{"html_url":"https:\/\/github.com\/huggingface\/datasets\/issues\/2945","title":"Protect master branch","comments":"Cool, I think we can do both :)","body":"After accidental merge commit (91c55355b634d0dc73350a7ddee1a6776dbbdd69) into `datasets` master branch, all commits present in the feature branch were permanently added to `datasets` master branch history, as e.g.:\r\n- 00cc036fea7c7745cfe722360036ed306796a3f2\r\n- 13ae8c98602bbad8197de3b9b425f4c78f582af1\r\n- ...\r\n\r\nI propose to protect our master branch, so that we avoid we can accidentally make this kind of mistakes in the future:\r\n- [x] For Pull Requests using GitHub, allow only squash merging, so that only a single commit per Pull Request is merged into the master branch\r\n - Currently, simple merge commits are already disabled\r\n - I propose to disable rebase merging as well\r\n- ~~Protect the master branch from direct pushes (to avoid accidentally pushing of merge commits)~~\r\n - ~~This protection would reject direct pushes to master branch~~\r\n - ~~If so, for each release (when we need to commit directly to the master branch), we should previously disable the protection and re-enable it again after the release~~\r\n- [x] Protect the master branch only from direct pushing of **merge commits**\r\n - GitHub offers the possibility to protect the master branch only from merge commits (which are the ones that introduce all the commits from the feature branch into the master branch).\r\n - No need to disable\/re-enable this protection on each release \r\n\r\nThis purpose of this Issue is to open a discussion about this problem and to agree in a solution.","comment_length":8,"text":"Protect master branch\nAfter accidental merge commit (91c55355b634d0dc73350a7ddee1a6776dbbdd69) into `datasets` master branch, all commits present in the feature branch were permanently added to `datasets` master branch history, as e.g.:\r\n- 00cc036fea7c7745cfe722360036ed306796a3f2\r\n- 13ae8c98602bbad8197de3b9b425f4c78f582af1\r\n- ...\r\n\r\nI propose to protect our master branch, so that we avoid we can accidentally make this kind of mistakes in the future:\r\n- [x] For Pull Requests using GitHub, allow only squash merging, so that only a single commit per Pull Request is merged into the master branch\r\n - Currently, simple merge commits are already disabled\r\n - I propose to disable rebase merging as well\r\n- ~~Protect the master branch from direct pushes (to avoid accidentally pushing of merge commits)~~\r\n - ~~This protection would reject direct pushes to master branch~~\r\n - ~~If so, for each release (when we need to commit directly to the master branch), we should previously disable the protection and re-enable it again after the release~~\r\n- [x] Protect the master branch only from direct pushing of **merge commits**\r\n - GitHub offers the possibility to protect the master branch only from merge commits (which are the ones that introduce all the commits from the feature branch into the master branch).\r\n - No need to disable\/re-enable this protection on each release \r\n\r\nThis purpose of this Issue is to open a discussion about this problem and to agree in a solution.\nCool, I think we can do both :)","embeddings":[-0.2390099317,-0.0966292247,-0.0759872422,-0.0984264985,-0.0993319154,-0.114640519,0.0078532966,0.239554435,-0.029255297,-0.0760675296,0.3045841157,-0.0697917044,-0.1556909084,0.2472542524,-0.0918809772,0.2528043091,0.1680651754,0.000099914,-0.099873215,0.0868200585,-0.0079817604,-0.1016465425,0.1242587939,0.0464673564,-0.2174060941,0.0417824537,0.0499481186,0.1391150504,-0.4184132516,-0.4594882727,0.1383522302,0.1707512289,0.0033366256,0.5652734041,-0.0001042576,0.0216173306,0.2390211523,0.0108919349,-0.1405786723,-0.1769673228,-0.5225075483,-0.4049667418,-0.1415199637,0.0287014097,0.0748746395,0.06865637,-0.0910198018,0.002452912,0.3400871456,0.0976495519,0.2408002317,-0.1941754073,0.1793913692,-0.0078934981,0.3311534822,0.5539640784,-0.0518133156,0.22529535,0.0045611048,-0.0102421539,0.1125852838,0.2086250186,-0.0837501734,-0.2613006532,0.2912853956,-0.1761568338,0.5298508406,-0.3999355435,-0.1809146106,-0.1078854129,0.0704470351,-0.201073736,-0.3955834508,-0.2669799924,-0.1179080084,-0.0097809853,0.0528227128,0.2314448655,-0.0157607514,0.0743468106,-0.3007844985,-0.3514812589,0.0444366112,0.0477003977,0.10768985,-0.1619907767,0.0425621495,-0.0867641419,0.3245029449,0.0032727672,-0.1678859442,-0.2417692542,-0.3024554849,-0.0731806457,-0.1582976431,-0.166840598,-0.2660009265,-0.3856663704,0.4048146307,0.3533258736,-0.360022366,-0.049508784,0.002726417,-0.0647690669,0.2959358394,0.0719453767,0.011164953,0.0792105719,0.5598432422,0.3058459461,0.0217627417,0.3349214792,0.3275439441,-0.0626471415,0.0689635947,0.5172939301,0.5336612463,-0.2621914148,0.0377011895,0.1680787653,-0.3323733509,-0.3190573454,0.0054372335,-0.0610207208,0.0830994621,0.0703226849,-0.1091590971,0.0525613427,0.1415696889,-0.0016033769,-0.3210552037,-0.1457319856,-0.3556134701,-0.0552582555,0.1311259568,-0.4680353403,0.0908330008,0.4519638121,-0.1013908461,-0.2686688304,0.0358462371,0.1716575772,0.0330468714,0.2908923328,-0.1433112174,-0.3825237751,0.2031245977,0.0965870917,-0.0456247479,0.2545187175,-0.1935967952,-0.2513652146,-0.1866476983,0.3089985549,-0.1121701747,0.3287965655,-0.548268497,0.0025912365,0.0102211721,0.3561609685,0.4972600937,0.385391444,0.2942132354,-0.1614470184,0.0424310081,0.1232627109,0.3384101689,-0.0511925742,-0.0100153778,-0.2738169432,0.1200275049,0.2083038092,-0.2946696877,-0.0103482995,-0.0507854298,-0.1232980564,0.0196767803,-0.048742909,-0.204722017,-0.0717563778,-0.4525829852,-0.0241770819,-0.0117112603,0.0191363525,0.1543500572,-0.3124671876,-0.0787971914,0.1779266298,-0.182664752,0.1171256378,-0.3127051294,-0.6241436601,-0.1159733236,-0.0647230893,0.1302614063,0.1962026656,0.3260959387,0.0762788281,0.0484594442,-0.1529644579,-0.0238246322,0.1797253937,0.3943619728,-0.1591625661,-0.2575499117,0.0904227868,-0.3276287019,0.0429003425,-0.0230592508,-0.0326709487,0.0328225382,-0.4201252162,0.2604811192,-0.0395855084,-0.089378275,-0.1092364043,0.2325994372,0.0994815007,-0.2429064363,-0.2273789346,-0.1815333217,0.2851662338,-0.242647782,0.2751372755,0.099704735,0.0815574303,-0.1571651399,-0.0062341182,-0.0228314847,0.3285116851,0.1736403704,-0.0701381266,-0.04674327,0.1801669151,-0.0945321396,0.1870126128,0.1769949794,0.4175725579,0.3424830437,-0.1347359419,-0.0105623221,-0.1067967415,-0.1460241973,0.022494385,-0.2684330642,0.2932992876,0.0991126224,-0.2107091695,-0.249318257,-0.0687692985,-0.1965132803,-0.265663594,-0.1541765183,0.0057348073,-0.1855473518,0.1418430507,-0.2901535332,0.2540666461,-0.2189239413,0.1275674105,0.0980846509,-0.201123789,-0.0243991297,0.0869311392,-0.0290957168,-0.057731878,0.100935325,0.3220874071,0.0902120993,0.183042407,-0.0160376783,0.0336617008,0.2433368862,-0.0592605695,0.362724781,0.1478046626,-0.0405387431,0.0476890579,0.3128943741,0.1199872941,0.0844289884,0.3880383074,-0.0403469466,0.1266814917,-0.2439862639,-0.0744038522,-0.2096193433,-0.5341181755,0.1785611957,-0.1322654784,-0.311395973,-0.0278486367,-0.053532742,0.1170226783,-0.4517551064,0.1408915371,0.2340205312,0.2509391308,-0.3069441617,-0.0698819757,0.1471458375,-0.0289604329,0.0071960338,0.1242603362,0.4291484952,-0.2824301422,0.4524761438,0.2389160544,-0.1856772155,-0.4897386432,-0.4018841088,0.0115547851,-0.1668282598,-0.1153612956,0.0861761943,-0.0717044175,0.1023176908,-0.401612252,-0.3951958418,0.0295078922,-0.3599407971,-0.1393553913,0.0470686667,0.1184691191,-0.2150190026,-0.0969456658,-0.2935731709,-0.2871156633,0.5013284087,-0.0580234416,0.0385896452,0.0112076271,-0.2732456625,-0.1094526052,0.1394193172,0.0409913287,-0.0648903027,-0.3088234365,-0.0299696531,0.0454311818,-0.0143893873,0.368963629,0.2126246095,-0.3675787747,0.0550749078,-0.350450635,-0.1397126317,-0.0122180469,0.4691388905,0.256077528,-0.1587453038,0.0662926659,0.0626281053,-0.1768465936,0.1262467802,-0.1262247413,0.1179754212,0.3565621078,0.3317129016,-0.0136891427,0.1207261309,0.0874625519,0.6525163054,0.3680907488,-0.0682478175,0.1437117904,0.2640963793,0.0372981504,0.0415912867,-0.2894337177,0.0679146126,-0.0695572942,-0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{"html_url":"https:\/\/github.com\/huggingface\/datasets\/issues\/2945","title":"Protect master branch","comments":"@lhoestq now the 2 are implemented.\r\n\r\nPlease note that for the the second protection, finally I have chosen to protect the master branch only from **merge commits** (see update comment above), so no need to disable\/re-enable the protection on each release (direct commits, different from merge commits, can be pushed to the remote master branch; and eventually reverted without messing up the repo history).","body":"After accidental merge commit (91c55355b634d0dc73350a7ddee1a6776dbbdd69) into `datasets` master branch, all commits present in the feature branch were permanently added to `datasets` master branch history, as e.g.:\r\n- 00cc036fea7c7745cfe722360036ed306796a3f2\r\n- 13ae8c98602bbad8197de3b9b425f4c78f582af1\r\n- ...\r\n\r\nI propose to protect our master branch, so that we avoid we can accidentally make this kind of mistakes in the future:\r\n- [x] For Pull Requests using GitHub, allow only squash merging, so that only a single commit per Pull Request is merged into the master branch\r\n - Currently, simple merge commits are already disabled\r\n - I propose to disable rebase merging as well\r\n- ~~Protect the master branch from direct pushes (to avoid accidentally pushing of merge commits)~~\r\n - ~~This protection would reject direct pushes to master branch~~\r\n - ~~If so, for each release (when we need to commit directly to the master branch), we should previously disable the protection and re-enable it again after the release~~\r\n- [x] Protect the master branch only from direct pushing of **merge commits**\r\n - GitHub offers the possibility to protect the master branch only from merge commits (which are the ones that introduce all the commits from the feature branch into the master branch).\r\n - No need to disable\/re-enable this protection on each release \r\n\r\nThis purpose of this Issue is to open a discussion about this problem and to agree in a solution.","comment_length":64,"text":"Protect master branch\nAfter accidental merge commit (91c55355b634d0dc73350a7ddee1a6776dbbdd69) into `datasets` master branch, all commits present in the feature branch were permanently added to `datasets` master branch history, as e.g.:\r\n- 00cc036fea7c7745cfe722360036ed306796a3f2\r\n- 13ae8c98602bbad8197de3b9b425f4c78f582af1\r\n- ...\r\n\r\nI propose to protect our master branch, so that we avoid we can accidentally make this kind of mistakes in the future:\r\n- [x] For Pull Requests using GitHub, allow only squash merging, so that only a single commit per Pull Request is merged into the master branch\r\n - Currently, simple merge commits are already disabled\r\n - I propose to disable rebase merging as well\r\n- ~~Protect the master branch from direct pushes (to avoid accidentally pushing of merge commits)~~\r\n - ~~This protection would reject direct pushes to master branch~~\r\n - ~~If so, for each release (when we need to commit directly to the master branch), we should previously disable the protection and re-enable it again after the release~~\r\n- [x] Protect the master branch only from direct pushing of **merge commits**\r\n - GitHub offers the possibility to protect the master branch only from merge commits (which are the ones that introduce all the commits from the feature branch into the master branch).\r\n - No need to disable\/re-enable this protection on each release \r\n\r\nThis purpose of this Issue is to open a discussion about this problem and to agree in a solution.\n@lhoestq now the 2 are implemented.\r\n\r\nPlease note that for the the second protection, finally I have chosen to protect the master branch only from **merge commits** (see update comment above), so no need to disable\/re-enable the protection on each release (direct commits, different from merge commits, can be pushed to the remote master branch; and eventually reverted without messing up the repo history).","embeddings":[-0.1553194672,-0.1002306268,-0.070321016,-0.0798171908,-0.1042534709,-0.1879874468,0.0104035577,0.2728635967,-0.0098489737,-0.0841399208,0.2926148772,-0.0778751224,-0.1324572563,0.2158903182,-0.0562292449,0.1705456823,0.2003232092,-0.0407981202,-0.0923330337,0.0279233586,0.0226084664,-0.0687029436,0.0832000151,0.0436301306,-0.1837819666,0.0054013738,-0.0148117775,0.1564962417,-0.4397666156,-0.4695702493,0.1452465504,0.2322402596,0.0251065325,0.570535183,-0.0001043637,-0.0003454557,0.1694866419,-0.0406010039,-0.1401022524,-0.2154636681,-0.4901072085,-0.4447958767,-0.07006336,-0.0083542913,0.1288067251,0.0528678969,-0.132499963,0.0238457564,0.3848451674,0.1055523977,0.2386109382,-0.1028517336,0.1812405735,0.0137694413,0.392008841,0.5474528074,-0.066634573,0.3031997979,0.0540179275,0.0006757497,0.0611058585,0.1741086096,-0.0823729113,-0.2866700888,0.2270194292,-0.1779912561,0.5181742311,-0.4243531227,-0.2415838391,-0.0831176639,0.0523793846,-0.1781465262,-0.4089041948,-0.2419367433,-0.1499476582,0.0076339091,0.0677802935,0.2468644679,0.039739579,0.0036496122,-0.3773649931,-0.2718090415,-0.004407926,0.0492040403,0.141940549,-0.1626293063,0.0664263666,-0.1270385236,0.2942230105,-0.0473279878,-0.1469833106,-0.3676346838,-0.2413333207,-0.0125874653,-0.1026133746,-0.1957468837,-0.2462065965,-0.3248277903,0.4251304567,0.3889683783,-0.3832727969,0.0137396995,-0.0304892361,-0.0810169131,0.2071851492,0.1178578511,-0.0104900496,0.0584886484,0.530713141,0.3144408166,0.0051584225,0.3255857527,0.3015701473,0.0030471815,0.0344459116,0.574118197,0.4289444685,-0.26292032,0.1276476383,0.1959735751,-0.3412007391,-0.3845081031,0.0711311549,-0.0861726329,0.1103260964,0.0271337908,-0.1147114784,0.0668015406,0.0451908335,-0.0380863659,-0.3079027534,-0.141485557,-0.3744001687,-0.0685305148,0.1860401928,-0.4571051598,0.1491677761,0.4325242639,-0.0680372268,-0.1992845535,0.0445522442,0.1292257011,0.0555874184,0.2961196899,-0.1537979692,-0.4526284337,0.2235967368,0.1313333064,-0.0668994114,0.1940043122,-0.1598993689,-0.2616221011,-0.1049757376,0.2977656722,-0.1482721567,0.3071561158,-0.5237665772,0.0363732092,-0.0044837268,0.3506707847,0.4910375476,0.3025480509,0.3280785084,-0.1685939282,0.0617566295,0.1903455257,0.3235116601,-0.0516864397,0.0623045377,-0.2877363563,0.1095039099,0.1450733244,-0.3121602237,0.0175557248,-0.0052360594,-0.1312822402,0.0107761566,-0.0714070052,-0.1119878516,-0.0989294425,-0.3666853309,0.0694030076,-0.0407375731,-0.1007770002,0.2079568803,-0.3201756477,-0.0947941095,0.2050814927,-0.1481231451,0.0208985135,-0.2919872105,-0.6061385274,-0.1636558473,-0.06343887,0.1249144152,0.2275642306,0.3607693315,-0.0050310493,-0.0283617247,-0.1714023799,-0.0093972106,0.1125069708,0.3421364129,-0.1496780068,-0.2415423691,0.1322465539,-0.3545884192,0.0911409035,-0.0785121024,-0.0097747799,0.0344485678,-0.3157884181,0.3412798941,-0.0440529883,-0.1208016798,-0.1141509414,0.2415172756,0.095951654,-0.1747335494,-0.2039210498,-0.2074069828,0.2601909339,-0.2408306301,0.3018657863,0.1300136894,0.1026374176,-0.1411807239,-0.0364533328,-0.0250960924,0.36193645,0.2363044769,-0.0899174288,-0.0591584407,0.1687609404,-0.0490626656,0.2035702467,0.2246947736,0.4252423346,0.3736152053,-0.1026815102,0.0626009405,-0.0917925984,-0.0787246898,0.0245947056,-0.3291273713,0.3280371726,0.073694557,-0.1394644678,-0.1740321219,-0.1033326834,-0.1740740985,-0.2202692628,-0.1707722992,-0.012951361,-0.1690982729,0.1943652332,-0.3365918398,0.2223692834,-0.2772080004,0.148579374,0.0150526064,-0.2516064942,-0.1024010181,0.0682980418,-0.0059674503,-0.1327149123,0.1241709664,0.3773829341,0.1278743744,0.1701000035,0.0042741634,0.0201513059,0.2572831213,-0.1164359227,0.353325963,0.1443069726,-0.1054452285,0.054064367,0.3364559114,0.1812186539,0.0643318817,0.346919328,-0.0033243245,0.0352581739,-0.2084068358,-0.0459848903,-0.2602106631,-0.5283415914,0.2192376256,-0.110637784,-0.3552163243,-0.0085041393,-0.0453661792,0.047418233,-0.5248722434,0.136029914,0.1835808307,0.2905260623,-0.3178029358,-0.1199780703,0.1263155937,-0.0225951802,0.0123441136,0.1285599768,0.3631602824,-0.2266809344,0.45945099,0.2389809936,-0.1695563644,-0.4726397991,-0.3434132934,0.0191301201,-0.2039363831,-0.1179748252,0.0098189935,-0.0989309773,0.0921538174,-0.3585401177,-0.3257124722,-0.018773742,-0.3730046451,-0.1600959152,0.0695955083,0.0628860295,-0.2346176803,-0.114040032,-0.386590004,-0.2821527123,0.4793556035,-0.0164330266,0.1156589612,-0.0431727543,-0.2923772335,-0.0459178463,0.0874827728,0.1366390288,-0.0000448525,-0.3609322906,0.0325667374,-0.0406603813,-0.0293471292,0.352609843,0.1854910851,-0.4302407205,0.0712517202,-0.3217604458,-0.1558190286,-0.0520843901,0.4864443243,0.3141199946,-0.0973737389,0.0710799471,0.1035510823,-0.1919001341,0.0550895296,-0.1816118211,0.1472238153,0.3454884887,0.2857186198,0.0378993787,0.1458674669,0.0254973993,0.7140879035,0.3778497577,-0.059657298,0.210337624,0.3106974065,-0.0277180839,0.0039902786,-0.2601569593,0.065046832,-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{"html_url":"https:\/\/github.com\/huggingface\/datasets\/issues\/2943","title":"Backwards compatibility broken for cached datasets that use `.filter()`","comments":"Hi ! I guess the caching mechanism should have considered the new `filter` to be different from the old one, and don't use cached results from the old `filter`.\r\nTo avoid other users from having this issue we could make the caching differentiate the two, what do you think ?","body":"## Describe the bug\r\nAfter upgrading to datasets `1.12.0`, some cached `.filter()` steps from `1.11.0` started failing with \r\n`ValueError: Keys mismatch: between {'indices': Value(dtype='uint64', id=None)} and {'file': Value(dtype='string', id=None), 'text': Value(dtype='string', id=None), 'speaker_id': Value(dtype='int64', id=None), 'chapter_id': Value(dtype='int64', id=None), 'id': Value(dtype='string', id=None)}`\r\n\r\nRelated feature: https:\/\/github.com\/huggingface\/datasets\/pull\/2836\r\n\r\n:question: This is probably a `wontfix` bug, since it can be solved by simply cleaning the related cache dirs, but the workaround could be useful for someone googling the error :) \r\n\r\n## Workaround\r\nRemove the cache for the given dataset, e.g. `rm -rf ~\/.cache\/huggingface\/datasets\/librispeech_asr`.\r\n\r\n## Steps to reproduce the bug\r\n1. Delete `~\/.cache\/huggingface\/datasets\/librispeech_asr` if it exists.\r\n\r\n2. `pip install datasets==1.11.0` and run the following snippet:\r\n\r\n```python\r\nfrom datasets import load_dataset\r\n\r\nids = [\"1272-141231-0000\"]\r\nds = load_dataset(\"patrickvonplaten\/librispeech_asr_dummy\", \"clean\", split=\"validation\")\r\nds = ds.filter(lambda x: x[\"id\"] in ids)\r\n```\r\n3. `pip install datasets==1.12.1` and re-run the code again\r\n\r\n## Expected results\r\nSame result as with the previous `datasets` version.\r\n\r\n## Actual results\r\n```bash\r\nReusing dataset librispeech_asr (.\/.cache\/huggingface\/datasets\/librispeech_asr\/clean\/2.1.0\/468ec03677f46a8714ac6b5b64dba02d246a228d92cbbad7f3dc190fa039eab1)\r\nLoading cached processed dataset at .\/.cache\/huggingface\/datasets\/librispeech_asr\/clean\/2.1.0\/468ec03677f46a8714ac6b5b64dba02d246a228d92cbbad7f3dc190fa039eab1\/cache-cd1c29844fdbc87a.arrow\r\nTraceback (most recent call last):\r\n File \".\/repos\/transformers\/src\/transformers\/models\/wav2vec2\/try_dataset.py\", line 5, in \r\n ds = ds.filter(lambda x: x[\"id\"] in ids)\r\n File \".\/envs\/transformers\/lib\/python3.8\/site-packages\/datasets\/arrow_dataset.py\", line 185, in wrapper\r\n out: Union[\"Dataset\", \"DatasetDict\"] = func(self, *args, **kwargs)\r\n File \".\/envs\/transformers\/lib\/python3.8\/site-packages\/datasets\/fingerprint.py\", line 398, in wrapper\r\n out = func(self, *args, **kwargs)\r\n File \".\/envs\/transformers\/lib\/python3.8\/site-packages\/datasets\/arrow_dataset.py\", line 2169, in filter\r\n indices = self.map(\r\n File \".\/envs\/transformers\/lib\/python3.8\/site-packages\/datasets\/arrow_dataset.py\", line 1686, in map\r\n return self._map_single(\r\n File \".\/envs\/transformers\/lib\/python3.8\/site-packages\/datasets\/arrow_dataset.py\", line 185, in wrapper\r\n out: Union[\"Dataset\", \"DatasetDict\"] = func(self, *args, **kwargs)\r\n File \".\/envs\/transformers\/lib\/python3.8\/site-packages\/datasets\/fingerprint.py\", line 398, in wrapper\r\n out = func(self, *args, **kwargs)\r\n File \".\/envs\/transformers\/lib\/python3.8\/site-packages\/datasets\/arrow_dataset.py\", line 1896, in _map_single\r\n return Dataset.from_file(cache_file_name, info=info, split=self.split)\r\n File \".\/envs\/transformers\/lib\/python3.8\/site-packages\/datasets\/arrow_dataset.py\", line 343, in from_file\r\n return cls(\r\n File \".\/envs\/transformers\/lib\/python3.8\/site-packages\/datasets\/arrow_dataset.py\", line 282, in __init__\r\n self.info.features = self.info.features.reorder_fields_as(inferred_features)\r\n File \".\/envs\/transformers\/lib\/python3.8\/site-packages\/datasets\/features.py\", line 1151, in reorder_fields_as\r\n return Features(recursive_reorder(self, other))\r\n File \".\/envs\/transformers\/lib\/python3.8\/site-packages\/datasets\/features.py\", line 1140, in recursive_reorder\r\n raise ValueError(f\"Keys mismatch: between {source} and {target}\" + stack_position)\r\nValueError: Keys mismatch: between {'indices': Value(dtype='uint64', id=None)} and {'file': Value(dtype='string', id=None), 'text': Value(dtype='string', id=None), 'speaker_id': Value(dtype='int64', id=None), 'chapter_id': Value(dtype='int64', id=None), 'id': Value(dtype='string', id=None)}\r\n\r\nProcess finished with exit code 1\r\n\r\n```\r\n\r\n## Environment info\r\n- `datasets` version: 1.12.1\r\n- Platform: Linux-5.11.0-34-generic-x86_64-with-glibc2.17\r\n- Python version: 3.8.10\r\n- PyArrow version: 5.0.0\r\n","comment_length":50,"text":"Backwards compatibility broken for cached datasets that use `.filter()`\n## Describe the bug\r\nAfter upgrading to datasets `1.12.0`, some cached `.filter()` steps from `1.11.0` started failing with \r\n`ValueError: Keys mismatch: between {'indices': Value(dtype='uint64', id=None)} and {'file': Value(dtype='string', id=None), 'text': Value(dtype='string', id=None), 'speaker_id': Value(dtype='int64', id=None), 'chapter_id': Value(dtype='int64', id=None), 'id': Value(dtype='string', id=None)}`\r\n\r\nRelated feature: https:\/\/github.com\/huggingface\/datasets\/pull\/2836\r\n\r\n:question: This is probably a `wontfix` bug, since it can be solved by simply cleaning the related cache dirs, but the workaround could be useful for someone googling the error :) \r\n\r\n## Workaround\r\nRemove the cache for the given dataset, e.g. `rm -rf ~\/.cache\/huggingface\/datasets\/librispeech_asr`.\r\n\r\n## Steps to reproduce the bug\r\n1. Delete `~\/.cache\/huggingface\/datasets\/librispeech_asr` if it exists.\r\n\r\n2. `pip install datasets==1.11.0` and run the following snippet:\r\n\r\n```python\r\nfrom datasets import load_dataset\r\n\r\nids = [\"1272-141231-0000\"]\r\nds = load_dataset(\"patrickvonplaten\/librispeech_asr_dummy\", \"clean\", split=\"validation\")\r\nds = ds.filter(lambda x: x[\"id\"] in ids)\r\n```\r\n3. `pip install datasets==1.12.1` and re-run the code again\r\n\r\n## Expected results\r\nSame result as with the previous `datasets` version.\r\n\r\n## Actual results\r\n```bash\r\nReusing dataset librispeech_asr (.\/.cache\/huggingface\/datasets\/librispeech_asr\/clean\/2.1.0\/468ec03677f46a8714ac6b5b64dba02d246a228d92cbbad7f3dc190fa039eab1)\r\nLoading cached processed dataset at .\/.cache\/huggingface\/datasets\/librispeech_asr\/clean\/2.1.0\/468ec03677f46a8714ac6b5b64dba02d246a228d92cbbad7f3dc190fa039eab1\/cache-cd1c29844fdbc87a.arrow\r\nTraceback (most recent call last):\r\n File \".\/repos\/transformers\/src\/transformers\/models\/wav2vec2\/try_dataset.py\", line 5, in \r\n ds = ds.filter(lambda x: x[\"id\"] in ids)\r\n File \".\/envs\/transformers\/lib\/python3.8\/site-packages\/datasets\/arrow_dataset.py\", line 185, in wrapper\r\n out: Union[\"Dataset\", \"DatasetDict\"] = func(self, *args, **kwargs)\r\n File \".\/envs\/transformers\/lib\/python3.8\/site-packages\/datasets\/fingerprint.py\", line 398, in wrapper\r\n out = func(self, *args, **kwargs)\r\n File \".\/envs\/transformers\/lib\/python3.8\/site-packages\/datasets\/arrow_dataset.py\", line 2169, in filter\r\n indices = self.map(\r\n File \".\/envs\/transformers\/lib\/python3.8\/site-packages\/datasets\/arrow_dataset.py\", line 1686, in map\r\n return self._map_single(\r\n File \".\/envs\/transformers\/lib\/python3.8\/site-packages\/datasets\/arrow_dataset.py\", line 185, in wrapper\r\n out: Union[\"Dataset\", \"DatasetDict\"] = func(self, *args, **kwargs)\r\n File \".\/envs\/transformers\/lib\/python3.8\/site-packages\/datasets\/fingerprint.py\", line 398, in wrapper\r\n out = func(self, *args, **kwargs)\r\n File \".\/envs\/transformers\/lib\/python3.8\/site-packages\/datasets\/arrow_dataset.py\", line 1896, in _map_single\r\n return Dataset.from_file(cache_file_name, info=info, split=self.split)\r\n File \".\/envs\/transformers\/lib\/python3.8\/site-packages\/datasets\/arrow_dataset.py\", line 343, in from_file\r\n return cls(\r\n File \".\/envs\/transformers\/lib\/python3.8\/site-packages\/datasets\/arrow_dataset.py\", line 282, in __init__\r\n self.info.features = self.info.features.reorder_fields_as(inferred_features)\r\n File \".\/envs\/transformers\/lib\/python3.8\/site-packages\/datasets\/features.py\", line 1151, in reorder_fields_as\r\n return Features(recursive_reorder(self, other))\r\n File \".\/envs\/transformers\/lib\/python3.8\/site-packages\/datasets\/features.py\", line 1140, in recursive_reorder\r\n raise ValueError(f\"Keys mismatch: between {source} and {target}\" + stack_position)\r\nValueError: Keys mismatch: between {'indices': Value(dtype='uint64', id=None)} and {'file': Value(dtype='string', id=None), 'text': Value(dtype='string', id=None), 'speaker_id': Value(dtype='int64', id=None), 'chapter_id': Value(dtype='int64', id=None), 'id': Value(dtype='string', id=None)}\r\n\r\nProcess finished with exit code 1\r\n\r\n```\r\n\r\n## Environment info\r\n- `datasets` version: 1.12.1\r\n- Platform: Linux-5.11.0-34-generic-x86_64-with-glibc2.17\r\n- Python version: 3.8.10\r\n- PyArrow version: 5.0.0\r\n\nHi ! I guess the caching mechanism should have considered the new `filter` to be different from the old one, and don't use cached results from the old `filter`.\r\nTo avoid other users from having this issue we could make the caching differentiate the two, what do you think ?","embeddings":[-0.3049958348,0.1243880838,-0.0465583764,0.2368971109,0.1517290324,-0.0666598156,-0.0069413707,0.3285393715,0.1737383604,0.0293814819,-0.2313861847,0.3581927419,-0.1503304988,0.2527370155,-0.2977862358,-0.1378120482,0.0444693454,0.0321247876,-0.0614953265,0.166412577,-0.1239175051,0.4988580644,-0.1957765073,0.0070795678,0.2221652865,0.0513210297,0.1091236398,0.138566196,0.0798620358,-0.3392693698,0.5981172919,0.1064069718,-0.0348691642,0.498096019,-0.0001213098,0.099867776,0.4398066998,-0.1073976383,-0.4579497874,-0.3686879575,-0.192854017,0.1319934726,0.2131137848,0.0811629742,-0.0715122968,0.1143416911,-0.303493619,-0.346046567,0.2488765419,0.1989328116,0.2267285585,0.2136144787,0.2657418847,0.066587165,0.170179069,0.0826525018,0.0259718653,-0.0490864292,0.2940285802,0.014261202,0.043685697,0.4806104004,-0.3283523321,-0.2146463245,0.3046185374,-0.2982533276,0.1030709222,-0.3957037926,0.2999309897,0.0781370997,0.1462642252,-0.255518198,-0.5773367882,-0.3483263254,-0.5121472478,-0.2868291736,0.4164735079,-0.216358915,-0.2009968162,0.2200830877,-0.3342647851,-0.0270038545,0.0934983194,0.0535101071,-0.0584819205,0.2941842079,-0.1318320185,-0.0048626242,0.0149830757,-0.132900551,0.1924084276,-0.2750048339,-0.1968749017,0.1725800484,-0.0155595224,-0.0546131916,0.0954222009,0.3323281705,0.140570581,0.0356287211,0.0192098524,0.0845401734,-0.0409641638,0.0536773168,0.1373275816,0.475378722,0.4901195765,0.2467906624,0.2727875113,0.2854887545,-0.0078853024,-0.0013800253,0.5281852484,-0.1666412055,0.0747071281,0.1517307162,0.196695298,-0.4048683643,-0.3602132797,0.1689626127,-0.0769995078,-0.1250401586,0.2207264006,0.0534940958,0.1005648673,0.2142349631,0.1320811808,0.0338568501,-0.1964725256,0.0612344779,-0.1725096703,-0.1739457697,0.0011616903,-0.1112991124,-0.0473052748,-0.89977777,0.1747701168,0.0482602492,0.0063488153,0.1058098748,-0.0697732121,0.207434997,0.1730166823,0.5229284167,-0.2213587761,0.0839003325,0.1671907902,-0.548327744,-0.1386865377,0.0125284484,-0.2415378094,-0.1112066656,0.2085879147,0.1735862345,-0.3296715915,0.0126515068,-0.17029953,0.4515431225,0.2592140436,-0.5466870666,-0.0779574588,-0.213380143,-0.4382343888,-0.1997140199,0.0971902013,0.4131370485,-0.5942543149,-0.3216674924,0.0216870401,0.0319067985,0.0399160981,-0.1229341999,-0.063439101,-0.2444722205,-0.1077689156,-0.1384182572,0.3606542647,-0.3195546865,-0.733892858,0.1537256837,0.2360213548,0.5342661738,-0.0709591061,-0.1374839246,0.2813950479,-0.2578290701,0.1459920853,0.0848445743,-0.1649033576,-0.0079940017,-0.3409692049,-0.2346946597,0.2238697708,-0.1502910852,0.0904174298,0.3348585963,0.0790057704,-0.2542206049,0.0502917804,0.0350500681,0.1049243882,0.1990925223,0.2453232259,0.2237652242,0.2863062918,-0.4365186989,-0.2801407278,0.2506181598,0.2141302824,-0.0600011088,-0.1524066925,-0.1982273012,-0.2678262889,-0.0884182155,-0.1589775681,-0.3044096231,0.0788738504,0.0225765873,0.3843636513,-0.0385034606,-0.1145259663,0.6218649745,0.3084535897,0.0944804996,-0.5049411654,0.229641512,-0.0548514128,0.0051691518,-0.0591857955,0.0377827175,0.2939595878,-0.0685256049,-0.2895757854,0.407875061,0.0392592028,0.084168233,-0.0276337974,0.1937460154,0.1773459613,0.2105839252,0.0639338642,0.2741822898,-0.0148837138,0.2087214142,-0.195456177,0.557248354,0.0293753464,0.3240505159,0.1208347678,0.148756206,0.3702490628,0.1305633932,-0.2189950645,-0.1370786875,0.3204495311,-0.4587228,0.0626674443,0.0123394234,-0.1297932267,0.3776263595,0.0716826022,0.0755119473,-0.0334524736,0.3119212389,-0.1593657285,-0.042190969,0.1813624054,0.4024861157,0.4014509916,0.2088322341,0.0507172868,0.0758817792,-0.1615392268,-0.0248774402,0.2082498521,0.1293462515,-0.1696399748,0.1535750628,0.3034210205,0.2732039392,-0.3061974645,0.0933934823,-0.2356239706,0.1195331365,-0.270581305,0.3616366684,-0.2810101509,-0.2280866951,0.076141715,-0.1678324938,-0.2615140676,-0.3875222206,0.089899376,0.3934392929,-0.086219281,0.2567750216,0.0147161586,0.2445412278,0.136167407,-0.1996820718,-0.4005717337,-0.2078815252,-0.3476482034,0.0053976052,0.2051323503,-0.1568491161,0.2970358729,-0.0763099566,-0.2066247612,-0.390294075,-0.9079098701,-0.0486286655,-0.123964861,0.6180298328,0.3176228106,-0.0916265026,-0.1352719367,-0.2327560335,0.1665794849,-0.0413150303,-0.4310828745,0.1568385363,0.0666227341,0.1682221591,-0.2003851682,-0.2270279974,-0.1135412008,-0.1458190233,0.0079398574,-0.1276904047,0.0849654675,0.3094039559,0.1123534068,-0.3165157139,-0.0872011557,0.1330857426,-0.3335401416,-0.0773374513,0.0739419162,-0.054162316,-0.2302434891,-0.034139622,-0.0319806486,0.0831893757,0.3736632466,-0.4069587886,-0.3791774213,-0.2713731527,-0.0048744264,0.2311773747,0.068360962,0.2319667786,0.1899949759,0.0391035639,-0.2794680893,-0.4342857301,-0.0886206329,-0.2365958691,0.1095489264,0.1482250243,0.6309939027,0.0345636867,0.4764270186,0.2947264612,0.0489611253,0.1398259848,0.1815548688,0.4972691238,-0.3258462548,-0.4095653892,-0.0467088632,-0.0461938493,0.0505124666,-0.1531914324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{"html_url":"https:\/\/github.com\/huggingface\/datasets\/issues\/2943","title":"Backwards compatibility broken for cached datasets that use `.filter()`","comments":"If it's easy enough to implement, then yes please \ud83d\ude04 But this issue can be low-priority, since I've only encountered it in a couple of `transformers` CI tests.","body":"## Describe the bug\r\nAfter upgrading to datasets `1.12.0`, some cached `.filter()` steps from `1.11.0` started failing with \r\n`ValueError: Keys mismatch: between {'indices': Value(dtype='uint64', id=None)} and {'file': Value(dtype='string', id=None), 'text': Value(dtype='string', id=None), 'speaker_id': Value(dtype='int64', id=None), 'chapter_id': Value(dtype='int64', id=None), 'id': Value(dtype='string', id=None)}`\r\n\r\nRelated feature: https:\/\/github.com\/huggingface\/datasets\/pull\/2836\r\n\r\n:question: This is probably a `wontfix` bug, since it can be solved by simply cleaning the related cache dirs, but the workaround could be useful for someone googling the error :) \r\n\r\n## Workaround\r\nRemove the cache for the given dataset, e.g. `rm -rf ~\/.cache\/huggingface\/datasets\/librispeech_asr`.\r\n\r\n## Steps to reproduce the bug\r\n1. Delete `~\/.cache\/huggingface\/datasets\/librispeech_asr` if it exists.\r\n\r\n2. `pip install datasets==1.11.0` and run the following snippet:\r\n\r\n```python\r\nfrom datasets import load_dataset\r\n\r\nids = [\"1272-141231-0000\"]\r\nds = load_dataset(\"patrickvonplaten\/librispeech_asr_dummy\", \"clean\", split=\"validation\")\r\nds = ds.filter(lambda x: x[\"id\"] in ids)\r\n```\r\n3. `pip install datasets==1.12.1` and re-run the code again\r\n\r\n## Expected results\r\nSame result as with the previous `datasets` version.\r\n\r\n## Actual results\r\n```bash\r\nReusing dataset librispeech_asr (.\/.cache\/huggingface\/datasets\/librispeech_asr\/clean\/2.1.0\/468ec03677f46a8714ac6b5b64dba02d246a228d92cbbad7f3dc190fa039eab1)\r\nLoading cached processed dataset at .\/.cache\/huggingface\/datasets\/librispeech_asr\/clean\/2.1.0\/468ec03677f46a8714ac6b5b64dba02d246a228d92cbbad7f3dc190fa039eab1\/cache-cd1c29844fdbc87a.arrow\r\nTraceback (most recent call last):\r\n File \".\/repos\/transformers\/src\/transformers\/models\/wav2vec2\/try_dataset.py\", line 5, in \r\n ds = ds.filter(lambda x: x[\"id\"] in ids)\r\n File \".\/envs\/transformers\/lib\/python3.8\/site-packages\/datasets\/arrow_dataset.py\", line 185, in wrapper\r\n out: Union[\"Dataset\", \"DatasetDict\"] = func(self, *args, **kwargs)\r\n File \".\/envs\/transformers\/lib\/python3.8\/site-packages\/datasets\/fingerprint.py\", line 398, in wrapper\r\n out = func(self, *args, **kwargs)\r\n File \".\/envs\/transformers\/lib\/python3.8\/site-packages\/datasets\/arrow_dataset.py\", line 2169, in filter\r\n indices = self.map(\r\n File \".\/envs\/transformers\/lib\/python3.8\/site-packages\/datasets\/arrow_dataset.py\", line 1686, in map\r\n return self._map_single(\r\n File \".\/envs\/transformers\/lib\/python3.8\/site-packages\/datasets\/arrow_dataset.py\", line 185, in wrapper\r\n out: Union[\"Dataset\", \"DatasetDict\"] = func(self, *args, **kwargs)\r\n File \".\/envs\/transformers\/lib\/python3.8\/site-packages\/datasets\/fingerprint.py\", line 398, in wrapper\r\n out = func(self, *args, **kwargs)\r\n File \".\/envs\/transformers\/lib\/python3.8\/site-packages\/datasets\/arrow_dataset.py\", line 1896, in _map_single\r\n return Dataset.from_file(cache_file_name, info=info, split=self.split)\r\n File \".\/envs\/transformers\/lib\/python3.8\/site-packages\/datasets\/arrow_dataset.py\", line 343, in from_file\r\n return cls(\r\n File \".\/envs\/transformers\/lib\/python3.8\/site-packages\/datasets\/arrow_dataset.py\", line 282, in __init__\r\n self.info.features = self.info.features.reorder_fields_as(inferred_features)\r\n File \".\/envs\/transformers\/lib\/python3.8\/site-packages\/datasets\/features.py\", line 1151, in reorder_fields_as\r\n return Features(recursive_reorder(self, other))\r\n File \".\/envs\/transformers\/lib\/python3.8\/site-packages\/datasets\/features.py\", line 1140, in recursive_reorder\r\n raise ValueError(f\"Keys mismatch: between {source} and {target}\" + stack_position)\r\nValueError: Keys mismatch: between {'indices': Value(dtype='uint64', id=None)} and {'file': Value(dtype='string', id=None), 'text': Value(dtype='string', id=None), 'speaker_id': Value(dtype='int64', id=None), 'chapter_id': Value(dtype='int64', id=None), 'id': Value(dtype='string', id=None)}\r\n\r\nProcess finished with exit code 1\r\n\r\n```\r\n\r\n## Environment info\r\n- `datasets` version: 1.12.1\r\n- Platform: Linux-5.11.0-34-generic-x86_64-with-glibc2.17\r\n- Python version: 3.8.10\r\n- PyArrow version: 5.0.0\r\n","comment_length":28,"text":"Backwards compatibility broken for cached datasets that use `.filter()`\n## Describe the bug\r\nAfter upgrading to datasets `1.12.0`, some cached `.filter()` steps from `1.11.0` started failing with \r\n`ValueError: Keys mismatch: between {'indices': Value(dtype='uint64', id=None)} and {'file': Value(dtype='string', id=None), 'text': Value(dtype='string', id=None), 'speaker_id': Value(dtype='int64', id=None), 'chapter_id': Value(dtype='int64', id=None), 'id': Value(dtype='string', id=None)}`\r\n\r\nRelated feature: https:\/\/github.com\/huggingface\/datasets\/pull\/2836\r\n\r\n:question: This is probably a `wontfix` bug, since it can be solved by simply cleaning the related cache dirs, but the workaround could be useful for someone googling the error :) \r\n\r\n## Workaround\r\nRemove the cache for the given dataset, e.g. `rm -rf ~\/.cache\/huggingface\/datasets\/librispeech_asr`.\r\n\r\n## Steps to reproduce the bug\r\n1. Delete `~\/.cache\/huggingface\/datasets\/librispeech_asr` if it exists.\r\n\r\n2. `pip install datasets==1.11.0` and run the following snippet:\r\n\r\n```python\r\nfrom datasets import load_dataset\r\n\r\nids = [\"1272-141231-0000\"]\r\nds = load_dataset(\"patrickvonplaten\/librispeech_asr_dummy\", \"clean\", split=\"validation\")\r\nds = ds.filter(lambda x: x[\"id\"] in ids)\r\n```\r\n3. `pip install datasets==1.12.1` and re-run the code again\r\n\r\n## Expected results\r\nSame result as with the previous `datasets` version.\r\n\r\n## Actual results\r\n```bash\r\nReusing dataset librispeech_asr (.\/.cache\/huggingface\/datasets\/librispeech_asr\/clean\/2.1.0\/468ec03677f46a8714ac6b5b64dba02d246a228d92cbbad7f3dc190fa039eab1)\r\nLoading cached processed dataset at .\/.cache\/huggingface\/datasets\/librispeech_asr\/clean\/2.1.0\/468ec03677f46a8714ac6b5b64dba02d246a228d92cbbad7f3dc190fa039eab1\/cache-cd1c29844fdbc87a.arrow\r\nTraceback (most recent call last):\r\n File \".\/repos\/transformers\/src\/transformers\/models\/wav2vec2\/try_dataset.py\", line 5, in \r\n ds = ds.filter(lambda x: x[\"id\"] in ids)\r\n File \".\/envs\/transformers\/lib\/python3.8\/site-packages\/datasets\/arrow_dataset.py\", line 185, in wrapper\r\n out: Union[\"Dataset\", \"DatasetDict\"] = func(self, *args, **kwargs)\r\n File \".\/envs\/transformers\/lib\/python3.8\/site-packages\/datasets\/fingerprint.py\", line 398, in wrapper\r\n out = func(self, *args, **kwargs)\r\n File \".\/envs\/transformers\/lib\/python3.8\/site-packages\/datasets\/arrow_dataset.py\", line 2169, in filter\r\n indices = self.map(\r\n File \".\/envs\/transformers\/lib\/python3.8\/site-packages\/datasets\/arrow_dataset.py\", line 1686, in map\r\n return self._map_single(\r\n File \".\/envs\/transformers\/lib\/python3.8\/site-packages\/datasets\/arrow_dataset.py\", line 185, in wrapper\r\n out: Union[\"Dataset\", \"DatasetDict\"] = func(self, *args, **kwargs)\r\n File \".\/envs\/transformers\/lib\/python3.8\/site-packages\/datasets\/fingerprint.py\", line 398, in wrapper\r\n out = func(self, *args, **kwargs)\r\n File \".\/envs\/transformers\/lib\/python3.8\/site-packages\/datasets\/arrow_dataset.py\", line 1896, in _map_single\r\n return Dataset.from_file(cache_file_name, info=info, split=self.split)\r\n File \".\/envs\/transformers\/lib\/python3.8\/site-packages\/datasets\/arrow_dataset.py\", line 343, in from_file\r\n return cls(\r\n File \".\/envs\/transformers\/lib\/python3.8\/site-packages\/datasets\/arrow_dataset.py\", line 282, in __init__\r\n self.info.features = self.info.features.reorder_fields_as(inferred_features)\r\n File \".\/envs\/transformers\/lib\/python3.8\/site-packages\/datasets\/features.py\", line 1151, in reorder_fields_as\r\n return Features(recursive_reorder(self, other))\r\n File \".\/envs\/transformers\/lib\/python3.8\/site-packages\/datasets\/features.py\", line 1140, in recursive_reorder\r\n raise ValueError(f\"Keys mismatch: between {source} and {target}\" + stack_position)\r\nValueError: Keys mismatch: between {'indices': Value(dtype='uint64', id=None)} and {'file': Value(dtype='string', id=None), 'text': Value(dtype='string', id=None), 'speaker_id': Value(dtype='int64', id=None), 'chapter_id': Value(dtype='int64', id=None), 'id': Value(dtype='string', id=None)}\r\n\r\nProcess finished with exit code 1\r\n\r\n```\r\n\r\n## Environment info\r\n- `datasets` version: 1.12.1\r\n- Platform: Linux-5.11.0-34-generic-x86_64-with-glibc2.17\r\n- Python version: 3.8.10\r\n- PyArrow version: 5.0.0\r\n\nIf it's easy enough to implement, then yes please \ud83d\ude04 But this issue can be low-priority, since I've only encountered it in a couple of `transformers` CI 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{"html_url":"https:\/\/github.com\/huggingface\/datasets\/issues\/2943","title":"Backwards compatibility broken for cached datasets that use `.filter()`","comments":"Well it can cause issue with anyone that updates `datasets` and re-run some code that uses filter, so I'm creating a PR","body":"## Describe the bug\r\nAfter upgrading to datasets `1.12.0`, some cached `.filter()` steps from `1.11.0` started failing with \r\n`ValueError: Keys mismatch: between {'indices': Value(dtype='uint64', id=None)} and {'file': Value(dtype='string', id=None), 'text': Value(dtype='string', id=None), 'speaker_id': Value(dtype='int64', id=None), 'chapter_id': Value(dtype='int64', id=None), 'id': Value(dtype='string', id=None)}`\r\n\r\nRelated feature: https:\/\/github.com\/huggingface\/datasets\/pull\/2836\r\n\r\n:question: This is probably a `wontfix` bug, since it can be solved by simply cleaning the related cache dirs, but the workaround could be useful for someone googling the error :) \r\n\r\n## Workaround\r\nRemove the cache for the given dataset, e.g. `rm -rf ~\/.cache\/huggingface\/datasets\/librispeech_asr`.\r\n\r\n## Steps to reproduce the bug\r\n1. Delete `~\/.cache\/huggingface\/datasets\/librispeech_asr` if it exists.\r\n\r\n2. `pip install datasets==1.11.0` and run the following snippet:\r\n\r\n```python\r\nfrom datasets import load_dataset\r\n\r\nids = [\"1272-141231-0000\"]\r\nds = load_dataset(\"patrickvonplaten\/librispeech_asr_dummy\", \"clean\", split=\"validation\")\r\nds = ds.filter(lambda x: x[\"id\"] in ids)\r\n```\r\n3. `pip install datasets==1.12.1` and re-run the code again\r\n\r\n## Expected results\r\nSame result as with the previous `datasets` version.\r\n\r\n## Actual results\r\n```bash\r\nReusing dataset librispeech_asr (.\/.cache\/huggingface\/datasets\/librispeech_asr\/clean\/2.1.0\/468ec03677f46a8714ac6b5b64dba02d246a228d92cbbad7f3dc190fa039eab1)\r\nLoading cached processed dataset at .\/.cache\/huggingface\/datasets\/librispeech_asr\/clean\/2.1.0\/468ec03677f46a8714ac6b5b64dba02d246a228d92cbbad7f3dc190fa039eab1\/cache-cd1c29844fdbc87a.arrow\r\nTraceback (most recent call last):\r\n File \".\/repos\/transformers\/src\/transformers\/models\/wav2vec2\/try_dataset.py\", line 5, in \r\n ds = ds.filter(lambda x: x[\"id\"] in ids)\r\n File \".\/envs\/transformers\/lib\/python3.8\/site-packages\/datasets\/arrow_dataset.py\", line 185, in wrapper\r\n out: Union[\"Dataset\", \"DatasetDict\"] = func(self, *args, **kwargs)\r\n File \".\/envs\/transformers\/lib\/python3.8\/site-packages\/datasets\/fingerprint.py\", line 398, in wrapper\r\n out = func(self, *args, **kwargs)\r\n File \".\/envs\/transformers\/lib\/python3.8\/site-packages\/datasets\/arrow_dataset.py\", line 2169, in filter\r\n indices = self.map(\r\n File \".\/envs\/transformers\/lib\/python3.8\/site-packages\/datasets\/arrow_dataset.py\", line 1686, in map\r\n return self._map_single(\r\n File \".\/envs\/transformers\/lib\/python3.8\/site-packages\/datasets\/arrow_dataset.py\", line 185, in wrapper\r\n out: Union[\"Dataset\", \"DatasetDict\"] = func(self, *args, **kwargs)\r\n File \".\/envs\/transformers\/lib\/python3.8\/site-packages\/datasets\/fingerprint.py\", line 398, in wrapper\r\n out = func(self, *args, **kwargs)\r\n File \".\/envs\/transformers\/lib\/python3.8\/site-packages\/datasets\/arrow_dataset.py\", line 1896, in _map_single\r\n return Dataset.from_file(cache_file_name, info=info, split=self.split)\r\n File \".\/envs\/transformers\/lib\/python3.8\/site-packages\/datasets\/arrow_dataset.py\", line 343, in from_file\r\n return cls(\r\n File \".\/envs\/transformers\/lib\/python3.8\/site-packages\/datasets\/arrow_dataset.py\", line 282, in __init__\r\n self.info.features = self.info.features.reorder_fields_as(inferred_features)\r\n File \".\/envs\/transformers\/lib\/python3.8\/site-packages\/datasets\/features.py\", line 1151, in reorder_fields_as\r\n return Features(recursive_reorder(self, other))\r\n File \".\/envs\/transformers\/lib\/python3.8\/site-packages\/datasets\/features.py\", line 1140, in recursive_reorder\r\n raise ValueError(f\"Keys mismatch: between {source} and {target}\" + stack_position)\r\nValueError: Keys mismatch: between {'indices': Value(dtype='uint64', id=None)} and {'file': Value(dtype='string', id=None), 'text': Value(dtype='string', id=None), 'speaker_id': Value(dtype='int64', id=None), 'chapter_id': Value(dtype='int64', id=None), 'id': Value(dtype='string', id=None)}\r\n\r\nProcess finished with exit code 1\r\n\r\n```\r\n\r\n## Environment info\r\n- `datasets` version: 1.12.1\r\n- Platform: Linux-5.11.0-34-generic-x86_64-with-glibc2.17\r\n- Python version: 3.8.10\r\n- PyArrow version: 5.0.0\r\n","comment_length":22,"text":"Backwards compatibility broken for cached datasets that use `.filter()`\n## Describe the bug\r\nAfter upgrading to datasets `1.12.0`, some cached `.filter()` steps from `1.11.0` started failing with \r\n`ValueError: Keys mismatch: between {'indices': Value(dtype='uint64', id=None)} and {'file': Value(dtype='string', id=None), 'text': Value(dtype='string', id=None), 'speaker_id': Value(dtype='int64', id=None), 'chapter_id': Value(dtype='int64', id=None), 'id': Value(dtype='string', id=None)}`\r\n\r\nRelated feature: https:\/\/github.com\/huggingface\/datasets\/pull\/2836\r\n\r\n:question: This is probably a `wontfix` bug, since it can be solved by simply cleaning the related cache dirs, but the workaround could be useful for someone googling the error :) \r\n\r\n## Workaround\r\nRemove the cache for the given dataset, e.g. `rm -rf ~\/.cache\/huggingface\/datasets\/librispeech_asr`.\r\n\r\n## Steps to reproduce the bug\r\n1. Delete `~\/.cache\/huggingface\/datasets\/librispeech_asr` if it exists.\r\n\r\n2. `pip install datasets==1.11.0` and run the following snippet:\r\n\r\n```python\r\nfrom datasets import load_dataset\r\n\r\nids = [\"1272-141231-0000\"]\r\nds = load_dataset(\"patrickvonplaten\/librispeech_asr_dummy\", \"clean\", split=\"validation\")\r\nds = ds.filter(lambda x: x[\"id\"] in ids)\r\n```\r\n3. `pip install datasets==1.12.1` and re-run the code again\r\n\r\n## Expected results\r\nSame result as with the previous `datasets` version.\r\n\r\n## Actual results\r\n```bash\r\nReusing dataset librispeech_asr (.\/.cache\/huggingface\/datasets\/librispeech_asr\/clean\/2.1.0\/468ec03677f46a8714ac6b5b64dba02d246a228d92cbbad7f3dc190fa039eab1)\r\nLoading cached processed dataset at .\/.cache\/huggingface\/datasets\/librispeech_asr\/clean\/2.1.0\/468ec03677f46a8714ac6b5b64dba02d246a228d92cbbad7f3dc190fa039eab1\/cache-cd1c29844fdbc87a.arrow\r\nTraceback (most recent call last):\r\n File \".\/repos\/transformers\/src\/transformers\/models\/wav2vec2\/try_dataset.py\", line 5, in \r\n ds = ds.filter(lambda x: x[\"id\"] in ids)\r\n File \".\/envs\/transformers\/lib\/python3.8\/site-packages\/datasets\/arrow_dataset.py\", line 185, in wrapper\r\n out: Union[\"Dataset\", \"DatasetDict\"] = func(self, *args, **kwargs)\r\n File \".\/envs\/transformers\/lib\/python3.8\/site-packages\/datasets\/fingerprint.py\", line 398, in wrapper\r\n out = func(self, *args, **kwargs)\r\n File \".\/envs\/transformers\/lib\/python3.8\/site-packages\/datasets\/arrow_dataset.py\", line 2169, in filter\r\n indices = self.map(\r\n File \".\/envs\/transformers\/lib\/python3.8\/site-packages\/datasets\/arrow_dataset.py\", line 1686, in map\r\n return self._map_single(\r\n File \".\/envs\/transformers\/lib\/python3.8\/site-packages\/datasets\/arrow_dataset.py\", line 185, in wrapper\r\n out: Union[\"Dataset\", \"DatasetDict\"] = func(self, *args, **kwargs)\r\n File \".\/envs\/transformers\/lib\/python3.8\/site-packages\/datasets\/fingerprint.py\", line 398, in wrapper\r\n out = func(self, *args, **kwargs)\r\n File \".\/envs\/transformers\/lib\/python3.8\/site-packages\/datasets\/arrow_dataset.py\", line 1896, in _map_single\r\n return Dataset.from_file(cache_file_name, info=info, split=self.split)\r\n File \".\/envs\/transformers\/lib\/python3.8\/site-packages\/datasets\/arrow_dataset.py\", line 343, in from_file\r\n return cls(\r\n File \".\/envs\/transformers\/lib\/python3.8\/site-packages\/datasets\/arrow_dataset.py\", line 282, in __init__\r\n self.info.features = self.info.features.reorder_fields_as(inferred_features)\r\n File \".\/envs\/transformers\/lib\/python3.8\/site-packages\/datasets\/features.py\", line 1151, in reorder_fields_as\r\n return Features(recursive_reorder(self, other))\r\n File \".\/envs\/transformers\/lib\/python3.8\/site-packages\/datasets\/features.py\", line 1140, in recursive_reorder\r\n raise ValueError(f\"Keys mismatch: between {source} and {target}\" + stack_position)\r\nValueError: Keys mismatch: between {'indices': Value(dtype='uint64', id=None)} and {'file': Value(dtype='string', id=None), 'text': Value(dtype='string', id=None), 'speaker_id': Value(dtype='int64', id=None), 'chapter_id': Value(dtype='int64', id=None), 'id': Value(dtype='string', id=None)}\r\n\r\nProcess finished with exit code 1\r\n\r\n```\r\n\r\n## Environment info\r\n- `datasets` version: 1.12.1\r\n- Platform: Linux-5.11.0-34-generic-x86_64-with-glibc2.17\r\n- Python version: 3.8.10\r\n- PyArrow version: 5.0.0\r\n\nWell it can cause issue with anyone that updates `datasets` and re-run some code that uses filter, so I'm creating a PR","embeddings":[-0.3049958348,0.1243880838,-0.0465583764,0.2368971109,0.1517290324,-0.0666598156,-0.0069413707,0.3285393715,0.1737383604,0.0293814819,-0.2313861847,0.3581927419,-0.1503304988,0.2527370155,-0.2977862358,-0.1378120482,0.0444693454,0.0321247876,-0.0614953265,0.166412577,-0.1239175051,0.4988580644,-0.1957765073,0.0070795678,0.2221652865,0.0513210297,0.1091236398,0.138566196,0.0798620358,-0.3392693698,0.5981172919,0.1064069718,-0.0348691642,0.498096019,-0.0001213098,0.099867776,0.4398066998,-0.1073976383,-0.4579497874,-0.3686879575,-0.192854017,0.1319934726,0.2131137848,0.0811629742,-0.0715122968,0.1143416911,-0.303493619,-0.346046567,0.2488765419,0.1989328116,0.2267285585,0.2136144787,0.2657418847,0.066587165,0.170179069,0.0826525018,0.0259718653,-0.0490864292,0.2940285802,0.014261202,0.043685697,0.4806104004,-0.3283523321,-0.2146463245,0.3046185374,-0.2982533276,0.1030709222,-0.3957037926,0.2999309897,0.0781370997,0.1462642252,-0.255518198,-0.5773367882,-0.3483263254,-0.5121472478,-0.2868291736,0.4164735079,-0.216358915,-0.2009968162,0.2200830877,-0.3342647851,-0.0270038545,0.0934983194,0.0535101071,-0.0584819205,0.2941842079,-0.1318320185,-0.0048626242,0.0149830757,-0.132900551,0.1924084276,-0.2750048339,-0.1968749017,0.1725800484,-0.0155595224,-0.0546131916,0.0954222009,0.3323281705,0.140570581,0.0356287211,0.0192098524,0.0845401734,-0.0409641638,0.0536773168,0.1373275816,0.475378722,0.4901195765,0.2467906624,0.2727875113,0.2854887545,-0.0078853024,-0.0013800253,0.5281852484,-0.1666412055,0.0747071281,0.1517307162,0.196695298,-0.4048683643,-0.3602132797,0.1689626127,-0.0769995078,-0.1250401586,0.2207264006,0.0534940958,0.1005648673,0.2142349631,0.1320811808,0.0338568501,-0.1964725256,0.0612344779,-0.1725096703,-0.1739457697,0.0011616903,-0.1112991124,-0.0473052748,-0.89977777,0.1747701168,0.0482602492,0.0063488153,0.1058098748,-0.0697732121,0.207434997,0.1730166823,0.5229284167,-0.2213587761,0.0839003325,0.1671907902,-0.548327744,-0.1386865377,0.0125284484,-0.2415378094,-0.1112066656,0.2085879147,0.1735862345,-0.3296715915,0.0126515068,-0.17029953,0.4515431225,0.2592140436,-0.5466870666,-0.0779574588,-0.213380143,-0.4382343888,-0.1997140199,0.0971902013,0.4131370485,-0.5942543149,-0.3216674924,0.0216870401,0.0319067985,0.0399160981,-0.1229341999,-0.063439101,-0.2444722205,-0.1077689156,-0.1384182572,0.3606542647,-0.3195546865,-0.733892858,0.1537256837,0.2360213548,0.5342661738,-0.0709591061,-0.1374839246,0.2813950479,-0.2578290701,0.1459920853,0.0848445743,-0.1649033576,-0.0079940017,-0.3409692049,-0.2346946597,0.2238697708,-0.1502910852,0.0904174298,0.3348585963,0.0790057704,-0.2542206049,0.0502917804,0.0350500681,0.1049243882,0.1990925223,0.2453232259,0.2237652242,0.2863062918,-0.4365186989,-0.2801407278,0.2506181598,0.2141302824,-0.0600011088,-0.1524066925,-0.1982273012,-0.2678262889,-0.0884182155,-0.1589775681,-0.3044096231,0.0788738504,0.0225765873,0.3843636513,-0.0385034606,-0.1145259663,0.6218649745,0.3084535897,0.0944804996,-0.5049411654,0.229641512,-0.0548514128,0.0051691518,-0.0591857955,0.0377827175,0.2939595878,-0.0685256049,-0.2895757854,0.407875061,0.0392592028,0.084168233,-0.0276337974,0.1937460154,0.1773459613,0.2105839252,0.0639338642,0.2741822898,-0.0148837138,0.2087214142,-0.195456177,0.557248354,0.0293753464,0.3240505159,0.1208347678,0.148756206,0.3702490628,0.1305633932,-0.2189950645,-0.1370786875,0.3204495311,-0.4587228,0.0626674443,0.0123394234,-0.1297932267,0.3776263595,0.0716826022,0.0755119473,-0.0334524736,0.3119212389,-0.1593657285,-0.042190969,0.1813624054,0.4024861157,0.4014509916,0.2088322341,0.0507172868,0.0758817792,-0.1615392268,-0.0248774402,0.2082498521,0.1293462515,-0.1696399748,0.1535750628,0.3034210205,0.2732039392,-0.3061974645,0.0933934823,-0.2356239706,0.1195331365,-0.270581305,0.3616366684,-0.2810101509,-0.2280866951,0.076141715,-0.1678324938,-0.2615140676,-0.3875222206,0.089899376,0.3934392929,-0.086219281,0.2567750216,0.0147161586,0.2445412278,0.136167407,-0.1996820718,-0.4005717337,-0.2078815252,-0.3476482034,0.0053976052,0.2051323503,-0.1568491161,0.2970358729,-0.0763099566,-0.2066247612,-0.390294075,-0.9079098701,-0.0486286655,-0.123964861,0.6180298328,0.3176228106,-0.0916265026,-0.1352719367,-0.2327560335,0.1665794849,-0.0413150303,-0.4310828745,0.1568385363,0.0666227341,0.1682221591,-0.2003851682,-0.2270279974,-0.1135412008,-0.1458190233,0.0079398574,-0.1276904047,0.0849654675,0.3094039559,0.1123534068,-0.3165157139,-0.0872011557,0.1330857426,-0.3335401416,-0.0773374513,0.0739419162,-0.054162316,-0.2302434891,-0.034139622,-0.0319806486,0.0831893757,0.3736632466,-0.4069587886,-0.3791774213,-0.2713731527,-0.0048744264,0.2311773747,0.068360962,0.2319667786,0.1899949759,0.0391035639,-0.2794680893,-0.4342857301,-0.0886206329,-0.2365958691,0.1095489264,0.1482250243,0.6309939027,0.0345636867,0.4764270186,0.2947264612,0.0489611253,0.1398259848,0.1815548688,0.4972691238,-0.3258462548,-0.4095653892,-0.0467088632,-0.0461938493,0.0505124666,-0.153191432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{"html_url":"https:\/\/github.com\/huggingface\/datasets\/issues\/2943","title":"Backwards compatibility broken for cached datasets that use `.filter()`","comments":"I just merged a fix, let me know if you're still having this kind of issues :)\r\n\r\nWe'll do a release soon to make this fix available","body":"## Describe the bug\r\nAfter upgrading to datasets `1.12.0`, some cached `.filter()` steps from `1.11.0` started failing with \r\n`ValueError: Keys mismatch: between {'indices': Value(dtype='uint64', id=None)} and {'file': Value(dtype='string', id=None), 'text': Value(dtype='string', id=None), 'speaker_id': Value(dtype='int64', id=None), 'chapter_id': Value(dtype='int64', id=None), 'id': Value(dtype='string', id=None)}`\r\n\r\nRelated feature: https:\/\/github.com\/huggingface\/datasets\/pull\/2836\r\n\r\n:question: This is probably a `wontfix` bug, since it can be solved by simply cleaning the related cache dirs, but the workaround could be useful for someone googling the error :) \r\n\r\n## Workaround\r\nRemove the cache for the given dataset, e.g. `rm -rf ~\/.cache\/huggingface\/datasets\/librispeech_asr`.\r\n\r\n## Steps to reproduce the bug\r\n1. Delete `~\/.cache\/huggingface\/datasets\/librispeech_asr` if it exists.\r\n\r\n2. `pip install datasets==1.11.0` and run the following snippet:\r\n\r\n```python\r\nfrom datasets import load_dataset\r\n\r\nids = [\"1272-141231-0000\"]\r\nds = load_dataset(\"patrickvonplaten\/librispeech_asr_dummy\", \"clean\", split=\"validation\")\r\nds = ds.filter(lambda x: x[\"id\"] in ids)\r\n```\r\n3. `pip install datasets==1.12.1` and re-run the code again\r\n\r\n## Expected results\r\nSame result as with the previous `datasets` version.\r\n\r\n## Actual results\r\n```bash\r\nReusing dataset librispeech_asr (.\/.cache\/huggingface\/datasets\/librispeech_asr\/clean\/2.1.0\/468ec03677f46a8714ac6b5b64dba02d246a228d92cbbad7f3dc190fa039eab1)\r\nLoading cached processed dataset at .\/.cache\/huggingface\/datasets\/librispeech_asr\/clean\/2.1.0\/468ec03677f46a8714ac6b5b64dba02d246a228d92cbbad7f3dc190fa039eab1\/cache-cd1c29844fdbc87a.arrow\r\nTraceback (most recent call last):\r\n File \".\/repos\/transformers\/src\/transformers\/models\/wav2vec2\/try_dataset.py\", line 5, in \r\n ds = ds.filter(lambda x: x[\"id\"] in ids)\r\n File \".\/envs\/transformers\/lib\/python3.8\/site-packages\/datasets\/arrow_dataset.py\", line 185, in wrapper\r\n out: Union[\"Dataset\", \"DatasetDict\"] = func(self, *args, **kwargs)\r\n File \".\/envs\/transformers\/lib\/python3.8\/site-packages\/datasets\/fingerprint.py\", line 398, in wrapper\r\n out = func(self, *args, **kwargs)\r\n File \".\/envs\/transformers\/lib\/python3.8\/site-packages\/datasets\/arrow_dataset.py\", line 2169, in filter\r\n indices = self.map(\r\n File \".\/envs\/transformers\/lib\/python3.8\/site-packages\/datasets\/arrow_dataset.py\", line 1686, in map\r\n return self._map_single(\r\n File \".\/envs\/transformers\/lib\/python3.8\/site-packages\/datasets\/arrow_dataset.py\", line 185, in wrapper\r\n out: Union[\"Dataset\", \"DatasetDict\"] = func(self, *args, **kwargs)\r\n File \".\/envs\/transformers\/lib\/python3.8\/site-packages\/datasets\/fingerprint.py\", line 398, in wrapper\r\n out = func(self, *args, **kwargs)\r\n File \".\/envs\/transformers\/lib\/python3.8\/site-packages\/datasets\/arrow_dataset.py\", line 1896, in _map_single\r\n return Dataset.from_file(cache_file_name, info=info, split=self.split)\r\n File \".\/envs\/transformers\/lib\/python3.8\/site-packages\/datasets\/arrow_dataset.py\", line 343, in from_file\r\n return cls(\r\n File \".\/envs\/transformers\/lib\/python3.8\/site-packages\/datasets\/arrow_dataset.py\", line 282, in __init__\r\n self.info.features = self.info.features.reorder_fields_as(inferred_features)\r\n File \".\/envs\/transformers\/lib\/python3.8\/site-packages\/datasets\/features.py\", line 1151, in reorder_fields_as\r\n return Features(recursive_reorder(self, other))\r\n File \".\/envs\/transformers\/lib\/python3.8\/site-packages\/datasets\/features.py\", line 1140, in recursive_reorder\r\n raise ValueError(f\"Keys mismatch: between {source} and {target}\" + stack_position)\r\nValueError: Keys mismatch: between {'indices': Value(dtype='uint64', id=None)} and {'file': Value(dtype='string', id=None), 'text': Value(dtype='string', id=None), 'speaker_id': Value(dtype='int64', id=None), 'chapter_id': Value(dtype='int64', id=None), 'id': Value(dtype='string', id=None)}\r\n\r\nProcess finished with exit code 1\r\n\r\n```\r\n\r\n## Environment info\r\n- `datasets` version: 1.12.1\r\n- Platform: Linux-5.11.0-34-generic-x86_64-with-glibc2.17\r\n- Python version: 3.8.10\r\n- PyArrow version: 5.0.0\r\n","comment_length":27,"text":"Backwards compatibility broken for cached datasets that use `.filter()`\n## Describe the bug\r\nAfter upgrading to datasets `1.12.0`, some cached `.filter()` steps from `1.11.0` started failing with \r\n`ValueError: Keys mismatch: between {'indices': Value(dtype='uint64', id=None)} and {'file': Value(dtype='string', id=None), 'text': Value(dtype='string', id=None), 'speaker_id': Value(dtype='int64', id=None), 'chapter_id': Value(dtype='int64', id=None), 'id': Value(dtype='string', id=None)}`\r\n\r\nRelated feature: https:\/\/github.com\/huggingface\/datasets\/pull\/2836\r\n\r\n:question: This is probably a `wontfix` bug, since it can be solved by simply cleaning the related cache dirs, but the workaround could be useful for someone googling the error :) \r\n\r\n## Workaround\r\nRemove the cache for the given dataset, e.g. `rm -rf ~\/.cache\/huggingface\/datasets\/librispeech_asr`.\r\n\r\n## Steps to reproduce the bug\r\n1. Delete `~\/.cache\/huggingface\/datasets\/librispeech_asr` if it exists.\r\n\r\n2. `pip install datasets==1.11.0` and run the following snippet:\r\n\r\n```python\r\nfrom datasets import load_dataset\r\n\r\nids = [\"1272-141231-0000\"]\r\nds = load_dataset(\"patrickvonplaten\/librispeech_asr_dummy\", \"clean\", split=\"validation\")\r\nds = ds.filter(lambda x: x[\"id\"] in ids)\r\n```\r\n3. `pip install datasets==1.12.1` and re-run the code again\r\n\r\n## Expected results\r\nSame result as with the previous `datasets` version.\r\n\r\n## Actual results\r\n```bash\r\nReusing dataset librispeech_asr (.\/.cache\/huggingface\/datasets\/librispeech_asr\/clean\/2.1.0\/468ec03677f46a8714ac6b5b64dba02d246a228d92cbbad7f3dc190fa039eab1)\r\nLoading cached processed dataset at .\/.cache\/huggingface\/datasets\/librispeech_asr\/clean\/2.1.0\/468ec03677f46a8714ac6b5b64dba02d246a228d92cbbad7f3dc190fa039eab1\/cache-cd1c29844fdbc87a.arrow\r\nTraceback (most recent call last):\r\n File \".\/repos\/transformers\/src\/transformers\/models\/wav2vec2\/try_dataset.py\", line 5, in \r\n ds = ds.filter(lambda x: x[\"id\"] in ids)\r\n File \".\/envs\/transformers\/lib\/python3.8\/site-packages\/datasets\/arrow_dataset.py\", line 185, in wrapper\r\n out: Union[\"Dataset\", \"DatasetDict\"] = func(self, *args, **kwargs)\r\n File \".\/envs\/transformers\/lib\/python3.8\/site-packages\/datasets\/fingerprint.py\", line 398, in wrapper\r\n out = func(self, *args, **kwargs)\r\n File \".\/envs\/transformers\/lib\/python3.8\/site-packages\/datasets\/arrow_dataset.py\", line 2169, in filter\r\n indices = self.map(\r\n File \".\/envs\/transformers\/lib\/python3.8\/site-packages\/datasets\/arrow_dataset.py\", line 1686, in map\r\n return self._map_single(\r\n File \".\/envs\/transformers\/lib\/python3.8\/site-packages\/datasets\/arrow_dataset.py\", line 185, in wrapper\r\n out: Union[\"Dataset\", \"DatasetDict\"] = func(self, *args, **kwargs)\r\n File \".\/envs\/transformers\/lib\/python3.8\/site-packages\/datasets\/fingerprint.py\", line 398, in wrapper\r\n out = func(self, *args, **kwargs)\r\n File \".\/envs\/transformers\/lib\/python3.8\/site-packages\/datasets\/arrow_dataset.py\", line 1896, in _map_single\r\n return Dataset.from_file(cache_file_name, info=info, split=self.split)\r\n File \".\/envs\/transformers\/lib\/python3.8\/site-packages\/datasets\/arrow_dataset.py\", line 343, in from_file\r\n return cls(\r\n File \".\/envs\/transformers\/lib\/python3.8\/site-packages\/datasets\/arrow_dataset.py\", line 282, in __init__\r\n self.info.features = self.info.features.reorder_fields_as(inferred_features)\r\n File \".\/envs\/transformers\/lib\/python3.8\/site-packages\/datasets\/features.py\", line 1151, in reorder_fields_as\r\n return Features(recursive_reorder(self, other))\r\n File \".\/envs\/transformers\/lib\/python3.8\/site-packages\/datasets\/features.py\", line 1140, in recursive_reorder\r\n raise ValueError(f\"Keys mismatch: between {source} and {target}\" + stack_position)\r\nValueError: Keys mismatch: between {'indices': Value(dtype='uint64', id=None)} and {'file': Value(dtype='string', id=None), 'text': Value(dtype='string', id=None), 'speaker_id': Value(dtype='int64', id=None), 'chapter_id': Value(dtype='int64', id=None), 'id': Value(dtype='string', id=None)}\r\n\r\nProcess finished with exit code 1\r\n\r\n```\r\n\r\n## Environment info\r\n- `datasets` version: 1.12.1\r\n- Platform: Linux-5.11.0-34-generic-x86_64-with-glibc2.17\r\n- Python version: 3.8.10\r\n- PyArrow version: 5.0.0\r\n\nI just merged a fix, let me know if you're still having this kind of issues :)\r\n\r\nWe'll do a release soon to make this fix 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{"html_url":"https:\/\/github.com\/huggingface\/datasets\/issues\/2943","title":"Backwards compatibility broken for cached datasets that use `.filter()`","comments":"Definitely works on several manual cases with our dummy datasets, thank you @lhoestq !","body":"## Describe the bug\r\nAfter upgrading to datasets `1.12.0`, some cached `.filter()` steps from `1.11.0` started failing with \r\n`ValueError: Keys mismatch: between {'indices': Value(dtype='uint64', id=None)} and {'file': Value(dtype='string', id=None), 'text': Value(dtype='string', id=None), 'speaker_id': Value(dtype='int64', id=None), 'chapter_id': Value(dtype='int64', id=None), 'id': Value(dtype='string', id=None)}`\r\n\r\nRelated feature: https:\/\/github.com\/huggingface\/datasets\/pull\/2836\r\n\r\n:question: This is probably a `wontfix` bug, since it can be solved by simply cleaning the related cache dirs, but the workaround could be useful for someone googling the error :) \r\n\r\n## Workaround\r\nRemove the cache for the given dataset, e.g. `rm -rf ~\/.cache\/huggingface\/datasets\/librispeech_asr`.\r\n\r\n## Steps to reproduce the bug\r\n1. Delete `~\/.cache\/huggingface\/datasets\/librispeech_asr` if it exists.\r\n\r\n2. `pip install datasets==1.11.0` and run the following snippet:\r\n\r\n```python\r\nfrom datasets import load_dataset\r\n\r\nids = [\"1272-141231-0000\"]\r\nds = load_dataset(\"patrickvonplaten\/librispeech_asr_dummy\", \"clean\", split=\"validation\")\r\nds = ds.filter(lambda x: x[\"id\"] in ids)\r\n```\r\n3. `pip install datasets==1.12.1` and re-run the code again\r\n\r\n## Expected results\r\nSame result as with the previous `datasets` version.\r\n\r\n## Actual results\r\n```bash\r\nReusing dataset librispeech_asr (.\/.cache\/huggingface\/datasets\/librispeech_asr\/clean\/2.1.0\/468ec03677f46a8714ac6b5b64dba02d246a228d92cbbad7f3dc190fa039eab1)\r\nLoading cached processed dataset at .\/.cache\/huggingface\/datasets\/librispeech_asr\/clean\/2.1.0\/468ec03677f46a8714ac6b5b64dba02d246a228d92cbbad7f3dc190fa039eab1\/cache-cd1c29844fdbc87a.arrow\r\nTraceback (most recent call last):\r\n File \".\/repos\/transformers\/src\/transformers\/models\/wav2vec2\/try_dataset.py\", line 5, in \r\n ds = ds.filter(lambda x: x[\"id\"] in ids)\r\n File \".\/envs\/transformers\/lib\/python3.8\/site-packages\/datasets\/arrow_dataset.py\", line 185, in wrapper\r\n out: Union[\"Dataset\", \"DatasetDict\"] = func(self, *args, **kwargs)\r\n File \".\/envs\/transformers\/lib\/python3.8\/site-packages\/datasets\/fingerprint.py\", line 398, in wrapper\r\n out = func(self, *args, **kwargs)\r\n File \".\/envs\/transformers\/lib\/python3.8\/site-packages\/datasets\/arrow_dataset.py\", line 2169, in filter\r\n indices = self.map(\r\n File \".\/envs\/transformers\/lib\/python3.8\/site-packages\/datasets\/arrow_dataset.py\", line 1686, in map\r\n return self._map_single(\r\n File \".\/envs\/transformers\/lib\/python3.8\/site-packages\/datasets\/arrow_dataset.py\", line 185, in wrapper\r\n out: Union[\"Dataset\", \"DatasetDict\"] = func(self, *args, **kwargs)\r\n File \".\/envs\/transformers\/lib\/python3.8\/site-packages\/datasets\/fingerprint.py\", line 398, in wrapper\r\n out = func(self, *args, **kwargs)\r\n File \".\/envs\/transformers\/lib\/python3.8\/site-packages\/datasets\/arrow_dataset.py\", line 1896, in _map_single\r\n return Dataset.from_file(cache_file_name, info=info, split=self.split)\r\n File \".\/envs\/transformers\/lib\/python3.8\/site-packages\/datasets\/arrow_dataset.py\", line 343, in from_file\r\n return cls(\r\n File \".\/envs\/transformers\/lib\/python3.8\/site-packages\/datasets\/arrow_dataset.py\", line 282, in __init__\r\n self.info.features = self.info.features.reorder_fields_as(inferred_features)\r\n File \".\/envs\/transformers\/lib\/python3.8\/site-packages\/datasets\/features.py\", line 1151, in reorder_fields_as\r\n return Features(recursive_reorder(self, other))\r\n File \".\/envs\/transformers\/lib\/python3.8\/site-packages\/datasets\/features.py\", line 1140, in recursive_reorder\r\n raise ValueError(f\"Keys mismatch: between {source} and {target}\" + stack_position)\r\nValueError: Keys mismatch: between {'indices': Value(dtype='uint64', id=None)} and {'file': Value(dtype='string', id=None), 'text': Value(dtype='string', id=None), 'speaker_id': Value(dtype='int64', id=None), 'chapter_id': Value(dtype='int64', id=None), 'id': Value(dtype='string', id=None)}\r\n\r\nProcess finished with exit code 1\r\n\r\n```\r\n\r\n## Environment info\r\n- `datasets` version: 1.12.1\r\n- Platform: Linux-5.11.0-34-generic-x86_64-with-glibc2.17\r\n- Python version: 3.8.10\r\n- PyArrow version: 5.0.0\r\n","comment_length":14,"text":"Backwards compatibility broken for cached datasets that use `.filter()`\n## Describe the bug\r\nAfter upgrading to datasets `1.12.0`, some cached `.filter()` steps from `1.11.0` started failing with \r\n`ValueError: Keys mismatch: between {'indices': Value(dtype='uint64', id=None)} and {'file': Value(dtype='string', id=None), 'text': Value(dtype='string', id=None), 'speaker_id': Value(dtype='int64', id=None), 'chapter_id': Value(dtype='int64', id=None), 'id': Value(dtype='string', id=None)}`\r\n\r\nRelated feature: https:\/\/github.com\/huggingface\/datasets\/pull\/2836\r\n\r\n:question: This is probably a `wontfix` bug, since it can be solved by simply cleaning the related cache dirs, but the workaround could be useful for someone googling the error :) \r\n\r\n## Workaround\r\nRemove the cache for the given dataset, e.g. `rm -rf ~\/.cache\/huggingface\/datasets\/librispeech_asr`.\r\n\r\n## Steps to reproduce the bug\r\n1. Delete `~\/.cache\/huggingface\/datasets\/librispeech_asr` if it exists.\r\n\r\n2. `pip install datasets==1.11.0` and run the following snippet:\r\n\r\n```python\r\nfrom datasets import load_dataset\r\n\r\nids = [\"1272-141231-0000\"]\r\nds = load_dataset(\"patrickvonplaten\/librispeech_asr_dummy\", \"clean\", split=\"validation\")\r\nds = ds.filter(lambda x: x[\"id\"] in ids)\r\n```\r\n3. `pip install datasets==1.12.1` and re-run the code again\r\n\r\n## Expected results\r\nSame result as with the previous `datasets` version.\r\n\r\n## Actual results\r\n```bash\r\nReusing dataset librispeech_asr (.\/.cache\/huggingface\/datasets\/librispeech_asr\/clean\/2.1.0\/468ec03677f46a8714ac6b5b64dba02d246a228d92cbbad7f3dc190fa039eab1)\r\nLoading cached processed dataset at .\/.cache\/huggingface\/datasets\/librispeech_asr\/clean\/2.1.0\/468ec03677f46a8714ac6b5b64dba02d246a228d92cbbad7f3dc190fa039eab1\/cache-cd1c29844fdbc87a.arrow\r\nTraceback (most recent call last):\r\n File \".\/repos\/transformers\/src\/transformers\/models\/wav2vec2\/try_dataset.py\", line 5, in \r\n ds = ds.filter(lambda x: x[\"id\"] in ids)\r\n File \".\/envs\/transformers\/lib\/python3.8\/site-packages\/datasets\/arrow_dataset.py\", line 185, in wrapper\r\n out: Union[\"Dataset\", \"DatasetDict\"] = func(self, *args, **kwargs)\r\n File \".\/envs\/transformers\/lib\/python3.8\/site-packages\/datasets\/fingerprint.py\", line 398, in wrapper\r\n out = func(self, *args, **kwargs)\r\n File \".\/envs\/transformers\/lib\/python3.8\/site-packages\/datasets\/arrow_dataset.py\", line 2169, in filter\r\n indices = self.map(\r\n File \".\/envs\/transformers\/lib\/python3.8\/site-packages\/datasets\/arrow_dataset.py\", line 1686, in map\r\n return self._map_single(\r\n File \".\/envs\/transformers\/lib\/python3.8\/site-packages\/datasets\/arrow_dataset.py\", line 185, in wrapper\r\n out: Union[\"Dataset\", \"DatasetDict\"] = func(self, *args, **kwargs)\r\n File \".\/envs\/transformers\/lib\/python3.8\/site-packages\/datasets\/fingerprint.py\", line 398, in wrapper\r\n out = func(self, *args, **kwargs)\r\n File \".\/envs\/transformers\/lib\/python3.8\/site-packages\/datasets\/arrow_dataset.py\", line 1896, in _map_single\r\n return Dataset.from_file(cache_file_name, info=info, split=self.split)\r\n File \".\/envs\/transformers\/lib\/python3.8\/site-packages\/datasets\/arrow_dataset.py\", line 343, in from_file\r\n return cls(\r\n File \".\/envs\/transformers\/lib\/python3.8\/site-packages\/datasets\/arrow_dataset.py\", line 282, in __init__\r\n self.info.features = self.info.features.reorder_fields_as(inferred_features)\r\n File \".\/envs\/transformers\/lib\/python3.8\/site-packages\/datasets\/features.py\", line 1151, in reorder_fields_as\r\n return Features(recursive_reorder(self, other))\r\n File \".\/envs\/transformers\/lib\/python3.8\/site-packages\/datasets\/features.py\", line 1140, in recursive_reorder\r\n raise ValueError(f\"Keys mismatch: between {source} and {target}\" + stack_position)\r\nValueError: Keys mismatch: between {'indices': Value(dtype='uint64', id=None)} and {'file': Value(dtype='string', id=None), 'text': Value(dtype='string', id=None), 'speaker_id': Value(dtype='int64', id=None), 'chapter_id': Value(dtype='int64', id=None), 'id': Value(dtype='string', id=None)}\r\n\r\nProcess finished with exit code 1\r\n\r\n```\r\n\r\n## Environment info\r\n- `datasets` version: 1.12.1\r\n- Platform: Linux-5.11.0-34-generic-x86_64-with-glibc2.17\r\n- Python version: 3.8.10\r\n- PyArrow version: 5.0.0\r\n\nDefinitely works on several manual cases with our dummy datasets, thank you @lhoestq !","embeddings":[-0.3049958348,0.1243880838,-0.0465583764,0.2368971109,0.1517290324,-0.0666598156,-0.0069413707,0.3285393715,0.1737383604,0.0293814819,-0.2313861847,0.3581927419,-0.1503304988,0.2527370155,-0.2977862358,-0.1378120482,0.0444693454,0.0321247876,-0.0614953265,0.166412577,-0.1239175051,0.4988580644,-0.1957765073,0.0070795678,0.2221652865,0.0513210297,0.1091236398,0.138566196,0.0798620358,-0.3392693698,0.5981172919,0.1064069718,-0.0348691642,0.498096019,-0.0001213098,0.099867776,0.4398066998,-0.1073976383,-0.4579497874,-0.3686879575,-0.192854017,0.1319934726,0.2131137848,0.0811629742,-0.0715122968,0.1143416911,-0.303493619,-0.346046567,0.2488765419,0.1989328116,0.2267285585,0.2136144787,0.2657418847,0.066587165,0.170179069,0.0826525018,0.0259718653,-0.0490864292,0.2940285802,0.014261202,0.043685697,0.4806104004,-0.3283523321,-0.2146463245,0.3046185374,-0.2982533276,0.1030709222,-0.3957037926,0.2999309897,0.0781370997,0.1462642252,-0.255518198,-0.5773367882,-0.3483263254,-0.5121472478,-0.2868291736,0.4164735079,-0.216358915,-0.2009968162,0.2200830877,-0.3342647851,-0.0270038545,0.0934983194,0.0535101071,-0.0584819205,0.2941842079,-0.1318320185,-0.0048626242,0.0149830757,-0.132900551,0.1924084276,-0.2750048339,-0.1968749017,0.1725800484,-0.0155595224,-0.0546131916,0.0954222009,0.3323281705,0.140570581,0.0356287211,0.0192098524,0.0845401734,-0.0409641638,0.0536773168,0.1373275816,0.475378722,0.4901195765,0.2467906624,0.2727875113,0.2854887545,-0.0078853024,-0.0013800253,0.5281852484,-0.1666412055,0.0747071281,0.1517307162,0.196695298,-0.4048683643,-0.3602132797,0.1689626127,-0.0769995078,-0.1250401586,0.2207264006,0.0534940958,0.1005648673,0.2142349631,0.1320811808,0.0338568501,-0.1964725256,0.0612344779,-0.1725096703,-0.1739457697,0.0011616903,-0.1112991124,-0.0473052748,-0.89977777,0.1747701168,0.0482602492,0.0063488153,0.1058098748,-0.0697732121,0.207434997,0.1730166823,0.5229284167,-0.2213587761,0.0839003325,0.1671907902,-0.548327744,-0.1386865377,0.0125284484,-0.2415378094,-0.1112066656,0.2085879147,0.1735862345,-0.3296715915,0.0126515068,-0.17029953,0.4515431225,0.2592140436,-0.5466870666,-0.0779574588,-0.213380143,-0.4382343888,-0.1997140199,0.0971902013,0.4131370485,-0.5942543149,-0.3216674924,0.0216870401,0.0319067985,0.0399160981,-0.1229341999,-0.063439101,-0.2444722205,-0.1077689156,-0.1384182572,0.3606542647,-0.3195546865,-0.733892858,0.1537256837,0.2360213548,0.5342661738,-0.0709591061,-0.1374839246,0.2813950479,-0.2578290701,0.1459920853,0.0848445743,-0.1649033576,-0.0079940017,-0.3409692049,-0.2346946597,0.2238697708,-0.1502910852,0.0904174298,0.3348585963,0.0790057704,-0.2542206049,0.0502917804,0.0350500681,0.1049243882,0.1990925223,0.2453232259,0.2237652242,0.2863062918,-0.4365186989,-0.2801407278,0.2506181598,0.2141302824,-0.0600011088,-0.1524066925,-0.1982273012,-0.2678262889,-0.0884182155,-0.1589775681,-0.3044096231,0.0788738504,0.0225765873,0.3843636513,-0.0385034606,-0.1145259663,0.6218649745,0.3084535897,0.0944804996,-0.5049411654,0.229641512,-0.0548514128,0.0051691518,-0.0591857955,0.0377827175,0.2939595878,-0.0685256049,-0.2895757854,0.407875061,0.0392592028,0.084168233,-0.0276337974,0.1937460154,0.1773459613,0.2105839252,0.0639338642,0.2741822898,-0.0148837138,0.2087214142,-0.195456177,0.557248354,0.0293753464,0.3240505159,0.1208347678,0.148756206,0.3702490628,0.1305633932,-0.2189950645,-0.1370786875,0.3204495311,-0.4587228,0.0626674443,0.0123394234,-0.1297932267,0.3776263595,0.0716826022,0.0755119473,-0.0334524736,0.3119212389,-0.1593657285,-0.042190969,0.1813624054,0.4024861157,0.4014509916,0.2088322341,0.0507172868,0.0758817792,-0.1615392268,-0.0248774402,0.2082498521,0.1293462515,-0.1696399748,0.1535750628,0.3034210205,0.2732039392,-0.3061974645,0.0933934823,-0.2356239706,0.1195331365,-0.270581305,0.3616366684,-0.2810101509,-0.2280866951,0.076141715,-0.1678324938,-0.2615140676,-0.3875222206,0.089899376,0.3934392929,-0.086219281,0.2567750216,0.0147161586,0.2445412278,0.136167407,-0.1996820718,-0.4005717337,-0.2078815252,-0.3476482034,0.0053976052,0.2051323503,-0.1568491161,0.2970358729,-0.0763099566,-0.2066247612,-0.390294075,-0.9079098701,-0.0486286655,-0.123964861,0.6180298328,0.3176228106,-0.0916265026,-0.1352719367,-0.2327560335,0.1665794849,-0.0413150303,-0.4310828745,0.1568385363,0.0666227341,0.1682221591,-0.2003851682,-0.2270279974,-0.1135412008,-0.1458190233,0.0079398574,-0.1276904047,0.0849654675,0.3094039559,0.1123534068,-0.3165157139,-0.0872011557,0.1330857426,-0.3335401416,-0.0773374513,0.0739419162,-0.054162316,-0.2302434891,-0.034139622,-0.0319806486,0.0831893757,0.3736632466,-0.4069587886,-0.3791774213,-0.2713731527,-0.0048744264,0.2311773747,0.068360962,0.2319667786,0.1899949759,0.0391035639,-0.2794680893,-0.4342857301,-0.0886206329,-0.2365958691,0.1095489264,0.1482250243,0.6309939027,0.0345636867,0.4764270186,0.2947264612,0.0489611253,0.1398259848,0.1815548688,0.4972691238,-0.3258462548,-0.4095653892,-0.0467088632,-0.0461938493,0.0505124666,-0.1531914324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{"html_url":"https:\/\/github.com\/huggingface\/datasets\/issues\/2943","title":"Backwards compatibility broken for cached datasets that use `.filter()`","comments":"Fixed by #2947.","body":"## Describe the bug\r\nAfter upgrading to datasets `1.12.0`, some cached `.filter()` steps from `1.11.0` started failing with \r\n`ValueError: Keys mismatch: between {'indices': Value(dtype='uint64', id=None)} and {'file': Value(dtype='string', id=None), 'text': Value(dtype='string', id=None), 'speaker_id': Value(dtype='int64', id=None), 'chapter_id': Value(dtype='int64', id=None), 'id': Value(dtype='string', id=None)}`\r\n\r\nRelated feature: https:\/\/github.com\/huggingface\/datasets\/pull\/2836\r\n\r\n:question: This is probably a `wontfix` bug, since it can be solved by simply cleaning the related cache dirs, but the workaround could be useful for someone googling the error :) \r\n\r\n## Workaround\r\nRemove the cache for the given dataset, e.g. `rm -rf ~\/.cache\/huggingface\/datasets\/librispeech_asr`.\r\n\r\n## Steps to reproduce the bug\r\n1. Delete `~\/.cache\/huggingface\/datasets\/librispeech_asr` if it exists.\r\n\r\n2. `pip install datasets==1.11.0` and run the following snippet:\r\n\r\n```python\r\nfrom datasets import load_dataset\r\n\r\nids = [\"1272-141231-0000\"]\r\nds = load_dataset(\"patrickvonplaten\/librispeech_asr_dummy\", \"clean\", split=\"validation\")\r\nds = ds.filter(lambda x: x[\"id\"] in ids)\r\n```\r\n3. `pip install datasets==1.12.1` and re-run the code again\r\n\r\n## Expected results\r\nSame result as with the previous `datasets` version.\r\n\r\n## Actual results\r\n```bash\r\nReusing dataset librispeech_asr (.\/.cache\/huggingface\/datasets\/librispeech_asr\/clean\/2.1.0\/468ec03677f46a8714ac6b5b64dba02d246a228d92cbbad7f3dc190fa039eab1)\r\nLoading cached processed dataset at .\/.cache\/huggingface\/datasets\/librispeech_asr\/clean\/2.1.0\/468ec03677f46a8714ac6b5b64dba02d246a228d92cbbad7f3dc190fa039eab1\/cache-cd1c29844fdbc87a.arrow\r\nTraceback (most recent call last):\r\n File \".\/repos\/transformers\/src\/transformers\/models\/wav2vec2\/try_dataset.py\", line 5, in \r\n ds = ds.filter(lambda x: x[\"id\"] in ids)\r\n File \".\/envs\/transformers\/lib\/python3.8\/site-packages\/datasets\/arrow_dataset.py\", line 185, in wrapper\r\n out: Union[\"Dataset\", \"DatasetDict\"] = func(self, *args, **kwargs)\r\n File \".\/envs\/transformers\/lib\/python3.8\/site-packages\/datasets\/fingerprint.py\", line 398, in wrapper\r\n out = func(self, *args, **kwargs)\r\n File \".\/envs\/transformers\/lib\/python3.8\/site-packages\/datasets\/arrow_dataset.py\", line 2169, in filter\r\n indices = self.map(\r\n File \".\/envs\/transformers\/lib\/python3.8\/site-packages\/datasets\/arrow_dataset.py\", line 1686, in map\r\n return self._map_single(\r\n File \".\/envs\/transformers\/lib\/python3.8\/site-packages\/datasets\/arrow_dataset.py\", line 185, in wrapper\r\n out: Union[\"Dataset\", \"DatasetDict\"] = func(self, *args, **kwargs)\r\n File \".\/envs\/transformers\/lib\/python3.8\/site-packages\/datasets\/fingerprint.py\", line 398, in wrapper\r\n out = func(self, *args, **kwargs)\r\n File \".\/envs\/transformers\/lib\/python3.8\/site-packages\/datasets\/arrow_dataset.py\", line 1896, in _map_single\r\n return Dataset.from_file(cache_file_name, info=info, split=self.split)\r\n File \".\/envs\/transformers\/lib\/python3.8\/site-packages\/datasets\/arrow_dataset.py\", line 343, in from_file\r\n return cls(\r\n File \".\/envs\/transformers\/lib\/python3.8\/site-packages\/datasets\/arrow_dataset.py\", line 282, in __init__\r\n self.info.features = self.info.features.reorder_fields_as(inferred_features)\r\n File \".\/envs\/transformers\/lib\/python3.8\/site-packages\/datasets\/features.py\", line 1151, in reorder_fields_as\r\n return Features(recursive_reorder(self, other))\r\n File \".\/envs\/transformers\/lib\/python3.8\/site-packages\/datasets\/features.py\", line 1140, in recursive_reorder\r\n raise ValueError(f\"Keys mismatch: between {source} and {target}\" + stack_position)\r\nValueError: Keys mismatch: between {'indices': Value(dtype='uint64', id=None)} and {'file': Value(dtype='string', id=None), 'text': Value(dtype='string', id=None), 'speaker_id': Value(dtype='int64', id=None), 'chapter_id': Value(dtype='int64', id=None), 'id': Value(dtype='string', id=None)}\r\n\r\nProcess finished with exit code 1\r\n\r\n```\r\n\r\n## Environment info\r\n- `datasets` version: 1.12.1\r\n- Platform: Linux-5.11.0-34-generic-x86_64-with-glibc2.17\r\n- Python version: 3.8.10\r\n- PyArrow version: 5.0.0\r\n","comment_length":3,"text":"Backwards compatibility broken for cached datasets that use `.filter()`\n## Describe the bug\r\nAfter upgrading to datasets `1.12.0`, some cached `.filter()` steps from `1.11.0` started failing with \r\n`ValueError: Keys mismatch: between {'indices': Value(dtype='uint64', id=None)} and {'file': Value(dtype='string', id=None), 'text': Value(dtype='string', id=None), 'speaker_id': Value(dtype='int64', id=None), 'chapter_id': Value(dtype='int64', id=None), 'id': Value(dtype='string', id=None)}`\r\n\r\nRelated feature: https:\/\/github.com\/huggingface\/datasets\/pull\/2836\r\n\r\n:question: This is probably a `wontfix` bug, since it can be solved by simply cleaning the related cache dirs, but the workaround could be useful for someone googling the error :) \r\n\r\n## Workaround\r\nRemove the cache for the given dataset, e.g. `rm -rf ~\/.cache\/huggingface\/datasets\/librispeech_asr`.\r\n\r\n## Steps to reproduce the bug\r\n1. Delete `~\/.cache\/huggingface\/datasets\/librispeech_asr` if it exists.\r\n\r\n2. `pip install datasets==1.11.0` and run the following snippet:\r\n\r\n```python\r\nfrom datasets import load_dataset\r\n\r\nids = [\"1272-141231-0000\"]\r\nds = load_dataset(\"patrickvonplaten\/librispeech_asr_dummy\", \"clean\", split=\"validation\")\r\nds = ds.filter(lambda x: x[\"id\"] in ids)\r\n```\r\n3. `pip install datasets==1.12.1` and re-run the code again\r\n\r\n## Expected results\r\nSame result as with the previous `datasets` version.\r\n\r\n## Actual results\r\n```bash\r\nReusing dataset librispeech_asr (.\/.cache\/huggingface\/datasets\/librispeech_asr\/clean\/2.1.0\/468ec03677f46a8714ac6b5b64dba02d246a228d92cbbad7f3dc190fa039eab1)\r\nLoading cached processed dataset at .\/.cache\/huggingface\/datasets\/librispeech_asr\/clean\/2.1.0\/468ec03677f46a8714ac6b5b64dba02d246a228d92cbbad7f3dc190fa039eab1\/cache-cd1c29844fdbc87a.arrow\r\nTraceback (most recent call last):\r\n File \".\/repos\/transformers\/src\/transformers\/models\/wav2vec2\/try_dataset.py\", line 5, in \r\n ds = ds.filter(lambda x: x[\"id\"] in ids)\r\n File \".\/envs\/transformers\/lib\/python3.8\/site-packages\/datasets\/arrow_dataset.py\", line 185, in wrapper\r\n out: Union[\"Dataset\", \"DatasetDict\"] = func(self, *args, **kwargs)\r\n File \".\/envs\/transformers\/lib\/python3.8\/site-packages\/datasets\/fingerprint.py\", line 398, in wrapper\r\n out = func(self, *args, **kwargs)\r\n File \".\/envs\/transformers\/lib\/python3.8\/site-packages\/datasets\/arrow_dataset.py\", line 2169, in filter\r\n indices = self.map(\r\n File \".\/envs\/transformers\/lib\/python3.8\/site-packages\/datasets\/arrow_dataset.py\", line 1686, in map\r\n return self._map_single(\r\n File \".\/envs\/transformers\/lib\/python3.8\/site-packages\/datasets\/arrow_dataset.py\", line 185, in wrapper\r\n out: Union[\"Dataset\", \"DatasetDict\"] = func(self, *args, **kwargs)\r\n File \".\/envs\/transformers\/lib\/python3.8\/site-packages\/datasets\/fingerprint.py\", line 398, in wrapper\r\n out = func(self, *args, **kwargs)\r\n File \".\/envs\/transformers\/lib\/python3.8\/site-packages\/datasets\/arrow_dataset.py\", line 1896, in _map_single\r\n return Dataset.from_file(cache_file_name, info=info, split=self.split)\r\n File \".\/envs\/transformers\/lib\/python3.8\/site-packages\/datasets\/arrow_dataset.py\", line 343, in from_file\r\n return cls(\r\n File \".\/envs\/transformers\/lib\/python3.8\/site-packages\/datasets\/arrow_dataset.py\", line 282, in __init__\r\n self.info.features = self.info.features.reorder_fields_as(inferred_features)\r\n File \".\/envs\/transformers\/lib\/python3.8\/site-packages\/datasets\/features.py\", line 1151, in reorder_fields_as\r\n return Features(recursive_reorder(self, other))\r\n File \".\/envs\/transformers\/lib\/python3.8\/site-packages\/datasets\/features.py\", line 1140, in recursive_reorder\r\n raise ValueError(f\"Keys mismatch: between {source} and {target}\" + stack_position)\r\nValueError: Keys mismatch: between {'indices': Value(dtype='uint64', id=None)} and {'file': Value(dtype='string', id=None), 'text': Value(dtype='string', id=None), 'speaker_id': Value(dtype='int64', id=None), 'chapter_id': Value(dtype='int64', id=None), 'id': Value(dtype='string', id=None)}\r\n\r\nProcess finished with exit code 1\r\n\r\n```\r\n\r\n## Environment info\r\n- `datasets` version: 1.12.1\r\n- Platform: Linux-5.11.0-34-generic-x86_64-with-glibc2.17\r\n- Python version: 3.8.10\r\n- PyArrow version: 5.0.0\r\n\nFixed by #2947.","embeddings":[-0.3049958348,0.1243880838,-0.0465583764,0.2368971109,0.1517290324,-0.0666598156,-0.0069413707,0.3285393715,0.1737383604,0.0293814819,-0.2313861847,0.3581927419,-0.1503304988,0.2527370155,-0.2977862358,-0.1378120482,0.0444693454,0.0321247876,-0.0614953265,0.166412577,-0.1239175051,0.4988580644,-0.1957765073,0.0070795678,0.2221652865,0.0513210297,0.1091236398,0.138566196,0.0798620358,-0.3392693698,0.5981172919,0.1064069718,-0.0348691642,0.498096019,-0.0001213098,0.099867776,0.4398066998,-0.1073976383,-0.4579497874,-0.3686879575,-0.192854017,0.1319934726,0.2131137848,0.0811629742,-0.0715122968,0.1143416911,-0.303493619,-0.346046567,0.2488765419,0.1989328116,0.2267285585,0.2136144787,0.2657418847,0.066587165,0.170179069,0.0826525018,0.0259718653,-0.0490864292,0.2940285802,0.014261202,0.043685697,0.4806104004,-0.3283523321,-0.2146463245,0.3046185374,-0.2982533276,0.1030709222,-0.3957037926,0.2999309897,0.0781370997,0.1462642252,-0.255518198,-0.5773367882,-0.3483263254,-0.5121472478,-0.2868291736,0.4164735079,-0.216358915,-0.2009968162,0.2200830877,-0.3342647851,-0.0270038545,0.0934983194,0.0535101071,-0.0584819205,0.2941842079,-0.1318320185,-0.0048626242,0.0149830757,-0.132900551,0.1924084276,-0.2750048339,-0.1968749017,0.1725800484,-0.0155595224,-0.0546131916,0.0954222009,0.3323281705,0.140570581,0.0356287211,0.0192098524,0.0845401734,-0.0409641638,0.0536773168,0.1373275816,0.475378722,0.4901195765,0.2467906624,0.2727875113,0.2854887545,-0.0078853024,-0.0013800253,0.5281852484,-0.1666412055,0.0747071281,0.1517307162,0.196695298,-0.4048683643,-0.3602132797,0.1689626127,-0.0769995078,-0.1250401586,0.2207264006,0.0534940958,0.1005648673,0.2142349631,0.1320811808,0.0338568501,-0.1964725256,0.0612344779,-0.1725096703,-0.1739457697,0.0011616903,-0.1112991124,-0.0473052748,-0.89977777,0.1747701168,0.0482602492,0.0063488153,0.1058098748,-0.0697732121,0.207434997,0.1730166823,0.5229284167,-0.2213587761,0.0839003325,0.1671907902,-0.548327744,-0.1386865377,0.0125284484,-0.2415378094,-0.1112066656,0.2085879147,0.1735862345,-0.3296715915,0.0126515068,-0.17029953,0.4515431225,0.2592140436,-0.5466870666,-0.0779574588,-0.213380143,-0.4382343888,-0.1997140199,0.0971902013,0.4131370485,-0.5942543149,-0.3216674924,0.0216870401,0.0319067985,0.0399160981,-0.1229341999,-0.063439101,-0.2444722205,-0.1077689156,-0.1384182572,0.3606542647,-0.3195546865,-0.733892858,0.1537256837,0.2360213548,0.5342661738,-0.0709591061,-0.1374839246,0.2813950479,-0.2578290701,0.1459920853,0.0848445743,-0.1649033576,-0.0079940017,-0.3409692049,-0.2346946597,0.2238697708,-0.1502910852,0.0904174298,0.3348585963,0.0790057704,-0.2542206049,0.0502917804,0.0350500681,0.1049243882,0.1990925223,0.2453232259,0.2237652242,0.2863062918,-0.4365186989,-0.2801407278,0.2506181598,0.2141302824,-0.0600011088,-0.1524066925,-0.1982273012,-0.2678262889,-0.0884182155,-0.1589775681,-0.3044096231,0.0788738504,0.0225765873,0.3843636513,-0.0385034606,-0.1145259663,0.6218649745,0.3084535897,0.0944804996,-0.5049411654,0.229641512,-0.0548514128,0.0051691518,-0.0591857955,0.0377827175,0.2939595878,-0.0685256049,-0.2895757854,0.407875061,0.0392592028,0.084168233,-0.0276337974,0.1937460154,0.1773459613,0.2105839252,0.0639338642,0.2741822898,-0.0148837138,0.2087214142,-0.195456177,0.557248354,0.0293753464,0.3240505159,0.1208347678,0.148756206,0.3702490628,0.1305633932,-0.2189950645,-0.1370786875,0.3204495311,-0.4587228,0.0626674443,0.0123394234,-0.1297932267,0.3776263595,0.0716826022,0.0755119473,-0.0334524736,0.3119212389,-0.1593657285,-0.042190969,0.1813624054,0.4024861157,0.4014509916,0.2088322341,0.0507172868,0.0758817792,-0.1615392268,-0.0248774402,0.2082498521,0.1293462515,-0.1696399748,0.1535750628,0.3034210205,0.2732039392,-0.3061974645,0.0933934823,-0.2356239706,0.1195331365,-0.270581305,0.3616366684,-0.2810101509,-0.2280866951,0.076141715,-0.1678324938,-0.2615140676,-0.3875222206,0.089899376,0.3934392929,-0.086219281,0.2567750216,0.0147161586,0.2445412278,0.136167407,-0.1996820718,-0.4005717337,-0.2078815252,-0.3476482034,0.0053976052,0.2051323503,-0.1568491161,0.2970358729,-0.0763099566,-0.2066247612,-0.390294075,-0.9079098701,-0.0486286655,-0.123964861,0.6180298328,0.3176228106,-0.0916265026,-0.1352719367,-0.2327560335,0.1665794849,-0.0413150303,-0.4310828745,0.1568385363,0.0666227341,0.1682221591,-0.2003851682,-0.2270279974,-0.1135412008,-0.1458190233,0.0079398574,-0.1276904047,0.0849654675,0.3094039559,0.1123534068,-0.3165157139,-0.0872011557,0.1330857426,-0.3335401416,-0.0773374513,0.0739419162,-0.054162316,-0.2302434891,-0.034139622,-0.0319806486,0.0831893757,0.3736632466,-0.4069587886,-0.3791774213,-0.2713731527,-0.0048744264,0.2311773747,0.068360962,0.2319667786,0.1899949759,0.0391035639,-0.2794680893,-0.4342857301,-0.0886206329,-0.2365958691,0.1095489264,0.1482250243,0.6309939027,0.0345636867,0.4764270186,0.2947264612,0.0489611253,0.1398259848,0.1815548688,0.4972691238,-0.3258462548,-0.4095653892,-0.0467088632,-0.0461938493,0.0505124666,-0.15319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{"html_url":"https:\/\/github.com\/huggingface\/datasets\/issues\/2941","title":"OSCAR unshuffled_original_ko: NonMatchingSplitsSizesError","comments":"I tried `unshuffled_original_da` and it is also not working","body":"## Describe the bug\r\n\r\nCannot download OSCAR `unshuffled_original_ko` due to `NonMatchingSplitsSizesError`.\r\n\r\n## Steps to reproduce the bug\r\n\r\n```python\r\n>>> dataset = datasets.load_dataset('oscar', 'unshuffled_original_ko')\r\nNonMatchingSplitsSizesError: [{'expected': SplitInfo(name='train', num_bytes=25292102197, num_examples=7345075, dataset_name='oscar'), 'recorded': SplitInfo(name='train', num_bytes=25284578514, num_examples=7344907, dataset_name='oscar')}]\r\n```\r\n\r\n## Expected results\r\n\r\nLoading is successful.\r\n\r\n## Actual results\r\n\r\nLoading throws above error.\r\n\r\n## Environment info\r\n\r\n- `datasets` version: 1.12.1\r\n- Platform: Linux-5.4.0-81-generic-x86_64-with-glibc2.29\r\n- Python version: 3.8.10\r\n- PyArrow version: 5.0.0\r\n","comment_length":9,"text":"OSCAR unshuffled_original_ko: NonMatchingSplitsSizesError\n## Describe the bug\r\n\r\nCannot download OSCAR `unshuffled_original_ko` due to `NonMatchingSplitsSizesError`.\r\n\r\n## Steps to reproduce the bug\r\n\r\n```python\r\n>>> dataset = datasets.load_dataset('oscar', 'unshuffled_original_ko')\r\nNonMatchingSplitsSizesError: [{'expected': SplitInfo(name='train', num_bytes=25292102197, num_examples=7345075, dataset_name='oscar'), 'recorded': SplitInfo(name='train', num_bytes=25284578514, num_examples=7344907, dataset_name='oscar')}]\r\n```\r\n\r\n## Expected results\r\n\r\nLoading is successful.\r\n\r\n## Actual results\r\n\r\nLoading throws above error.\r\n\r\n## Environment info\r\n\r\n- `datasets` version: 1.12.1\r\n- Platform: Linux-5.4.0-81-generic-x86_64-with-glibc2.29\r\n- Python version: 3.8.10\r\n- PyArrow version: 5.0.0\r\n\nI tried `unshuffled_original_da` and it is also not 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{"html_url":"https:\/\/github.com\/huggingface\/datasets\/issues\/2937","title":"load_dataset using default cache on Windows causes PermissionError: [WinError 5] Access is denied","comments":"Hi @daqieq, thanks for reporting.\r\n\r\nUnfortunately, I was not able to reproduce this bug:\r\n```ipython\r\nIn [1]: from datasets import load_dataset\r\n ...: ds = load_dataset('wiki_bio')\r\nDownloading: 7.58kB [00:00, 26.3kB\/s]\r\nDownloading: 2.71kB [00:00, ?B\/s]\r\nUsing custom data configuration default\r\nDownloading and preparing dataset wiki_bio\/default (download: 318.53 MiB, generated: 736.94 MiB, post-processed: Unknown size, total: 1.03 GiB) to C:\\Users\\username\\.cache\\huggingface\\datasets\\wiki_bio\\default\\\r\n1.1.0\\5293ce565954ba965dada626f1e79684e98172d950371d266bf3caaf87e911c9...\r\nDownloading: 334MB [01:17, 4.32MB\/s]\r\nDataset wiki_bio downloaded and prepared to C:\\Users\\username\\.cache\\huggingface\\datasets\\wiki_bio\\default\\1.1.0\\5293ce565954ba965dada626f1e79684e98172d950371d266bf3caaf87e911c9. Subsequent calls will reuse thi\r\ns data.\r\n```\r\n\r\nThis kind of error messages usually happen because:\r\n- Your running Python script hasn't write access to that directory\r\n- You have another program (the File Explorer?) already browsing inside that directory","body":"## Describe the bug\r\nStandard process to download and load the wiki_bio dataset causes PermissionError in Windows 10 and 11.\r\n\r\n## Steps to reproduce the bug\r\n```python\r\nfrom datasets import load_dataset\r\nds = load_dataset('wiki_bio')\r\n```\r\n\r\n## Expected results\r\nIt is expected that the dataset downloads without any errors.\r\n\r\n## Actual results\r\nPermissionError see trace below:\r\n```\r\nUsing custom data configuration default\r\nDownloading and preparing dataset wiki_bio\/default (download: 318.53 MiB, generated: 736.94 MiB, post-processed: Unknown size, total: 1.03 GiB) to C:\\Users\\username\\.cache\\huggingface\\datasets\\wiki_bio\\default\\1.1.0\\5293ce565954ba965dada626f1e79684e98172d950371d266bf3caaf87e911c9...\r\nTraceback (most recent call last):\r\n File \"\", line 1, in \r\n File \"C:\\Users\\username\\.conda\\envs\\hf\\lib\\site-packages\\datasets\\load.py\", line 1112, in load_dataset\r\n builder_instance.download_and_prepare(\r\n File \"C:\\Users\\username\\.conda\\envs\\hf\\lib\\site-packages\\datasets\\builder.py\", line 644, in download_and_prepare\r\n self._save_info()\r\n File \"C:\\Users\\username\\.conda\\envs\\hf\\lib\\contextlib.py\", line 120, in __exit__\r\n next(self.gen)\r\n File \"C:\\Users\\username\\.conda\\envs\\hf\\lib\\site-packages\\datasets\\builder.py\", line 598, in incomplete_dir\r\n os.rename(tmp_dir, dirname)\r\nPermissionError: [WinError 5] Access is denied: 'C:\\\\Users\\\\username\\\\.cache\\\\huggingface\\\\datasets\\\\wiki_bio\\\\default\\\\1.1.0\\\\5293ce565954ba965dada626f1e79684e98172d950371d266bf3caaf87e911c9.incomplete' -> 'C:\\\\Users\\\\username\\\\.cache\\\\huggingface\\\\datasets\\\\wiki_bio\\\\default\\\\1.1.0\\\\5293ce565954ba965dada626f1e79684e98172d950371d266bf3caaf87e911c9'\r\n```\r\nBy commenting out the os.rename() [L604](https:\/\/github.com\/huggingface\/datasets\/blob\/master\/src\/datasets\/builder.py#L604) and the shutil.rmtree() [L607](https:\/\/github.com\/huggingface\/datasets\/blob\/master\/src\/datasets\/builder.py#L607) lines, in my virtual environment, I was able to get the load process to complete, rename the directory manually and then rerun the `load_dataset('wiki_bio')` to get what I needed.\r\n\r\nIt seems that os.rename() in the `incomplete_dir` content manager is the culprit. Here's another project [Conan](https:\/\/github.com\/conan-io\/conan\/issues\/6560) with similar issue with os.rename() if it helps debug this issue.\r\n\r\n## Environment info\r\n- `datasets` version: 1.12.1\r\n- Platform: Windows-10-10.0.22449-SP0\r\n- Python version: 3.8.12\r\n- PyArrow version: 5.0.0\r\n","comment_length":109,"text":"load_dataset using default cache on Windows causes PermissionError: [WinError 5] Access is denied\n## Describe the bug\r\nStandard process to download and load the wiki_bio dataset causes PermissionError in Windows 10 and 11.\r\n\r\n## Steps to reproduce the bug\r\n```python\r\nfrom datasets import load_dataset\r\nds = load_dataset('wiki_bio')\r\n```\r\n\r\n## Expected results\r\nIt is expected that the dataset downloads without any errors.\r\n\r\n## Actual results\r\nPermissionError see trace below:\r\n```\r\nUsing custom data configuration default\r\nDownloading and preparing dataset wiki_bio\/default (download: 318.53 MiB, generated: 736.94 MiB, post-processed: Unknown size, total: 1.03 GiB) to C:\\Users\\username\\.cache\\huggingface\\datasets\\wiki_bio\\default\\1.1.0\\5293ce565954ba965dada626f1e79684e98172d950371d266bf3caaf87e911c9...\r\nTraceback (most recent call last):\r\n File \"\", line 1, in \r\n File \"C:\\Users\\username\\.conda\\envs\\hf\\lib\\site-packages\\datasets\\load.py\", line 1112, in load_dataset\r\n builder_instance.download_and_prepare(\r\n File \"C:\\Users\\username\\.conda\\envs\\hf\\lib\\site-packages\\datasets\\builder.py\", line 644, in download_and_prepare\r\n self._save_info()\r\n File \"C:\\Users\\username\\.conda\\envs\\hf\\lib\\contextlib.py\", line 120, in __exit__\r\n next(self.gen)\r\n File \"C:\\Users\\username\\.conda\\envs\\hf\\lib\\site-packages\\datasets\\builder.py\", line 598, in incomplete_dir\r\n os.rename(tmp_dir, dirname)\r\nPermissionError: [WinError 5] Access is denied: 'C:\\\\Users\\\\username\\\\.cache\\\\huggingface\\\\datasets\\\\wiki_bio\\\\default\\\\1.1.0\\\\5293ce565954ba965dada626f1e79684e98172d950371d266bf3caaf87e911c9.incomplete' -> 'C:\\\\Users\\\\username\\\\.cache\\\\huggingface\\\\datasets\\\\wiki_bio\\\\default\\\\1.1.0\\\\5293ce565954ba965dada626f1e79684e98172d950371d266bf3caaf87e911c9'\r\n```\r\nBy commenting out the os.rename() [L604](https:\/\/github.com\/huggingface\/datasets\/blob\/master\/src\/datasets\/builder.py#L604) and the shutil.rmtree() [L607](https:\/\/github.com\/huggingface\/datasets\/blob\/master\/src\/datasets\/builder.py#L607) lines, in my virtual environment, I was able to get the load process to complete, rename the directory manually and then rerun the `load_dataset('wiki_bio')` to get what I needed.\r\n\r\nIt seems that os.rename() in the `incomplete_dir` content manager is the culprit. Here's another project [Conan](https:\/\/github.com\/conan-io\/conan\/issues\/6560) with similar issue with os.rename() if it helps debug this issue.\r\n\r\n## Environment info\r\n- `datasets` version: 1.12.1\r\n- Platform: Windows-10-10.0.22449-SP0\r\n- Python version: 3.8.12\r\n- PyArrow version: 5.0.0\r\n\nHi @daqieq, thanks for reporting.\r\n\r\nUnfortunately, I was not able to reproduce this bug:\r\n```ipython\r\nIn [1]: from datasets import load_dataset\r\n ...: ds = load_dataset('wiki_bio')\r\nDownloading: 7.58kB [00:00, 26.3kB\/s]\r\nDownloading: 2.71kB [00:00, ?B\/s]\r\nUsing custom data configuration default\r\nDownloading and preparing dataset wiki_bio\/default (download: 318.53 MiB, generated: 736.94 MiB, post-processed: Unknown size, total: 1.03 GiB) to C:\\Users\\username\\.cache\\huggingface\\datasets\\wiki_bio\\default\\\r\n1.1.0\\5293ce565954ba965dada626f1e79684e98172d950371d266bf3caaf87e911c9...\r\nDownloading: 334MB [01:17, 4.32MB\/s]\r\nDataset wiki_bio downloaded and prepared to C:\\Users\\username\\.cache\\huggingface\\datasets\\wiki_bio\\default\\1.1.0\\5293ce565954ba965dada626f1e79684e98172d950371d266bf3caaf87e911c9. Subsequent calls will reuse thi\r\ns data.\r\n```\r\n\r\nThis kind of error messages usually happen because:\r\n- Your running Python script hasn't write access to that directory\r\n- You have another program (the File Explorer?) already browsing inside that 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{"html_url":"https:\/\/github.com\/huggingface\/datasets\/issues\/2937","title":"load_dataset using default cache on Windows causes PermissionError: [WinError 5] Access is denied","comments":"Thanks @albertvillanova for looking at it! I tried on my personal Windows machine and it downloaded just fine.\r\n\r\nRunning on my work machine and on a colleague's machine it is consistently hitting this error. It's not a write access issue because the `.incomplete` directory is written just fine. It just won't rename and then it deletes the directory in the `finally` step. Also the zip file is written and extracted fine in the downloads directory.\r\n\r\nThat leaves another program that might be interfering, and there are plenty of those in my work machine ... (full antivirus, data loss prevention, etc.). So the question remains, why not extend the `try` block to allow catching the error and circle back to the rename after the unknown program is finished doing its 'stuff'. This is the approach that I read about in the linked repo (see my comments above).\r\n\r\nIf it's not high priority, that's fine. However, if someone were to write an PR that solved this issue in our environment in an `except` clause, would it be reviewed for inclusion in a future release? Just wondering whether I should spend any more time on this issue.","body":"## Describe the bug\r\nStandard process to download and load the wiki_bio dataset causes PermissionError in Windows 10 and 11.\r\n\r\n## Steps to reproduce the bug\r\n```python\r\nfrom datasets import load_dataset\r\nds = load_dataset('wiki_bio')\r\n```\r\n\r\n## Expected results\r\nIt is expected that the dataset downloads without any errors.\r\n\r\n## Actual results\r\nPermissionError see trace below:\r\n```\r\nUsing custom data configuration default\r\nDownloading and preparing dataset wiki_bio\/default (download: 318.53 MiB, generated: 736.94 MiB, post-processed: Unknown size, total: 1.03 GiB) to C:\\Users\\username\\.cache\\huggingface\\datasets\\wiki_bio\\default\\1.1.0\\5293ce565954ba965dada626f1e79684e98172d950371d266bf3caaf87e911c9...\r\nTraceback (most recent call last):\r\n File \"\", line 1, in \r\n File \"C:\\Users\\username\\.conda\\envs\\hf\\lib\\site-packages\\datasets\\load.py\", line 1112, in load_dataset\r\n builder_instance.download_and_prepare(\r\n File \"C:\\Users\\username\\.conda\\envs\\hf\\lib\\site-packages\\datasets\\builder.py\", line 644, in download_and_prepare\r\n self._save_info()\r\n File \"C:\\Users\\username\\.conda\\envs\\hf\\lib\\contextlib.py\", line 120, in __exit__\r\n next(self.gen)\r\n File \"C:\\Users\\username\\.conda\\envs\\hf\\lib\\site-packages\\datasets\\builder.py\", line 598, in incomplete_dir\r\n os.rename(tmp_dir, dirname)\r\nPermissionError: [WinError 5] Access is denied: 'C:\\\\Users\\\\username\\\\.cache\\\\huggingface\\\\datasets\\\\wiki_bio\\\\default\\\\1.1.0\\\\5293ce565954ba965dada626f1e79684e98172d950371d266bf3caaf87e911c9.incomplete' -> 'C:\\\\Users\\\\username\\\\.cache\\\\huggingface\\\\datasets\\\\wiki_bio\\\\default\\\\1.1.0\\\\5293ce565954ba965dada626f1e79684e98172d950371d266bf3caaf87e911c9'\r\n```\r\nBy commenting out the os.rename() [L604](https:\/\/github.com\/huggingface\/datasets\/blob\/master\/src\/datasets\/builder.py#L604) and the shutil.rmtree() [L607](https:\/\/github.com\/huggingface\/datasets\/blob\/master\/src\/datasets\/builder.py#L607) lines, in my virtual environment, I was able to get the load process to complete, rename the directory manually and then rerun the `load_dataset('wiki_bio')` to get what I needed.\r\n\r\nIt seems that os.rename() in the `incomplete_dir` content manager is the culprit. Here's another project [Conan](https:\/\/github.com\/conan-io\/conan\/issues\/6560) with similar issue with os.rename() if it helps debug this issue.\r\n\r\n## Environment info\r\n- `datasets` version: 1.12.1\r\n- Platform: Windows-10-10.0.22449-SP0\r\n- Python version: 3.8.12\r\n- PyArrow version: 5.0.0\r\n","comment_length":194,"text":"load_dataset using default cache on Windows causes PermissionError: [WinError 5] Access is denied\n## Describe the bug\r\nStandard process to download and load the wiki_bio dataset causes PermissionError in Windows 10 and 11.\r\n\r\n## Steps to reproduce the bug\r\n```python\r\nfrom datasets import load_dataset\r\nds = load_dataset('wiki_bio')\r\n```\r\n\r\n## Expected results\r\nIt is expected that the dataset downloads without any errors.\r\n\r\n## Actual results\r\nPermissionError see trace below:\r\n```\r\nUsing custom data configuration default\r\nDownloading and preparing dataset wiki_bio\/default (download: 318.53 MiB, generated: 736.94 MiB, post-processed: Unknown size, total: 1.03 GiB) to C:\\Users\\username\\.cache\\huggingface\\datasets\\wiki_bio\\default\\1.1.0\\5293ce565954ba965dada626f1e79684e98172d950371d266bf3caaf87e911c9...\r\nTraceback (most recent call last):\r\n File \"\", line 1, in \r\n File \"C:\\Users\\username\\.conda\\envs\\hf\\lib\\site-packages\\datasets\\load.py\", line 1112, in load_dataset\r\n builder_instance.download_and_prepare(\r\n File \"C:\\Users\\username\\.conda\\envs\\hf\\lib\\site-packages\\datasets\\builder.py\", line 644, in download_and_prepare\r\n self._save_info()\r\n File \"C:\\Users\\username\\.conda\\envs\\hf\\lib\\contextlib.py\", line 120, in __exit__\r\n next(self.gen)\r\n File \"C:\\Users\\username\\.conda\\envs\\hf\\lib\\site-packages\\datasets\\builder.py\", line 598, in incomplete_dir\r\n os.rename(tmp_dir, dirname)\r\nPermissionError: [WinError 5] Access is denied: 'C:\\\\Users\\\\username\\\\.cache\\\\huggingface\\\\datasets\\\\wiki_bio\\\\default\\\\1.1.0\\\\5293ce565954ba965dada626f1e79684e98172d950371d266bf3caaf87e911c9.incomplete' -> 'C:\\\\Users\\\\username\\\\.cache\\\\huggingface\\\\datasets\\\\wiki_bio\\\\default\\\\1.1.0\\\\5293ce565954ba965dada626f1e79684e98172d950371d266bf3caaf87e911c9'\r\n```\r\nBy commenting out the os.rename() [L604](https:\/\/github.com\/huggingface\/datasets\/blob\/master\/src\/datasets\/builder.py#L604) and the shutil.rmtree() [L607](https:\/\/github.com\/huggingface\/datasets\/blob\/master\/src\/datasets\/builder.py#L607) lines, in my virtual environment, I was able to get the load process to complete, rename the directory manually and then rerun the `load_dataset('wiki_bio')` to get what I needed.\r\n\r\nIt seems that os.rename() in the `incomplete_dir` content manager is the culprit. Here's another project [Conan](https:\/\/github.com\/conan-io\/conan\/issues\/6560) with similar issue with os.rename() if it helps debug this issue.\r\n\r\n## Environment info\r\n- `datasets` version: 1.12.1\r\n- Platform: Windows-10-10.0.22449-SP0\r\n- Python version: 3.8.12\r\n- PyArrow version: 5.0.0\r\n\nThanks @albertvillanova for looking at it! I tried on my personal Windows machine and it downloaded just fine.\r\n\r\nRunning on my work machine and on a colleague's machine it is consistently hitting this error. It's not a write access issue because the `.incomplete` directory is written just fine. It just won't rename and then it deletes the directory in the `finally` step. Also the zip file is written and extracted fine in the downloads directory.\r\n\r\nThat leaves another program that might be interfering, and there are plenty of those in my work machine ... (full antivirus, data loss prevention, etc.). So the question remains, why not extend the `try` block to allow catching the error and circle back to the rename after the unknown program is finished doing its 'stuff'. This is the approach that I read about in the linked repo (see my comments above).\r\n\r\nIf it's not high priority, that's fine. However, if someone were to write an PR that solved this issue in our environment in an `except` clause, would it be reviewed for inclusion in a future release? Just wondering whether I should spend any more time on this 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{"html_url":"https:\/\/github.com\/huggingface\/datasets\/issues\/2934","title":"to_tf_dataset keeps a reference to the open data somewhere, causing issues on windows","comments":"I did some investigation and, as it seems, the bug stems from [this line](https:\/\/github.com\/huggingface\/datasets\/blob\/8004d7c3e1d74b29c3e5b0d1660331cd26758363\/src\/datasets\/arrow_dataset.py#L325). The lifecycle of the dataset from the linked line is bound to one of the returned `tf.data.Dataset`. So my (hacky) solution involves wrapping the linked dataset with `weakref.proxy` and adding a custom `__del__` to `tf.python.data.ops.dataset_ops.TensorSliceDataset` (this is the type of a dataset that is returned by `tf.data.Dataset.from_tensor_slices`; this works for TF 2.x, but I'm not sure `tf.python.data.ops.dataset_ops` is a valid path for TF 1.x) that deletes the linked dataset, which is assigned to the dataset object as a property. Will open a draft PR soon!","body":"To reproduce:\r\n```python\r\nimport datasets as ds\r\nimport weakref\r\nimport gc\r\n\r\nd = ds.load_dataset(\"mnist\", split=\"train\")\r\nref = weakref.ref(d._data.table)\r\ntfd = d.to_tf_dataset(\"image\", batch_size=1, shuffle=False, label_cols=\"label\")\r\ndel tfd, d\r\ngc.collect()\r\nassert ref() is None, \"Error: there is at least one reference left\"\r\n```\r\n\r\nThis causes issues because the table holds a reference to an open arrow file that should be closed. So on windows it's not possible to delete or move the arrow file afterwards.\r\n\r\nMoreover the CI test of the `to_tf_dataset` method isn't able to clean up the temporary arrow files because of this.\r\n\r\ncc @Rocketknight1 ","comment_length":99,"text":"to_tf_dataset keeps a reference to the open data somewhere, causing issues on windows\nTo reproduce:\r\n```python\r\nimport datasets as ds\r\nimport weakref\r\nimport gc\r\n\r\nd = ds.load_dataset(\"mnist\", split=\"train\")\r\nref = weakref.ref(d._data.table)\r\ntfd = d.to_tf_dataset(\"image\", batch_size=1, shuffle=False, label_cols=\"label\")\r\ndel tfd, d\r\ngc.collect()\r\nassert ref() is None, \"Error: there is at least one reference left\"\r\n```\r\n\r\nThis causes issues because the table holds a reference to an open arrow file that should be closed. So on windows it's not possible to delete or move the arrow file afterwards.\r\n\r\nMoreover the CI test of the `to_tf_dataset` method isn't able to clean up the temporary arrow files because of this.\r\n\r\ncc @Rocketknight1 \nI did some investigation and, as it seems, the bug stems from [this line](https:\/\/github.com\/huggingface\/datasets\/blob\/8004d7c3e1d74b29c3e5b0d1660331cd26758363\/src\/datasets\/arrow_dataset.py#L325). The lifecycle of the dataset from the linked line is bound to one of the returned `tf.data.Dataset`. So my (hacky) solution involves wrapping the linked dataset with `weakref.proxy` and adding a custom `__del__` to `tf.python.data.ops.dataset_ops.TensorSliceDataset` (this is the type of a dataset that is returned by `tf.data.Dataset.from_tensor_slices`; this works for TF 2.x, but I'm not sure `tf.python.data.ops.dataset_ops` is a valid path for TF 1.x) that deletes the linked dataset, which is assigned to the dataset object as a property. Will open a draft PR soon!","embeddings":[0.0456905663,0.334508419,0.1148890257,0.080451034,0.2304305583,0.118887417,0.3988847136,0.2548755109,-0.1099170819,0.2674386799,-0.3353850842,0.4040720165,-0.1644811481,-0.1074629501,-0.0342661999,-0.1066431478,0.0511111543,0.1451645941,-0.1666638404,-0.1226122081,-0.2103365809,0.0351907536,-0.1274755448,0.0721159056,-0.1387630254,-0.2811448276,0.0105889933,0.1006439403,0.2441080213,-0.0183550231,0.0682790056,0.0211760364,-0.177796185,0.4797623456,-0.0001166652,0.4142073989,-0.0061280271,0.1301537901,-0.235580802,0.0565112084,-0.0690313205,0.1760492623,0.1245584637,-0.0599729307,0.1349684894,-0.2177767009,-0.0255724229,0.0832738504,0.2824953496,0.4290578365,0.1738771498,0.6678111553,0.049049627,-0.0070738695,0.3827846944,0.0638971701,-0.2742590308,0.1161701158,-0.2370098084,-0.1935149431,0.0655605793,0.5781883597,0.0173822027,-0.0747985765,-0.1346866488,0.0793781951,-0.2474848479,-0.2132097334,0.099155657,0.3864619732,0.2942140996,-0.3177287579,-0.0704426542,-0.1286842972,-0.0647842139,-0.1454346925,0.1913480461,-0.046207156,0.017386917,0.1348499954,0.0582152456,-0.1622668952,-0.2615066469,-0.0061726281,-0.2810887992,-0.1072200239,0.0857438222,0.0801384374,0.414106369,-0.0271513741,-0.068637602,0.0416288003,0.0201638471,0.1307915747,-0.2023018003,-0.1820205152,-0.039258711,-0.1770301908,-0.0281791855,-0.0708624199,0.2392520756,-0.1570445448,-0.2443445474,0.0677398071,0.1644194871,-0.0705889463,-0.6655332446,0.1451997906,0.2680508792,-0.1933338344,-0.1119194329,-0.0286712628,0.0027096812,-0.4981434345,0.2861165106,-0.2107922286,0.4638061523,0.0174133722,-0.5834477544,0.1703355759,-0.1943601817,0.4077934325,-0.0392662771,0.3421227634,-0.0097245919,0.2793687582,0.3236465752,-0.0036117642,-0.4970700741,-0.0162012,-0.0546676517,-0.0396631137,-0.2995604873,-0.3319226205,0.1675927192,0.0439531542,-0.0478496067,0.1728075743,0.0610827021,0.1025774032,0.2539456487,-0.2808155119,0.5246316791,0.4812721908,-0.0750983432,0.2450949699,0.094958134,-0.2507022023,-0.1292222887,0.3534173071,-0.271070987,-0.1585650742,-0.2603646219,0.1245645955,-0.1202328429,-0.1552468389,-0.2238021493,0.0450044461,0.0343124531,-0.0595466755,0.0862919539,-0.1370920688,-0.5523484945,-0.2646558583,0.0109041464,0.3650912344,-0.1991525888,0.1795514375,-0.0254384503,-0.3931910396,-0.1594114155,0.2656008899,0.0646049082,0.315382421,-0.4097916186,-0.1304864734,0.4838194549,-0.2320678681,-0.4512842894,0.2350057364,-0.2089946121,0.2142525762,-0.3518173099,0.1303172708,0.2125170231,-0.2066658884,0.0123629132,-0.0070450343,-0.0623103678,-0.009127249,-0.3236092329,-0.0108151864,-0.0680190474,-0.1952706128,-0.0157284178,-0.0124156829,0.1318649352,-0.0493954197,0.3902634382,0.2196914554,0.1933261156,0.1417482793,0.3756607473,-0.1729133576,-0.0790883675,0.0432887785,-0.3775175512,0.1395243853,0.4056796134,0.0777031407,-0.1249045953,-0.1479362994,-0.0149693638,0.1985371262,-0.1919496357,0.0200802218,0.0784440786,-0.1194245666,-0.3427836597,0.2097568363,-0.0087737013,-0.1780350357,-0.2768910527,0.0820086896,0.1028026193,0.4687242806,0.2687642872,-0.0055732708,-0.2448134571,0.1943388134,-0.1494650543,-0.0300622191,-0.2719019651,0.4269395471,0.1747722477,0.3178130984,-0.1541973948,0.5814441442,0.1066848487,-0.5981359482,-0.2948667407,0.345680505,0.1319477111,-0.028623648,-0.0179552659,0.1456983984,-0.2007223219,0.171325475,0.0537690409,0.2392641008,0.0408273004,0.0214642044,-0.0363970362,-0.3410162628,-0.048381418,0.3614164293,-0.3146298528,0.2986148596,-0.6084029675,0.1965304911,0.3866648376,0.0311069563,-0.1689249873,0.2358304709,-0.1694948971,0.0617295206,0.0715380758,0.3181353509,0.476054281,0.0223621521,0.1727905273,0.1367983967,-0.193739742,-0.0902521908,0.2373642176,0.0887134001,0.3124321103,0.2039208263,0.25704512,0.193103686,-0.3024033606,0.0330183432,0.130203411,0.1728958189,-0.4525787234,0.1747216135,-0.1807385981,-0.0388464183,-0.307489574,0.0117461048,0.0377215482,-0.134800002,0.3154597282,0.3967652917,-0.4561233222,0.153922379,-0.3289419115,0.1290333718,0.0227688793,-0.2959315479,0.1803024411,-0.169179216,-0.2714836895,0.0344429053,0.3643865883,-0.24427782,0.4180335999,-0.0013580687,-0.0747494251,-0.1528537571,-0.2631629407,0.0897795483,-0.0949533954,0.1932279766,0.2937165499,0.3368107378,-0.1232672036,-0.1301765293,0.1462340504,-0.4851278663,-0.1786133051,0.1372669786,-0.1227175817,0.209010601,0.0203978065,-0.1520884037,0.0741765723,-0.3432299793,0.0670822263,-0.191379711,0.127728045,0.6203324199,0.1520412415,-0.0800016448,0.3789710402,0.10767968,-0.1736004055,-0.1127346307,0.200914368,-0.2037230134,-0.4296337366,0.2909044921,0.0338889062,0.1325789392,0.295558393,-0.6434158087,-0.1689423323,-0.0437356085,0.1776636243,-0.1088872477,-0.0470069051,0.1665738225,0.1164368093,-0.1197147071,-0.3432469964,0.0827863663,0.3821257651,-0.227080822,-0.0485802852,0.0422705784,0.0882449597,0.313570559,0.6243577003,0.0508327,-0.3429434299,0.0208036136,-0.2982521057,0.5073607564,-0.159090668,-0.2795748413,0.0158007275,0.0420313291,-0.13273637,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{"html_url":"https:\/\/github.com\/huggingface\/datasets\/issues\/2934","title":"to_tf_dataset keeps a reference to the open data somewhere, causing issues on windows","comments":"Thanks a lot for investigating !","body":"To reproduce:\r\n```python\r\nimport datasets as ds\r\nimport weakref\r\nimport gc\r\n\r\nd = ds.load_dataset(\"mnist\", split=\"train\")\r\nref = weakref.ref(d._data.table)\r\ntfd = d.to_tf_dataset(\"image\", batch_size=1, shuffle=False, label_cols=\"label\")\r\ndel tfd, d\r\ngc.collect()\r\nassert ref() is None, \"Error: there is at least one reference left\"\r\n```\r\n\r\nThis causes issues because the table holds a reference to an open arrow file that should be closed. So on windows it's not possible to delete or move the arrow file afterwards.\r\n\r\nMoreover the CI test of the `to_tf_dataset` method isn't able to clean up the temporary arrow files because of this.\r\n\r\ncc @Rocketknight1 ","comment_length":6,"text":"to_tf_dataset keeps a reference to the open data somewhere, causing issues on windows\nTo reproduce:\r\n```python\r\nimport datasets as ds\r\nimport weakref\r\nimport gc\r\n\r\nd = ds.load_dataset(\"mnist\", split=\"train\")\r\nref = weakref.ref(d._data.table)\r\ntfd = d.to_tf_dataset(\"image\", batch_size=1, shuffle=False, label_cols=\"label\")\r\ndel tfd, d\r\ngc.collect()\r\nassert ref() is None, \"Error: there is at least one reference left\"\r\n```\r\n\r\nThis causes issues because the table holds a reference to an open arrow file that should be closed. So on windows it's not possible to delete or move the arrow file afterwards.\r\n\r\nMoreover the CI test of the `to_tf_dataset` method isn't able to clean up the temporary arrow files because of this.\r\n\r\ncc @Rocketknight1 \nThanks a lot for investigating 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{"html_url":"https:\/\/github.com\/huggingface\/datasets\/issues\/2932","title":"Conda build fails","comments":"Why 1.9 ?\r\n\r\nhttps:\/\/anaconda.org\/HuggingFace\/datasets currently says 1.11","body":"## Describe the bug\r\nCurrent `datasets` version in conda is 1.9 instead of 1.12.\r\n\r\nThe build of the conda package fails.\r\n","comment_length":7,"text":"Conda build fails\n## Describe the bug\r\nCurrent `datasets` version in conda is 1.9 instead of 1.12.\r\n\r\nThe build of the conda package fails.\r\n\nWhy 1.9 ?\r\n\r\nhttps:\/\/anaconda.org\/HuggingFace\/datasets currently says 1.11","embeddings":[-0.4641540647,0.0640663132,-0.1675162613,0.0911422074,0.0242198519,-0.1167648509,0.0046573328,0.4584467709,-0.1488352567,0.0218314379,-0.0215561539,0.1672185659,0.1742765456,0.4530319273,-0.0687930807,-0.0687004402,0.2214742899,0.1521472484,-0.3632488847,-0.0315286219,-0.2346586734,0.3556938469,-0.1418616623,0.042954348,-0.1667399108,-0.1111968309,-0.0725726113,-0.0722472295,-0.3525906503,-0.309075892,0.5409511924,-0.1080390662,0.0791708231,0.5754619241,-0.0001009286,-0.0032789898,0.5302111506,0.0530045889,-0.2712946236,-0.1803977042,-0.3502198458,-0.0796987191,-0.0399142914,0.1341502666,-0.025258461,-0.3136871755,-0.0039637419,-0.0812839568,0.2737195194,0.1549416184,0.3308033943,0.0851677209,0.4155243039,-0.4425582886,-0.4505780041,0.2404605895,-0.3100655675,0.1933912933,-0.0950218216,-0.0654576346,0.3125593662,0.2748890221,-0.0108567653,-0.2175896168,0.0571583509,-0.1458919346,-0.1065396816,-0.1608880013,0.2085220516,0.082794033,0.5673777461,-0.3365124464,-0.4459105134,0.116349265,0.0547229312,-0.1276331395,0.2863733172,-0.0994853154,-0.1852693409,0.2289404869,-0.3072545826,-0.1659559906,-0.0998794213,0.0542916507,0.0351006612,0.0843344927,-0.2238170356,0.0395075008,0.0850799978,-0.0795847178,0.0889546126,-0.017170459,-0.0044747209,-0.0164357647,-0.2207264602,-0.1931111813,-0.1908893287,0.0040363134,0.290076822,0.0416318104,-0.2814685106,-0.2031846493,0.0022204889,0.0698052868,-0.057144139,0.0626951158,0.4606170654,0.077450268,0.145462364,0.2514044344,0.1704059988,0.0070430655,0.0021299806,-0.3041796386,-0.2461816221,-0.0392220579,0.2495234162,-0.2043797076,-0.1059830338,0.1044391021,0.0609567054,-0.0279124603,0.0041456302,0.0737251118,-0.098237142,-0.1265262663,0.0580965318,0.1755417287,0.0175039303,0.0253259484,-0.2480798662,-0.19085069,-0.0307969321,0.012003894,0.2311127931,-0.3263165355,0.4701302052,-0.1246728003,0.1668923795,0.0062446366,-0.1614912301,0.078064017,-0.3607374132,0.4940434396,-0.2841105163,0.1163817868,-0.0540333688,-0.0690908656,-0.0722390711,0.009847221,-0.1026801541,-0.0875664055,-0.4648688734,0.2391197681,0.1965070069,0.1023772582,-0.1715472639,-0.0221420992,0.0536693893,-0.0455940999,-0.0935841873,0.0454386994,-0.0532059669,-0.1457587034,0.2406151742,0.2125408351,-0.1583735943,0.1107756644,0.1150244549,-0.2624725103,-0.2327995002,0.0774188638,-0.0671860948,-0.1484007537,-0.0090418523,-0.2777799666,0.1654792577,-0.4407516718,-0.5214886069,0.0708692968,0.1568902731,-0.1486879587,-0.0275132805,-0.1143989637,0.1878380924,-0.1143638939,0.1809624135,0.0786408931,0.005723747,-0.2770836055,-0.1697548479,-0.275437206,-0.241848886,0.0517572574,-0.0461218953,0.1780284196,0.042331446,-0.2044620216,0.1104750931,0.143998459,-0.2036080956,0.2029421628,0.4378283918,-0.1347674131,0.0343065746,-0.1481755525,-0.5067104101,0.0793106779,0.1355768591,-0.0216997135,-0.0685060993,-0.1599982828,-0.3334034681,0.2762608528,0.0679381713,-0.217081219,0.2146058977,-0.1150966808,0.1284432113,0.0793034956,-0.1678137481,0.7080689073,-0.137064442,0.2539079785,-0.1866996437,0.1552656889,-0.2871536613,-0.1260931939,0.2259406298,0.1750397533,0.2101011425,-0.1859097183,-0.1064059809,0.3100512624,-0.1132724211,-0.1732333302,0.1977748871,-0.2093673199,0.0850393698,-0.0069135549,0.1005597562,-0.0600860864,-0.1986969262,0.2078127116,0.0507737435,-0.0467366427,0.1806784272,-0.0241424423,-0.0142447082,0.2336301059,0.4198890924,0.0989498571,0.0189749841,-0.1595772505,0.1046580002,0.1180455238,0.2821315825,0.0236277115,-0.2964583933,0.1159171835,0.3984741867,-0.1097792536,-0.0726787671,0.2535006702,0.0919811726,0.1495138854,0.3302159607,0.2453039587,0.1037769392,0.0531402938,-0.095606029,0.2007383853,-0.3480142057,-0.0298374817,0.1060575619,0.0322836116,0.2812074125,-0.0845356435,-0.1015645266,0.0695140362,-0.0760767683,-0.0694011524,-0.1414503753,0.0929866508,0.0086003188,0.0421742573,-0.1739253849,-0.0793259069,-0.255325973,-0.2271770239,-0.2650867403,-0.020353796,-0.0007564036,0.1378406584,0.210903734,0.4877940714,0.0403436422,0.0395924002,-0.0504935272,0.0288728029,-0.1351604462,0.1316828281,-0.1524738818,0.1581934094,0.0931431428,-0.0417627729,0.1490940601,-0.4683484435,0.1915226579,-0.0488868766,-0.5337531567,0.162495628,-0.2750543058,0.4730536938,0.2408772856,0.0991982818,0.0325651355,-0.2097351402,0.1801730543,-0.1365850121,-0.1675789505,-0.219999522,-0.0309822541,-0.3386186361,-0.0958381668,-0.1521292478,-0.135665834,-0.1753454208,0.3863707185,-0.0221327562,0.0562425889,0.3409312069,0.4604199231,0.0470611565,-0.1918748617,0.0891415849,-0.1435628682,-0.4498470724,0.056229163,-0.2765717506,-0.2497362643,-0.0989951342,0.2478436381,0.4750163257,0.0358756483,-0.5661168694,-0.1986954808,-0.0168789178,0.1650635004,0.0965101942,-0.013050952,0.2417072803,0.0429633595,-0.1247905418,-0.2604943216,-0.3049151897,0.1147871763,-0.2061828226,0.3588501811,-0.0101826554,0.4184118509,-0.1961857677,0.2067244649,0.570433557,-0.193848446,0.1289104074,0.1958947629,0.646984756,-0.2325790077,-0.2919476926,0.1788204014,0.24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{"html_url":"https:\/\/github.com\/huggingface\/datasets\/issues\/2932","title":"Conda build fails","comments":"Alright I added 1.12.0 and 1.12.1 and fixed the conda build #2952 ","body":"## Describe the bug\r\nCurrent `datasets` version in conda is 1.9 instead of 1.12.\r\n\r\nThe build of the conda package fails.\r\n","comment_length":12,"text":"Conda build fails\n## Describe the bug\r\nCurrent `datasets` version in conda is 1.9 instead of 1.12.\r\n\r\nThe build of the conda package fails.\r\n\nAlright I added 1.12.0 and 1.12.1 and fixed the conda build #2952 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{"html_url":"https:\/\/github.com\/huggingface\/datasets\/issues\/2930","title":"Mutable columns argument breaks set_format","comments":"Pushed a fix to my branch #2731 ","body":"## Describe the bug\r\nIf you pass a mutable list to the `columns` argument of `set_format` and then change the list afterwards, the returned columns also change.\r\n\r\n## Steps to reproduce the bug\r\n```python\r\nfrom datasets import load_dataset\r\ndataset = load_dataset(\"glue\", \"cola\")\r\n\r\ncolumn_list = [\"idx\", \"label\"]\r\ndataset.set_format(\"python\", columns=column_list)\r\ncolumn_list[1] = \"foo\" # Change the list after we call `set_format`\r\ndataset['train'][:4].keys()\r\n```\r\n\r\n## Expected results\r\n```python\r\ndict_keys(['idx', 'label'])\r\n```\r\n\r\n## Actual results\r\n```python\r\ndict_keys(['idx'])\r\n```","comment_length":7,"text":"Mutable columns argument breaks set_format\n## Describe the bug\r\nIf you pass a mutable list to the `columns` argument of `set_format` and then change the list afterwards, the returned columns also change.\r\n\r\n## Steps to reproduce the bug\r\n```python\r\nfrom datasets import load_dataset\r\ndataset = load_dataset(\"glue\", \"cola\")\r\n\r\ncolumn_list = [\"idx\", \"label\"]\r\ndataset.set_format(\"python\", columns=column_list)\r\ncolumn_list[1] = \"foo\" # Change the list after we call `set_format`\r\ndataset['train'][:4].keys()\r\n```\r\n\r\n## Expected results\r\n```python\r\ndict_keys(['idx', 'label'])\r\n```\r\n\r\n## Actual results\r\n```python\r\ndict_keys(['idx'])\r\n```\nPushed a fix to my branch #2731 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{"html_url":"https:\/\/github.com\/huggingface\/datasets\/issues\/2927","title":"Datasets 1.12 dataset.filter TypeError: get_indices_from_mask_function() got an unexpected keyword argument","comments":"Thanks for reporting, I'm looking into it :)","body":"## Describe the bug\r\nUpgrading to 1.12 caused `dataset.filter` call to fail with \r\n\r\n> get_indices_from_mask_function() got an unexpected keyword argument valid_rel_labels\r\n\r\n\r\n## Steps to reproduce the bug\r\n```pythondef \r\n\r\nfilter_good_rows(\r\n ex: Dict,\r\n valid_rel_labels: Set[str],\r\n valid_ner_labels: Set[str],\r\n tokenizer: PreTrainedTokenizerFast,\r\n) -> bool:\r\n \"\"\"Get the good rows\"\"\"\r\n encoding = get_encoding_for_text(text=ex[\"text\"], tokenizer=tokenizer)\r\n ex[\"encoding\"] = encoding\r\n for relation in ex[\"relations\"]:\r\n if not is_valid_relation(relation, valid_rel_labels):\r\n return False\r\n for span in ex[\"spans\"]:\r\n if not is_valid_span(span, valid_ner_labels, encoding):\r\n return False\r\n return True\r\n \r\ndef get_dataset(): \r\n loader_path = str(Path(__file__).parent \/ \"prodigy_dataset_builder.py\")\r\n ds = load_dataset(\r\n loader_path,\r\n name=\"prodigy-dataset\",\r\n data_files=sorted(file_paths),\r\n cache_dir=cache_dir,\r\n )[\"train\"]\r\n\r\n valid_ner_labels = set(vocab.ner_category)\r\n valid_relations = set(vocab.relation_types.keys())\r\n ds = ds.filter(\r\n filter_good_rows,\r\n fn_kwargs=dict(\r\n valid_rel_labels=valid_relations,\r\n valid_ner_labels=valid_ner_labels,\r\n tokenizer=vocab.tokenizer,\r\n ),\r\n keep_in_memory=True,\r\n num_proc=num_proc,\r\n )\r\n\r\n```\r\n\r\n`ds` is a `DatasetDict` produced by a jsonl dataset.\r\nThis runs fine on 1.11 but fails on 1.12\r\n\r\n**Stack Trace**\r\n\r\n\r\n\r\n## Expected results\r\n\r\nI expect 1.12 datasets filter to filter the dataset without raising as it does on 1.11\r\n\r\n## Actual results\r\n```\r\ntf_ner_rel_lib\/dataset.py:695: in load_prodigy_arrow_datasets_from_jsonl\r\n ds = ds.filter(\r\n..\/..\/..\/..\/.pyenv\/versions\/tf_ner_rel_lib\/lib\/python3.8\/site-packages\/datasets\/arrow_dataset.py:185: in wrapper\r\n out: Union[\"Dataset\", \"DatasetDict\"] = func(self, *args, **kwargs)\r\n..\/..\/..\/..\/.pyenv\/versions\/tf_ner_rel_lib\/lib\/python3.8\/site-packages\/datasets\/fingerprint.py:398: in wrapper\r\n out = func(self, *args, **kwargs)\r\n..\/..\/..\/..\/.pyenv\/versions\/tf_ner_rel_lib\/lib\/python3.8\/site-packages\/datasets\/arrow_dataset.py:2169: in filter\r\n indices = self.map(\r\n..\/..\/..\/..\/.pyenv\/versions\/tf_ner_rel_lib\/lib\/python3.8\/site-packages\/datasets\/arrow_dataset.py:1686: in map\r\n return self._map_single(\r\n..\/..\/..\/..\/.pyenv\/versions\/tf_ner_rel_lib\/lib\/python3.8\/site-packages\/datasets\/arrow_dataset.py:185: in wrapper\r\n out: Union[\"Dataset\", \"DatasetDict\"] = func(self, *args, **kwargs)\r\n..\/..\/..\/..\/.pyenv\/versions\/tf_ner_rel_lib\/lib\/python3.8\/site-packages\/datasets\/fingerprint.py:398: in wrapper\r\n out = func(self, *args, **kwargs)\r\n..\/..\/..\/..\/.pyenv\/versions\/tf_ner_rel_lib\/lib\/python3.8\/site-packages\/datasets\/arrow_dataset.py:2048: in _map_single\r\n batch = apply_function_on_filtered_inputs(\r\n_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ \r\n\r\ninputs = {'_input_hash': [2108817714, 1477695082, -1021597032, 2130671338, -1260483858, -1203431639, ...], '_task_hash': [18070...ons', 'relations', 'relations', ...], 'answer': ['accept', 'accept', 'accept', 'accept', 'accept', 'accept', ...], ...}\r\nindices = [0, 1, 2, 3, 4, 5, ...], check_same_num_examples = False, offset = 0\r\n\r\n def apply_function_on_filtered_inputs(inputs, indices, check_same_num_examples=False, offset=0):\r\n \"\"\"Utility to apply the function on a selection of columns.\"\"\"\r\n nonlocal update_data\r\n fn_args = [inputs] if input_columns is None else [inputs[col] for col in input_columns]\r\n if offset == 0:\r\n effective_indices = indices\r\n else:\r\n effective_indices = [i + offset for i in indices] if isinstance(indices, list) else indices + offset\r\n processed_inputs = (\r\n> function(*fn_args, effective_indices, **fn_kwargs) if with_indices else function(*fn_args, **fn_kwargs)\r\n )\r\nE TypeError: get_indices_from_mask_function() got an unexpected keyword argument 'valid_rel_labels'\r\n\r\n..\/..\/..\/..\/.pyenv\/versions\/tf_ner_rel_lib\/lib\/python3.8\/site-packages\/datasets\/arrow_dataset.py:1939: TypeError\r\n```\r\n\r\n## Environment info\r\n\r\n- `datasets` version: 1.12.1\r\n- Platform: Mac\r\n- Python version: 3.8.9\r\n- PyArrow version: pyarrow==5.0.0\r\n\r\n","comment_length":8,"text":"Datasets 1.12 dataset.filter TypeError: get_indices_from_mask_function() got an unexpected keyword argument\n## Describe the bug\r\nUpgrading to 1.12 caused `dataset.filter` call to fail with \r\n\r\n> get_indices_from_mask_function() got an unexpected keyword argument valid_rel_labels\r\n\r\n\r\n## Steps to reproduce the bug\r\n```pythondef \r\n\r\nfilter_good_rows(\r\n ex: Dict,\r\n valid_rel_labels: Set[str],\r\n valid_ner_labels: Set[str],\r\n tokenizer: PreTrainedTokenizerFast,\r\n) -> bool:\r\n \"\"\"Get the good rows\"\"\"\r\n encoding = get_encoding_for_text(text=ex[\"text\"], tokenizer=tokenizer)\r\n ex[\"encoding\"] = encoding\r\n for relation in ex[\"relations\"]:\r\n if not is_valid_relation(relation, valid_rel_labels):\r\n return False\r\n for span in ex[\"spans\"]:\r\n if not is_valid_span(span, valid_ner_labels, encoding):\r\n return False\r\n return True\r\n \r\ndef get_dataset(): \r\n loader_path = str(Path(__file__).parent \/ \"prodigy_dataset_builder.py\")\r\n ds = load_dataset(\r\n loader_path,\r\n name=\"prodigy-dataset\",\r\n data_files=sorted(file_paths),\r\n cache_dir=cache_dir,\r\n )[\"train\"]\r\n\r\n valid_ner_labels = set(vocab.ner_category)\r\n valid_relations = set(vocab.relation_types.keys())\r\n ds = ds.filter(\r\n filter_good_rows,\r\n fn_kwargs=dict(\r\n valid_rel_labels=valid_relations,\r\n valid_ner_labels=valid_ner_labels,\r\n tokenizer=vocab.tokenizer,\r\n ),\r\n keep_in_memory=True,\r\n num_proc=num_proc,\r\n )\r\n\r\n```\r\n\r\n`ds` is a `DatasetDict` produced by a jsonl dataset.\r\nThis runs fine on 1.11 but fails on 1.12\r\n\r\n**Stack Trace**\r\n\r\n\r\n\r\n## Expected results\r\n\r\nI expect 1.12 datasets filter to filter the dataset without raising as it does on 1.11\r\n\r\n## Actual results\r\n```\r\ntf_ner_rel_lib\/dataset.py:695: in load_prodigy_arrow_datasets_from_jsonl\r\n ds = ds.filter(\r\n..\/..\/..\/..\/.pyenv\/versions\/tf_ner_rel_lib\/lib\/python3.8\/site-packages\/datasets\/arrow_dataset.py:185: in wrapper\r\n out: Union[\"Dataset\", \"DatasetDict\"] = func(self, *args, **kwargs)\r\n..\/..\/..\/..\/.pyenv\/versions\/tf_ner_rel_lib\/lib\/python3.8\/site-packages\/datasets\/fingerprint.py:398: in wrapper\r\n out = func(self, *args, **kwargs)\r\n..\/..\/..\/..\/.pyenv\/versions\/tf_ner_rel_lib\/lib\/python3.8\/site-packages\/datasets\/arrow_dataset.py:2169: in filter\r\n indices = self.map(\r\n..\/..\/..\/..\/.pyenv\/versions\/tf_ner_rel_lib\/lib\/python3.8\/site-packages\/datasets\/arrow_dataset.py:1686: in map\r\n return self._map_single(\r\n..\/..\/..\/..\/.pyenv\/versions\/tf_ner_rel_lib\/lib\/python3.8\/site-packages\/datasets\/arrow_dataset.py:185: in wrapper\r\n out: Union[\"Dataset\", \"DatasetDict\"] = func(self, *args, **kwargs)\r\n..\/..\/..\/..\/.pyenv\/versions\/tf_ner_rel_lib\/lib\/python3.8\/site-packages\/datasets\/fingerprint.py:398: in wrapper\r\n out = func(self, *args, **kwargs)\r\n..\/..\/..\/..\/.pyenv\/versions\/tf_ner_rel_lib\/lib\/python3.8\/site-packages\/datasets\/arrow_dataset.py:2048: in _map_single\r\n batch = apply_function_on_filtered_inputs(\r\n_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ \r\n\r\ninputs = {'_input_hash': [2108817714, 1477695082, -1021597032, 2130671338, -1260483858, -1203431639, ...], '_task_hash': [18070...ons', 'relations', 'relations', ...], 'answer': ['accept', 'accept', 'accept', 'accept', 'accept', 'accept', ...], ...}\r\nindices = [0, 1, 2, 3, 4, 5, ...], check_same_num_examples = False, offset = 0\r\n\r\n def apply_function_on_filtered_inputs(inputs, indices, check_same_num_examples=False, offset=0):\r\n \"\"\"Utility to apply the function on a selection of columns.\"\"\"\r\n nonlocal update_data\r\n fn_args = [inputs] if input_columns is None else [inputs[col] for col in input_columns]\r\n if offset == 0:\r\n effective_indices = indices\r\n else:\r\n effective_indices = [i + offset for i in indices] if isinstance(indices, list) else indices + offset\r\n processed_inputs = (\r\n> function(*fn_args, effective_indices, **fn_kwargs) if with_indices else function(*fn_args, **fn_kwargs)\r\n )\r\nE TypeError: get_indices_from_mask_function() got an unexpected keyword argument 'valid_rel_labels'\r\n\r\n..\/..\/..\/..\/.pyenv\/versions\/tf_ner_rel_lib\/lib\/python3.8\/site-packages\/datasets\/arrow_dataset.py:1939: TypeError\r\n```\r\n\r\n## Environment info\r\n\r\n- `datasets` version: 1.12.1\r\n- Platform: Mac\r\n- Python version: 3.8.9\r\n- PyArrow version: pyarrow==5.0.0\r\n\r\n\nThanks for reporting, I'm looking into it 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{"html_url":"https:\/\/github.com\/huggingface\/datasets\/issues\/2927","title":"Datasets 1.12 dataset.filter TypeError: get_indices_from_mask_function() got an unexpected keyword argument","comments":"Fixed by #2950.","body":"## Describe the bug\r\nUpgrading to 1.12 caused `dataset.filter` call to fail with \r\n\r\n> get_indices_from_mask_function() got an unexpected keyword argument valid_rel_labels\r\n\r\n\r\n## Steps to reproduce the bug\r\n```pythondef \r\n\r\nfilter_good_rows(\r\n ex: Dict,\r\n valid_rel_labels: Set[str],\r\n valid_ner_labels: Set[str],\r\n tokenizer: PreTrainedTokenizerFast,\r\n) -> bool:\r\n \"\"\"Get the good rows\"\"\"\r\n encoding = get_encoding_for_text(text=ex[\"text\"], tokenizer=tokenizer)\r\n ex[\"encoding\"] = encoding\r\n for relation in ex[\"relations\"]:\r\n if not is_valid_relation(relation, valid_rel_labels):\r\n return False\r\n for span in ex[\"spans\"]:\r\n if not is_valid_span(span, valid_ner_labels, encoding):\r\n return False\r\n return True\r\n \r\ndef get_dataset(): \r\n loader_path = str(Path(__file__).parent \/ \"prodigy_dataset_builder.py\")\r\n ds = load_dataset(\r\n loader_path,\r\n name=\"prodigy-dataset\",\r\n data_files=sorted(file_paths),\r\n cache_dir=cache_dir,\r\n )[\"train\"]\r\n\r\n valid_ner_labels = set(vocab.ner_category)\r\n valid_relations = set(vocab.relation_types.keys())\r\n ds = ds.filter(\r\n filter_good_rows,\r\n fn_kwargs=dict(\r\n valid_rel_labels=valid_relations,\r\n valid_ner_labels=valid_ner_labels,\r\n tokenizer=vocab.tokenizer,\r\n ),\r\n keep_in_memory=True,\r\n num_proc=num_proc,\r\n )\r\n\r\n```\r\n\r\n`ds` is a `DatasetDict` produced by a jsonl dataset.\r\nThis runs fine on 1.11 but fails on 1.12\r\n\r\n**Stack Trace**\r\n\r\n\r\n\r\n## Expected results\r\n\r\nI expect 1.12 datasets filter to filter the dataset without raising as it does on 1.11\r\n\r\n## Actual results\r\n```\r\ntf_ner_rel_lib\/dataset.py:695: in load_prodigy_arrow_datasets_from_jsonl\r\n ds = ds.filter(\r\n..\/..\/..\/..\/.pyenv\/versions\/tf_ner_rel_lib\/lib\/python3.8\/site-packages\/datasets\/arrow_dataset.py:185: in wrapper\r\n out: Union[\"Dataset\", \"DatasetDict\"] = func(self, *args, **kwargs)\r\n..\/..\/..\/..\/.pyenv\/versions\/tf_ner_rel_lib\/lib\/python3.8\/site-packages\/datasets\/fingerprint.py:398: in wrapper\r\n out = func(self, *args, **kwargs)\r\n..\/..\/..\/..\/.pyenv\/versions\/tf_ner_rel_lib\/lib\/python3.8\/site-packages\/datasets\/arrow_dataset.py:2169: in filter\r\n indices = self.map(\r\n..\/..\/..\/..\/.pyenv\/versions\/tf_ner_rel_lib\/lib\/python3.8\/site-packages\/datasets\/arrow_dataset.py:1686: in map\r\n return self._map_single(\r\n..\/..\/..\/..\/.pyenv\/versions\/tf_ner_rel_lib\/lib\/python3.8\/site-packages\/datasets\/arrow_dataset.py:185: in wrapper\r\n out: Union[\"Dataset\", \"DatasetDict\"] = func(self, *args, **kwargs)\r\n..\/..\/..\/..\/.pyenv\/versions\/tf_ner_rel_lib\/lib\/python3.8\/site-packages\/datasets\/fingerprint.py:398: in wrapper\r\n out = func(self, *args, **kwargs)\r\n..\/..\/..\/..\/.pyenv\/versions\/tf_ner_rel_lib\/lib\/python3.8\/site-packages\/datasets\/arrow_dataset.py:2048: in _map_single\r\n batch = apply_function_on_filtered_inputs(\r\n_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ \r\n\r\ninputs = {'_input_hash': [2108817714, 1477695082, -1021597032, 2130671338, -1260483858, -1203431639, ...], '_task_hash': [18070...ons', 'relations', 'relations', ...], 'answer': ['accept', 'accept', 'accept', 'accept', 'accept', 'accept', ...], ...}\r\nindices = [0, 1, 2, 3, 4, 5, ...], check_same_num_examples = False, offset = 0\r\n\r\n def apply_function_on_filtered_inputs(inputs, indices, check_same_num_examples=False, offset=0):\r\n \"\"\"Utility to apply the function on a selection of columns.\"\"\"\r\n nonlocal update_data\r\n fn_args = [inputs] if input_columns is None else [inputs[col] for col in input_columns]\r\n if offset == 0:\r\n effective_indices = indices\r\n else:\r\n effective_indices = [i + offset for i in indices] if isinstance(indices, list) else indices + offset\r\n processed_inputs = (\r\n> function(*fn_args, effective_indices, **fn_kwargs) if with_indices else function(*fn_args, **fn_kwargs)\r\n )\r\nE TypeError: get_indices_from_mask_function() got an unexpected keyword argument 'valid_rel_labels'\r\n\r\n..\/..\/..\/..\/.pyenv\/versions\/tf_ner_rel_lib\/lib\/python3.8\/site-packages\/datasets\/arrow_dataset.py:1939: TypeError\r\n```\r\n\r\n## Environment info\r\n\r\n- `datasets` version: 1.12.1\r\n- Platform: Mac\r\n- Python version: 3.8.9\r\n- PyArrow version: pyarrow==5.0.0\r\n\r\n","comment_length":3,"text":"Datasets 1.12 dataset.filter TypeError: get_indices_from_mask_function() got an unexpected keyword argument\n## Describe the bug\r\nUpgrading to 1.12 caused `dataset.filter` call to fail with \r\n\r\n> get_indices_from_mask_function() got an unexpected keyword argument valid_rel_labels\r\n\r\n\r\n## Steps to reproduce the bug\r\n```pythondef \r\n\r\nfilter_good_rows(\r\n ex: Dict,\r\n valid_rel_labels: Set[str],\r\n valid_ner_labels: Set[str],\r\n tokenizer: PreTrainedTokenizerFast,\r\n) -> bool:\r\n \"\"\"Get the good rows\"\"\"\r\n encoding = get_encoding_for_text(text=ex[\"text\"], tokenizer=tokenizer)\r\n ex[\"encoding\"] = encoding\r\n for relation in ex[\"relations\"]:\r\n if not is_valid_relation(relation, valid_rel_labels):\r\n return False\r\n for span in ex[\"spans\"]:\r\n if not is_valid_span(span, valid_ner_labels, encoding):\r\n return False\r\n return True\r\n \r\ndef get_dataset(): \r\n loader_path = str(Path(__file__).parent \/ \"prodigy_dataset_builder.py\")\r\n ds = load_dataset(\r\n loader_path,\r\n name=\"prodigy-dataset\",\r\n data_files=sorted(file_paths),\r\n cache_dir=cache_dir,\r\n )[\"train\"]\r\n\r\n valid_ner_labels = set(vocab.ner_category)\r\n valid_relations = set(vocab.relation_types.keys())\r\n ds = ds.filter(\r\n filter_good_rows,\r\n fn_kwargs=dict(\r\n valid_rel_labels=valid_relations,\r\n valid_ner_labels=valid_ner_labels,\r\n tokenizer=vocab.tokenizer,\r\n ),\r\n keep_in_memory=True,\r\n num_proc=num_proc,\r\n )\r\n\r\n```\r\n\r\n`ds` is a `DatasetDict` produced by a jsonl dataset.\r\nThis runs fine on 1.11 but fails on 1.12\r\n\r\n**Stack Trace**\r\n\r\n\r\n\r\n## Expected results\r\n\r\nI expect 1.12 datasets filter to filter the dataset without raising as it does on 1.11\r\n\r\n## Actual results\r\n```\r\ntf_ner_rel_lib\/dataset.py:695: in load_prodigy_arrow_datasets_from_jsonl\r\n ds = ds.filter(\r\n..\/..\/..\/..\/.pyenv\/versions\/tf_ner_rel_lib\/lib\/python3.8\/site-packages\/datasets\/arrow_dataset.py:185: in wrapper\r\n out: Union[\"Dataset\", \"DatasetDict\"] = func(self, *args, **kwargs)\r\n..\/..\/..\/..\/.pyenv\/versions\/tf_ner_rel_lib\/lib\/python3.8\/site-packages\/datasets\/fingerprint.py:398: in wrapper\r\n out = func(self, *args, **kwargs)\r\n..\/..\/..\/..\/.pyenv\/versions\/tf_ner_rel_lib\/lib\/python3.8\/site-packages\/datasets\/arrow_dataset.py:2169: in filter\r\n indices = self.map(\r\n..\/..\/..\/..\/.pyenv\/versions\/tf_ner_rel_lib\/lib\/python3.8\/site-packages\/datasets\/arrow_dataset.py:1686: in map\r\n return self._map_single(\r\n..\/..\/..\/..\/.pyenv\/versions\/tf_ner_rel_lib\/lib\/python3.8\/site-packages\/datasets\/arrow_dataset.py:185: in wrapper\r\n out: Union[\"Dataset\", \"DatasetDict\"] = func(self, *args, **kwargs)\r\n..\/..\/..\/..\/.pyenv\/versions\/tf_ner_rel_lib\/lib\/python3.8\/site-packages\/datasets\/fingerprint.py:398: in wrapper\r\n out = func(self, *args, **kwargs)\r\n..\/..\/..\/..\/.pyenv\/versions\/tf_ner_rel_lib\/lib\/python3.8\/site-packages\/datasets\/arrow_dataset.py:2048: in _map_single\r\n batch = apply_function_on_filtered_inputs(\r\n_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ \r\n\r\ninputs = {'_input_hash': [2108817714, 1477695082, -1021597032, 2130671338, -1260483858, -1203431639, ...], '_task_hash': [18070...ons', 'relations', 'relations', ...], 'answer': ['accept', 'accept', 'accept', 'accept', 'accept', 'accept', ...], ...}\r\nindices = [0, 1, 2, 3, 4, 5, ...], check_same_num_examples = False, offset = 0\r\n\r\n def apply_function_on_filtered_inputs(inputs, indices, check_same_num_examples=False, offset=0):\r\n \"\"\"Utility to apply the function on a selection of columns.\"\"\"\r\n nonlocal update_data\r\n fn_args = [inputs] if input_columns is None else [inputs[col] for col in input_columns]\r\n if offset == 0:\r\n effective_indices = indices\r\n else:\r\n effective_indices = [i + offset for i in indices] if isinstance(indices, list) else indices + offset\r\n processed_inputs = (\r\n> function(*fn_args, effective_indices, **fn_kwargs) if with_indices else function(*fn_args, **fn_kwargs)\r\n )\r\nE TypeError: get_indices_from_mask_function() got an unexpected keyword argument 'valid_rel_labels'\r\n\r\n..\/..\/..\/..\/.pyenv\/versions\/tf_ner_rel_lib\/lib\/python3.8\/site-packages\/datasets\/arrow_dataset.py:1939: TypeError\r\n```\r\n\r\n## Environment info\r\n\r\n- `datasets` version: 1.12.1\r\n- Platform: Mac\r\n- Python version: 3.8.9\r\n- PyArrow version: pyarrow==5.0.0\r\n\r\n\nFixed by #2950.","embeddings":[-0.2829699218,0.2777142525,-0.0049711736,0.1934766024,0.0521399193,-0.0259464737,0.2624210715,0.4762106538,-0.1806556284,0.0836368799,-0.1311300099,0.3645940423,-0.1310276091,0.1083575189,-0.1563561261,-0.2259478569,0.0624334253,0.0486550108,-0.0484171957,0.0325096361,-0.3871050477,0.4132161736,-0.4211299717,0.1235269159,0.008685492,-0.1654407978,0.1102141142,-0.0451107584,0.0099631641,-0.4235654175,0.4564154148,0.0699703991,0.0381553695,0.2566260993,-0.0001171321,0.2396819293,0.366684258,-0.0842551515,-0.2250449806,-0.405336529,-0.1809279323,-0.0339020006,0.1180416197,-0.1935501844,-0.0818986967,0.0174917243,-0.249624908,-0.2356579155,0.2611079514,0.4325256944,0.1972838491,0.0717553645,-0.0651654452,0.0339874998,0.0209346432,0.2666781545,0.0185224004,-0.0007021982,0.0587287322,-0.1879914552,0.2919843793,0.5048494339,-0.2368306518,-0.2743239999,0.2988181114,-0.2294282466,0.223627314,-0.3586401939,0.2479036301,-0.0648386478,0.0247386377,-0.0833451524,-0.3223271072,-0.1697276235,-0.2576664686,-0.1120616794,0.3498052657,-0.1584596336,-0.244265154,-0.0208345316,-0.07258939,-0.0044453437,-0.0193625409,0.0009967602,-0.0110017173,0.5335709453,0.051298894,0.0513096936,0.0644063577,-0.1729889512,0.3066953421,-0.1076792255,-0.1007701308,0.0617668256,-0.170351848,0.0725231543,0.2387878448,-0.100716427,0.0953230187,-0.0929184034,-0.0909817293,-0.1606885344,0.0432230234,-0.0053427084,-0.0694495961,0.0904969722,0.3082866073,0.6318762302,0.0672923923,0.058587458,-0.0616019517,0.0373482928,-0.0557346605,0.0807316527,0.0906984136,0.2471883744,0.3795975745,-0.3135053217,-0.6206545234,0.1616326869,-0.5770969987,-0.1607390344,0.237689808,0.0602699555,0.2683047056,0.0857219398,-0.0559860393,0.0184919778,-0.0884351358,0.0967272371,-0.1329723448,0.0300575532,-0.0661212951,-0.288934499,0.1902687103,-0.5589625239,0.0788640752,-0.1231013834,-0.090868406,-0.0088757947,-0.0592832975,0.0524173155,0.1024440303,0.3941213489,-0.4122775495,0.1066873968,0.2004962116,-0.5756821632,-0.1944885254,0.2767592072,-0.4454355538,-0.0839394405,0.0214587208,0.2041888237,0.1366723627,-0.1789838076,-0.3426181376,0.5803837776,0.0123843485,-0.2307372093,-0.0704242811,-0.2239028215,-0.3683023453,-0.0239756647,0.1340631545,0.1689580381,-0.8365051746,-0.2499466091,0.2335013896,-0.0030520104,-0.0626956224,-0.2084405422,-0.1818583906,-0.0219790861,-0.1172454953,-0.0013353637,0.3576940596,-0.485848546,-0.6574547887,0.1390666068,0.1225496158,0.1945742369,0.1314316094,-0.1025109664,0.3647884429,-0.0999429971,0.1756555587,0.2742819488,0.0124631217,-0.0347300246,-0.2012303621,0.0749125555,0.2941287458,-0.0680078194,0.1666537374,0.27637887,-0.1620120853,-0.0194072127,0.2861098051,0.1138249561,-0.0791773424,0.0638790876,0.2018899024,-0.1175088063,0.232898742,-0.4075967968,-0.1830836833,-0.0563953593,0.0861253291,0.130115062,-0.3378736973,-0.214805305,-0.3373113871,0.0745304599,-0.2046329081,-0.1652506888,0.1793425977,0.1503546089,-0.0387527496,-0.0507269315,-0.0374752656,0.4382365942,0.2428901494,0.1432452947,-0.2216559798,0.2244707495,-0.0795594603,-0.080613561,0.0983143821,-0.0522183664,0.36705634,-0.0016741207,-0.3518117964,0.2039128393,0.0591471232,-0.3466408551,-0.0529597551,0.0107605178,0.0352687202,0.0651153848,0.1794721931,0.3757503927,-0.0078226523,0.1407691687,-0.1973981559,0.4055749774,-0.0408483408,0.4687744379,0.0199777223,0.0771241337,0.3994879127,0.2407884896,-0.1524210721,-0.1703110337,0.0525777675,0.3843008578,0.2716696858,-0.1308127642,-0.0575984567,0.2184957862,0.3087431192,-0.0334121324,0.0245152265,0.3014428616,0.015626844,0.0211441629,0.1812755615,0.4510476291,0.150378406,0.1575035751,-0.1162214428,0.0736183971,-0.2908762395,-0.0003076858,0.2586247027,0.1770678163,-0.2355194986,-0.0977184847,0.2307223231,0.0618963651,-0.3269087672,-0.4945808649,-0.1177324802,0.1571106911,-0.2101406008,0.4223466814,-0.2563110888,-0.360643059,0.2112966329,-0.1757659763,-0.1615487486,-0.4771515429,0.0838324726,0.1105318815,0.0211028233,0.4142694175,-0.1944871247,0.2770675421,0.1981321126,-0.2057120055,-0.2324998677,-0.3686217666,-0.3903269172,0.0790402368,0.1200828031,0.0579170287,0.1671684682,-0.0545275025,0.1354142576,0.083329387,-0.7349098921,0.1015762314,-0.3447894454,0.4745761752,0.1016463861,0.038837377,-0.3328078389,-0.2596282661,0.1520894766,0.304394573,-0.2402678132,0.1635410488,0.0431459434,0.2904618084,-0.2289169878,-0.4294990301,-0.2123535424,-0.1180267707,-0.1469037235,-0.1846597344,0.3282931149,0.5408180952,0.0719795302,-0.0907747671,0.1236420944,0.184421882,-0.3507416248,-0.4034056962,0.144136861,-0.0788861513,-0.1249593124,-0.1968769431,-0.2286561579,0.0848402604,0.2183059156,-0.5605329275,-0.4192049503,0.0349870473,0.0410459861,0.1570181549,-0.1139656156,0.1058698297,0.2774389684,0.030448081,-0.2724855542,-0.3693442345,0.0357308015,-0.2114411592,0.0473164991,0.0135325789,0.9601014853,0.0164168794,0.0682780072,0.1694822013,0.2156193256,0.3308912516,0.0938933268,0.4396736324,-0.2999125719,-0.2751029134,-0.1022948325,0.0323724411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{"html_url":"https:\/\/github.com\/huggingface\/datasets\/issues\/2924","title":"\"File name too long\" error for file locks","comments":"Hi, the filename here is less than 255\r\n```python\r\n>>> len(\"_home_garrett_.cache_huggingface_datasets_csv_test-7c856aea083a7043_0.0.0_9144e0a4e8435090117cea53e6c7537173ef2304525df4a077c435d8ee7828ff.incomplete.lock\")\r\n154\r\n```\r\nso not sure why it's considered too long for your filesystem.\r\n(also note that the lock files we use always have smaller filenames than 255)\r\n\r\nhttps:\/\/github.com\/huggingface\/datasets\/blob\/5d1a9f1e3c6c495dc0610b459e39d2eb8893f152\/src\/datasets\/utils\/filelock.py#L135-L135","body":"## Describe the bug\r\n\r\nGetting the following error when calling `load_dataset(\"gar1t\/test\")`:\r\n\r\n```\r\nOSError: [Errno 36] File name too long: '\/.cache\/huggingface\/datasets\/_home_garrett_.cache_huggingface_datasets_csv_test-7c856aea083a7043_0.0.0_9144e0a4e8435090117cea53e6c7537173ef2304525df4a077c435d8ee7828ff.incomplete.lock'\r\n```\r\n\r\n## Steps to reproduce the bug\r\n\r\nWhere the user cache dir (e.g. `~\/.cache`) is on a file system that limits filenames to 255 chars (e.g. ext4):\r\n\r\n```python\r\nfrom datasets import load_dataset\r\nload_dataset(\"gar1t\/test\")\r\n```\r\n\r\n## Expected results\r\n\r\nExpect the function to return without an error.\r\n\r\n## Actual results\r\n\r\n```\r\nTraceback (most recent call last):\r\n File \"\", line 1, in \r\n File \"\/lib\/python3.9\/site-packages\/datasets\/load.py\", line 1112, in load_dataset\r\n builder_instance.download_and_prepare(\r\n File \"\/lib\/python3.9\/site-packages\/datasets\/builder.py\", line 644, in download_and_prepare\r\n self._save_info()\r\n File \"\/lib\/python3.9\/site-packages\/datasets\/builder.py\", line 765, in _save_info\r\n with FileLock(lock_path):\r\n File \"\/lib\/python3.9\/site-packages\/datasets\/utils\/filelock.py\", line 323, in __enter__\r\n self.acquire()\r\n File \"\/lib\/python3.9\/site-packages\/datasets\/utils\/filelock.py\", line 272, in acquire\r\n self._acquire()\r\n File \"\/lib\/python3.9\/site-packages\/datasets\/utils\/filelock.py\", line 403, in _acquire\r\n fd = os.open(self._lock_file, open_mode)\r\nOSError: [Errno 36] File name too long: '\/.cache\/huggingface\/datasets\/_home_garrett_.cache_huggingface_datasets_csv_test-7c856aea083a7043_0.0.0_9144e0a4e8435090117cea53e6c7537173ef2304525df4a077c435d8ee7828ff.incomplete.lock'\r\n```\r\n\r\n## Environment info\r\n\r\n- `datasets` version: 1.12.1\r\n- Platform: Linux-5.11.0-27-generic-x86_64-with-glibc2.31\r\n- Python version: 3.9.7\r\n- PyArrow version: 5.0.0\r\n","comment_length":39,"text":"\"File name too long\" error for file locks\n## Describe the bug\r\n\r\nGetting the following error when calling `load_dataset(\"gar1t\/test\")`:\r\n\r\n```\r\nOSError: [Errno 36] File name too long: '\/.cache\/huggingface\/datasets\/_home_garrett_.cache_huggingface_datasets_csv_test-7c856aea083a7043_0.0.0_9144e0a4e8435090117cea53e6c7537173ef2304525df4a077c435d8ee7828ff.incomplete.lock'\r\n```\r\n\r\n## Steps to reproduce the bug\r\n\r\nWhere the user cache dir (e.g. `~\/.cache`) is on a file system that limits filenames to 255 chars (e.g. ext4):\r\n\r\n```python\r\nfrom datasets import load_dataset\r\nload_dataset(\"gar1t\/test\")\r\n```\r\n\r\n## Expected results\r\n\r\nExpect the function to return without an error.\r\n\r\n## Actual results\r\n\r\n```\r\nTraceback (most recent call last):\r\n File \"\", line 1, in \r\n File \"\/lib\/python3.9\/site-packages\/datasets\/load.py\", line 1112, in load_dataset\r\n builder_instance.download_and_prepare(\r\n File \"\/lib\/python3.9\/site-packages\/datasets\/builder.py\", line 644, in download_and_prepare\r\n self._save_info()\r\n File \"\/lib\/python3.9\/site-packages\/datasets\/builder.py\", line 765, in _save_info\r\n with FileLock(lock_path):\r\n File \"\/lib\/python3.9\/site-packages\/datasets\/utils\/filelock.py\", line 323, in __enter__\r\n self.acquire()\r\n File \"\/lib\/python3.9\/site-packages\/datasets\/utils\/filelock.py\", line 272, in acquire\r\n self._acquire()\r\n File \"\/lib\/python3.9\/site-packages\/datasets\/utils\/filelock.py\", line 403, in _acquire\r\n fd = os.open(self._lock_file, open_mode)\r\nOSError: [Errno 36] File name too long: '\/.cache\/huggingface\/datasets\/_home_garrett_.cache_huggingface_datasets_csv_test-7c856aea083a7043_0.0.0_9144e0a4e8435090117cea53e6c7537173ef2304525df4a077c435d8ee7828ff.incomplete.lock'\r\n```\r\n\r\n## Environment info\r\n\r\n- `datasets` version: 1.12.1\r\n- Platform: Linux-5.11.0-27-generic-x86_64-with-glibc2.31\r\n- Python version: 3.9.7\r\n- PyArrow version: 5.0.0\r\n\nHi, the filename here is less than 255\r\n```python\r\n>>> len(\"_home_garrett_.cache_huggingface_datasets_csv_test-7c856aea083a7043_0.0.0_9144e0a4e8435090117cea53e6c7537173ef2304525df4a077c435d8ee7828ff.incomplete.lock\")\r\n154\r\n```\r\nso not sure why it's considered too long for your filesystem.\r\n(also note that the lock files we use always have smaller filenames than 255)\r\n\r\nhttps:\/\/github.com\/huggingface\/datasets\/blob\/5d1a9f1e3c6c495dc0610b459e39d2eb8893f152\/src\/datasets\/utils\/filelock.py#L135-L135","embeddings":[0.0493393503,0.0920015797,-0.0714939609,0.401848346,0.4151671231,0.2358950824,0.636662364,0.2435030937,0.2364584506,0.2313297093,0.0185690634,0.0077435691,-0.1442685276,-0.3100371361,-0.2015356272,-0.2443143427,-0.1581087261,-0.0416213386,-0.177078411,0.2177188098,-0.1549794227,0.3787363768,0.0246380791,0.1570838541,-0.536006391,0.0900789052,-0.1295958906,0.3886749148,-0.0215065964,-0.3729913831,0.051118616,-0.0467165932,0.068814449,0.811357677,-0.0001220686,-0.3334413767,0.3542338908,-0.0562992319,-0.3271892667,-0.2243083715,-0.1685816944,-0.5117114782,-0.0345845483,-0.4988024831,0.1872130185,-0.0951189175,-0.0331902057,-0.8021566272,0.0410523824,0.3733062148,0.1268745214,-0.0783471763,0.0910248384,-0.2579201758,0.3323119283,-0.2779891491,0.0040849657,0.3564026058,0.3556975722,-0.0988676101,-0.1546510309,0.2032182515,-0.0368982404,0.027324792,0.2471582741,0.079537563,-0.0874831453,-0.3261650205,0.2958056033,0.4273941517,0.4714379907,-0.0930620804,-0.2793129385,-0.4676968753,0.1424980164,-0.1251056492,0.4885306358,-0.0762403011,-0.222074613,0.1514885277,-0.1704189926,-0.0196209587,-0.1235974729,-0.1102384105,-0.101975739,0.0650619194,0.0405084826,0.0362790599,0.3111130893,-0.3483147621,0.2395959646,0.0048368936,0.0742740706,0.2694604397,-0.6542128921,0.1469123662,0.0842684656,0.3854928017,0.2534896433,0.0576435812,-0.2659727335,-0.1483078748,0.1947487742,-0.0104660569,-0.0649549589,0.374206692,0.1944440156,0.1612814218,0.3012429774,0.0779469088,-0.3367469013,-0.1069349945,-0.0764871612,-0.5534629822,0.445138216,0.133591041,0.0741629824,-0.3437117934,0.1504577994,0.5705774426,0.1057047993,-0.0104902675,0.2287023664,0.2733797133,0.005434711,0.1107665151,0.0830746666,-0.0899362862,-0.0889612734,0.1008345857,-0.1473124325,-0.1107303202,-0.2270203382,0.1124099791,0.0892055854,-0.3083957136,0.2792796195,-0.227465108,0.3152402341,-0.1249576584,-0.0829687566,-0.2377426773,-0.0667323172,0.1559667885,-0.0591069087,0.0739138052,0.2452137023,-0.2607334852,-0.1920612901,-0.0771116391,-0.3397490382,-0.2851879001,-0.0617215782,0.0947027877,-0.165494591,0.2159024328,0.2415551245,-0.2671947181,0.6555529237,-0.0798197463,0.0127971917,-0.2747654617,-0.1418095827,0.0071923966,0.0408245735,0.5534662008,0.1325238049,-0.0175623372,-0.0687896535,0.1282823086,0.0086658485,0.3722593188,0.1806448698,-0.0535966232,-0.4219526947,0.3124936521,0.2268715054,-0.2402810752,-0.486985594,0.3215382397,-0.394307375,0.0994933695,0.3229117393,0.0228835605,0.0517107099,-0.024582373,0.2853384614,0.1822132915,-0.0260959864,0.0234954469,-0.202524215,-0.1073302031,-0.0406307913,0.1691952497,0.0109546073,0.0670013204,0.0796193257,-0.1751918048,0.2715687454,0.0314637274,-0.2040443867,0.3736798167,0.1382424235,0.3267191648,0.1860822737,-0.106327638,-0.5937504768,0.2637207508,-0.1218474954,-0.1787318438,-0.2175928354,-0.1868047863,-0.1749890149,-0.0072898441,0.0034599197,0.1351371109,-0.0204084311,0.2832494974,0.1417792886,-0.1317639649,0.0934399366,0.5883799791,-0.1702439636,0.0175973084,-0.4781179428,-0.17112647,-0.1178534552,-0.1125399023,0.0315071233,0.0016395918,0.3693964779,-0.1089679822,-0.3443510532,0.4480753541,0.3332768381,0.0975331739,-0.1789251119,-0.0044306694,-0.0568642691,0.220775336,0.0726701096,0.0398121551,0.0077854944,0.0142915584,-0.2135758698,0.3000163734,-0.14081572,0.27127707,0.0286138058,0.1682408899,0.2461784184,-0.1318140626,0.0643453151,-0.1761228889,0.6458842158,-0.2163528502,0.3048405647,0.0547799319,0.0452316478,-0.1448624283,0.6458194852,0.045392707,-0.0001772655,0.1977425367,0.1562013328,-0.0408157147,-0.0193730351,0.3064890206,0.5111442208,0.1920584738,-0.0301231258,-0.2065057307,0.3941576481,-0.2238344699,0.256319046,0.0159084182,-0.0027928618,0.3657026589,0.0496179983,-0.1290875971,0.0514840819,-0.8095089197,-0.050442148,0.223697722,-0.3198090196,0.1480822265,-0.1393256634,-0.0432045013,-0.0237995591,0.0004903108,-0.0803984031,-0.4162820876,-0.1888936311,0.2785063386,-0.1418365985,0.2145332694,-0.1188357845,-0.0032407197,0.2712830305,-0.0145387761,-0.2950273752,-0.0016123495,0.0803438574,-0.0837603286,0.2720724344,-0.5375033021,0.0838043541,-0.1256639212,-0.0631072521,-0.5083525181,-0.2498805523,0.1116677895,-0.1684809178,0.2935642302,0.3413662016,0.2078575194,0.1666625887,0.0588969849,0.0049610781,-0.2622792721,0.0236695651,0.2326634228,0.1779004186,-0.0228179917,-0.1917994022,-0.023455549,-0.3463737965,-0.4160333872,0.3207841218,0.0303027015,0.2279285491,0.3411892653,-0.0708731115,0.2978130579,-0.0179723371,0.2472920567,-0.0879067779,-0.446370095,0.4170611799,-0.1525222659,-0.2990590334,-0.4318080544,0.2980134785,-0.0355094597,0.1055189967,-0.4223643541,-0.1790713519,-0.401260376,0.3389849961,-0.3357198238,0.0054093283,0.1970204413,-0.0852006599,-0.0563488565,-0.1069383249,0.0849020928,0.2325119227,0.0110361269,-0.009520852,0.0929816738,0.2951667607,-0.11607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{"html_url":"https:\/\/github.com\/huggingface\/datasets\/issues\/2924","title":"\"File name too long\" error for file locks","comments":"Yes, you're right! I need to get you more info here. Either there's something going with the name itself that the file system doesn't like (an encoding that blows up the name length??) or perhaps there's something with the path that's causing the entire string to be used as a name. I haven't seen this on any system before and the Internet's not forthcoming with any info.","body":"## Describe the bug\r\n\r\nGetting the following error when calling `load_dataset(\"gar1t\/test\")`:\r\n\r\n```\r\nOSError: [Errno 36] File name too long: '\/.cache\/huggingface\/datasets\/_home_garrett_.cache_huggingface_datasets_csv_test-7c856aea083a7043_0.0.0_9144e0a4e8435090117cea53e6c7537173ef2304525df4a077c435d8ee7828ff.incomplete.lock'\r\n```\r\n\r\n## Steps to reproduce the bug\r\n\r\nWhere the user cache dir (e.g. `~\/.cache`) is on a file system that limits filenames to 255 chars (e.g. ext4):\r\n\r\n```python\r\nfrom datasets import load_dataset\r\nload_dataset(\"gar1t\/test\")\r\n```\r\n\r\n## Expected results\r\n\r\nExpect the function to return without an error.\r\n\r\n## Actual results\r\n\r\n```\r\nTraceback (most recent call last):\r\n File \"\", line 1, in \r\n File \"\/lib\/python3.9\/site-packages\/datasets\/load.py\", line 1112, in load_dataset\r\n builder_instance.download_and_prepare(\r\n File \"\/lib\/python3.9\/site-packages\/datasets\/builder.py\", line 644, in download_and_prepare\r\n self._save_info()\r\n File \"\/lib\/python3.9\/site-packages\/datasets\/builder.py\", line 765, in _save_info\r\n with FileLock(lock_path):\r\n File \"\/lib\/python3.9\/site-packages\/datasets\/utils\/filelock.py\", line 323, in __enter__\r\n self.acquire()\r\n File \"\/lib\/python3.9\/site-packages\/datasets\/utils\/filelock.py\", line 272, in acquire\r\n self._acquire()\r\n File \"\/lib\/python3.9\/site-packages\/datasets\/utils\/filelock.py\", line 403, in _acquire\r\n fd = os.open(self._lock_file, open_mode)\r\nOSError: [Errno 36] File name too long: '\/.cache\/huggingface\/datasets\/_home_garrett_.cache_huggingface_datasets_csv_test-7c856aea083a7043_0.0.0_9144e0a4e8435090117cea53e6c7537173ef2304525df4a077c435d8ee7828ff.incomplete.lock'\r\n```\r\n\r\n## Environment info\r\n\r\n- `datasets` version: 1.12.1\r\n- Platform: Linux-5.11.0-27-generic-x86_64-with-glibc2.31\r\n- Python version: 3.9.7\r\n- PyArrow version: 5.0.0\r\n","comment_length":67,"text":"\"File name too long\" error for file locks\n## Describe the bug\r\n\r\nGetting the following error when calling `load_dataset(\"gar1t\/test\")`:\r\n\r\n```\r\nOSError: [Errno 36] File name too long: '\/.cache\/huggingface\/datasets\/_home_garrett_.cache_huggingface_datasets_csv_test-7c856aea083a7043_0.0.0_9144e0a4e8435090117cea53e6c7537173ef2304525df4a077c435d8ee7828ff.incomplete.lock'\r\n```\r\n\r\n## Steps to reproduce the bug\r\n\r\nWhere the user cache dir (e.g. `~\/.cache`) is on a file system that limits filenames to 255 chars (e.g. ext4):\r\n\r\n```python\r\nfrom datasets import load_dataset\r\nload_dataset(\"gar1t\/test\")\r\n```\r\n\r\n## Expected results\r\n\r\nExpect the function to return without an error.\r\n\r\n## Actual results\r\n\r\n```\r\nTraceback (most recent call last):\r\n File \"\", line 1, in \r\n File \"\/lib\/python3.9\/site-packages\/datasets\/load.py\", line 1112, in load_dataset\r\n builder_instance.download_and_prepare(\r\n File \"\/lib\/python3.9\/site-packages\/datasets\/builder.py\", line 644, in download_and_prepare\r\n self._save_info()\r\n File \"\/lib\/python3.9\/site-packages\/datasets\/builder.py\", line 765, in _save_info\r\n with FileLock(lock_path):\r\n File \"\/lib\/python3.9\/site-packages\/datasets\/utils\/filelock.py\", line 323, in __enter__\r\n self.acquire()\r\n File \"\/lib\/python3.9\/site-packages\/datasets\/utils\/filelock.py\", line 272, in acquire\r\n self._acquire()\r\n File \"\/lib\/python3.9\/site-packages\/datasets\/utils\/filelock.py\", line 403, in _acquire\r\n fd = os.open(self._lock_file, open_mode)\r\nOSError: [Errno 36] File name too long: '\/.cache\/huggingface\/datasets\/_home_garrett_.cache_huggingface_datasets_csv_test-7c856aea083a7043_0.0.0_9144e0a4e8435090117cea53e6c7537173ef2304525df4a077c435d8ee7828ff.incomplete.lock'\r\n```\r\n\r\n## Environment info\r\n\r\n- `datasets` version: 1.12.1\r\n- Platform: Linux-5.11.0-27-generic-x86_64-with-glibc2.31\r\n- Python version: 3.9.7\r\n- PyArrow version: 5.0.0\r\n\nYes, you're right! I need to get you more info here. Either there's something going with the name itself that the file system doesn't like (an encoding that blows up the name length??) or perhaps there's something with the path that's causing the entire string to be used as a name. I haven't seen this on any system before and the Internet's not forthcoming with any info.","embeddings":[0.0493393503,0.0920015797,-0.0714939609,0.401848346,0.4151671231,0.2358950824,0.636662364,0.2435030937,0.2364584506,0.2313297093,0.0185690634,0.0077435691,-0.1442685276,-0.3100371361,-0.2015356272,-0.2443143427,-0.1581087261,-0.0416213386,-0.177078411,0.2177188098,-0.1549794227,0.3787363768,0.0246380791,0.1570838541,-0.536006391,0.0900789052,-0.1295958906,0.3886749148,-0.0215065964,-0.3729913831,0.051118616,-0.0467165932,0.068814449,0.811357677,-0.0001220686,-0.3334413767,0.3542338908,-0.0562992319,-0.3271892667,-0.2243083715,-0.1685816944,-0.5117114782,-0.0345845483,-0.4988024831,0.1872130185,-0.0951189175,-0.0331902057,-0.8021566272,0.0410523824,0.3733062148,0.1268745214,-0.0783471763,0.0910248384,-0.2579201758,0.3323119283,-0.2779891491,0.0040849657,0.3564026058,0.3556975722,-0.0988676101,-0.1546510309,0.2032182515,-0.0368982404,0.027324792,0.2471582741,0.079537563,-0.0874831453,-0.3261650205,0.2958056033,0.4273941517,0.4714379907,-0.0930620804,-0.2793129385,-0.4676968753,0.1424980164,-0.1251056492,0.4885306358,-0.0762403011,-0.222074613,0.1514885277,-0.1704189926,-0.0196209587,-0.1235974729,-0.1102384105,-0.101975739,0.0650619194,0.0405084826,0.0362790599,0.3111130893,-0.3483147621,0.2395959646,0.0048368936,0.0742740706,0.2694604397,-0.6542128921,0.1469123662,0.0842684656,0.3854928017,0.2534896433,0.0576435812,-0.2659727335,-0.1483078748,0.1947487742,-0.0104660569,-0.0649549589,0.374206692,0.1944440156,0.1612814218,0.3012429774,0.0779469088,-0.3367469013,-0.1069349945,-0.0764871612,-0.5534629822,0.445138216,0.133591041,0.0741629824,-0.3437117934,0.1504577994,0.5705774426,0.1057047993,-0.0104902675,0.2287023664,0.2733797133,0.005434711,0.1107665151,0.0830746666,-0.0899362862,-0.0889612734,0.1008345857,-0.1473124325,-0.1107303202,-0.2270203382,0.1124099791,0.0892055854,-0.3083957136,0.2792796195,-0.227465108,0.3152402341,-0.1249576584,-0.0829687566,-0.2377426773,-0.0667323172,0.1559667885,-0.0591069087,0.0739138052,0.2452137023,-0.2607334852,-0.1920612901,-0.0771116391,-0.3397490382,-0.2851879001,-0.0617215782,0.0947027877,-0.165494591,0.2159024328,0.2415551245,-0.2671947181,0.6555529237,-0.0798197463,0.0127971917,-0.2747654617,-0.1418095827,0.0071923966,0.0408245735,0.5534662008,0.1325238049,-0.0175623372,-0.0687896535,0.1282823086,0.0086658485,0.3722593188,0.1806448698,-0.0535966232,-0.4219526947,0.3124936521,0.2268715054,-0.2402810752,-0.486985594,0.3215382397,-0.394307375,0.0994933695,0.3229117393,0.0228835605,0.0517107099,-0.024582373,0.2853384614,0.1822132915,-0.0260959864,0.0234954469,-0.202524215,-0.1073302031,-0.0406307913,0.1691952497,0.0109546073,0.0670013204,0.0796193257,-0.1751918048,0.2715687454,0.0314637274,-0.2040443867,0.3736798167,0.1382424235,0.3267191648,0.1860822737,-0.106327638,-0.5937504768,0.2637207508,-0.1218474954,-0.1787318438,-0.2175928354,-0.1868047863,-0.1749890149,-0.0072898441,0.0034599197,0.1351371109,-0.0204084311,0.2832494974,0.1417792886,-0.1317639649,0.0934399366,0.5883799791,-0.1702439636,0.0175973084,-0.4781179428,-0.17112647,-0.1178534552,-0.1125399023,0.0315071233,0.0016395918,0.3693964779,-0.1089679822,-0.3443510532,0.4480753541,0.3332768381,0.0975331739,-0.1789251119,-0.0044306694,-0.0568642691,0.220775336,0.0726701096,0.0398121551,0.0077854944,0.0142915584,-0.2135758698,0.3000163734,-0.14081572,0.27127707,0.0286138058,0.1682408899,0.2461784184,-0.1318140626,0.0643453151,-0.1761228889,0.6458842158,-0.2163528502,0.3048405647,0.0547799319,0.0452316478,-0.1448624283,0.6458194852,0.045392707,-0.0001772655,0.1977425367,0.1562013328,-0.0408157147,-0.0193730351,0.3064890206,0.5111442208,0.1920584738,-0.0301231258,-0.2065057307,0.3941576481,-0.2238344699,0.256319046,0.0159084182,-0.0027928618,0.3657026589,0.0496179983,-0.1290875971,0.0514840819,-0.8095089197,-0.050442148,0.223697722,-0.3198090196,0.1480822265,-0.1393256634,-0.0432045013,-0.0237995591,0.0004903108,-0.0803984031,-0.4162820876,-0.1888936311,0.2785063386,-0.1418365985,0.2145332694,-0.1188357845,-0.0032407197,0.2712830305,-0.0145387761,-0.2950273752,-0.0016123495,0.0803438574,-0.0837603286,0.2720724344,-0.5375033021,0.0838043541,-0.1256639212,-0.0631072521,-0.5083525181,-0.2498805523,0.1116677895,-0.1684809178,0.2935642302,0.3413662016,0.2078575194,0.1666625887,0.0588969849,0.0049610781,-0.2622792721,0.0236695651,0.2326634228,0.1779004186,-0.0228179917,-0.1917994022,-0.023455549,-0.3463737965,-0.4160333872,0.3207841218,0.0303027015,0.2279285491,0.3411892653,-0.0708731115,0.2978130579,-0.0179723371,0.2472920567,-0.0879067779,-0.446370095,0.4170611799,-0.1525222659,-0.2990590334,-0.4318080544,0.2980134785,-0.0355094597,0.1055189967,-0.4223643541,-0.1790713519,-0.401260376,0.3389849961,-0.3357198238,0.0054093283,0.1970204413,-0.0852006599,-0.0563488565,-0.1069383249,0.0849020928,0.2325119227,0.0110361269,-0.009520852,0.0929816738,0.2951667607,-0.1160778701,0.4532136321,0.3616654873,0.0753480569,0.1953798383,-0.1376693696,0.1843574643,-0.2590326071,-0.1269457787,0.0239379536,0.1744713485,-0.2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{"html_url":"https:\/\/github.com\/huggingface\/datasets\/issues\/2919","title":"Unwanted progress bars when accessing examples","comments":"doing a patch release now :)","body":"When accessing examples from a dataset formatted for pytorch, some progress bars appear when accessing examples:\r\n```python\r\nIn [1]: import datasets as ds \r\n\r\nIn [2]: d = ds.Dataset.from_dict({\"a\": [0, 1, 2]}).with_format(\"torch\") \r\n\r\nIn [3]: d[0] \r\n100%|\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588| 1\/1 [00:00<00:00, 3172.70it\/s]\r\nOut[3]: {'a': tensor(0)}\r\n```\r\n\r\nThis is because the pytorch formatter calls `map_nested` that uses progress bars\r\n\r\ncc @sgugger ","comment_length":6,"text":"Unwanted progress bars when accessing examples\nWhen accessing examples from a dataset formatted for pytorch, some progress bars appear when accessing examples:\r\n```python\r\nIn [1]: import datasets as ds \r\n\r\nIn [2]: d = ds.Dataset.from_dict({\"a\": [0, 1, 2]}).with_format(\"torch\") \r\n\r\nIn [3]: d[0] \r\n100%|\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588| 1\/1 [00:00<00:00, 3172.70it\/s]\r\nOut[3]: {'a': tensor(0)}\r\n```\r\n\r\nThis is because the pytorch formatter calls `map_nested` that uses progress bars\r\n\r\ncc @sgugger \ndoing a patch release now :)","embeddings":[-0.0175480675,-0.2450868934,0.0048732767,0.10296496,0.1993564218,0.0186564457,0.5456594825,0.2686175108,-0.3862209618,0.233160913,0.1730663329,0.4301009178,-0.0968903452,0.163059175,0.0540391617,-0.2791727781,-0.0362301841,0.0565355495,0.0279643871,0.077860862,-0.0710608438,-0.1534580141,-0.1631929129,0.2491238117,-0.4254311323,-0.1769718528,0.0204986315,-0.2497630417,0.2259828746,-0.6468563676,0.3624524474,0.017228175,0.0925377831,0.4540927112,-0.0001153858,0.1453293562,0.4285333753,0.2160392404,-0.2830248773,-0.1382309198,0.3537838757,-0.5126107335,0.3308672309,-0.3774378598,-0.0873389021,-0.5281800628,-0.2761972547,-0.4125932157,0.2876147032,0.0940865576,0.219826296,0.4712334275,-0.2902378142,-0.0055018864,0.5578870773,0.1376104206,-0.3271023929,0.1375557929,0.5464651585,0.0474077016,-0.2612787783,0.6541268229,-0.1014311612,0.3388422132,0.1663392037,-0.0622609183,0.0632258281,-0.1512287408,-0.1227654591,0.3897278607,0.1704966575,-0.0895446837,-0.2203477472,-0.810939312,-0.0621742755,-0.0982443467,-0.1076176837,0.1732621491,0.0438141599,0.0303935967,-0.5963996649,0.0290020015,-0.27146402,0.0937305689,0.0468122512,-0.1223285422,-0.0942170694,0.0973090604,-0.0284371544,0.083435297,0.1243030503,-0.1994698942,0.0932707414,0.0010196987,-0.046441935,0.1807018369,0.1487292498,-0.170697704,-0.1335336268,0.1875990182,-0.0521841459,0.1841346174,-0.0532620661,0.1660489142,-0.0838754028,-0.0406403653,0.1388794035,-0.2265987098,0.3347287774,0.0163198523,0.2717235684,0.1755359918,-0.037750639,-0.2225538939,0.1156542897,0.1093147323,0.1090649813,0.0211095344,-0.1003607139,0.2777105868,-0.3311299682,0.0219744816,0.2182542384,0.2214021385,-0.1495082378,-0.2717446387,0.1687204242,0.2794264853,-0.0600529797,0.2132000178,-0.0743473321,-0.1063092723,-0.4007383883,-0.1235286221,0.2540350258,0.1500249505,0.2033672929,0.1358764023,0.1028876156,-0.0182450209,0.3721589446,0.0875214264,0.4961630404,0.1029135436,-0.2043189853,0.2071680725,0.075304538,0.4455814064,-0.0290748477,0.271618098,-0.0666681826,-0.3141361773,-0.5112269521,0.1377267092,-0.4437701702,0.2145982981,-0.0623717569,-0.0707329661,0.3211912811,-0.0310045853,-0.0121499458,-0.3121537268,-0.247789517,0.0666654259,0.2291434705,0.2016990036,-0.1900742948,-0.2416289151,0.2099226117,0.0013152339,0.0640754029,0.1185466424,-0.23551175,0.3596096337,-0.2419328094,-0.026544407,0.1872638762,-0.1814072579,-0.3709934652,-0.1421762258,0.0234307237,0.3529036641,0.0315213762,-0.1182669997,0.3322122693,0.0806723833,-0.0189346746,0.1771131754,0.2529223263,-0.0826835856,-0.2729922533,0.0518658161,0.3036836088,0.2500388026,-0.0991399065,-0.1353184134,-0.2295820117,-0.2512821555,0.1488023102,-0.2186713666,0.1137110293,-0.0783538818,-0.0333985202,-0.1114408895,0.128908366,-0.200934723,-0.1400860697,0.334146589,-0.1916212291,-0.0585839003,-0.1843481064,-0.1486791074,-0.2163711041,-0.0423979238,-0.2948601842,-0.0875450671,0.0705130398,-0.1551547796,-0.0718744695,-0.0183241945,0.0944948941,-0.1325680614,-0.3775212765,-0.0472109951,0.0076890243,0.3263398409,0.1543031484,-0.3600919843,-0.1122498885,0.4872063696,0.0123988334,-0.0924425796,-0.1616445929,0.0323477574,0.1806732118,0.020979777,-0.418775171,0.1870616972,0.3267300427,0.1015354991,-0.0053531798,0.2033281922,-0.110525474,-0.0142406756,-0.2096013576,0.275554955,0.3913537562,0.3871296048,-0.0188226514,0.1576325446,0.2299357653,0.2287838459,-0.19063811,-0.3708530664,0.3794209957,0.1164492592,0.2275259644,-0.1128709093,-0.056149736,0.2054813057,0.2419088632,-0.0129112294,0.0710802451,0.1942463815,-0.3472250402,-0.0823546052,0.2821888328,-0.0113203805,0.0383519419,0.0980123356,0.1815937907,0.1735030264,-0.0646654963,-0.2774474919,-0.002367727,0.252974838,0.1037782133,0.0188018717,0.1014887169,0.0363334157,-0.0996529385,-0.2078756839,-0.2531711757,0.0459414124,-0.4298397005,0.2782195807,-0.3938655257,-0.2349227965,-0.1121626124,-0.1845910549,0.0518490709,-0.2728966475,0.1209051535,0.5843741298,-0.1538914591,0.3002850115,0.3276317716,0.1584009379,0.0250879861,0.1818053126,-0.0510158017,-0.510720849,-0.2618277967,0.0802433044,-0.0849289596,-0.1672920287,0.5056151152,-0.1877110004,0.4327579141,-0.3314349949,-0.1271976531,0.1429183781,-0.0935863927,0.0404994525,-0.0486703366,0.1458623558,-0.0911693275,0.233792603,0.160188511,-0.1380708367,-0.0214611273,0.16621387,-0.2902114093,-0.0336513706,-0.2353742123,0.0583931804,-0.3945191801,-0.1011367291,0.0673801824,-0.1514690369,0.1948434263,-0.0160474218,-0.3517791629,0.4189587235,0.1875047535,-0.1340169311,-0.1357982308,-0.3630971313,0.2848750055,-0.41330266,-0.2727486789,-0.0870164037,-0.2269277126,-0.2414059043,0.2249186188,-0.4970188141,-0.4690299034,-0.2819567025,0.2401717752,0.0068959016,-0.0885310322,0.2275055051,0.2388396263,-0.0490171686,-0.1502595395,-0.1286485642,0.2684911489,-0.2023165226,-0.0526690036,-0.2280971706,-0.1113249883,0.1931582093,0.4944924414,0.1924008131,-0.2912912369,0.1187348738,0.0306330826,0.1123298332,-0.1802462488,-0.1826853007,-0.1176944822,-0.4110670388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{"html_url":"https:\/\/github.com\/huggingface\/datasets\/issues\/2918","title":"`Can not decode content-encoding: gzip` when loading `scitldr` dataset with streaming","comments":"Hi @SBrandeis, thanks for reporting! ^^\r\n\r\nI think this is an issue with `fsspec`: https:\/\/github.com\/intake\/filesystem_spec\/issues\/389\r\n\r\nI will ask them if they are planning to fix it...","body":"## Describe the bug\r\n\r\nTrying to load the `\"FullText\"` config of the `\"scitldr\"` dataset with `streaming=True` raises an error from `aiohttp`:\r\n```python\r\nClientPayloadError: 400, message='Can not decode content-encoding: gzip'\r\n```\r\n\r\ncc @lhoestq \r\n\r\n## Steps to reproduce the bug\r\n```python\r\nfrom datasets import load_dataset\r\n\r\niter_dset = iter(\r\n load_dataset(\"scitldr\", name=\"FullText\", split=\"test\", streaming=True)\r\n)\r\n\r\nnext(iter_dset)\r\n```\r\n\r\n## Expected results\r\nReturns the first sample of the dataset\r\n\r\n## Actual results\r\nCalling `__next__` crashes with the following Traceback:\r\n\r\n```python\r\n----> 1 next(dset_iter)\r\n\r\n~\\miniconda3\\envs\\datasets\\lib\\site-packages\\datasets\\iterable_dataset.py in __iter__(self)\r\n 339\r\n 340 def __iter__(self):\r\n--> 341 for key, example in self._iter():\r\n 342 if self.features:\r\n 343 # we encode the example for ClassLabel feature types for example\r\n\r\n~\\miniconda3\\envs\\datasets\\lib\\site-packages\\datasets\\iterable_dataset.py in _iter(self)\r\n 336 else:\r\n 337 ex_iterable = self._ex_iterable\r\n--> 338 yield from ex_iterable\r\n 339\r\n 340 def __iter__(self):\r\n\r\n~\\miniconda3\\envs\\datasets\\lib\\site-packages\\datasets\\iterable_dataset.py in __iter__(self)\r\n 76\r\n 77 def __iter__(self):\r\n---> 78 for key, example in self.generate_examples_fn(**self.kwargs):\r\n 79 yield key, example\r\n 80\r\n\r\n~\\.cache\\huggingface\\modules\\datasets_modules\\datasets\\scitldr\\72d6e2195786c57e1d343066fb2cc4f93ea39c5e381e53e6ae7c44bbfd1f05ef\\scitldr.py in _generate_examples(self, filepath, split)\r\n 162\r\n 163 with open(filepath, encoding=\"utf-8\") as f:\r\n--> 164 for id_, row in enumerate(f):\r\n 165 data = json.loads(row)\r\n 166 if self.config.name == \"AIC\":\r\n\r\n~\\miniconda3\\envs\\datasets\\lib\\site-packages\\fsspec\\implementations\\http.py in read(self, length)\r\n 496 else:\r\n 497 length = min(self.size - self.loc, length)\r\n--> 498 return super().read(length)\r\n 499\r\n 500 async def async_fetch_all(self):\r\n\r\n~\\miniconda3\\envs\\datasets\\lib\\site-packages\\fsspec\\spec.py in read(self, length)\r\n 1481 # don't even bother calling fetch\r\n 1482 return b\"\"\r\n-> 1483 out = self.cache._fetch(self.loc, self.loc + length)\r\n 1484 self.loc += len(out)\r\n 1485 return out\r\n\r\n~\\miniconda3\\envs\\datasets\\lib\\site-packages\\fsspec\\caching.py in _fetch(self, start, end)\r\n 378 elif start < self.start:\r\n 379 if self.end - end > self.blocksize:\r\n--> 380 self.cache = self.fetcher(start, bend)\r\n 381 self.start = start\r\n 382 else:\r\n\r\n~\\miniconda3\\envs\\datasets\\lib\\site-packages\\fsspec\\asyn.py in wrapper(*args, **kwargs)\r\n 86 def wrapper(*args, **kwargs):\r\n 87 self = obj or args[0]\r\n---> 88 return sync(self.loop, func, *args, **kwargs)\r\n 89\r\n 90 return wrapper\r\n\r\n~\\miniconda3\\envs\\datasets\\lib\\site-packages\\fsspec\\asyn.py in sync(loop, func, timeout, *args, **kwargs)\r\n 67 raise FSTimeoutError\r\n 68 if isinstance(result[0], BaseException):\r\n---> 69 raise result[0]\r\n 70 return result[0]\r\n 71\r\n\r\n~\\miniconda3\\envs\\datasets\\lib\\site-packages\\fsspec\\asyn.py in _runner(event, coro, result, timeout)\r\n 23 coro = asyncio.wait_for(coro, timeout=timeout)\r\n 24 try:\r\n---> 25 result[0] = await coro\r\n 26 except Exception as ex:\r\n 27 result[0] = ex\r\n\r\n~\\miniconda3\\envs\\datasets\\lib\\site-packages\\fsspec\\implementations\\http.py in async_fetch_range(self, start, end)\r\n 538 if r.status == 206:\r\n 539 # partial content, as expected\r\n--> 540 out = await r.read()\r\n 541 elif \"Content-Length\" in r.headers:\r\n 542 cl = int(r.headers[\"Content-Length\"])\r\n\r\n~\\miniconda3\\envs\\datasets\\lib\\site-packages\\aiohttp\\client_reqrep.py in read(self)\r\n 1030 if self._body is None:\r\n 1031 try:\r\n-> 1032 self._body = await self.content.read()\r\n 1033 for trace in self._traces:\r\n 1034 await trace.send_response_chunk_received(\r\n\r\n~\\miniconda3\\envs\\datasets\\lib\\site-packages\\aiohttp\\streams.py in read(self, n)\r\n 342 async def read(self, n: int = -1) -> bytes:\r\n 343 if self._exception is not None:\r\n--> 344 raise self._exception\r\n 345\r\n 346 # migration problem; with DataQueue you have to catch\r\n\r\nClientPayloadError: 400, message='Can not decode content-encoding: gzip'\r\n```\r\n\r\n## Environment info\r\n\r\n- `datasets` version: 1.12.0\r\n- Platform: Windows-10-10.0.19041-SP0\r\n- Python version: 3.8.5\r\n- PyArrow version: 2.0.0\r\n- aiohttp version: 3.7.4.post0\r\n","comment_length":26,"text":"`Can not decode content-encoding: gzip` when loading `scitldr` dataset with streaming\n## Describe the bug\r\n\r\nTrying to load the `\"FullText\"` config of the `\"scitldr\"` dataset with `streaming=True` raises an error from `aiohttp`:\r\n```python\r\nClientPayloadError: 400, message='Can not decode content-encoding: gzip'\r\n```\r\n\r\ncc @lhoestq \r\n\r\n## Steps to reproduce the bug\r\n```python\r\nfrom datasets import load_dataset\r\n\r\niter_dset = iter(\r\n load_dataset(\"scitldr\", name=\"FullText\", split=\"test\", streaming=True)\r\n)\r\n\r\nnext(iter_dset)\r\n```\r\n\r\n## Expected results\r\nReturns the first sample of the dataset\r\n\r\n## Actual results\r\nCalling `__next__` crashes with the following Traceback:\r\n\r\n```python\r\n----> 1 next(dset_iter)\r\n\r\n~\\miniconda3\\envs\\datasets\\lib\\site-packages\\datasets\\iterable_dataset.py in __iter__(self)\r\n 339\r\n 340 def __iter__(self):\r\n--> 341 for key, example in self._iter():\r\n 342 if self.features:\r\n 343 # we encode the example for ClassLabel feature types for example\r\n\r\n~\\miniconda3\\envs\\datasets\\lib\\site-packages\\datasets\\iterable_dataset.py in _iter(self)\r\n 336 else:\r\n 337 ex_iterable = self._ex_iterable\r\n--> 338 yield from ex_iterable\r\n 339\r\n 340 def __iter__(self):\r\n\r\n~\\miniconda3\\envs\\datasets\\lib\\site-packages\\datasets\\iterable_dataset.py in __iter__(self)\r\n 76\r\n 77 def __iter__(self):\r\n---> 78 for key, example in self.generate_examples_fn(**self.kwargs):\r\n 79 yield key, example\r\n 80\r\n\r\n~\\.cache\\huggingface\\modules\\datasets_modules\\datasets\\scitldr\\72d6e2195786c57e1d343066fb2cc4f93ea39c5e381e53e6ae7c44bbfd1f05ef\\scitldr.py in _generate_examples(self, filepath, split)\r\n 162\r\n 163 with open(filepath, encoding=\"utf-8\") as f:\r\n--> 164 for id_, row in enumerate(f):\r\n 165 data = json.loads(row)\r\n 166 if self.config.name == \"AIC\":\r\n\r\n~\\miniconda3\\envs\\datasets\\lib\\site-packages\\fsspec\\implementations\\http.py in read(self, length)\r\n 496 else:\r\n 497 length = min(self.size - self.loc, length)\r\n--> 498 return super().read(length)\r\n 499\r\n 500 async def async_fetch_all(self):\r\n\r\n~\\miniconda3\\envs\\datasets\\lib\\site-packages\\fsspec\\spec.py in read(self, length)\r\n 1481 # don't even bother calling fetch\r\n 1482 return b\"\"\r\n-> 1483 out = self.cache._fetch(self.loc, self.loc + length)\r\n 1484 self.loc += len(out)\r\n 1485 return out\r\n\r\n~\\miniconda3\\envs\\datasets\\lib\\site-packages\\fsspec\\caching.py in _fetch(self, start, end)\r\n 378 elif start < self.start:\r\n 379 if self.end - end > self.blocksize:\r\n--> 380 self.cache = self.fetcher(start, bend)\r\n 381 self.start = start\r\n 382 else:\r\n\r\n~\\miniconda3\\envs\\datasets\\lib\\site-packages\\fsspec\\asyn.py in wrapper(*args, **kwargs)\r\n 86 def wrapper(*args, **kwargs):\r\n 87 self = obj or args[0]\r\n---> 88 return sync(self.loop, func, *args, **kwargs)\r\n 89\r\n 90 return wrapper\r\n\r\n~\\miniconda3\\envs\\datasets\\lib\\site-packages\\fsspec\\asyn.py in sync(loop, func, timeout, *args, **kwargs)\r\n 67 raise FSTimeoutError\r\n 68 if isinstance(result[0], BaseException):\r\n---> 69 raise result[0]\r\n 70 return result[0]\r\n 71\r\n\r\n~\\miniconda3\\envs\\datasets\\lib\\site-packages\\fsspec\\asyn.py in _runner(event, coro, result, timeout)\r\n 23 coro = asyncio.wait_for(coro, timeout=timeout)\r\n 24 try:\r\n---> 25 result[0] = await coro\r\n 26 except Exception as ex:\r\n 27 result[0] = ex\r\n\r\n~\\miniconda3\\envs\\datasets\\lib\\site-packages\\fsspec\\implementations\\http.py in async_fetch_range(self, start, end)\r\n 538 if r.status == 206:\r\n 539 # partial content, as expected\r\n--> 540 out = await r.read()\r\n 541 elif \"Content-Length\" in r.headers:\r\n 542 cl = int(r.headers[\"Content-Length\"])\r\n\r\n~\\miniconda3\\envs\\datasets\\lib\\site-packages\\aiohttp\\client_reqrep.py in read(self)\r\n 1030 if self._body is None:\r\n 1031 try:\r\n-> 1032 self._body = await self.content.read()\r\n 1033 for trace in self._traces:\r\n 1034 await trace.send_response_chunk_received(\r\n\r\n~\\miniconda3\\envs\\datasets\\lib\\site-packages\\aiohttp\\streams.py in read(self, n)\r\n 342 async def read(self, n: int = -1) -> bytes:\r\n 343 if self._exception is not None:\r\n--> 344 raise self._exception\r\n 345\r\n 346 # migration problem; with DataQueue you have to catch\r\n\r\nClientPayloadError: 400, message='Can not decode content-encoding: gzip'\r\n```\r\n\r\n## Environment info\r\n\r\n- `datasets` version: 1.12.0\r\n- Platform: Windows-10-10.0.19041-SP0\r\n- Python version: 3.8.5\r\n- PyArrow version: 2.0.0\r\n- aiohttp version: 3.7.4.post0\r\n\nHi @SBrandeis, thanks for reporting! ^^\r\n\r\nI think this is an issue with `fsspec`: https:\/\/github.com\/intake\/filesystem_spec\/issues\/389\r\n\r\nI will ask them if they are planning to fix it...","embeddings":[-0.3864652812,-0.2204957455,0.0894813165,0.4387415349,0.2157086134,0.1229229122,-0.0369101539,0.3093882799,0.2422775328,0.0955026373,-0.2264311761,0.4261536002,-0.0641756877,0.3035327792,-0.0094654057,-0.2584786713,-0.0045060921,0.2337655127,-0.1049250886,0.1268760264,-0.0005556306,0.1941259056,-0.2469931841,-0.0304233823,0.0522285365,-0.1081046611,0.0124065261,0.2805253863,-0.059583243,-0.4462363422,0.1259049624,-0.0615482517,0.4121141732,0.3375741839,-0.0001145032,0.2734483778,0.4721427262,-0.1227841005,-0.4853440225,-0.2121149004,-0.218665719,0.0961271524,0.0428345352,-0.1566341817,-0.0091345869,0.0711133331,-0.0400046185,-0.7192984819,0.3178500533,0.3456359804,0.1778500229,0.0426006429,-0.0489487238,0.0268223863,0.0688386783,-0.2986646891,-0.0920066237,0.1218173951,0.2367273569,0.2287404835,-0.359573245,0.3845903277,-0.0658884048,0.0601840951,0.0944148228,0.0967238322,-0.1749501675,-0.4655051231,-0.0073944977,0.1884141415,0.6776787043,-0.2555166781,-0.4161978662,-0.2319881767,-0.0021713183,-0.6144573689,0.1465310603,0.1489273459,-0.4673651159,0.1280597448,0.2459630817,-0.0087530883,-0.327994585,-0.0458728224,-0.246799171,0.3882223368,-0.0767857283,-0.0237270929,0.0272943433,-0.0866289064,0.4627741277,-0.2110346556,-0.28459391,-0.0639790371,-0.4397552311,-0.0420655161,-0.0765123144,-0.094109349,0.1681466401,0.215389967,0.2953203917,0.0882523879,0.1543645263,0.0926442817,0.3555265665,0.3608669043,0.0598316565,0.1853774786,0.1311810017,0.0994313955,0.1366237104,-0.1818924248,-0.146488592,-0.0372809209,0.0281632524,-0.0131140957,0.262858659,-0.1912374645,-0.4138476253,0.0297920927,-0.3057210743,-0.0794672072,0.0055905147,0.2817168832,0.0958337784,0.4187803268,0.0194585584,0.1178673506,-0.2396837324,-0.4573938251,-0.0878112167,-0.1247182265,-0.0010082311,0.1233467832,0.0153353186,-0.1886832416,0.1515737325,-0.0465371571,0.1621010602,-0.0012185768,-0.0208561029,-0.1100598425,0.0216292907,0.2049841285,0.2180556655,0.1640353203,0.1795151085,-0.1997850239,-0.130682379,0.0886226073,-0.1416330338,-0.1671275645,-0.1788374335,0.2166469097,-0.0094024939,-0.3506264389,-0.0937892571,0.2814688385,0.1409590542,-0.2037903368,-0.1410701424,-0.0391465798,-0.1241319329,-0.0865156949,0.1792775393,0.5796180964,-0.1855915189,-0.1380216628,-0.1521381587,0.0008939481,0.5833057165,0.3344025612,-0.0607914478,0.0897968188,-0.170722425,0.1476544142,0.5017140508,-0.1436584145,-0.5796036124,0.5436661243,-0.0396082066,0.5637281537,0.2707032561,0.0225366186,0.1742344499,0.0431196988,0.313529104,0.3299024701,-0.1484592706,0.0074822325,-0.3334669471,-0.0399836674,0.4416950643,0.1912285686,0.0674290732,0.2502861321,-0.0288742278,-0.0626677349,0.3689779639,-0.0752563924,0.0763451308,0.0410080664,0.1468882412,0.0120370993,0.1379382461,-0.2090770155,-0.310682714,0.2972311378,0.0749307722,0.0127100674,-0.3174338341,0.2031723857,-0.1906455308,0.0017469627,-0.2628925443,-0.2590405643,0.1243173257,0.3170801699,0.0756207108,0.0505895875,-0.3084766865,0.7747215033,-0.078255251,-0.0131349247,-0.4549141228,0.2024929523,-0.0707768798,-0.2014573216,0.0164402872,-0.1265656799,0.0672556311,-0.0161919687,-0.2579914331,0.3313200772,-0.2366138101,0.2640713155,-0.1954375803,-0.1731976271,0.2897448838,-0.3644788265,-0.0866843313,0.2706028223,0.1075036898,0.125209704,-0.0176983867,0.1480839401,0.2070915401,0.2361335903,0.0434485786,0.1143487841,0.1961675137,-0.0816748515,-0.0686837137,-0.0850090533,0.3240485787,-0.3548789024,0.0467500761,-0.1989230514,-0.2722995877,0.0823631957,0.3586791456,-0.1403829306,-0.0492737889,0.3303245604,-0.2885322273,-0.1639078707,0.3145605624,0.1940099597,0.4723360538,0.0815219656,0.2573353648,-0.0455882996,-0.1886483878,-0.0735060051,0.3051697612,0.1374839693,0.0314638466,0.2464114279,0.0300063528,0.1635747999,-0.3229160309,-0.3497166634,-0.0566318817,0.1123416647,-0.3201874793,0.306302011,-0.38826859,-0.4651744664,0.0463509336,0.1560085416,0.0689049438,-0.3235587776,-0.1817550063,0.2325055152,-0.0038823313,0.1636838913,-0.2682063282,0.0895485729,0.2671770155,-0.0134687945,-0.3418698907,-0.1808474958,-0.2729629874,0.0591977872,0.1749951094,-0.4228037596,0.1769195795,0.0074260007,-0.1160532683,-0.1401735991,-0.076423429,0.3197818398,-0.1554995328,0.1568958163,-0.2774865627,0.1493911892,0.2213633955,-0.2503491044,0.1219482496,0.0908335969,-0.1187844425,0.2801248431,0.0940897167,-0.0241610892,-0.0305832736,-0.3010807931,-0.3024666011,-0.6177494526,0.0549683981,0.0454257689,0.2135515809,0.1643050015,0.0170347784,0.0968246013,0.3292069137,0.0991601422,-0.114212282,-0.4199434817,0.3747448325,-0.1670146883,-0.2868409157,-0.3248367012,-0.0681597292,0.1972901672,0.2589270473,-0.5295243263,0.2950162292,-0.4081035852,0.0848925635,-0.3834516108,-0.0974560827,-0.0261515174,0.0762885734,-0.0349893123,-0.1878576428,0.0707459524,-0.1169513538,0.0990076959,0.2066107839,0.1086307243,0.3698712885,0.1008493528,0.3327504098,0.4812112153,-0.0195496976,0.3407551944,0.082891129,0.1538007557,-0.1004334986,-0.0627885908,-0.0955621898,-0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{"html_url":"https:\/\/github.com\/huggingface\/datasets\/issues\/2918","title":"`Can not decode content-encoding: gzip` when loading `scitldr` dataset with streaming","comments":"Code to reproduce the bug: `ClientPayloadError: 400, message='Can not decode content-encoding: gzip'`\r\n```python\r\nIn [1]: import fsspec\r\n\r\nIn [2]: import json\r\n\r\nIn [3]: with fsspec.open('https:\/\/raw.githubusercontent.com\/allenai\/scitldr\/master\/SciTLDR-Data\/SciTLDR-FullText\/test.jsonl', encoding=\"utf-8\") as f:\r\n ...: for row in f:\r\n ...: data = json.loads(row)\r\n ...:\r\n---------------------------------------------------------------------------\r\nClientPayloadError Traceback (most recent call last)\r\n```","body":"## Describe the bug\r\n\r\nTrying to load the `\"FullText\"` config of the `\"scitldr\"` dataset with `streaming=True` raises an error from `aiohttp`:\r\n```python\r\nClientPayloadError: 400, message='Can not decode content-encoding: gzip'\r\n```\r\n\r\ncc @lhoestq \r\n\r\n## Steps to reproduce the bug\r\n```python\r\nfrom datasets import load_dataset\r\n\r\niter_dset = iter(\r\n load_dataset(\"scitldr\", name=\"FullText\", split=\"test\", streaming=True)\r\n)\r\n\r\nnext(iter_dset)\r\n```\r\n\r\n## Expected results\r\nReturns the first sample of the dataset\r\n\r\n## Actual results\r\nCalling `__next__` crashes with the following Traceback:\r\n\r\n```python\r\n----> 1 next(dset_iter)\r\n\r\n~\\miniconda3\\envs\\datasets\\lib\\site-packages\\datasets\\iterable_dataset.py in __iter__(self)\r\n 339\r\n 340 def __iter__(self):\r\n--> 341 for key, example in self._iter():\r\n 342 if self.features:\r\n 343 # we encode the example for ClassLabel feature types for example\r\n\r\n~\\miniconda3\\envs\\datasets\\lib\\site-packages\\datasets\\iterable_dataset.py in _iter(self)\r\n 336 else:\r\n 337 ex_iterable = self._ex_iterable\r\n--> 338 yield from ex_iterable\r\n 339\r\n 340 def __iter__(self):\r\n\r\n~\\miniconda3\\envs\\datasets\\lib\\site-packages\\datasets\\iterable_dataset.py in __iter__(self)\r\n 76\r\n 77 def __iter__(self):\r\n---> 78 for key, example in self.generate_examples_fn(**self.kwargs):\r\n 79 yield key, example\r\n 80\r\n\r\n~\\.cache\\huggingface\\modules\\datasets_modules\\datasets\\scitldr\\72d6e2195786c57e1d343066fb2cc4f93ea39c5e381e53e6ae7c44bbfd1f05ef\\scitldr.py in _generate_examples(self, filepath, split)\r\n 162\r\n 163 with open(filepath, encoding=\"utf-8\") as f:\r\n--> 164 for id_, row in enumerate(f):\r\n 165 data = json.loads(row)\r\n 166 if self.config.name == \"AIC\":\r\n\r\n~\\miniconda3\\envs\\datasets\\lib\\site-packages\\fsspec\\implementations\\http.py in read(self, length)\r\n 496 else:\r\n 497 length = min(self.size - self.loc, length)\r\n--> 498 return super().read(length)\r\n 499\r\n 500 async def async_fetch_all(self):\r\n\r\n~\\miniconda3\\envs\\datasets\\lib\\site-packages\\fsspec\\spec.py in read(self, length)\r\n 1481 # don't even bother calling fetch\r\n 1482 return b\"\"\r\n-> 1483 out = self.cache._fetch(self.loc, self.loc + length)\r\n 1484 self.loc += len(out)\r\n 1485 return out\r\n\r\n~\\miniconda3\\envs\\datasets\\lib\\site-packages\\fsspec\\caching.py in _fetch(self, start, end)\r\n 378 elif start < self.start:\r\n 379 if self.end - end > self.blocksize:\r\n--> 380 self.cache = self.fetcher(start, bend)\r\n 381 self.start = start\r\n 382 else:\r\n\r\n~\\miniconda3\\envs\\datasets\\lib\\site-packages\\fsspec\\asyn.py in wrapper(*args, **kwargs)\r\n 86 def wrapper(*args, **kwargs):\r\n 87 self = obj or args[0]\r\n---> 88 return sync(self.loop, func, *args, **kwargs)\r\n 89\r\n 90 return wrapper\r\n\r\n~\\miniconda3\\envs\\datasets\\lib\\site-packages\\fsspec\\asyn.py in sync(loop, func, timeout, *args, **kwargs)\r\n 67 raise FSTimeoutError\r\n 68 if isinstance(result[0], BaseException):\r\n---> 69 raise result[0]\r\n 70 return result[0]\r\n 71\r\n\r\n~\\miniconda3\\envs\\datasets\\lib\\site-packages\\fsspec\\asyn.py in _runner(event, coro, result, timeout)\r\n 23 coro = asyncio.wait_for(coro, timeout=timeout)\r\n 24 try:\r\n---> 25 result[0] = await coro\r\n 26 except Exception as ex:\r\n 27 result[0] = ex\r\n\r\n~\\miniconda3\\envs\\datasets\\lib\\site-packages\\fsspec\\implementations\\http.py in async_fetch_range(self, start, end)\r\n 538 if r.status == 206:\r\n 539 # partial content, as expected\r\n--> 540 out = await r.read()\r\n 541 elif \"Content-Length\" in r.headers:\r\n 542 cl = int(r.headers[\"Content-Length\"])\r\n\r\n~\\miniconda3\\envs\\datasets\\lib\\site-packages\\aiohttp\\client_reqrep.py in read(self)\r\n 1030 if self._body is None:\r\n 1031 try:\r\n-> 1032 self._body = await self.content.read()\r\n 1033 for trace in self._traces:\r\n 1034 await trace.send_response_chunk_received(\r\n\r\n~\\miniconda3\\envs\\datasets\\lib\\site-packages\\aiohttp\\streams.py in read(self, n)\r\n 342 async def read(self, n: int = -1) -> bytes:\r\n 343 if self._exception is not None:\r\n--> 344 raise self._exception\r\n 345\r\n 346 # migration problem; with DataQueue you have to catch\r\n\r\nClientPayloadError: 400, message='Can not decode content-encoding: gzip'\r\n```\r\n\r\n## Environment info\r\n\r\n- `datasets` version: 1.12.0\r\n- Platform: Windows-10-10.0.19041-SP0\r\n- Python version: 3.8.5\r\n- PyArrow version: 2.0.0\r\n- aiohttp version: 3.7.4.post0\r\n","comment_length":46,"text":"`Can not decode content-encoding: gzip` when loading `scitldr` dataset with streaming\n## Describe the bug\r\n\r\nTrying to load the `\"FullText\"` config of the `\"scitldr\"` dataset with `streaming=True` raises an error from `aiohttp`:\r\n```python\r\nClientPayloadError: 400, message='Can not decode content-encoding: gzip'\r\n```\r\n\r\ncc @lhoestq \r\n\r\n## Steps to reproduce the bug\r\n```python\r\nfrom datasets import load_dataset\r\n\r\niter_dset = iter(\r\n load_dataset(\"scitldr\", name=\"FullText\", split=\"test\", streaming=True)\r\n)\r\n\r\nnext(iter_dset)\r\n```\r\n\r\n## Expected results\r\nReturns the first sample of the dataset\r\n\r\n## Actual results\r\nCalling `__next__` crashes with the following Traceback:\r\n\r\n```python\r\n----> 1 next(dset_iter)\r\n\r\n~\\miniconda3\\envs\\datasets\\lib\\site-packages\\datasets\\iterable_dataset.py in __iter__(self)\r\n 339\r\n 340 def __iter__(self):\r\n--> 341 for key, example in self._iter():\r\n 342 if self.features:\r\n 343 # we encode the example for ClassLabel feature types for example\r\n\r\n~\\miniconda3\\envs\\datasets\\lib\\site-packages\\datasets\\iterable_dataset.py in _iter(self)\r\n 336 else:\r\n 337 ex_iterable = self._ex_iterable\r\n--> 338 yield from ex_iterable\r\n 339\r\n 340 def __iter__(self):\r\n\r\n~\\miniconda3\\envs\\datasets\\lib\\site-packages\\datasets\\iterable_dataset.py in __iter__(self)\r\n 76\r\n 77 def __iter__(self):\r\n---> 78 for key, example in self.generate_examples_fn(**self.kwargs):\r\n 79 yield key, example\r\n 80\r\n\r\n~\\.cache\\huggingface\\modules\\datasets_modules\\datasets\\scitldr\\72d6e2195786c57e1d343066fb2cc4f93ea39c5e381e53e6ae7c44bbfd1f05ef\\scitldr.py in _generate_examples(self, filepath, split)\r\n 162\r\n 163 with open(filepath, encoding=\"utf-8\") as f:\r\n--> 164 for id_, row in enumerate(f):\r\n 165 data = json.loads(row)\r\n 166 if self.config.name == \"AIC\":\r\n\r\n~\\miniconda3\\envs\\datasets\\lib\\site-packages\\fsspec\\implementations\\http.py in read(self, length)\r\n 496 else:\r\n 497 length = min(self.size - self.loc, length)\r\n--> 498 return super().read(length)\r\n 499\r\n 500 async def async_fetch_all(self):\r\n\r\n~\\miniconda3\\envs\\datasets\\lib\\site-packages\\fsspec\\spec.py in read(self, length)\r\n 1481 # don't even bother calling fetch\r\n 1482 return b\"\"\r\n-> 1483 out = self.cache._fetch(self.loc, self.loc + length)\r\n 1484 self.loc += len(out)\r\n 1485 return out\r\n\r\n~\\miniconda3\\envs\\datasets\\lib\\site-packages\\fsspec\\caching.py in _fetch(self, start, end)\r\n 378 elif start < self.start:\r\n 379 if self.end - end > self.blocksize:\r\n--> 380 self.cache = self.fetcher(start, bend)\r\n 381 self.start = start\r\n 382 else:\r\n\r\n~\\miniconda3\\envs\\datasets\\lib\\site-packages\\fsspec\\asyn.py in wrapper(*args, **kwargs)\r\n 86 def wrapper(*args, **kwargs):\r\n 87 self = obj or args[0]\r\n---> 88 return sync(self.loop, func, *args, **kwargs)\r\n 89\r\n 90 return wrapper\r\n\r\n~\\miniconda3\\envs\\datasets\\lib\\site-packages\\fsspec\\asyn.py in sync(loop, func, timeout, *args, **kwargs)\r\n 67 raise FSTimeoutError\r\n 68 if isinstance(result[0], BaseException):\r\n---> 69 raise result[0]\r\n 70 return result[0]\r\n 71\r\n\r\n~\\miniconda3\\envs\\datasets\\lib\\site-packages\\fsspec\\asyn.py in _runner(event, coro, result, timeout)\r\n 23 coro = asyncio.wait_for(coro, timeout=timeout)\r\n 24 try:\r\n---> 25 result[0] = await coro\r\n 26 except Exception as ex:\r\n 27 result[0] = ex\r\n\r\n~\\miniconda3\\envs\\datasets\\lib\\site-packages\\fsspec\\implementations\\http.py in async_fetch_range(self, start, end)\r\n 538 if r.status == 206:\r\n 539 # partial content, as expected\r\n--> 540 out = await r.read()\r\n 541 elif \"Content-Length\" in r.headers:\r\n 542 cl = int(r.headers[\"Content-Length\"])\r\n\r\n~\\miniconda3\\envs\\datasets\\lib\\site-packages\\aiohttp\\client_reqrep.py in read(self)\r\n 1030 if self._body is None:\r\n 1031 try:\r\n-> 1032 self._body = await self.content.read()\r\n 1033 for trace in self._traces:\r\n 1034 await trace.send_response_chunk_received(\r\n\r\n~\\miniconda3\\envs\\datasets\\lib\\site-packages\\aiohttp\\streams.py in read(self, n)\r\n 342 async def read(self, n: int = -1) -> bytes:\r\n 343 if self._exception is not None:\r\n--> 344 raise self._exception\r\n 345\r\n 346 # migration problem; with DataQueue you have to catch\r\n\r\nClientPayloadError: 400, message='Can not decode content-encoding: gzip'\r\n```\r\n\r\n## Environment info\r\n\r\n- `datasets` version: 1.12.0\r\n- Platform: Windows-10-10.0.19041-SP0\r\n- Python version: 3.8.5\r\n- PyArrow version: 2.0.0\r\n- aiohttp version: 3.7.4.post0\r\n\nCode to reproduce the bug: `ClientPayloadError: 400, message='Can not decode content-encoding: gzip'`\r\n```python\r\nIn [1]: import fsspec\r\n\r\nIn [2]: import json\r\n\r\nIn [3]: with fsspec.open('https:\/\/raw.githubusercontent.com\/allenai\/scitldr\/master\/SciTLDR-Data\/SciTLDR-FullText\/test.jsonl', encoding=\"utf-8\") as f:\r\n ...: for row in f:\r\n ...: data = json.loads(row)\r\n ...:\r\n---------------------------------------------------------------------------\r\nClientPayloadError Traceback (most recent call last)\r\n```","embeddings":[-0.3864652812,-0.2204957455,0.0894813165,0.4387415349,0.2157086134,0.1229229122,-0.0369101539,0.3093882799,0.2422775328,0.0955026373,-0.2264311761,0.4261536002,-0.0641756877,0.3035327792,-0.0094654057,-0.2584786713,-0.0045060921,0.2337655127,-0.1049250886,0.1268760264,-0.0005556306,0.1941259056,-0.2469931841,-0.0304233823,0.0522285365,-0.1081046611,0.0124065261,0.2805253863,-0.059583243,-0.4462363422,0.1259049624,-0.0615482517,0.4121141732,0.3375741839,-0.0001145032,0.2734483778,0.4721427262,-0.1227841005,-0.4853440225,-0.2121149004,-0.218665719,0.0961271524,0.0428345352,-0.1566341817,-0.0091345869,0.0711133331,-0.0400046185,-0.7192984819,0.3178500533,0.3456359804,0.1778500229,0.0426006429,-0.0489487238,0.0268223863,0.0688386783,-0.2986646891,-0.0920066237,0.1218173951,0.2367273569,0.2287404835,-0.359573245,0.3845903277,-0.0658884048,0.0601840951,0.0944148228,0.0967238322,-0.1749501675,-0.4655051231,-0.0073944977,0.1884141415,0.6776787043,-0.2555166781,-0.4161978662,-0.2319881767,-0.0021713183,-0.6144573689,0.1465310603,0.1489273459,-0.4673651159,0.1280597448,0.2459630817,-0.0087530883,-0.327994585,-0.0458728224,-0.246799171,0.3882223368,-0.0767857283,-0.0237270929,0.0272943433,-0.0866289064,0.4627741277,-0.2110346556,-0.28459391,-0.0639790371,-0.4397552311,-0.0420655161,-0.0765123144,-0.094109349,0.1681466401,0.215389967,0.2953203917,0.0882523879,0.1543645263,0.0926442817,0.3555265665,0.3608669043,0.0598316565,0.1853774786,0.1311810017,0.0994313955,0.1366237104,-0.1818924248,-0.146488592,-0.0372809209,0.0281632524,-0.0131140957,0.262858659,-0.1912374645,-0.4138476253,0.0297920927,-0.3057210743,-0.0794672072,0.0055905147,0.2817168832,0.0958337784,0.4187803268,0.0194585584,0.1178673506,-0.2396837324,-0.4573938251,-0.0878112167,-0.1247182265,-0.0010082311,0.1233467832,0.0153353186,-0.1886832416,0.1515737325,-0.0465371571,0.1621010602,-0.0012185768,-0.0208561029,-0.1100598425,0.0216292907,0.2049841285,0.2180556655,0.1640353203,0.1795151085,-0.1997850239,-0.130682379,0.0886226073,-0.1416330338,-0.1671275645,-0.1788374335,0.2166469097,-0.0094024939,-0.3506264389,-0.0937892571,0.2814688385,0.1409590542,-0.2037903368,-0.1410701424,-0.0391465798,-0.1241319329,-0.0865156949,0.1792775393,0.5796180964,-0.1855915189,-0.1380216628,-0.1521381587,0.0008939481,0.5833057165,0.3344025612,-0.0607914478,0.0897968188,-0.170722425,0.1476544142,0.5017140508,-0.1436584145,-0.5796036124,0.5436661243,-0.0396082066,0.5637281537,0.2707032561,0.0225366186,0.1742344499,0.0431196988,0.313529104,0.3299024701,-0.1484592706,0.0074822325,-0.3334669471,-0.0399836674,0.4416950643,0.1912285686,0.0674290732,0.2502861321,-0.0288742278,-0.0626677349,0.3689779639,-0.0752563924,0.0763451308,0.0410080664,0.1468882412,0.0120370993,0.1379382461,-0.2090770155,-0.310682714,0.2972311378,0.0749307722,0.0127100674,-0.3174338341,0.2031723857,-0.1906455308,0.0017469627,-0.2628925443,-0.2590405643,0.1243173257,0.3170801699,0.0756207108,0.0505895875,-0.3084766865,0.7747215033,-0.078255251,-0.0131349247,-0.4549141228,0.2024929523,-0.0707768798,-0.2014573216,0.0164402872,-0.1265656799,0.0672556311,-0.0161919687,-0.2579914331,0.3313200772,-0.2366138101,0.2640713155,-0.1954375803,-0.1731976271,0.2897448838,-0.3644788265,-0.0866843313,0.2706028223,0.1075036898,0.125209704,-0.0176983867,0.1480839401,0.2070915401,0.2361335903,0.0434485786,0.1143487841,0.1961675137,-0.0816748515,-0.0686837137,-0.0850090533,0.3240485787,-0.3548789024,0.0467500761,-0.1989230514,-0.2722995877,0.0823631957,0.3586791456,-0.1403829306,-0.0492737889,0.3303245604,-0.2885322273,-0.1639078707,0.3145605624,0.1940099597,0.4723360538,0.0815219656,0.2573353648,-0.0455882996,-0.1886483878,-0.0735060051,0.3051697612,0.1374839693,0.0314638466,0.2464114279,0.0300063528,0.1635747999,-0.3229160309,-0.3497166634,-0.0566318817,0.1123416647,-0.3201874793,0.306302011,-0.38826859,-0.4651744664,0.0463509336,0.1560085416,0.0689049438,-0.3235587776,-0.1817550063,0.2325055152,-0.0038823313,0.1636838913,-0.2682063282,0.0895485729,0.2671770155,-0.0134687945,-0.3418698907,-0.1808474958,-0.2729629874,0.0591977872,0.1749951094,-0.4228037596,0.1769195795,0.0074260007,-0.1160532683,-0.1401735991,-0.076423429,0.3197818398,-0.1554995328,0.1568958163,-0.2774865627,0.1493911892,0.2213633955,-0.2503491044,0.1219482496,0.0908335969,-0.1187844425,0.2801248431,0.0940897167,-0.0241610892,-0.0305832736,-0.3010807931,-0.3024666011,-0.6177494526,0.0549683981,0.0454257689,0.2135515809,0.1643050015,0.0170347784,0.0968246013,0.3292069137,0.0991601422,-0.114212282,-0.4199434817,0.3747448325,-0.1670146883,-0.2868409157,-0.3248367012,-0.0681597292,0.1972901672,0.2589270473,-0.5295243263,0.2950162292,-0.4081035852,0.0848925635,-0.3834516108,-0.0974560827,-0.0261515174,0.0762885734,-0.0349893123,-0.1878576428,0.0707459524,-0.1169513538,0.0990076959,0.2066107839,0.1086307243,0.3698712885,0.1008493528,0.3327504098,0.4812112153,-0.0195496976,0.3407551944,0.082891129,0.1538007557,-0.1004334986,-0.0627885908,-0.095562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{"html_url":"https:\/\/github.com\/huggingface\/datasets\/issues\/2918","title":"`Can not decode content-encoding: gzip` when loading `scitldr` dataset with streaming","comments":"Thanks for investigating @albertvillanova ! \ud83e\udd17 ","body":"## Describe the bug\r\n\r\nTrying to load the `\"FullText\"` config of the `\"scitldr\"` dataset with `streaming=True` raises an error from `aiohttp`:\r\n```python\r\nClientPayloadError: 400, message='Can not decode content-encoding: gzip'\r\n```\r\n\r\ncc @lhoestq \r\n\r\n## Steps to reproduce the bug\r\n```python\r\nfrom datasets import load_dataset\r\n\r\niter_dset = iter(\r\n load_dataset(\"scitldr\", name=\"FullText\", split=\"test\", streaming=True)\r\n)\r\n\r\nnext(iter_dset)\r\n```\r\n\r\n## Expected results\r\nReturns the first sample of the dataset\r\n\r\n## Actual results\r\nCalling `__next__` crashes with the following Traceback:\r\n\r\n```python\r\n----> 1 next(dset_iter)\r\n\r\n~\\miniconda3\\envs\\datasets\\lib\\site-packages\\datasets\\iterable_dataset.py in __iter__(self)\r\n 339\r\n 340 def __iter__(self):\r\n--> 341 for key, example in self._iter():\r\n 342 if self.features:\r\n 343 # we encode the example for ClassLabel feature types for example\r\n\r\n~\\miniconda3\\envs\\datasets\\lib\\site-packages\\datasets\\iterable_dataset.py in _iter(self)\r\n 336 else:\r\n 337 ex_iterable = self._ex_iterable\r\n--> 338 yield from ex_iterable\r\n 339\r\n 340 def __iter__(self):\r\n\r\n~\\miniconda3\\envs\\datasets\\lib\\site-packages\\datasets\\iterable_dataset.py in __iter__(self)\r\n 76\r\n 77 def __iter__(self):\r\n---> 78 for key, example in self.generate_examples_fn(**self.kwargs):\r\n 79 yield key, example\r\n 80\r\n\r\n~\\.cache\\huggingface\\modules\\datasets_modules\\datasets\\scitldr\\72d6e2195786c57e1d343066fb2cc4f93ea39c5e381e53e6ae7c44bbfd1f05ef\\scitldr.py in _generate_examples(self, filepath, split)\r\n 162\r\n 163 with open(filepath, encoding=\"utf-8\") as f:\r\n--> 164 for id_, row in enumerate(f):\r\n 165 data = json.loads(row)\r\n 166 if self.config.name == \"AIC\":\r\n\r\n~\\miniconda3\\envs\\datasets\\lib\\site-packages\\fsspec\\implementations\\http.py in read(self, length)\r\n 496 else:\r\n 497 length = min(self.size - self.loc, length)\r\n--> 498 return super().read(length)\r\n 499\r\n 500 async def async_fetch_all(self):\r\n\r\n~\\miniconda3\\envs\\datasets\\lib\\site-packages\\fsspec\\spec.py in read(self, length)\r\n 1481 # don't even bother calling fetch\r\n 1482 return b\"\"\r\n-> 1483 out = self.cache._fetch(self.loc, self.loc + length)\r\n 1484 self.loc += len(out)\r\n 1485 return out\r\n\r\n~\\miniconda3\\envs\\datasets\\lib\\site-packages\\fsspec\\caching.py in _fetch(self, start, end)\r\n 378 elif start < self.start:\r\n 379 if self.end - end > self.blocksize:\r\n--> 380 self.cache = self.fetcher(start, bend)\r\n 381 self.start = start\r\n 382 else:\r\n\r\n~\\miniconda3\\envs\\datasets\\lib\\site-packages\\fsspec\\asyn.py in wrapper(*args, **kwargs)\r\n 86 def wrapper(*args, **kwargs):\r\n 87 self = obj or args[0]\r\n---> 88 return sync(self.loop, func, *args, **kwargs)\r\n 89\r\n 90 return wrapper\r\n\r\n~\\miniconda3\\envs\\datasets\\lib\\site-packages\\fsspec\\asyn.py in sync(loop, func, timeout, *args, **kwargs)\r\n 67 raise FSTimeoutError\r\n 68 if isinstance(result[0], BaseException):\r\n---> 69 raise result[0]\r\n 70 return result[0]\r\n 71\r\n\r\n~\\miniconda3\\envs\\datasets\\lib\\site-packages\\fsspec\\asyn.py in _runner(event, coro, result, timeout)\r\n 23 coro = asyncio.wait_for(coro, timeout=timeout)\r\n 24 try:\r\n---> 25 result[0] = await coro\r\n 26 except Exception as ex:\r\n 27 result[0] = ex\r\n\r\n~\\miniconda3\\envs\\datasets\\lib\\site-packages\\fsspec\\implementations\\http.py in async_fetch_range(self, start, end)\r\n 538 if r.status == 206:\r\n 539 # partial content, as expected\r\n--> 540 out = await r.read()\r\n 541 elif \"Content-Length\" in r.headers:\r\n 542 cl = int(r.headers[\"Content-Length\"])\r\n\r\n~\\miniconda3\\envs\\datasets\\lib\\site-packages\\aiohttp\\client_reqrep.py in read(self)\r\n 1030 if self._body is None:\r\n 1031 try:\r\n-> 1032 self._body = await self.content.read()\r\n 1033 for trace in self._traces:\r\n 1034 await trace.send_response_chunk_received(\r\n\r\n~\\miniconda3\\envs\\datasets\\lib\\site-packages\\aiohttp\\streams.py in read(self, n)\r\n 342 async def read(self, n: int = -1) -> bytes:\r\n 343 if self._exception is not None:\r\n--> 344 raise self._exception\r\n 345\r\n 346 # migration problem; with DataQueue you have to catch\r\n\r\nClientPayloadError: 400, message='Can not decode content-encoding: gzip'\r\n```\r\n\r\n## Environment info\r\n\r\n- `datasets` version: 1.12.0\r\n- Platform: Windows-10-10.0.19041-SP0\r\n- Python version: 3.8.5\r\n- PyArrow version: 2.0.0\r\n- aiohttp version: 3.7.4.post0\r\n","comment_length":6,"text":"`Can not decode content-encoding: gzip` when loading `scitldr` dataset with streaming\n## Describe the bug\r\n\r\nTrying to load the `\"FullText\"` config of the `\"scitldr\"` dataset with `streaming=True` raises an error from `aiohttp`:\r\n```python\r\nClientPayloadError: 400, message='Can not decode content-encoding: gzip'\r\n```\r\n\r\ncc @lhoestq \r\n\r\n## Steps to reproduce the bug\r\n```python\r\nfrom datasets import load_dataset\r\n\r\niter_dset = iter(\r\n load_dataset(\"scitldr\", name=\"FullText\", split=\"test\", streaming=True)\r\n)\r\n\r\nnext(iter_dset)\r\n```\r\n\r\n## Expected results\r\nReturns the first sample of the dataset\r\n\r\n## Actual results\r\nCalling `__next__` crashes with the following Traceback:\r\n\r\n```python\r\n----> 1 next(dset_iter)\r\n\r\n~\\miniconda3\\envs\\datasets\\lib\\site-packages\\datasets\\iterable_dataset.py in __iter__(self)\r\n 339\r\n 340 def __iter__(self):\r\n--> 341 for key, example in self._iter():\r\n 342 if self.features:\r\n 343 # we encode the example for ClassLabel feature types for example\r\n\r\n~\\miniconda3\\envs\\datasets\\lib\\site-packages\\datasets\\iterable_dataset.py in _iter(self)\r\n 336 else:\r\n 337 ex_iterable = self._ex_iterable\r\n--> 338 yield from ex_iterable\r\n 339\r\n 340 def __iter__(self):\r\n\r\n~\\miniconda3\\envs\\datasets\\lib\\site-packages\\datasets\\iterable_dataset.py in __iter__(self)\r\n 76\r\n 77 def __iter__(self):\r\n---> 78 for key, example in self.generate_examples_fn(**self.kwargs):\r\n 79 yield key, example\r\n 80\r\n\r\n~\\.cache\\huggingface\\modules\\datasets_modules\\datasets\\scitldr\\72d6e2195786c57e1d343066fb2cc4f93ea39c5e381e53e6ae7c44bbfd1f05ef\\scitldr.py in _generate_examples(self, filepath, split)\r\n 162\r\n 163 with open(filepath, encoding=\"utf-8\") as f:\r\n--> 164 for id_, row in enumerate(f):\r\n 165 data = json.loads(row)\r\n 166 if self.config.name == \"AIC\":\r\n\r\n~\\miniconda3\\envs\\datasets\\lib\\site-packages\\fsspec\\implementations\\http.py in read(self, length)\r\n 496 else:\r\n 497 length = min(self.size - self.loc, length)\r\n--> 498 return super().read(length)\r\n 499\r\n 500 async def async_fetch_all(self):\r\n\r\n~\\miniconda3\\envs\\datasets\\lib\\site-packages\\fsspec\\spec.py in read(self, length)\r\n 1481 # don't even bother calling fetch\r\n 1482 return b\"\"\r\n-> 1483 out = self.cache._fetch(self.loc, self.loc + length)\r\n 1484 self.loc += len(out)\r\n 1485 return out\r\n\r\n~\\miniconda3\\envs\\datasets\\lib\\site-packages\\fsspec\\caching.py in _fetch(self, start, end)\r\n 378 elif start < self.start:\r\n 379 if self.end - end > self.blocksize:\r\n--> 380 self.cache = self.fetcher(start, bend)\r\n 381 self.start = start\r\n 382 else:\r\n\r\n~\\miniconda3\\envs\\datasets\\lib\\site-packages\\fsspec\\asyn.py in wrapper(*args, **kwargs)\r\n 86 def wrapper(*args, **kwargs):\r\n 87 self = obj or args[0]\r\n---> 88 return sync(self.loop, func, *args, **kwargs)\r\n 89\r\n 90 return wrapper\r\n\r\n~\\miniconda3\\envs\\datasets\\lib\\site-packages\\fsspec\\asyn.py in sync(loop, func, timeout, *args, **kwargs)\r\n 67 raise FSTimeoutError\r\n 68 if isinstance(result[0], BaseException):\r\n---> 69 raise result[0]\r\n 70 return result[0]\r\n 71\r\n\r\n~\\miniconda3\\envs\\datasets\\lib\\site-packages\\fsspec\\asyn.py in _runner(event, coro, result, timeout)\r\n 23 coro = asyncio.wait_for(coro, timeout=timeout)\r\n 24 try:\r\n---> 25 result[0] = await coro\r\n 26 except Exception as ex:\r\n 27 result[0] = ex\r\n\r\n~\\miniconda3\\envs\\datasets\\lib\\site-packages\\fsspec\\implementations\\http.py in async_fetch_range(self, start, end)\r\n 538 if r.status == 206:\r\n 539 # partial content, as expected\r\n--> 540 out = await r.read()\r\n 541 elif \"Content-Length\" in r.headers:\r\n 542 cl = int(r.headers[\"Content-Length\"])\r\n\r\n~\\miniconda3\\envs\\datasets\\lib\\site-packages\\aiohttp\\client_reqrep.py in read(self)\r\n 1030 if self._body is None:\r\n 1031 try:\r\n-> 1032 self._body = await self.content.read()\r\n 1033 for trace in self._traces:\r\n 1034 await trace.send_response_chunk_received(\r\n\r\n~\\miniconda3\\envs\\datasets\\lib\\site-packages\\aiohttp\\streams.py in read(self, n)\r\n 342 async def read(self, n: int = -1) -> bytes:\r\n 343 if self._exception is not None:\r\n--> 344 raise self._exception\r\n 345\r\n 346 # migration problem; with DataQueue you have to catch\r\n\r\nClientPayloadError: 400, message='Can not decode content-encoding: gzip'\r\n```\r\n\r\n## Environment info\r\n\r\n- `datasets` version: 1.12.0\r\n- Platform: Windows-10-10.0.19041-SP0\r\n- Python version: 3.8.5\r\n- PyArrow version: 2.0.0\r\n- aiohttp version: 3.7.4.post0\r\n\nThanks for investigating @albertvillanova ! \ud83e\udd17 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{"html_url":"https:\/\/github.com\/huggingface\/datasets\/issues\/2917","title":"windows download abnormal","comments":"Hi ! Is there some kind of proxy that is configured in your browser that gives you access to internet ? If it's the case it could explain why it doesn't work in the code, since the proxy wouldn't be used","body":"## Describe the bug\r\nThe script clearly exists (accessible from the browser), but the script download fails on windows. Then I tried it again and it can be downloaded normally on linux. why??\r\n## Steps to reproduce the bug\r\n```python3.7 + windows\r\n![image](https:\/\/user-images.githubusercontent.com\/52347799\/133436174-4303f847-55d5-434f-a749-08da3bb9b654.png)\r\n\r\n\r\n# Sample code to reproduce the bug\r\n```\r\n\r\n## Expected results\r\nIt can be downloaded normally.\r\n\r\n## Actual results\r\nit cann't\r\n\r\n## Environment info\r\n\r\n- `datasets` version:1.11.0\r\n- Platform:windows\r\n- Python version:3.7\r\n- PyArrow version:\r\n","comment_length":41,"text":"windows download abnormal\n## Describe the bug\r\nThe script clearly exists (accessible from the browser), but the script download fails on windows. Then I tried it again and it can be downloaded normally on linux. why??\r\n## Steps to reproduce the bug\r\n```python3.7 + windows\r\n![image](https:\/\/user-images.githubusercontent.com\/52347799\/133436174-4303f847-55d5-434f-a749-08da3bb9b654.png)\r\n\r\n\r\n# Sample code to reproduce the bug\r\n```\r\n\r\n## Expected results\r\nIt can be downloaded normally.\r\n\r\n## Actual results\r\nit cann't\r\n\r\n## Environment info\r\n\r\n- `datasets` version:1.11.0\r\n- Platform:windows\r\n- Python version:3.7\r\n- PyArrow version:\r\n\nHi ! Is there some kind of proxy that is configured in your browser that gives you access to internet ? If it's the case it could explain why it doesn't work in the code, since the proxy wouldn't be 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{"html_url":"https:\/\/github.com\/huggingface\/datasets\/issues\/2917","title":"windows download abnormal","comments":"It is indeed an agency problem, thank you very, very much","body":"## Describe the bug\r\nThe script clearly exists (accessible from the browser), but the script download fails on windows. Then I tried it again and it can be downloaded normally on linux. why??\r\n## Steps to reproduce the bug\r\n```python3.7 + windows\r\n![image](https:\/\/user-images.githubusercontent.com\/52347799\/133436174-4303f847-55d5-434f-a749-08da3bb9b654.png)\r\n\r\n\r\n# Sample code to reproduce the bug\r\n```\r\n\r\n## Expected results\r\nIt can be downloaded normally.\r\n\r\n## Actual results\r\nit cann't\r\n\r\n## Environment info\r\n\r\n- `datasets` version:1.11.0\r\n- Platform:windows\r\n- Python version:3.7\r\n- PyArrow version:\r\n","comment_length":11,"text":"windows download abnormal\n## Describe the bug\r\nThe script clearly exists (accessible from the browser), but the script download fails on windows. Then I tried it again and it can be downloaded normally on linux. why??\r\n## Steps to reproduce the bug\r\n```python3.7 + windows\r\n![image](https:\/\/user-images.githubusercontent.com\/52347799\/133436174-4303f847-55d5-434f-a749-08da3bb9b654.png)\r\n\r\n\r\n# Sample code to reproduce the bug\r\n```\r\n\r\n## Expected results\r\nIt can be downloaded normally.\r\n\r\n## Actual results\r\nit cann't\r\n\r\n## Environment info\r\n\r\n- `datasets` version:1.11.0\r\n- Platform:windows\r\n- Python version:3.7\r\n- PyArrow version:\r\n\nIt is indeed an agency problem, thank you very, very much","embeddings":[-0.1819480658,-0.0709162951,-0.0238682684,0.1505493969,0.2515722811,-0.0938239619,0.0032474413,0.11024113,0.362229526,0.0661070794,0.1379210949,0.2134204656,0.0683521777,0.2074184567,-0.0183983669,-0.2120418251,0.0408425257,0.169603616,-0.109351553,0.0084896954,-0.3778957427,0.1222561225,-0.244990021,-0.0322376676,0.118513003,-0.1276566386,-0.3018625379,0.1873607785,-0.2236432433,-0.0762688667,0.1565723866,-0.1330198646,0.1389445513,0.3082361221,-0.0001110555,0.0270548705,0.4110395014,0.0012154208,0.0296383202,-0.0051854542,-0.1887717694,-0.1067669615,-0.2910613418,-0.3018800914,0.163738668,0.3125549555,0.0271366872,-0.1251074672,0.2193036079,0.4728519619,0.3004208505,0.4755435586,-0.1050411314,-0.052202031,0.2329810262,0.0768294707,-0.1763174385,0.1497375667,0.254214406,-0.2113004178,0.2170041651,-0.0690852255,-0.0610753894,-0.0063095409,-0.0902022049,0.0914523378,0.0073744375,-0.5927760601,0.1896012127,0.1313620359,0.401514262,-0.1367759109,-0.1460928917,0.1193385571,0.0144195985,-0.0813066885,0.2506349087,0.3949985504,-0.1587389559,0.0788909793,-0.2028708309,0.2833296955,-0.2204892486,0.1841510683,-0.1222558171,0.1910018027,-0.0857133567,0.2090443075,-0.0596853197,0.2083201408,0.0718123764,-0.2013075501,-0.0888202116,-0.0001829596,-0.0580069907,0.0415586531,-0.0736014992,0.6245207787,0.131835565,0.1753447503,0.0869020745,-0.1592784822,0.183907643,0.0403068922,0.224851951,0.1441524029,-0.2167216688,0.1089039966,0.2697381079,0.2915846109,-0.0308599882,0.1096391231,-0.107437022,-0.41546157,0.2698237002,0.0131871272,0.4077474773,-0.1028263122,-0.62843436,0.0436515138,0.1092530116,0.1674703658,-0.1276319921,0.1536726505,-0.2733654976,0.013872534,0.0132438513,0.32657969,-0.1094577163,0.206694752,0.0036723863,0.0926430076,-0.2967072725,-0.2458346933,0.3576645553,-0.1119512916,0.1324940771,0.2963950634,-0.1462853998,-0.01702445,0.0105879866,-0.1995972842,-0.1029506847,0.439473182,0.2255644649,0.3950291276,-0.1827936321,0.0152565576,-0.0298757032,0.1933602542,-0.1561779529,-0.0408560708,-0.0541052632,0.2324374616,-0.3855369985,-0.1968738139,-0.039049834,-0.2038401216,-0.0327578783,0.1615654677,0.2465070188,-0.2326217592,-0.2305738032,-0.3819427192,-0.0289984588,0.5887625813,-0.2635556459,0.2373927385,-0.0847060829,-0.328499347,0.4486765862,0.1127441376,0.1913455874,0.1516014636,-0.4607596099,0.0760051906,0.0519300029,-0.4398525655,-0.5598500967,0.4142220914,-0.0977686942,0.1617045999,0.1845384538,0.1501137912,0.3036822379,-0.0325751416,-0.0825505555,0.4031662345,-0.0779337958,0.1080535129,-0.1352510601,-0.134622559,0.201709345,0.168760702,0.1124669537,0.0058825724,0.1826209873,-0.1374899149,0.298524797,0.2201125473,0.0792058855,0.2166628391,0.2818375528,0.1327269375,0.1090106666,-0.1992979348,-0.0353883244,0.2307479382,-0.068367891,-0.1274635792,-0.1719899923,-0.2560539246,-0.4767713547,0.0034119347,-0.2548181415,-0.193141818,0.1301574707,0.0973193944,0.1700637639,0.1048899293,0.1259745061,0.1654153466,0.0545962192,0.0097603537,0.1804260761,0.302990526,-0.2577700615,-0.0457421467,0.0831682235,-0.0594857298,0.2411195934,0.0036581284,-0.0234827735,0.1238250807,0.0947378799,-0.0369792469,0.0112583535,-0.0961492285,-0.0187985655,-0.0046684532,0.208516553,0.5677586794,0.1324958503,-0.0747223124,0.0045230803,0.1056594327,-0.1307276189,-0.0183902904,-0.0695005655,0.2344380766,0.2568475604,-0.3220861256,-0.0040925732,-0.0434314571,0.2829574049,0.0912019163,-0.0603718944,-0.0726193562,-0.1558505297,0.1877027154,0.700084269,-0.1830890179,0.3046873808,0.1612100303,-0.1223507747,0.1930639148,-0.137361154,0.2834716737,0.7158129215,0.0091603678,0.0177161265,0.2950727344,0.002406968,-0.1490684301,0.2080704123,0.0997634754,-0.2241471559,0.3055584729,-0.0021933594,0.2467545569,-0.3240906596,-0.2490481883,0.0142765054,0.1652551144,-0.2981254756,0.098265931,-0.1337950528,-0.2373199612,-0.4030925632,0.0705316141,-0.0321190171,-0.0354837701,0.0339757949,0.1372851282,-0.0919834822,-0.1605060548,-0.3157018423,0.1221830696,0.1355545223,-0.1588221341,-0.0273725614,0.2578795254,-0.2540774345,0.1120541915,0.2056393325,-0.0287933052,0.5603420734,-0.496091783,0.2043798715,-0.4564227164,-0.2349586785,0.0054968395,0.0737605169,0.429425329,0.1740064919,0.074083142,0.0736172348,0.0474765077,0.1184441224,-0.2257839143,-0.2208357453,0.1726488918,-0.0539861992,-0.4455712736,-0.3471133113,-0.2787243128,-0.2446427345,-0.3425118625,0.228518784,0.1010006294,0.1010874212,-0.1100757569,0.0160217341,-0.098175019,-0.0168962702,0.1484609544,-0.0863984153,0.1665725112,0.4026210904,-0.0914862081,-0.6598010659,0.226908803,-0.0615841225,-0.2246567756,0.2210018188,-0.4095596969,-0.1262203753,-0.342110157,0.129096806,0.0070878975,0.2367419153,0.2802664936,-0.2545440495,0.0095087094,-0.2566065192,-0.113554813,-0.3147451878,-0.0000580463,0.1881667525,0.0922107473,0.2084324509,0.0313312039,0.1634161323,0.3067245483,-0.0213810727,0.3113523424,-0.4172437489,0.4244993925,0.0466330908,-0.2214517891,0.0983962864,-0.2498928308,0.1606142521,0.3106133938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{"html_url":"https:\/\/github.com\/huggingface\/datasets\/issues\/2917","title":"windows download abnormal","comments":"Let me know if you have other questions :)\r\n\r\nClosing this issue now","body":"## Describe the bug\r\nThe script clearly exists (accessible from the browser), but the script download fails on windows. Then I tried it again and it can be downloaded normally on linux. why??\r\n## Steps to reproduce the bug\r\n```python3.7 + windows\r\n![image](https:\/\/user-images.githubusercontent.com\/52347799\/133436174-4303f847-55d5-434f-a749-08da3bb9b654.png)\r\n\r\n\r\n# Sample code to reproduce the bug\r\n```\r\n\r\n## Expected results\r\nIt can be downloaded normally.\r\n\r\n## Actual results\r\nit cann't\r\n\r\n## Environment info\r\n\r\n- `datasets` version:1.11.0\r\n- Platform:windows\r\n- Python version:3.7\r\n- PyArrow version:\r\n","comment_length":13,"text":"windows download abnormal\n## Describe the bug\r\nThe script clearly exists (accessible from the browser), but the script download fails on windows. Then I tried it again and it can be downloaded normally on linux. why??\r\n## Steps to reproduce the bug\r\n```python3.7 + windows\r\n![image](https:\/\/user-images.githubusercontent.com\/52347799\/133436174-4303f847-55d5-434f-a749-08da3bb9b654.png)\r\n\r\n\r\n# Sample code to reproduce the bug\r\n```\r\n\r\n## Expected results\r\nIt can be downloaded normally.\r\n\r\n## Actual results\r\nit cann't\r\n\r\n## Environment info\r\n\r\n- `datasets` version:1.11.0\r\n- Platform:windows\r\n- Python version:3.7\r\n- PyArrow version:\r\n\nLet me know if you have other questions :)\r\n\r\nClosing this issue now","embeddings":[-0.2207755297,-0.1332957745,-0.0691334903,0.1743752807,0.2265743613,-0.0526336357,-0.0071485089,0.1346075088,0.2471247762,0.0947366357,0.1111562997,0.2011167854,0.0616793036,0.1772477627,0.0522657,-0.2514466345,0.0436070114,0.1884659082,-0.1824369282,-0.0201365389,-0.3421658576,0.1442255229,-0.2688935101,-0.0980346352,0.1254004985,0.0039493642,-0.3086517155,0.2426276207,-0.2599076033,-0.1714349836,0.0742157772,-0.0879949704,0.163236618,0.2892299891,-0.0001064452,0.0440166481,0.3960578144,0.0089306133,0.0559709035,-0.0490174629,-0.2207491994,-0.1396053284,-0.2216101736,-0.293787092,0.1231971383,0.110559687,0.0049971924,-0.0325602069,0.0923154652,0.3783936501,0.3404253125,0.4842950702,-0.0722764954,-0.0313038714,0.4031114578,0.1105747074,-0.1911314577,0.1367606372,0.2970518172,-0.2148809433,0.1355500519,-0.0241315197,-0.0474884994,0.0302238148,-0.0182570498,0.0709910616,-0.0401855186,-0.4947353899,0.1354618669,0.1454054117,0.3116519451,-0.1623973399,-0.1045670062,0.1049401239,-0.0483258218,-0.2304380387,0.1805960536,0.3206201792,-0.1367184669,0.1314445287,-0.2873217762,0.2481869012,-0.2228381038,0.1704220772,-0.0978163704,0.2842999995,-0.0906764865,0.1684194952,-0.0802360848,0.2447992563,0.0083303163,-0.2286545932,-0.100851655,0.0021302979,-0.0380191021,0.0297235865,0.0100703146,0.5421230197,0.129743889,0.2278441787,-0.0172666032,-0.1089707017,0.2379163802,0.0550119728,0.1292164624,0.1321160793,-0.1616736948,0.0282277036,0.3516741991,0.2831096351,0.0294332672,0.1096406654,-0.0448804945,-0.409791708,0.3063461781,0.067747876,0.3790406585,-0.1142017618,-0.6207893491,0.0279846601,0.070389539,0.1223948523,-0.0729451403,0.2053755671,-0.2115510255,-0.038713377,0.0287676323,0.3363829255,-0.1473443359,0.1672639549,-0.0085347267,0.1934087873,-0.2336297184,-0.2789251506,0.4062646925,-0.1174081489,0.1876767725,0.2447303534,-0.1050559208,0.0019086151,-0.0402509943,-0.1226891279,-0.0408820026,0.4309408367,0.2251872271,0.4080506861,-0.1081939563,0.1009383127,-0.0526482053,0.2426031977,-0.1097498462,-0.0552099682,-0.0306426436,0.2899795175,-0.3140995502,-0.2293543965,-0.0028886429,-0.2081403881,-0.0288612023,0.0473718569,0.2172876298,-0.2606675923,-0.2903537452,-0.4342307448,0.0709750429,0.4895909131,-0.4144755304,0.227134794,0.0270461105,-0.3316147923,0.4150057137,0.1907803267,0.1251585186,0.099305965,-0.4734765291,0.1356181353,0.0067809615,-0.3681674302,-0.4561111033,0.3584577441,-0.1014415994,0.1938920319,0.1865333468,0.0966966599,0.288595438,-0.0928407386,-0.0392433889,0.2651661932,-0.0274843816,0.0421565957,-0.1506972611,-0.1795333475,0.1650287658,0.1755206734,0.0803700909,0.0484602489,0.2923360765,-0.1043607593,0.3381632268,0.1145961881,0.0599303469,0.2569989264,0.3408920169,0.1021471545,0.1144763753,-0.243753314,-0.0720155612,0.2930027843,-0.0744742453,-0.1432997733,-0.2104561776,-0.2281522602,-0.4664123952,0.0491136797,-0.2203699201,-0.2093744129,0.1927045584,0.105987832,0.2294083834,0.0463595949,0.0988513902,0.1179435104,-0.0049782838,-0.0852696747,0.1478122175,0.2976120412,-0.358138293,-0.0983111262,0.0428683013,-0.0618663542,0.2359926403,-0.0797711536,0.0072130114,0.1748583913,0.0909606516,0.0120516652,-0.0225844402,-0.0551137365,-0.0018050423,-0.0328586437,0.2095954716,0.4951538742,0.1691873521,-0.1198024824,-0.0891863182,0.0866177082,-0.056391336,-0.0603130832,-0.0616283976,0.135127157,0.2473331243,-0.2254518121,-0.0318140052,-0.0709816664,0.4197178185,0.1235188469,-0.1139750704,-0.1175652519,-0.1888785362,0.154271245,0.6313266158,-0.2423981279,0.2632025778,0.0781768337,-0.0857005417,0.1904331446,-0.1035305858,0.2065784037,0.6523746848,0.0705253407,0.0944053531,0.3197440505,-0.0311157349,-0.1489898115,0.2229828387,0.0814782307,-0.1967646033,0.2652468383,-0.0275604334,0.1729865074,-0.3210701644,-0.1806147695,0.0191281699,0.1348359287,-0.3165897727,0.1074841619,-0.1814183295,-0.0536290482,-0.3422429264,-0.0320985392,-0.0447294153,-0.053749226,0.0935111567,0.2653478086,-0.1439779252,-0.1548583955,-0.2668774128,0.0174200926,0.2345918864,-0.2694985569,-0.021260269,0.218075648,-0.2366104424,0.1633965224,0.1720452756,-0.0932388231,0.5667923689,-0.4775111079,0.1574508995,-0.4538096488,-0.1942285001,-0.0239324756,-0.0515272506,0.3197554946,0.2340763509,0.1066003665,0.0461247899,0.0562638193,0.1573544741,-0.2489885539,-0.2195565104,0.2129605412,0.0073923734,-0.4503124058,-0.3328312635,-0.2961428463,-0.202512458,-0.3447829485,0.1225349307,0.1772155315,0.1006463096,-0.0672200993,0.0358825438,-0.0186522044,0.047959242,0.0959775299,-0.1114475951,0.1012131572,0.4370254278,-0.108312875,-0.668559134,0.2404889315,0.0451720208,-0.2690183222,0.241299063,-0.5037426949,-0.088098526,-0.3273606896,0.2437198013,0.0548889041,0.2400947064,0.2727158964,-0.2193379253,-0.0504980423,-0.2639805079,-0.0492048077,-0.2931634486,-0.0319044366,0.1824257225,0.0572677068,0.2499418408,0.0213780347,0.2403487414,0.2202055752,0.0174942203,0.3821138442,-0.3713877201,0.4587187171,-0.0153283775,-0.1699820757,0.0924867392,-0.2480327785,0.0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{"html_url":"https:\/\/github.com\/huggingface\/datasets\/issues\/2914","title":"Having a dependency defining fsspec entrypoint raises an AttributeError when importing datasets","comments":"Closed by #2915.","body":"## Describe the bug\r\nIn one of my project, I defined a custom fsspec filesystem with an entrypoint.\r\nMy guess is that by doing so, a variable named `spec` is created in the module `fsspec` (created by entering a for loop as there are entrypoints defined, see the loop in question [here](https:\/\/github.com\/intake\/filesystem_spec\/blob\/0589358d8a029ed6b60d031018f52be2eb721291\/fsspec\/__init__.py#L55)).\r\nSo that `fsspec.spec`, that was previously referring to the `spec` submodule, is now referring to that `spec` variable.\r\nThis make the import of datasets failing as it is using that `fsspec.spec`.\r\n\r\n## Steps to reproduce the bug\r\nI could reproduce the bug with a dummy poetry project.\r\n\r\nHere is the pyproject.toml:\r\n```toml\r\n[tool.poetry]\r\nname = \"debug-datasets\"\r\nversion = \"0.1.0\"\r\ndescription = \"\"\r\nauthors = [\"Pierre Godard\"]\r\n\r\n[tool.poetry.dependencies]\r\npython = \"^3.8\"\r\ndatasets = \"^1.11.0\"\r\n\r\n[tool.poetry.dev-dependencies]\r\n\r\n[build-system]\r\nrequires = [\"poetry-core>=1.0.0\"]\r\nbuild-backend = \"poetry.core.masonry.api\"\r\n\r\n[tool.poetry.plugins.\"fsspec.specs\"]\r\n\"file2\" = \"fsspec.implementations.local.LocalFileSystem\"\r\n```\r\n\r\nThe only other file being a `debug_datasets\/__init__.py` empty file.\r\n\r\nThe overall structure of the project is as follows:\r\n```\r\n.\r\n\u251c\u2500\u2500 pyproject.toml\r\n\u2514\u2500\u2500 debug_datasets\r\n \u2514\u2500\u2500 __init__.py\r\n```\r\n\r\nThen, within the project folder run:\r\n\r\n```\r\npoetry install\r\npoetry run python\r\n```\r\n\r\nAnd in the python interpreter, try to import `datasets`:\r\n\r\n```\r\nimport datasets\r\n```\r\n\r\n## Expected results\r\nThe import should run successfully.\r\n\r\n## Actual results\r\n\r\nHere is the trace of the error I get:\r\n\r\n```\r\nTraceback (most recent call last):\r\n File \"\", line 1, in \r\n File \"\/home\/godarpi\/.cache\/pypoetry\/virtualenvs\/debug-datasets-JuFzTKL--py3.8\/lib\/python3.8\/site-packages\/datasets\/__init__.py\", line 33, in \r\n from .arrow_dataset import Dataset, concatenate_datasets\r\n File \"\/home\/godarpi\/.cache\/pypoetry\/virtualenvs\/debug-datasets-JuFzTKL--py3.8\/lib\/python3.8\/site-packages\/datasets\/arrow_dataset.py\", line 48, in \r\n from .filesystems import extract_path_from_uri, is_remote_filesystem\r\n File \"\/home\/godarpi\/.cache\/pypoetry\/virtualenvs\/debug-datasets-JuFzTKL--py3.8\/lib\/python3.8\/site-packages\/datasets\/filesystems\/__init__.py\", line 30, in \r\n def is_remote_filesystem(fs: fsspec.spec.AbstractFileSystem) -> bool:\r\nAttributeError: 'EntryPoint' object has no attribute 'AbstractFileSystem'\r\n```\r\n\r\n## Suggested fix\r\n\r\n`datasets\/filesystems\/__init__.py`, line 30, replace:\r\n```\r\n def is_remote_filesystem(fs: fsspec.spec.AbstractFileSystem) -> bool:\r\n```\r\nby:\r\n```\r\n def is_remote_filesystem(fs: fsspec.AbstractFileSystem) -> bool:\r\n```\r\n\r\nI will come up with a PR soon if this effectively solves the issue.\r\n\r\n## Environment info\r\n\r\n- `datasets` version: 1.11.0\r\n- Platform: WSL2 (Ubuntu 20.04.1 LTS)\r\n- Python version: 3.8.5\r\n- PyArrow version: 5.0.0\r\n- `fsspec` version: 2021.8.1\r\n","comment_length":3,"text":"Having a dependency defining fsspec entrypoint raises an AttributeError when importing datasets\n## Describe the bug\r\nIn one of my project, I defined a custom fsspec filesystem with an entrypoint.\r\nMy guess is that by doing so, a variable named `spec` is created in the module `fsspec` (created by entering a for loop as there are entrypoints defined, see the loop in question [here](https:\/\/github.com\/intake\/filesystem_spec\/blob\/0589358d8a029ed6b60d031018f52be2eb721291\/fsspec\/__init__.py#L55)).\r\nSo that `fsspec.spec`, that was previously referring to the `spec` submodule, is now referring to that `spec` variable.\r\nThis make the import of datasets failing as it is using that `fsspec.spec`.\r\n\r\n## Steps to reproduce the bug\r\nI could reproduce the bug with a dummy poetry project.\r\n\r\nHere is the pyproject.toml:\r\n```toml\r\n[tool.poetry]\r\nname = \"debug-datasets\"\r\nversion = \"0.1.0\"\r\ndescription = \"\"\r\nauthors = [\"Pierre Godard\"]\r\n\r\n[tool.poetry.dependencies]\r\npython = \"^3.8\"\r\ndatasets = \"^1.11.0\"\r\n\r\n[tool.poetry.dev-dependencies]\r\n\r\n[build-system]\r\nrequires = [\"poetry-core>=1.0.0\"]\r\nbuild-backend = \"poetry.core.masonry.api\"\r\n\r\n[tool.poetry.plugins.\"fsspec.specs\"]\r\n\"file2\" = \"fsspec.implementations.local.LocalFileSystem\"\r\n```\r\n\r\nThe only other file being a `debug_datasets\/__init__.py` empty file.\r\n\r\nThe overall structure of the project is as follows:\r\n```\r\n.\r\n\u251c\u2500\u2500 pyproject.toml\r\n\u2514\u2500\u2500 debug_datasets\r\n \u2514\u2500\u2500 __init__.py\r\n```\r\n\r\nThen, within the project folder run:\r\n\r\n```\r\npoetry install\r\npoetry run python\r\n```\r\n\r\nAnd in the python interpreter, try to import `datasets`:\r\n\r\n```\r\nimport datasets\r\n```\r\n\r\n## Expected results\r\nThe import should run successfully.\r\n\r\n## Actual results\r\n\r\nHere is the trace of the error I get:\r\n\r\n```\r\nTraceback (most recent call last):\r\n File \"\", line 1, in \r\n File \"\/home\/godarpi\/.cache\/pypoetry\/virtualenvs\/debug-datasets-JuFzTKL--py3.8\/lib\/python3.8\/site-packages\/datasets\/__init__.py\", line 33, in \r\n from .arrow_dataset import Dataset, concatenate_datasets\r\n File \"\/home\/godarpi\/.cache\/pypoetry\/virtualenvs\/debug-datasets-JuFzTKL--py3.8\/lib\/python3.8\/site-packages\/datasets\/arrow_dataset.py\", line 48, in \r\n from .filesystems import extract_path_from_uri, is_remote_filesystem\r\n File \"\/home\/godarpi\/.cache\/pypoetry\/virtualenvs\/debug-datasets-JuFzTKL--py3.8\/lib\/python3.8\/site-packages\/datasets\/filesystems\/__init__.py\", line 30, in \r\n def is_remote_filesystem(fs: fsspec.spec.AbstractFileSystem) -> bool:\r\nAttributeError: 'EntryPoint' object has no attribute 'AbstractFileSystem'\r\n```\r\n\r\n## Suggested fix\r\n\r\n`datasets\/filesystems\/__init__.py`, line 30, replace:\r\n```\r\n def is_remote_filesystem(fs: fsspec.spec.AbstractFileSystem) -> bool:\r\n```\r\nby:\r\n```\r\n def is_remote_filesystem(fs: fsspec.AbstractFileSystem) -> bool:\r\n```\r\n\r\nI will come up with a PR soon if this effectively solves the issue.\r\n\r\n## Environment info\r\n\r\n- `datasets` version: 1.11.0\r\n- Platform: WSL2 (Ubuntu 20.04.1 LTS)\r\n- Python version: 3.8.5\r\n- PyArrow version: 5.0.0\r\n- `fsspec` version: 2021.8.1\r\n\nClosed by #2915.","embeddings":[-0.0430343486,0.1729615629,-0.0092811715,0.3249996006,0.3824198246,-0.027578136,0.3696733415,-0.0281330235,0.0717082471,0.1155877039,0.2619345784,0.4130678475,-0.0622646138,0.0250749756,-0.1028326377,0.2277256399,0.0461320765,0.1964916587,-0.1118648723,0.0814422593,-0.2052113712,0.284779042,-0.20766671,0.1609047055,-0.3610854745,0.1328371316,0.0609395057,0.3386956155,-0.1826843172,-0.6150017977,0.3787585497,-0.1575102955,-0.0890944004,0.1836814284,-0.0001192106,0.0413611904,0.1138052121,-0.081654422,-0.2293013632,0.0555289909,-0.5713313818,-0.1993799657,0.1814039797,-0.2640390098,0.4023723602,-0.2299601138,-0.0492568053,-0.720643878,-0.0350703783,0.4295676649,0.151214689,-0.0737293884,0.1342175305,-0.078581512,0.1368629038,0.0073615429,-0.2163085043,0.0665525422,-0.1590744555,-0.1137120426,-0.0247463994,0.028592892,-0.073725529,0.2388720959,0.7355163693,-0.1537969112,0.4934940934,-0.2031433433,-0.0418940075,0.1256616861,0.4556665123,-0.4343681931,-0.3134965301,-0.1376075,0.0694526061,-0.099931106,0.2053408474,-0.0052408343,0.0365756415,0.1147769988,0.4034270048,-0.0220960025,-0.1103871539,0.0099158697,-0.1579840034,-0.1949465275,-0.2566277087,0.1149913594,-0.0176931862,-0.1435348243,-0.293458432,-0.1421632171,-0.1392901242,0.0056826379,-0.3321452439,0.0178288762,0.2203202993,-0.1728139669,-0.2198076099,0.2929090858,-0.0524715595,0.0291536804,0.1376225799,0.3627481163,0.1954966486,0.1123554483,0.3818732202,0.0941143036,0.2671805918,0.0105761532,-0.1745803803,-0.0067355181,-0.0322308168,-0.4421686828,0.196050778,-0.0149847884,0.7509050965,-0.3278894126,-0.3549762964,0.2934402525,-0.2196975499,0.0935777649,-0.0379396789,0.2039052695,0.2131644487,0.5074154139,0.110865429,0.1131861806,-0.2682110369,0.0728461221,-0.0765823871,0.0951074734,-0.1301978678,0.1238338947,-0.0917826369,-0.0535966307,0.3636643291,-0.0692519695,0.087492682,-0.2760293186,0.0895308852,-0.1705299467,-0.2756180465,0.268853128,-0.4858374298,0.0634655356,0.0485958643,-0.1812145114,-0.1407059431,-0.0891787559,-0.223374173,-0.2241652012,-0.2036291659,0.1713668406,-0.227433145,0.0364288799,-0.2167210281,-0.2242526561,0.2060965598,-0.0521678776,0.1591060758,-0.2531750202,-0.255607456,-0.1615860462,-0.0356961787,0.3934701979,-0.2941275835,-0.1991277188,-0.1428540945,-0.2064636052,-0.1786636561,-0.1581739336,-0.1267938614,0.4374836683,-0.2819532752,0.1414128244,0.2326729596,-0.4089822769,-0.1433600485,0.2878492177,0.0954126939,-0.1979203522,0.1566754282,-0.1284318268,0.1135182604,-0.1574372202,0.1001495048,0.0776153803,0.1633862257,0.007081199,-0.0188464597,0.036375124,-0.1544718444,-0.0118767954,-0.2443443686,0.0282867458,0.1455506384,0.1295337081,-0.1325169504,-0.1437395513,0.102992475,0.446297735,0.5053649545,0.2926414013,0.0018090364,-0.3757137656,-0.1410629153,0.2792656124,0.0050844387,-0.0705011711,-0.0173224658,-0.0369233266,-0.2272954136,0.4188956916,-0.0087850615,-0.3483618796,-0.0021392696,0.1587059945,-0.2076747417,-0.1167936027,-0.2682749331,0.0590893626,-0.1198496372,0.0904613882,-0.2748877108,0.0908167139,-0.0701375529,-0.0042610434,-0.0235086512,0.3459734023,0.1507644355,0.122721754,-0.1112476885,0.3404074907,0.5937909484,0.2090201527,-0.0751314461,0.3819218576,0.1279350668,-0.2491389513,-0.0412581488,0.1454391629,0.2145802826,-0.0491112433,0.4379881918,0.2754839659,0.2406787723,0.3727573156,-0.0556240045,0.1264895946,0.242703557,-0.1039064452,-0.1578007489,-0.2713132203,0.1646561325,-0.0192193184,0.4520537555,0.2100941837,0.0325967483,-0.0543766543,-0.0089729214,0.0672252774,0.0164770391,-0.0364091173,-0.2857769132,0.0901333243,0.2991757691,0.056403894,0.7109383345,0.0651810989,-0.3750181198,0.1294146478,-0.020994015,-0.0924102291,0.232258141,0.1908759773,0.1215177178,0.1294934303,0.2365701497,-0.1311048567,0.0034811324,-0.2910775244,-0.0515734069,0.1591396928,-0.7218433022,0.3073238134,-0.4488064051,0.0365511924,-0.3674746156,-0.1099792644,0.039912153,-0.3449760377,-0.1204983741,0.0795523301,-0.1346967071,0.1161788553,-0.3224466741,0.0531995259,-0.0138493739,-0.4038999677,0.0998631865,-0.1038146392,-0.0749545097,-0.0629806891,0.2017032653,0.4358921051,0.1518811285,0.2062558383,-0.0725508034,-0.2794296741,-0.1373124123,0.0227615349,-0.0418572798,0.7079858184,0.4984821379,-0.0236271117,0.334713608,-0.052583456,-0.022778647,-0.0508446507,-0.1497538984,0.1021313444,-0.0401481725,-0.159014374,-0.1050804704,-0.2614705265,0.0915697217,-0.2867671847,-0.0876169056,-0.0462019704,0.0119425636,0.4248128235,0.1451478451,0.4045563936,0.2662217617,-0.0025793067,-0.0106500452,0.1442047656,0.1359658688,-0.1538913995,-0.2444033176,0.2343038619,-0.2083691955,-0.0294864997,-0.0952343643,-0.4535905719,-0.25282076,-0.1953865737,0.3757030666,-0.0514751524,-0.1112330109,0.4003503621,0.165106073,-0.0839413479,-0.3391038477,-0.2688634694,0.0547733977,0.0875356421,-0.0184010845,0.1420247257,-0.1215976626,-0.1658104211,0.374792397,0.2599062324,-0.1457856596,0.2739960253,-0.1751101613,0.2564560473,0.1007364541,-0.3438697755,-0.0130711058,-0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{"html_url":"https:\/\/github.com\/huggingface\/datasets\/issues\/2913","title":"timit_asr dataset only includes one text phrase","comments":"Hi @margotwagner, \r\nThis bug was fixed in #1995. Upgrading the datasets should work (min v1.8.0 ideally)","body":"## Describe the bug\r\nThe dataset 'timit_asr' only includes one text phrase. It only includes the transcription \"Would such an act of refusal be useful?\" multiple times rather than different phrases.\r\n\r\n## Steps to reproduce the bug\r\nNote: I am following the tutorial https:\/\/huggingface.co\/blog\/fine-tune-wav2vec2-english\r\n\r\n1. Install the dataset and other packages\r\n```python\r\n!pip install datasets>=1.5.0\r\n!pip install transformers==4.4.0\r\n!pip install soundfile\r\n!pip install jiwer\r\n```\r\n2. Load the dataset\r\n```python\r\nfrom datasets import load_dataset, load_metric\r\n\r\ntimit = load_dataset(\"timit_asr\")\r\n```\r\n3. Remove columns that we don't want\r\n```python\r\ntimit = timit.remove_columns([\"phonetic_detail\", \"word_detail\", \"dialect_region\", \"id\", \"sentence_type\", \"speaker_id\"])\r\n```\r\n4. Write a short function to display some random samples of the dataset.\r\n```python\r\nfrom datasets import ClassLabel\r\nimport random\r\nimport pandas as pd\r\nfrom IPython.display import display, HTML\r\n\r\ndef show_random_elements(dataset, num_examples=10):\r\n assert num_examples <= len(dataset), \"Can't pick more elements than there are in the dataset.\"\r\n picks = []\r\n for _ in range(num_examples):\r\n pick = random.randint(0, len(dataset)-1)\r\n while pick in picks:\r\n pick = random.randint(0, len(dataset)-1)\r\n picks.append(pick)\r\n \r\n df = pd.DataFrame(dataset[picks])\r\n display(HTML(df.to_html()))\r\n\r\nshow_random_elements(timit[\"train\"].remove_columns([\"file\"]))\r\n```\r\n\r\n## Expected results\r\n10 random different transcription phrases.\r\n\r\n## Actual results\r\n10 of the same transcription phrase \"Would such an act of refusal be useful?\"\r\n\r\n## Environment info\r\n\r\n- `datasets` version: 1.4.1\r\n- Platform: macOS-10.15.7-x86_64-i386-64bit\r\n- Python version: 3.8.5\r\n- PyArrow version: not listed\r\n","comment_length":16,"text":"timit_asr dataset only includes one text phrase\n## Describe the bug\r\nThe dataset 'timit_asr' only includes one text phrase. It only includes the transcription \"Would such an act of refusal be useful?\" multiple times rather than different phrases.\r\n\r\n## Steps to reproduce the bug\r\nNote: I am following the tutorial https:\/\/huggingface.co\/blog\/fine-tune-wav2vec2-english\r\n\r\n1. Install the dataset and other packages\r\n```python\r\n!pip install datasets>=1.5.0\r\n!pip install transformers==4.4.0\r\n!pip install soundfile\r\n!pip install jiwer\r\n```\r\n2. Load the dataset\r\n```python\r\nfrom datasets import load_dataset, load_metric\r\n\r\ntimit = load_dataset(\"timit_asr\")\r\n```\r\n3. Remove columns that we don't want\r\n```python\r\ntimit = timit.remove_columns([\"phonetic_detail\", \"word_detail\", \"dialect_region\", \"id\", \"sentence_type\", \"speaker_id\"])\r\n```\r\n4. Write a short function to display some random samples of the dataset.\r\n```python\r\nfrom datasets import ClassLabel\r\nimport random\r\nimport pandas as pd\r\nfrom IPython.display import display, HTML\r\n\r\ndef show_random_elements(dataset, num_examples=10):\r\n assert num_examples <= len(dataset), \"Can't pick more elements than there are in the dataset.\"\r\n picks = []\r\n for _ in range(num_examples):\r\n pick = random.randint(0, len(dataset)-1)\r\n while pick in picks:\r\n pick = random.randint(0, len(dataset)-1)\r\n picks.append(pick)\r\n \r\n df = pd.DataFrame(dataset[picks])\r\n display(HTML(df.to_html()))\r\n\r\nshow_random_elements(timit[\"train\"].remove_columns([\"file\"]))\r\n```\r\n\r\n## Expected results\r\n10 random different transcription phrases.\r\n\r\n## Actual results\r\n10 of the same transcription phrase \"Would such an act of refusal be useful?\"\r\n\r\n## Environment info\r\n\r\n- `datasets` version: 1.4.1\r\n- Platform: macOS-10.15.7-x86_64-i386-64bit\r\n- Python version: 3.8.5\r\n- PyArrow version: not listed\r\n\nHi @margotwagner, \r\nThis bug was fixed in #1995. Upgrading the datasets should work (min v1.8.0 ideally)","embeddings":[0.161290288,-0.138053894,-0.0205535926,0.2273859978,0.1176241562,-0.1575979143,0.2670057416,0.2965668142,-0.5154975653,0.2804078162,0.1454417259,0.4508609474,-0.0935282409,-0.1810210198,0.0642997622,-0.0575755686,0.1221427992,0.2484459728,-0.0715344921,-0.2342915833,0.1333340406,0.3007956445,-0.257455945,-0.064661555,-0.2737528682,0.0847610086,-0.0766698644,-0.2693378031,-0.0532864109,-0.4953400791,0.1539394706,0.011122671,0.1049450785,0.3338848054,-0.0001155144,-0.0851756856,0.0369373672,0.0938122272,-0.2382418513,-0.157761991,-0.0969283953,0.0886226818,-0.0698391646,-0.0270568896,-0.0834104121,0.1937476546,0.0529345647,-0.0981618613,0.5147064328,0.2915839553,0.1400677115,-0.0102435909,-0.252120316,-0.0659528822,0.3198694587,0.0313076191,0.0850752518,-0.1780210286,0.2137241364,0.0314790495,-0.0516679287,0.6180685163,-0.2813329399,0.3980180621,-0.2359145284,0.1242306605,-0.3711774647,-0.5308332443,0.266836673,0.2161651254,0.6465566754,-0.2296243757,-0.2044666409,-0.2378615439,0.3199083507,-0.1560648084,-0.0421698391,-0.0161785055,-0.2728021145,0.2350625694,-0.0094950655,0.047647696,-0.2204018831,0.1288805455,0.055811543,-0.1074196771,-0.0731363669,0.1385569125,-0.177100122,0.0994098634,-0.085863933,0.2703583241,-0.1442609429,0.0341251902,-0.149762243,-0.0674901083,0.1925161034,-0.3049418628,0.2917183042,-0.0602199472,0.3855580986,0.0374377258,-0.1856715977,-0.0881002173,0.3239292204,0.019808244,-0.0233677495,0.1496766508,0.2298973352,-0.2340689301,-0.2531856596,0.0737158284,-0.0144180572,0.2162737995,0.331879437,-0.265029937,0.2829249203,-0.367644608,-0.5658828616,0.0642497912,-0.4400376976,-0.1030385345,-0.2528013289,0.1816738844,0.1693309546,0.3009272516,0.1449253261,0.2226763219,0.0693393499,-0.5628325343,-0.1818621904,0.0267980024,0.2098604441,0.0308408234,0.211329326,-0.3237065375,0.3398516774,0.3417337835,0.195452705,-0.4315991104,-0.2874112725,-0.1301766485,0.0255610328,0.0091858534,-0.0264903046,0.3916681111,0.0738202855,-0.0182900447,0.0072356486,0.0610072762,-0.106556423,0.0068646008,0.2134016603,0.1364102364,-0.0805880576,-0.167114675,0.1232486665,0.3605877757,0.0779450312,-0.3008698225,0.0030109661,-0.1827422976,-0.3919641674,-0.0386591218,0.0773314983,0.3024609089,-0.3355484903,0.0954273939,0.178534463,0.4213981032,0.3944750428,0.2484540939,-0.0465098992,0.3734385073,-0.1168166548,0.3001444042,0.2041984349,-0.4621309042,-0.3920545578,0.2112352401,-0.049603641,0.2464142591,0.1548911184,-0.1627833694,0.3027338982,0.1427075267,0.3759355247,0.153478384,0.1457359195,0.0334793814,-0.2446887195,-0.057892438,0.0868306831,-0.0131406998,0.0021537091,0.0955836996,0.0370380655,0.232970655,0.5271517038,0.0745838359,0.0924516246,0.1029673368,0.1659648567,-0.1012314931,0.2239779383,-0.2631005943,0.2564861178,-0.0488976687,0.2068272382,0.2837985456,0.1540315896,0.0793671757,-0.3015450537,-0.2188883722,-0.2991884053,-0.3250726461,0.1145813912,0.3257373571,-0.0144317094,0.0420466512,-0.1898579001,0.3409897685,-0.2203264982,-0.0741857439,0.0233059432,0.0991652608,0.0638567135,-0.2447074801,0.5248855948,0.2528992593,0.18514961,0.2016844004,-0.1669406295,0.22355178,-0.0147676235,0.0219675135,-0.2637168467,-0.3288924992,0.1099477187,-0.5017575622,-0.1113948897,0.2344709635,0.3181717396,-0.0044999369,-0.3036757708,-0.1834709942,0.1390077174,0.2569387853,-0.0554442666,-0.0651190057,0.3011586368,0.0662527978,-0.3537996113,-0.3246975541,0.2526897192,-0.234980613,0.2662216723,0.0202848148,-0.3417770565,0.1585986316,0.3928949833,0.2245113999,0.085208714,0.2943886817,-0.2468326539,-0.027017273,-0.1227289811,0.0949118286,0.2907651365,0.2881050408,0.0543022901,-0.3764547706,-0.1287488788,-0.1132321432,0.1393357515,-0.1307561845,-0.3556297719,0.4351170063,0.3585450351,0.0863876268,-0.4493839145,-0.1341284662,-0.1424040794,0.4703677297,-0.3349600732,-0.2316320539,-0.4356057346,-0.1205524206,-0.109005712,0.0662912875,0.0797044486,0.225550577,0.2190566808,-0.0127121434,0.0054567931,0.1367991865,0.1719908118,0.2340619862,0.0712051392,0.1117983311,0.086305052,0.3191404045,-0.306956917,-0.0242702272,0.0259727612,0.271612525,0.1074295118,-0.1422471404,-0.1639021039,-0.4067965448,0.0590180866,0.2675643563,-0.3301686049,0.5113489628,0.1133633777,0.1602485478,-0.2251659632,-0.3130813539,0.0026724054,-0.0268870983,-0.1686861217,0.3555683196,0.0829433203,-0.0056142341,-0.0482558794,-0.698974669,0.099392809,-0.2500089407,0.2359433323,-0.0328674652,0.2103785425,0.3641358316,0.0606071204,0.0812700838,-0.0587117635,0.2053699046,-0.410209775,-0.2545935214,0.3561107218,0.2606044412,-0.3348694146,-0.3405695558,0.0509579033,0.2652952075,-0.0322290994,-0.4001087248,0.1003541723,-0.2674658895,0.1453480273,-0.2972603142,0.2826198339,0.3854675591,0.0129939336,-0.1495047212,-0.1692215204,-0.1078334898,0.2658356428,0.3553734422,0.0058843573,0.1011472642,0.0699137822,-0.0593266487,0.3506978154,0.6459594965,0.2025162727,0.2600066662,-0.1765539348,0.0942354053,-0.0324934721,-0.3995798528,0.0270797946,-0.1173247173,-0.062567167,0.4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{"html_url":"https:\/\/github.com\/huggingface\/datasets\/issues\/2913","title":"timit_asr dataset only includes one text phrase","comments":"Hi @margotwagner,\r\n\r\nYes, as @bhavitvyamalik has commented, this bug was fixed in `datasets` version 1.5.0. You need to update it, as your current version is 1.4.1:\r\n> Environment info\r\n> - `datasets` version: 1.4.1","body":"## Describe the bug\r\nThe dataset 'timit_asr' only includes one text phrase. It only includes the transcription \"Would such an act of refusal be useful?\" multiple times rather than different phrases.\r\n\r\n## Steps to reproduce the bug\r\nNote: I am following the tutorial https:\/\/huggingface.co\/blog\/fine-tune-wav2vec2-english\r\n\r\n1. Install the dataset and other packages\r\n```python\r\n!pip install datasets>=1.5.0\r\n!pip install transformers==4.4.0\r\n!pip install soundfile\r\n!pip install jiwer\r\n```\r\n2. Load the dataset\r\n```python\r\nfrom datasets import load_dataset, load_metric\r\n\r\ntimit = load_dataset(\"timit_asr\")\r\n```\r\n3. Remove columns that we don't want\r\n```python\r\ntimit = timit.remove_columns([\"phonetic_detail\", \"word_detail\", \"dialect_region\", \"id\", \"sentence_type\", \"speaker_id\"])\r\n```\r\n4. Write a short function to display some random samples of the dataset.\r\n```python\r\nfrom datasets import ClassLabel\r\nimport random\r\nimport pandas as pd\r\nfrom IPython.display import display, HTML\r\n\r\ndef show_random_elements(dataset, num_examples=10):\r\n assert num_examples <= len(dataset), \"Can't pick more elements than there are in the dataset.\"\r\n picks = []\r\n for _ in range(num_examples):\r\n pick = random.randint(0, len(dataset)-1)\r\n while pick in picks:\r\n pick = random.randint(0, len(dataset)-1)\r\n picks.append(pick)\r\n \r\n df = pd.DataFrame(dataset[picks])\r\n display(HTML(df.to_html()))\r\n\r\nshow_random_elements(timit[\"train\"].remove_columns([\"file\"]))\r\n```\r\n\r\n## Expected results\r\n10 random different transcription phrases.\r\n\r\n## Actual results\r\n10 of the same transcription phrase \"Would such an act of refusal be useful?\"\r\n\r\n## Environment info\r\n\r\n- `datasets` version: 1.4.1\r\n- Platform: macOS-10.15.7-x86_64-i386-64bit\r\n- Python version: 3.8.5\r\n- PyArrow version: not listed\r\n","comment_length":34,"text":"timit_asr dataset only includes one text phrase\n## Describe the bug\r\nThe dataset 'timit_asr' only includes one text phrase. It only includes the transcription \"Would such an act of refusal be useful?\" multiple times rather than different phrases.\r\n\r\n## Steps to reproduce the bug\r\nNote: I am following the tutorial https:\/\/huggingface.co\/blog\/fine-tune-wav2vec2-english\r\n\r\n1. Install the dataset and other packages\r\n```python\r\n!pip install datasets>=1.5.0\r\n!pip install transformers==4.4.0\r\n!pip install soundfile\r\n!pip install jiwer\r\n```\r\n2. Load the dataset\r\n```python\r\nfrom datasets import load_dataset, load_metric\r\n\r\ntimit = load_dataset(\"timit_asr\")\r\n```\r\n3. Remove columns that we don't want\r\n```python\r\ntimit = timit.remove_columns([\"phonetic_detail\", \"word_detail\", \"dialect_region\", \"id\", \"sentence_type\", \"speaker_id\"])\r\n```\r\n4. Write a short function to display some random samples of the dataset.\r\n```python\r\nfrom datasets import ClassLabel\r\nimport random\r\nimport pandas as pd\r\nfrom IPython.display import display, HTML\r\n\r\ndef show_random_elements(dataset, num_examples=10):\r\n assert num_examples <= len(dataset), \"Can't pick more elements than there are in the dataset.\"\r\n picks = []\r\n for _ in range(num_examples):\r\n pick = random.randint(0, len(dataset)-1)\r\n while pick in picks:\r\n pick = random.randint(0, len(dataset)-1)\r\n picks.append(pick)\r\n \r\n df = pd.DataFrame(dataset[picks])\r\n display(HTML(df.to_html()))\r\n\r\nshow_random_elements(timit[\"train\"].remove_columns([\"file\"]))\r\n```\r\n\r\n## Expected results\r\n10 random different transcription phrases.\r\n\r\n## Actual results\r\n10 of the same transcription phrase \"Would such an act of refusal be useful?\"\r\n\r\n## Environment info\r\n\r\n- `datasets` version: 1.4.1\r\n- Platform: macOS-10.15.7-x86_64-i386-64bit\r\n- Python version: 3.8.5\r\n- PyArrow version: not listed\r\n\nHi @margotwagner,\r\n\r\nYes, as @bhavitvyamalik has commented, this bug was fixed in `datasets` version 1.5.0. You need to update it, as your current version is 1.4.1:\r\n> Environment info\r\n> - `datasets` version: 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{"html_url":"https:\/\/github.com\/huggingface\/datasets\/issues\/2904","title":"FORCE_REDOWNLOAD does not work","comments":"Hi ! Thanks for reporting. The error seems to happen only if you use compressed files.\r\n\r\nThe second dataset is prepared in another dataset cache directory than the first - which is normal, since the source file is different. However, it doesn't uncompress the new data file because it finds the old uncompressed data in the extraction cache directory.\r\n\r\nIf we fix the extraction cache mechanism to uncompress a local file if it changed then it should fix the issue.\r\nCurrently the extraction cache mechanism only takes into account the path of the compressed file, which is an issue.","body":"## Describe the bug\r\nWith GenerateMode.FORCE_REDOWNLOAD, the documentation says \r\n +------------------------------------+-----------+---------+\r\n | | Downloads | Dataset |\r\n +====================================+===========+=========+\r\n | `REUSE_DATASET_IF_EXISTS` (default)| Reuse | Reuse |\r\n +------------------------------------+-----------+---------+\r\n | `REUSE_CACHE_IF_EXISTS` | Reuse | Fresh |\r\n +------------------------------------+-----------+---------+\r\n | `FORCE_REDOWNLOAD` | Fresh | Fresh |\r\n +------------------------------------+-----------+---------+\r\n\r\nHowever, the old dataset is loaded even when FORCE_REDOWNLOAD is chosen.\r\n\r\n## Steps to reproduce the bug\r\n```python\r\n\r\nimport pandas as pd\r\nfrom datasets import load_dataset, GenerateMode\r\npd.DataFrame(range(5), columns=['numbers']).to_csv('\/tmp\/test.tsv.gz', index=False)\r\nee = load_dataset('csv', data_files=['\/tmp\/test.tsv.gz'], delimiter='\\t', split='train', download_mode=GenerateMode.FORCE_REDOWNLOAD)\r\nprint(ee)\r\npd.DataFrame(range(10), columns=['numerals']).to_csv('\/tmp\/test.tsv.gz', index=False)\r\nee = load_dataset('csv', data_files=['\/tmp\/test.tsv.gz'], delimiter='\\t', split='train', download_mode=GenerateMode.FORCE_REDOWNLOAD)\r\nprint(ee)\r\n\r\n```\r\n\r\n## Expected results\r\nDataset({\r\n features: ['numbers'],\r\n num_rows: 5\r\n})\r\nDataset({\r\n features: ['numerals'],\r\n num_rows: 10\r\n})\r\n\r\n## Actual results\r\nDataset({\r\n features: ['numbers'],\r\n num_rows: 5\r\n})\r\nDataset({\r\n features: ['numbers'],\r\n num_rows: 5\r\n})\r\n\r\n\r\n## Environment info\r\n\r\n- `datasets` version: 1.8.0\r\n- Platform: Linux-4.14.181-108.257.amzn1.x86_64-x86_64-with-glibc2.10\r\n- Python version: 3.7.10\r\n- PyArrow version: 3.0.0\r\n","comment_length":99,"text":"FORCE_REDOWNLOAD does not work\n## Describe the bug\r\nWith GenerateMode.FORCE_REDOWNLOAD, the documentation says \r\n +------------------------------------+-----------+---------+\r\n | | Downloads | Dataset |\r\n +====================================+===========+=========+\r\n | `REUSE_DATASET_IF_EXISTS` (default)| Reuse | Reuse |\r\n +------------------------------------+-----------+---------+\r\n | `REUSE_CACHE_IF_EXISTS` | Reuse | Fresh |\r\n +------------------------------------+-----------+---------+\r\n | `FORCE_REDOWNLOAD` | Fresh | Fresh |\r\n +------------------------------------+-----------+---------+\r\n\r\nHowever, the old dataset is loaded even when FORCE_REDOWNLOAD is chosen.\r\n\r\n## Steps to reproduce the bug\r\n```python\r\n\r\nimport pandas as pd\r\nfrom datasets import load_dataset, GenerateMode\r\npd.DataFrame(range(5), columns=['numbers']).to_csv('\/tmp\/test.tsv.gz', index=False)\r\nee = load_dataset('csv', data_files=['\/tmp\/test.tsv.gz'], delimiter='\\t', split='train', download_mode=GenerateMode.FORCE_REDOWNLOAD)\r\nprint(ee)\r\npd.DataFrame(range(10), columns=['numerals']).to_csv('\/tmp\/test.tsv.gz', index=False)\r\nee = load_dataset('csv', data_files=['\/tmp\/test.tsv.gz'], delimiter='\\t', split='train', download_mode=GenerateMode.FORCE_REDOWNLOAD)\r\nprint(ee)\r\n\r\n```\r\n\r\n## Expected results\r\nDataset({\r\n features: ['numbers'],\r\n num_rows: 5\r\n})\r\nDataset({\r\n features: ['numerals'],\r\n num_rows: 10\r\n})\r\n\r\n## Actual results\r\nDataset({\r\n features: ['numbers'],\r\n num_rows: 5\r\n})\r\nDataset({\r\n features: ['numbers'],\r\n num_rows: 5\r\n})\r\n\r\n\r\n## Environment info\r\n\r\n- `datasets` version: 1.8.0\r\n- Platform: Linux-4.14.181-108.257.amzn1.x86_64-x86_64-with-glibc2.10\r\n- Python version: 3.7.10\r\n- PyArrow version: 3.0.0\r\n\nHi ! Thanks for reporting. The error seems to happen only if you use compressed files.\r\n\r\nThe second dataset is prepared in another dataset cache directory than the first - which is normal, since the source file is different. However, it doesn't uncompress the new data file because it finds the old uncompressed data in the extraction cache directory.\r\n\r\nIf we fix the extraction cache mechanism to uncompress a local file if it changed then it should fix the issue.\r\nCurrently the extraction cache mechanism only takes into account the path of the compressed file, which is an issue.","embeddings":[-0.1253399998,0.0207806267,0.0185128842,0.0257142521,0.1821040809,0.0251934566,0.4788660109,0.2438331693,0.145796001,-0.2338699996,-0.1162827983,0.2687395215,0.0850747079,0.1121127084,-0.0132031068,0.3137487769,0.1101020277,0.2144826502,-0.0722493976,-0.0051923986,-0.2910993099,0.0634416714,-0.1833185554,-0.2195639163,-0.2065767497,0.2937990129,-0.0426936708,0.3026081324,0.0084062209,-0.3731202781,0.1836812794,0.2550069988,0.1820975393,0.6611076593,-0.000109716,0.0617883578,0.1586239785,-0.1793150753,-0.1704429984,-0.1858083606,0.0419964381,-0.0455286205,-0.3461463749,-0.0842089579,-0.0005326021,-0.2207529545,-0.1748757958,-0.346149832,0.5304936767,0.4028010368,0.2320071906,-0.1104161516,0.1202409416,-0.0721178129,0.1787120998,0.1414143443,-0.0699524358,0.3557813466,0.1045252755,-0.2072234303,-0.090631254,0.071447365,-0.1301329136,0.0515931137,-0.100776881,-0.0266386643,0.3280929923,-0.1360899806,0.0856568739,0.1706219614,0.60730654,-0.3377125561,-0.4172843397,0.0072846375,0.0183145087,-0.2442766875,0.1650239527,0.1792488247,-0.0074186656,0.368666321,-0.0126197841,-0.0254521314,0.0721380934,-0.1948064566,-0.0308580399,-0.117819868,-0.1004166082,-0.0362135768,0.0516206622,0.0602480508,0.4213585556,-0.021616118,-0.0566806123,0.0342854224,-0.029152602,-0.1342204958,0.2153061479,-0.3018356264,0.0747370049,-0.0281923842,-0.0764476284,-0.1077359319,-0.0592024475,0.04897983,0.0738615543,0.2261448056,-0.1387252212,-0.090120174,0.3910107613,0.1767412573,-0.251378119,-0.0726185068,0.0294129029,-0.2027270049,0.5261009336,-0.0647600591,0.2614033818,-0.1472218335,-0.4044618905,-0.0181814078,-0.1070784777,0.0032655229,-0.2742642164,0.1767624319,0.0207748469,0.3530659974,0.1341454834,0.1856252104,0.0581398085,-0.206356883,-0.2226337641,-0.1226871684,-0.3091842234,0.0251519568,0.2947835922,-0.1708823442,0.3440055251,0.5470414758,-0.3297725916,-0.5027301908,-0.0621353462,-0.0314323269,0.145252198,0.2964736819,-0.0633033141,-0.0863981768,0.2666994929,-0.0901367441,-0.0168399569,0.3552974463,-0.0914471522,-0.3069287539,0.1436540186,0.1975634098,0.0511063114,0.1958488077,-0.127950877,0.0155278603,0.3042720556,-0.0705190748,-0.0457962081,-0.0500780307,-0.0136534544,-0.2484514862,0.0476663969,0.7337824702,-0.4681162834,0.0081418594,-0.2922013104,-0.0890840143,0.2404547036,0.0785243809,0.0047035967,0.0423925631,-0.4686902165,-0.3603310585,0.1667583287,-0.1669529229,-0.4369600415,0.0058205375,-0.2142421305,0.2575328946,0.1665146351,-0.0449789762,-0.1501356214,-0.0172337554,0.0970478505,0.2490073442,0.0923939124,0.044823505,-0.3246170282,-0.288821131,0.0455681123,-0.1329662651,0.2855156362,0.405680865,0.3521436155,0.166620627,0.3699389398,0.0625928491,0.0155102359,0.0143866315,0.3367364407,-0.0180097409,0.0330760218,0.1765212268,-0.5221686959,0.3075172901,-0.0336038433,-0.2515624166,0.0728959292,-0.0701956674,-0.5719491243,-0.228338778,-0.0720977858,-0.3877546787,0.1725475937,0.4713421166,-0.033435449,0.0184990596,-0.1434893459,0.4725981653,-0.3845011294,-0.0480710976,-0.0413006209,0.2855195999,-0.1932275444,-0.1814612597,-0.2195678353,-0.1187965199,0.2447945774,-0.1625743508,-0.1215589717,0.4299698472,0.186048165,0.2720868587,-0.3619140387,-0.1950368881,0.1652197391,-0.016063001,-0.1465488821,0.1185459942,0.2459682077,-0.0776265264,-0.0173951481,0.2789047956,-0.4031001925,-0.0885871574,0.071374476,-0.0462191142,0.2666130662,-0.2367841005,0.2828178704,-0.340397954,-0.3403313756,0.2947020233,0.0018241567,0.1279272139,0.0989251807,0.1121156812,0.2867794335,0.0066712452,0.047518149,-0.0890123621,-0.1765656024,-0.2945634723,0.0131209595,0.3500014544,0.5929611921,0.1657259762,-0.0166612044,0.1451728493,-0.0583074652,-0.253809005,0.2043861002,-0.2613338828,-0.0064706737,0.3311824501,0.1878525168,0.0763362646,-0.6370830536,0.186015591,0.5140926838,0.2118025422,-0.2195149958,-0.1909853667,-0.1673647612,0.4125154912,0.0630170777,0.1435509324,-0.0300561506,-0.1065669209,-0.138272956,0.5955287814,-0.0589990281,0.073292084,-0.1850811243,-0.0749260634,-0.0461661331,-0.1426966637,-0.1472802907,-0.1764150858,-0.1414824873,0.0910957381,0.2313582748,-0.3419226408,0.1477311403,-0.1828764379,-0.1267566234,-0.3267619908,-0.0833430737,-0.1293941885,0.0340040401,0.2224018276,-0.2250489891,0.1471374333,0.169757694,0.0325683653,-0.004715248,-0.3554546833,-0.3341683745,-0.1029282659,0.1511999369,0.2633759975,-0.2600107789,-0.4554549158,0.1090232432,-0.3711392879,-0.0406573713,0.1015611589,-0.0919760466,0.3833361566,0.2238112539,-0.0310825258,0.4020083547,0.2698833942,-0.4376188517,-0.2790603936,0.0805035159,-0.1014774665,-0.2633699775,0.0147913452,0.0609156676,-0.0121200867,0.2737061381,-0.4307680726,-0.0845259503,-0.2824058533,0.0837785453,-0.0585749894,0.2336959094,0.2081224918,0.1207609028,-0.2136411071,-0.1040799022,-0.0956789032,-0.041495204,0.233491689,0.126982823,0.0212046467,0.2292778045,0.0935394913,0.6404239535,0.0503102541,-0.1254811585,0.5086991787,0.125423491,0.7294853926,-0.074036397,-0.339060694,-0.0056349542,-0.1845843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{"html_url":"https:\/\/github.com\/huggingface\/datasets\/issues\/2902","title":"Add WIT Dataset","comments":"@hassiahk is working on it #2810 ","body":"## Adding a Dataset\r\n- **Name:** *WIT*\r\n- **Description:** *Wikipedia-based Image Text Dataset*\r\n- **Paper:** *[WIT: Wikipedia-based Image Text Dataset for Multimodal Multilingual Machine Learning\r\n](https:\/\/arxiv.org\/abs\/2103.01913)*\r\n- **Data:** *https:\/\/github.com\/google-research-datasets\/wit*\r\n- **Motivation:** (excerpt from their Github README.md)\r\n\r\n> - The largest multimodal dataset (publicly available at the time of this writing) by the number of image-text examples.\r\n> - A massively multilingual dataset (first of its kind) with coverage for over 100+ languages.\r\n> - A collection of diverse set of concepts and real world entities.\r\n> - Brings forth challenging real-world test sets.\r\n\r\nInstructions to add a new dataset can be found [here](https:\/\/github.com\/huggingface\/datasets\/blob\/master\/ADD_NEW_DATASET.md).\r\n","comment_length":6,"text":"Add WIT Dataset\n## Adding a Dataset\r\n- **Name:** *WIT*\r\n- **Description:** *Wikipedia-based Image Text Dataset*\r\n- **Paper:** *[WIT: Wikipedia-based Image Text Dataset for Multimodal Multilingual Machine Learning\r\n](https:\/\/arxiv.org\/abs\/2103.01913)*\r\n- **Data:** *https:\/\/github.com\/google-research-datasets\/wit*\r\n- **Motivation:** (excerpt from their Github README.md)\r\n\r\n> - The largest multimodal dataset (publicly available at the time of this writing) by the number of image-text examples.\r\n> - A massively multilingual dataset (first of its kind) with coverage for over 100+ languages.\r\n> - A collection of diverse set of concepts and real world entities.\r\n> - Brings forth challenging real-world test sets.\r\n\r\nInstructions to add a new dataset can be found [here](https:\/\/github.com\/huggingface\/datasets\/blob\/master\/ADD_NEW_DATASET.md).\r\n\n@hassiahk is working on it #2810 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{"html_url":"https:\/\/github.com\/huggingface\/datasets\/issues\/2902","title":"Add WIT Dataset","comments":"WikiMedia is now hosting the pixel values directly which should make it a lot easier!\r\nThe files can be found here:\r\nhttps:\/\/techblog.wikimedia.org\/2021\/09\/09\/the-wikipedia-image-caption-matching-challenge-and-a-huge-release-of-image-data-for-research\/\r\nhttps:\/\/analytics.wikimedia.org\/published\/datasets\/one-off\/caption_competition\/training\/image_pixels\/","body":"## Adding a Dataset\r\n- **Name:** *WIT*\r\n- **Description:** *Wikipedia-based Image Text Dataset*\r\n- **Paper:** *[WIT: Wikipedia-based Image Text Dataset for Multimodal Multilingual Machine Learning\r\n](https:\/\/arxiv.org\/abs\/2103.01913)*\r\n- **Data:** *https:\/\/github.com\/google-research-datasets\/wit*\r\n- **Motivation:** (excerpt from their Github README.md)\r\n\r\n> - The largest multimodal dataset (publicly available at the time of this writing) by the number of image-text examples.\r\n> - A massively multilingual dataset (first of its kind) with coverage for over 100+ languages.\r\n> - A collection of diverse set of concepts and real world entities.\r\n> - Brings forth challenging real-world test sets.\r\n\r\nInstructions to add a new dataset can be found [here](https:\/\/github.com\/huggingface\/datasets\/blob\/master\/ADD_NEW_DATASET.md).\r\n","comment_length":23,"text":"Add WIT Dataset\n## Adding a Dataset\r\n- **Name:** *WIT*\r\n- **Description:** *Wikipedia-based Image Text Dataset*\r\n- **Paper:** *[WIT: Wikipedia-based Image Text Dataset for Multimodal Multilingual Machine Learning\r\n](https:\/\/arxiv.org\/abs\/2103.01913)*\r\n- **Data:** *https:\/\/github.com\/google-research-datasets\/wit*\r\n- **Motivation:** (excerpt from their Github README.md)\r\n\r\n> - The largest multimodal dataset (publicly available at the time of this writing) by the number of image-text examples.\r\n> - A massively multilingual dataset (first of its kind) with coverage for over 100+ languages.\r\n> - A collection of diverse set of concepts and real world entities.\r\n> - Brings forth challenging real-world test sets.\r\n\r\nInstructions to add a new dataset can be found [here](https:\/\/github.com\/huggingface\/datasets\/blob\/master\/ADD_NEW_DATASET.md).\r\n\nWikiMedia is now hosting the pixel values directly which should make it a lot easier!\r\nThe files can be found here:\r\nhttps:\/\/techblog.wikimedia.org\/2021\/09\/09\/the-wikipedia-image-caption-matching-challenge-and-a-huge-release-of-image-data-for-research\/\r\nhttps:\/\/analytics.wikimedia.org\/published\/datasets\/one-off\/caption_competition\/training\/image_pixels\/","embeddings":[-0.0497860387,-0.0547444522,-0.1280666143,0.0003004618,-0.039354112,-0.0071313963,0.3546046019,0.1659145951,0.1936855763,0.2055810541,0.0639799759,0.1460067928,-0.0242924541,0.1997424215,-0.01487609,-0.2061530054,-0.0474179126,0.007556899,-0.095439963,-0.0953547359,-0.1237470731,0.1472279876,-0.1375917941,-0.2112537324,-0.4580724537,-0.0875768587,-0.1730553955,-0.0253455658,-0.1721293628,-0.0776184052,-0.1209003329,0.1993556619,-0.1435845345,0.3342963159,-0.0000973471,-0.1190001816,0.2309069782,-0.1802841574,-0.0871033221,0.0576613285,0.1233595088,-0.0183559302,-0.3197696805,-0.4497412145,-0.1917415559,-0.0919262245,0.250259757,0.0872264057,0.220723629,-0.0171945672,0.3280394673,-0.1210444793,0.10394793,0.0242980495,0.0408663712,0.1121114418,-0.1330121011,-0.0007004917,0.0150091052,-0.3246178627,-0.1604316086,0.6847470999,0.0171727017,0.0272535756,0.0614412166,0.0563659444,-0.1721417308,-0.3297246695,0.1790350825,0.4349500835,0.5310005546,0.0022286586,-0.1660662293,-0.0531980433,-0.0875593573,-0.0650093853,0.1432961375,0.2999428809,-0.1368227452,0.0239426252,-0.2666973472,0.0028924709,-0.2545390427,0.1593592912,-0.1959471852,0.4131114483,0.021566527,0.0326524712,-0.000182682,-0.2101522833,-0.4369372725,-0.0127320085,0.2125476599,0.0099950507,0.0381090157,-0.2509273589,-0.0503862202,-0.1546090245,0.3692227602,-0.197378248,-0.0403584838,0.0733242184,-0.1825137436,0.3001660109,0.1151571646,-0.273580879,-0.1728782058,0.0215191487,0.1827842295,0.0874059945,-0.2466638684,0.1344642043,0.2253025919,-0.1029728055,-0.1772990078,-0.087800771,0.1070196927,-0.0466616042,-0.1075610965,-0.008583298,-0.146704793,-0.0710129738,-0.4360583127,0.2165165991,-0.0335759073,0.1098506823,0.1278970838,0.1473859996,-0.2188078016,-0.1533710063,-0.1057867333,0.1009146869,0.0557398014,0.2790454626,0.1211024597,0.3311381638,0.256734401,-0.0824029595,0.1746457219,0.0947210044,0.0416478626,-0.0953107029,0.2555246055,-0.008912893,0.0164618678,-0.1227277219,-0.051241383,-0.2793621719,-0.19902426,0.0270652734,-0.1084727868,0.2036321461,-0.3212899566,0.2668367922,0.1446974277,-0.0583522059,-0.0045630517,0.6982100606,0.0959759504,0.0308421906,0.1365310401,0.1445246041,-0.3958842158,-0.0636466146,0.2453516275,0.4490688741,-0.0886282399,0.0255282167,0.3061005771,0.3564617038,0.0193696469,0.1932205856,0.0245506912,-0.0181516893,0.0488758124,0.2630175054,-0.1056210548,-0.2358684838,-0.1195387468,-0.1759907305,-0.0965119898,0.1020767167,0.0326437689,0.2860510647,0.270050317,-0.102775909,0.093534559,0.6013006568,-0.1892949641,0.2137656063,-0.2130067647,-0.3863318861,-0.0069002835,0.3273807466,0.1285638511,-0.4685770869,0.2250231653,0.1909397244,0.0319985636,-0.3845872879,0.0987228006,0.0075334026,-0.0310356412,0.0995845795,0.091828078,-0.2398949265,-0.0079521695,0.0546789393,0.2049163729,0.4832212925,-0.1773444712,-0.2481343299,-0.038995903,-0.1513748169,-0.1065134704,-0.3698706031,0.3537430465,0.1205859855,-0.0045493012,0.2210574299,0.0784497336,-0.0594155379,-0.3163442016,-0.0456017256,0.1199062467,0.1867598891,-0.2623721361,-0.0185750853,0.1218658164,0.0860536918,-0.0835613608,0.1807654053,0.0911751837,0.1913248599,0.0461769477,0.3455418348,0.2677955925,0.0530583225,0.3559015393,-0.6900479198,-0.0690085068,0.19123438,0.0030610359,0.0122600449,-0.2659728527,0.1819274873,0.2717046738,0.0452171303,-0.0804450437,-0.0083080204,0.1473466903,-0.0244393963,0.0583699085,-0.3646619022,-0.0810375884,0.2129996717,-0.2515609264,-0.2326057106,-0.1890244186,0.1808756441,0.5132712126,0.2433172613,0.3687907755,0.1684178561,-0.1199678779,-0.1401048452,-0.0068703438,-0.0960154831,-0.1688125134,0.3742109239,-0.053772375,-0.1260635257,-0.2724704742,-0.0922951698,0.1780956537,-0.0158920027,0.2231312841,0.0486644618,0.2492239773,-0.0249909423,-0.4577436149,-0.1271143258,-0.1574495137,-0.1973033249,0.056282267,0.0784645602,-0.071331881,-0.1991609186,-0.066964902,0.2833610475,-0.3087125421,-0.1143085882,0.3522223532,0.0152183259,-0.1103117839,0.183313325,0.0873240456,0.1733572483,0.1862344891,0.0245582648,-0.1337936968,-0.5455780029,-0.0611162968,0.2686133087,0.1743589044,-0.0161512867,0.5377413034,-0.1781012118,0.0367738679,-0.2884244919,-0.705337286,0.1020467132,-0.1840780228,0.1969555616,0.0155186988,0.2760653794,-0.2234164178,-0.1541128606,0.1546294391,-0.1017461941,-0.1935486495,-0.2209588885,0.0480773337,0.0420998633,-0.2291868478,-0.2148961276,-0.1442073584,-0.2447485179,0.1066020951,0.3489588201,0.1721870601,0.0647365227,0.3838174343,0.0280661602,-0.0238699075,0.2764138281,-0.3571163714,-0.032202661,0.3682462573,-0.3039095998,-0.403814584,-0.1645279825,-0.182767272,0.1699132323,0.0591959544,-0.3852038085,-0.3932056427,0.0160926636,0.1964574009,0.2606021762,0.0776553899,0.1688962281,0.1696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{"html_url":"https:\/\/github.com\/huggingface\/datasets\/issues\/2902","title":"Add WIT Dataset","comments":"> @hassiahk is working on it #2810\r\n\r\nThank you @bhavitvyamalik! Added this issue so we could track progress \ud83d\ude04 . Just linked the PR as well for visibility. ","body":"## Adding a Dataset\r\n- **Name:** *WIT*\r\n- **Description:** *Wikipedia-based Image Text Dataset*\r\n- **Paper:** *[WIT: Wikipedia-based Image Text Dataset for Multimodal Multilingual Machine Learning\r\n](https:\/\/arxiv.org\/abs\/2103.01913)*\r\n- **Data:** *https:\/\/github.com\/google-research-datasets\/wit*\r\n- **Motivation:** (excerpt from their Github README.md)\r\n\r\n> - The largest multimodal dataset (publicly available at the time of this writing) by the number of image-text examples.\r\n> - A massively multilingual dataset (first of its kind) with coverage for over 100+ languages.\r\n> - A collection of diverse set of concepts and real world entities.\r\n> - Brings forth challenging real-world test sets.\r\n\r\nInstructions to add a new dataset can be found [here](https:\/\/github.com\/huggingface\/datasets\/blob\/master\/ADD_NEW_DATASET.md).\r\n","comment_length":28,"text":"Add WIT Dataset\n## Adding a Dataset\r\n- **Name:** *WIT*\r\n- **Description:** *Wikipedia-based Image Text Dataset*\r\n- **Paper:** *[WIT: Wikipedia-based Image Text Dataset for Multimodal Multilingual Machine Learning\r\n](https:\/\/arxiv.org\/abs\/2103.01913)*\r\n- **Data:** *https:\/\/github.com\/google-research-datasets\/wit*\r\n- **Motivation:** (excerpt from their Github README.md)\r\n\r\n> - The largest multimodal dataset (publicly available at the time of this writing) by the number of image-text examples.\r\n> - A massively multilingual dataset (first of its kind) with coverage for over 100+ languages.\r\n> - A collection of diverse set of concepts and real world entities.\r\n> - Brings forth challenging real-world test sets.\r\n\r\nInstructions to add a new dataset can be found [here](https:\/\/github.com\/huggingface\/datasets\/blob\/master\/ADD_NEW_DATASET.md).\r\n\n> @hassiahk is working on it #2810\r\n\r\nThank you @bhavitvyamalik! Added this issue so we could track progress \ud83d\ude04 . Just linked the PR as well for visibility. 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{"html_url":"https:\/\/github.com\/huggingface\/datasets\/issues\/2901","title":"Incompatibility with pytest","comments":"Sorry, my bad... When implementing `xpathopen`, I just considered the use case in the COUNTER dataset... I'm fixing it!","body":"## Describe the bug\r\n\r\npytest complains about xpathopen \/ path.open(\"w\")\r\n\r\n## Steps to reproduce the bug\r\n\r\nCreate a test file, `test.py`:\r\n\r\n```python\r\nimport datasets as ds\r\ndef load_dataset():\r\n ds.load_dataset(\"counter\", split=\"train\", streaming=True)\r\n```\r\n\r\nAnd launch it with pytest:\r\n\r\n```bash\r\npython -m pytest test.py\r\n```\r\n\r\n## Expected results\r\n\r\nIt should give something like:\r\n\r\n```\r\ncollected 1 item\r\n\r\ntest.py . [100%]\r\n\r\n======= 1 passed in 3.15s =======\r\n```\r\n\r\n## Actual results\r\n\r\n```\r\n============================================================================================================================= test session starts ==============================================================================================================================\r\nplatform linux -- Python 3.8.11, pytest-6.2.5, py-1.10.0, pluggy-1.0.0\r\nrootdir: \/home\/slesage\/hf\/datasets-preview-backend, configfile: pyproject.toml\r\nplugins: anyio-3.3.1\r\ncollected 1 item\r\n\r\ntests\/queries\/test_rows.py . [100%]Traceback (most recent call last):\r\n File \"\/home\/slesage\/.pyenv\/versions\/3.8.11\/lib\/python3.8\/runpy.py\", line 194, in _run_module_as_main\r\n return _run_code(code, main_globals, None,\r\n File \"\/home\/slesage\/.pyenv\/versions\/3.8.11\/lib\/python3.8\/runpy.py\", line 87, in _run_code\r\n exec(code, run_globals)\r\n File \"\/home\/slesage\/hf\/datasets-preview-backend\/.venv\/lib\/python3.8\/site-packages\/pytest\/__main__.py\", line 5, in \r\n raise SystemExit(pytest.console_main())\r\n File \"\/home\/slesage\/hf\/datasets-preview-backend\/.venv\/lib\/python3.8\/site-packages\/_pytest\/config\/__init__.py\", line 185, in console_main\r\n code = main()\r\n File \"\/home\/slesage\/hf\/datasets-preview-backend\/.venv\/lib\/python3.8\/site-packages\/_pytest\/config\/__init__.py\", line 162, in main\r\n ret: Union[ExitCode, int] = config.hook.pytest_cmdline_main(\r\n File \"\/home\/slesage\/hf\/datasets-preview-backend\/.venv\/lib\/python3.8\/site-packages\/pluggy\/_hooks.py\", line 265, in __call__\r\n return self._hookexec(self.name, self.get_hookimpls(), kwargs, firstresult)\r\n File \"\/home\/slesage\/hf\/datasets-preview-backend\/.venv\/lib\/python3.8\/site-packages\/pluggy\/_manager.py\", line 80, in _hookexec\r\n return self._inner_hookexec(hook_name, methods, kwargs, firstresult)\r\n File \"\/home\/slesage\/hf\/datasets-preview-backend\/.venv\/lib\/python3.8\/site-packages\/pluggy\/_callers.py\", line 60, in _multicall\r\n return outcome.get_result()\r\n File \"\/home\/slesage\/hf\/datasets-preview-backend\/.venv\/lib\/python3.8\/site-packages\/pluggy\/_result.py\", line 60, in get_result\r\n raise ex[1].with_traceback(ex[2])\r\n File \"\/home\/slesage\/hf\/datasets-preview-backend\/.venv\/lib\/python3.8\/site-packages\/pluggy\/_callers.py\", line 39, in _multicall\r\n res = hook_impl.function(*args)\r\n File \"\/home\/slesage\/hf\/datasets-preview-backend\/.venv\/lib\/python3.8\/site-packages\/_pytest\/main.py\", line 316, in pytest_cmdline_main\r\n return wrap_session(config, _main)\r\n File \"\/home\/slesage\/hf\/datasets-preview-backend\/.venv\/lib\/python3.8\/site-packages\/_pytest\/main.py\", line 304, in wrap_session\r\n config.hook.pytest_sessionfinish(\r\n File \"\/home\/slesage\/hf\/datasets-preview-backend\/.venv\/lib\/python3.8\/site-packages\/pluggy\/_hooks.py\", line 265, in __call__\r\n return self._hookexec(self.name, self.get_hookimpls(), kwargs, firstresult)\r\n File \"\/home\/slesage\/hf\/datasets-preview-backend\/.venv\/lib\/python3.8\/site-packages\/pluggy\/_manager.py\", line 80, in _hookexec\r\n return self._inner_hookexec(hook_name, methods, kwargs, firstresult)\r\n File \"\/home\/slesage\/hf\/datasets-preview-backend\/.venv\/lib\/python3.8\/site-packages\/pluggy\/_callers.py\", line 55, in _multicall\r\n gen.send(outcome)\r\n File \"\/home\/slesage\/hf\/datasets-preview-backend\/.venv\/lib\/python3.8\/site-packages\/_pytest\/terminal.py\", line 803, in pytest_sessionfinish\r\n outcome.get_result()\r\n File \"\/home\/slesage\/hf\/datasets-preview-backend\/.venv\/lib\/python3.8\/site-packages\/pluggy\/_result.py\", line 60, in get_result\r\n raise ex[1].with_traceback(ex[2])\r\n File \"\/home\/slesage\/hf\/datasets-preview-backend\/.venv\/lib\/python3.8\/site-packages\/pluggy\/_callers.py\", line 39, in _multicall\r\n res = hook_impl.function(*args)\r\n File \"\/home\/slesage\/hf\/datasets-preview-backend\/.venv\/lib\/python3.8\/site-packages\/_pytest\/cacheprovider.py\", line 428, in pytest_sessionfinish\r\n config.cache.set(\"cache\/nodeids\", sorted(self.cached_nodeids))\r\n File \"\/home\/slesage\/hf\/datasets-preview-backend\/.venv\/lib\/python3.8\/site-packages\/_pytest\/cacheprovider.py\", line 188, in set\r\n f = path.open(\"w\")\r\nTypeError: xpathopen() takes 1 positional argument but 2 were given\r\n```\r\n\r\n## Environment info\r\n\r\n- `datasets` version: 1.12.0\r\n- Platform: Linux-5.11.0-1017-aws-x86_64-with-glibc2.29\r\n- Python version: 3.8.11\r\n- PyArrow version: 4.0.1\r\n","comment_length":19,"text":"Incompatibility with pytest\n## Describe the bug\r\n\r\npytest complains about xpathopen \/ path.open(\"w\")\r\n\r\n## Steps to reproduce the bug\r\n\r\nCreate a test file, `test.py`:\r\n\r\n```python\r\nimport datasets as ds\r\ndef load_dataset():\r\n ds.load_dataset(\"counter\", split=\"train\", streaming=True)\r\n```\r\n\r\nAnd launch it with pytest:\r\n\r\n```bash\r\npython -m pytest test.py\r\n```\r\n\r\n## Expected results\r\n\r\nIt should give something like:\r\n\r\n```\r\ncollected 1 item\r\n\r\ntest.py . [100%]\r\n\r\n======= 1 passed in 3.15s =======\r\n```\r\n\r\n## Actual results\r\n\r\n```\r\n============================================================================================================================= test session starts ==============================================================================================================================\r\nplatform linux -- Python 3.8.11, pytest-6.2.5, py-1.10.0, pluggy-1.0.0\r\nrootdir: \/home\/slesage\/hf\/datasets-preview-backend, configfile: pyproject.toml\r\nplugins: anyio-3.3.1\r\ncollected 1 item\r\n\r\ntests\/queries\/test_rows.py . [100%]Traceback (most recent call last):\r\n File \"\/home\/slesage\/.pyenv\/versions\/3.8.11\/lib\/python3.8\/runpy.py\", line 194, in _run_module_as_main\r\n return _run_code(code, main_globals, None,\r\n File \"\/home\/slesage\/.pyenv\/versions\/3.8.11\/lib\/python3.8\/runpy.py\", line 87, in _run_code\r\n exec(code, run_globals)\r\n File \"\/home\/slesage\/hf\/datasets-preview-backend\/.venv\/lib\/python3.8\/site-packages\/pytest\/__main__.py\", line 5, in \r\n raise SystemExit(pytest.console_main())\r\n File \"\/home\/slesage\/hf\/datasets-preview-backend\/.venv\/lib\/python3.8\/site-packages\/_pytest\/config\/__init__.py\", line 185, in console_main\r\n code = main()\r\n File \"\/home\/slesage\/hf\/datasets-preview-backend\/.venv\/lib\/python3.8\/site-packages\/_pytest\/config\/__init__.py\", line 162, in main\r\n ret: Union[ExitCode, int] = config.hook.pytest_cmdline_main(\r\n File \"\/home\/slesage\/hf\/datasets-preview-backend\/.venv\/lib\/python3.8\/site-packages\/pluggy\/_hooks.py\", line 265, in __call__\r\n return self._hookexec(self.name, self.get_hookimpls(), kwargs, firstresult)\r\n File \"\/home\/slesage\/hf\/datasets-preview-backend\/.venv\/lib\/python3.8\/site-packages\/pluggy\/_manager.py\", line 80, in _hookexec\r\n return self._inner_hookexec(hook_name, methods, kwargs, firstresult)\r\n File \"\/home\/slesage\/hf\/datasets-preview-backend\/.venv\/lib\/python3.8\/site-packages\/pluggy\/_callers.py\", line 60, in _multicall\r\n return outcome.get_result()\r\n File \"\/home\/slesage\/hf\/datasets-preview-backend\/.venv\/lib\/python3.8\/site-packages\/pluggy\/_result.py\", line 60, in get_result\r\n raise ex[1].with_traceback(ex[2])\r\n File \"\/home\/slesage\/hf\/datasets-preview-backend\/.venv\/lib\/python3.8\/site-packages\/pluggy\/_callers.py\", line 39, in _multicall\r\n res = hook_impl.function(*args)\r\n File \"\/home\/slesage\/hf\/datasets-preview-backend\/.venv\/lib\/python3.8\/site-packages\/_pytest\/main.py\", line 316, in pytest_cmdline_main\r\n return wrap_session(config, _main)\r\n File \"\/home\/slesage\/hf\/datasets-preview-backend\/.venv\/lib\/python3.8\/site-packages\/_pytest\/main.py\", line 304, in wrap_session\r\n config.hook.pytest_sessionfinish(\r\n File \"\/home\/slesage\/hf\/datasets-preview-backend\/.venv\/lib\/python3.8\/site-packages\/pluggy\/_hooks.py\", line 265, in __call__\r\n return self._hookexec(self.name, self.get_hookimpls(), kwargs, firstresult)\r\n File \"\/home\/slesage\/hf\/datasets-preview-backend\/.venv\/lib\/python3.8\/site-packages\/pluggy\/_manager.py\", line 80, in _hookexec\r\n return self._inner_hookexec(hook_name, methods, kwargs, firstresult)\r\n File \"\/home\/slesage\/hf\/datasets-preview-backend\/.venv\/lib\/python3.8\/site-packages\/pluggy\/_callers.py\", line 55, in _multicall\r\n gen.send(outcome)\r\n File \"\/home\/slesage\/hf\/datasets-preview-backend\/.venv\/lib\/python3.8\/site-packages\/_pytest\/terminal.py\", line 803, in pytest_sessionfinish\r\n outcome.get_result()\r\n File \"\/home\/slesage\/hf\/datasets-preview-backend\/.venv\/lib\/python3.8\/site-packages\/pluggy\/_result.py\", line 60, in get_result\r\n raise ex[1].with_traceback(ex[2])\r\n File \"\/home\/slesage\/hf\/datasets-preview-backend\/.venv\/lib\/python3.8\/site-packages\/pluggy\/_callers.py\", line 39, in _multicall\r\n res = hook_impl.function(*args)\r\n File \"\/home\/slesage\/hf\/datasets-preview-backend\/.venv\/lib\/python3.8\/site-packages\/_pytest\/cacheprovider.py\", line 428, in pytest_sessionfinish\r\n config.cache.set(\"cache\/nodeids\", sorted(self.cached_nodeids))\r\n File \"\/home\/slesage\/hf\/datasets-preview-backend\/.venv\/lib\/python3.8\/site-packages\/_pytest\/cacheprovider.py\", line 188, in set\r\n f = path.open(\"w\")\r\nTypeError: xpathopen() takes 1 positional argument but 2 were given\r\n```\r\n\r\n## Environment info\r\n\r\n- `datasets` version: 1.12.0\r\n- Platform: Linux-5.11.0-1017-aws-x86_64-with-glibc2.29\r\n- Python version: 3.8.11\r\n- PyArrow version: 4.0.1\r\n\nSorry, my bad... When implementing `xpathopen`, I just considered the use case in the COUNTER dataset... I'm fixing it!","embeddings":[-0.3016421199,-0.1903872788,-0.0241492949,0.0990293771,0.3130396903,-0.1301406622,0.3114988804,0.2646481693,-0.1026777923,0.2515563369,-0.0329412185,0.3579744101,-0.0499672778,0.0878717452,-0.1619878709,0.1268500686,-0.0505254902,0.0685187206,0.0408935249,0.1136002094,-0.5712682605,-0.1570851803,-0.2168759555,0.0572490133,0.0126794586,0.0686679184,0.0916296914,0.2422822416,-0.0774931237,-0.469889611,0.1960368752,-0.0263328627,-0.0422579646,0.4722329378,-0.0001045304,-0.0628838241,0.4584931433,0.2026191652,-0.4859764278,-0.2095862329,-0.1490363926,0.2063343376,0.2531500459,-0.254386425,-0.1224498227,-0.2215161026,0.0800599158,-0.5224384069,0.0328101404,0.2298583686,0.2646005154,0.5858446956,0.1764907539,-0.1220323741,0.3920012712,-0.1865620166,-0.0988033265,0.1512855589,0.2791418135,0.0342658088,-0.1552184969,0.2738155425,-0.251668334,0.2581939995,0.0830629617,0.0403149687,0.1071799397,0.1236744151,0.0054618004,0.1477268934,-0.0244684517,-0.2447356284,-0.187163204,0.0132809207,-0.1560323685,-0.4709234536,0.0660294145,0.2990931869,-0.1927743256,0.0707391798,-0.1533978879,0.285312593,-0.2178352475,0.2086598277,-0.0414643139,0.474942416,0.0106467595,0.0582296215,0.0282520335,0.0535728522,0.3718392253,-0.029089909,0.0258877985,-0.2166382521,-0.0353300683,0.0447735675,0.3098430037,0.1299089044,0.1545516998,0.1684173644,-0.0050124396,-0.0647071525,0.1099802256,0.1257055998,-0.0156705379,-0.0932240486,-0.0586434491,0.1788802147,0.1195266545,0.1648669541,-0.1360280216,0.0084639816,-0.0668128058,-0.3070941567,0.0900237709,0.1294367164,0.5126132965,-0.0637784451,-0.3585409522,-0.1637833863,-0.3168658912,0.1315581501,-0.0887979344,0.1646843106,-0.3344549835,0.0525158457,0.2567434609,0.1665857583,-0.3985979259,0.1734841466,-0.1729390174,-0.0025560693,-0.2071772516,-0.1981868595,0.0244949311,-0.0061054123,0.0369944014,0.0258494634,-0.0169168096,-0.0553664789,0.1729714125,-0.0304495171,0.0978282169,0.0834876001,0.1038077101,0.0230855085,0.0646957457,0.2365556359,0.0004667089,0.4194874167,-0.1334887296,-0.2440327555,0.1164916679,0.35601753,0.0041353889,-0.1565836817,0.1820517033,-0.3767896891,-0.1220365986,-0.0800681487,0.0026528577,-0.2985853255,-0.1478585154,-0.2639125884,0.2254217416,0.2425370067,0.2605780959,0.0505344905,-0.0282764584,0.1627342254,0.3797311783,0.1669939458,-0.1939092427,-0.0896540061,-0.1379573345,0.1596079618,-0.0486291014,-0.4239594936,-0.3278156817,-0.1607752889,-0.1731502265,-0.07503663,0.049631156,0.0323371701,0.3179516196,0.0479337424,-0.0318585075,0.412953496,-0.0740917623,0.0748518556,-0.4666415751,-0.3798174262,0.5641200542,0.151705578,0.0570301823,-0.3497416377,0.2054489851,0.0608398058,0.5148956776,-0.1897210926,-0.1518411934,-0.2993411422,0.752466023,-0.0474503674,0.0110379141,-0.0960698351,0.0665274039,0.0204419531,0.2316295356,-0.2802456915,-0.1499119997,-0.0891542956,-0.1524517834,0.1319255084,-0.3466832638,-0.382157594,0.2474724054,0.2782579362,-0.1456098258,-0.1309039593,-0.0118953492,-0.0933069214,-0.3045151532,0.100494273,0.0386145003,0.1053284183,-0.0396609642,-0.094124943,-0.1193556041,0.298748225,0.162939176,-0.0600543693,-0.1179709211,0.3312662542,0.2449479997,0.1955460757,-0.0468190089,0.0072500408,0.0820179358,-0.3156889975,-0.2024822384,0.3221007586,0.0975408554,-0.002581625,0.0227513239,0.1893991232,-0.2354014963,0.1002926081,0.2986403406,0.0012304113,-0.0390900932,-0.1708397567,0.0159268808,0.3352193236,0.253539145,0.4091952145,0.4384313226,0.0471034944,-0.1185913086,-0.0291307271,0.1720729768,0.0163347404,0.2327632755,0.1700844765,-0.400013119,-0.0777249038,-0.0843988508,0.3033127487,0.3952056468,0.275578618,0.0969723538,0.0887242258,-0.1324126124,-0.2870429754,0.2992976308,0.1152055711,0.0208725408,0.2415834963,0.2297437042,0.1021427438,-0.2554797828,-0.0732267201,-0.1389924884,0.0566167571,-0.1460091919,-0.0478304811,-0.1640127003,0.2011270672,-0.1140680909,-0.5005605817,0.1816411167,-0.2008605003,0.0143492073,0.3810865879,-0.2022752911,0.1200969592,-0.2770578265,0.0942122191,0.0885685533,-0.0884958655,0.1415922791,-0.2295171022,-0.1760160774,0.1794952601,0.0489556827,-0.1769523323,0.3923769593,-0.0935229883,-0.0265750755,-0.1780978739,-0.1293669194,0.0228035133,0.2426382601,0.0753103569,0.111499086,0.05481565,0.108430557,-0.2810976505,0.3347675204,-0.2257572263,-0.0527905673,0.1222594678,0.0974359363,-0.0859619454,-0.1513888091,-0.6303583384,-0.1750367284,-0.3355152905,0.0044383388,-0.107812658,0.1289740354,0.4354717731,0.0387976281,0.1618680805,-0.1536655724,0.121643737,-0.0872593373,-0.0211115107,0.2169368863,-0.1349027008,-0.4010963142,-0.198347047,-0.1213339865,-0.1045445204,0.433926791,-0.2609218061,-0.2592196167,-0.2313274145,0.0872525126,-0.1294534057,-0.0232153423,0.3986389339,0.2484317124,-0.2569616735,-0.2864581048,0.0643375963,-0.0433718897,-0.0686287582,0.0381864421,-0.087544404,0.1517151892,0.1127801165,0.2772788107,-0.0392005183,-0.0387176163,0.1129376143,-0.2495963275,0.1978779882,-0.0433227047,-0.256916225,0.1621052921,-0.2219971567,-0.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{"html_url":"https:\/\/github.com\/huggingface\/datasets\/issues\/2892","title":"Error when encoding a dataset with None objects with a Sequence feature","comments":"This has been fixed by https:\/\/github.com\/huggingface\/datasets\/pull\/2900\r\nWe're doing a new release 1.12 today to make the fix available :)","body":"There is an error when encoding a dataset with None objects with a Sequence feature\r\n\r\nTo reproduce:\r\n```python\r\nfrom datasets import Dataset, Features, Value, Sequence\r\ndata = {\"a\": [[0], None]}\r\nfeatures = Features({\"a\": Sequence(Value(\"int32\"))})\r\ndataset = Dataset.from_dict(data, features=features)\r\n```\r\nraises\r\n\r\n```python\r\n---------------------------------------------------------------------------\r\nTypeError Traceback (most recent call last)\r\n in \r\n 2 data = {\"a\": [[0], None]}\r\n 3 features = Features({\"a\": Sequence(Value(\"int32\"))})\r\n----> 4 dataset = Dataset.from_dict(data, features=features)\r\n[...]\r\n~\/datasets\/features.py in encode_nested_example(schema, obj)\r\n 888 if isinstance(obj, str): # don't interpret a string as a list\r\n 889 raise ValueError(\"Got a string but expected a list instead: '{}'\".format(obj))\r\n--> 890 return [encode_nested_example(schema.feature, o) for o in obj]\r\n 891 # Object with special encoding:\r\n 892 # ClassLabel will convert from string to int, TranslationVariableLanguages does some checks\r\n\r\nTypeError: 'NoneType' object is not iterable\r\n```\r\n\r\nInstead, if should run without error, as if the `features` were not passed","comment_length":19,"text":"Error when encoding a dataset with None objects with a Sequence feature\nThere is an error when encoding a dataset with None objects with a Sequence feature\r\n\r\nTo reproduce:\r\n```python\r\nfrom datasets import Dataset, Features, Value, Sequence\r\ndata = {\"a\": [[0], None]}\r\nfeatures = Features({\"a\": Sequence(Value(\"int32\"))})\r\ndataset = Dataset.from_dict(data, features=features)\r\n```\r\nraises\r\n\r\n```python\r\n---------------------------------------------------------------------------\r\nTypeError Traceback (most recent call last)\r\n in \r\n 2 data = {\"a\": [[0], None]}\r\n 3 features = Features({\"a\": Sequence(Value(\"int32\"))})\r\n----> 4 dataset = Dataset.from_dict(data, features=features)\r\n[...]\r\n~\/datasets\/features.py in encode_nested_example(schema, obj)\r\n 888 if isinstance(obj, str): # don't interpret a string as a list\r\n 889 raise ValueError(\"Got a string but expected a list instead: '{}'\".format(obj))\r\n--> 890 return [encode_nested_example(schema.feature, o) for o in obj]\r\n 891 # Object with special encoding:\r\n 892 # ClassLabel will convert from string to int, TranslationVariableLanguages does some checks\r\n\r\nTypeError: 'NoneType' object is not iterable\r\n```\r\n\r\nInstead, if should run without error, as if the `features` were not passed\nThis has been fixed by https:\/\/github.com\/huggingface\/datasets\/pull\/2900\r\nWe're doing a new release 1.12 today to make the fix available :)","embeddings":[-0.0830345005,-0.2502439916,-0.0386255719,0.3015390933,0.2076890171,0.0788367391,0.5001555681,0.1494158357,0.1255292594,0.1421060264,0.3285635114,0.0508400798,-0.2967574298,0.3420606554,-0.2623355389,-0.2604982257,0.034535449,0.2224871665,0.0154916933,-0.0224474147,-0.3130667508,0.1709361374,-0.364877075,-0.2221626192,-0.1550774723,-0.2108083814,-0.2160241902,-0.2960246801,0.0542513058,-0.5987132192,0.2819482088,-0.0695153251,0.0921094567,0.2383219302,-0.0001144155,0.105492115,0.6381921768,-0.1453159899,-0.2628047466,-0.3394742906,-0.5618312359,-0.4227804542,0.1241655424,-0.3578473926,-0.0965121984,-0.0749715865,0.094718039,-0.3724971116,0.2042126656,0.2839667201,0.2130593061,0.3830007613,-0.0845509171,-0.0744024739,0.206820786,0.4162615538,-0.2607547045,-0.1798537225,0.035758879,0.2699487805,0.0946282744,0.3492396772,-0.111404337,-0.1305068284,0.2094347328,0.0849353001,0.0885619074,-0.4521939754,-0.2671251595,0.2014953941,0.2924095094,0.0013618435,-0.4084641933,-0.2180884778,0.0188714378,-0.6638943553,0.2666681111,0.055101525,-0.0386278071,-0.0269636735,-0.168392688,0.145001635,-0.2138954103,0.1660171747,-0.1209752634,0.0807909966,-0.1394540668,0.0341406651,-0.2402791679,-0.2700186968,0.0587494336,-0.3706248403,-0.0257517211,0.2111456394,-0.2515126467,-0.1936304271,0.167350471,0.0101849167,0.1697200388,0.1379934251,-0.0658706501,-0.1037984863,-0.0693587288,-0.0169596635,0.2529113591,0.2136024684,0.4112964869,-0.0680698305,-0.0581751354,0.1961243749,0.0996622145,-0.017598724,0.11928422,0.0517393723,0.0169261098,0.4072842002,0.6467193961,-0.0525628068,-0.393481791,0.2361950129,-0.3853594661,-0.0304983035,-0.0211958643,0.0061908085,0.0841203332,0.2916485071,-0.1465796083,0.2161677331,-0.2736037076,-0.250882864,-0.2348464429,0.0721954107,0.1018402427,0.0885985568,-0.0547403768,-0.0332995802,0.0658167601,0.1560105532,0.0796802491,0.1009850726,-0.0555230267,-0.365808934,0.0925061703,0.1523720324,-0.0070467223,0.2921590805,0.3762358427,-0.2581280768,-0.1091617644,0.1182429716,-0.2788110375,-0.217513755,-0.005827894,0.1725294441,-0.3795679808,0.0441264361,-0.0734036565,0.2263460904,0.2108575404,-0.0775901377,-0.0965437591,-0.0869289562,-0.2196164131,-0.1535144448,-0.0919654146,0.6465163827,-0.1091333926,-0.012587267,0.1191491932,-0.0898958445,0.3521232605,0.2327210456,0.0481479168,-0.057248231,-0.3404105306,0.1900297105,0.0831347257,-0.1115483418,-0.208607167,0.5657577515,0.0239907876,0.3598697484,0.1134639457,0.0959460512,0.1585700363,-0.3072297871,0.0130883651,0.1975988597,-0.1874651164,-0.2206795961,-0.2418985963,-0.1231627762,0.5206927657,-0.0743467882,-0.0640905127,0.2067834437,-0.3010617197,-0.0231112521,-0.1406468004,-0.3186881542,0.1288141012,0.5053259134,0.21923922,0.0375964157,-0.0967492238,-0.4028446674,-0.362685889,0.1312113106,-0.179603979,0.2839995623,-0.4932679534,-0.1799385846,0.0735202953,0.019041175,-0.3947464824,0.0015028021,0.1547810137,0.1214925349,-0.1535111219,0.1999227107,-0.0773415267,0.2772004306,0.1561118215,-0.1248554364,-0.567322731,0.3699874282,0.0695009977,-0.2262255251,0.0071669328,0.3033728004,0.3317838013,-0.0899984464,0.018318845,0.1505862623,0.0259702709,-0.3309904039,-0.3067805767,-0.1571382731,0.3190588653,-0.4185928106,-0.2214330137,0.3747457862,0.1825433373,-0.1073410809,-0.1209880039,0.6575422287,0.0157377589,0.4647912979,0.0042069959,0.1760141253,0.0537278354,-0.0018615762,-0.327142626,-0.2869090438,0.1755582988,0.2026411891,-0.1650790572,-0.0399117619,-0.3015944362,0.0058686477,0.4108926356,0.088436991,0.0354627408,0.0570802502,-0.0770579055,-0.0368382186,0.1092406586,-0.159593001,0.299883306,0.0505586714,-0.0732196867,0.0735782608,0.0075443899,0.0745387301,0.1758682579,-0.0350369215,0.2430823594,0.0285679307,0.1076480895,0.1176982298,-0.1779721677,-0.5438489318,-0.0398581289,0.1430890262,-0.2491215467,0.2492818087,-0.4795846939,-0.2056158483,0.130416885,-0.2173653096,-0.386295557,-0.4440228939,-0.1639413238,0.0275585894,-0.14083983,0.2236978859,0.0148140779,-0.26245597,0.2985749543,-0.0243482068,-0.0831376389,-0.1392291635,-0.0242327861,0.0463199466,0.1656642258,-0.0481024235,0.2467772514,0.0332959294,-0.3499837816,-0.2126868665,-0.2085345984,0.0565498397,-0.3503640592,0.0777079239,0.3079494238,0.1277272999,-0.04940686,-0.1998873949,0.4227285385,0.4243725836,-0.2120994478,0.3217695653,-0.102062583,-0.0354735814,-0.2110650837,-0.0959021896,-0.1572214514,-0.2278930247,0.1076047793,0.2109689265,0.2101610452,0.4471023381,0.0470193811,0.120879285,0.1355805248,-0.175628379,0.0008855638,-0.0150361629,0.4824748039,-0.1827880889,-0.2712480724,0.1487995982,-0.2987255454,0.0875612497,0.2041173279,-0.3575526774,-0.1763042659,-0.1413789541,0.1611307114,0.0745360553,-0.1046581939,0.3775742948,0.2816057205,-0.1083455756,-0.0367697217,-0.0508110113,0.2569004595,-0.0705505088,0.0287486054,0.229687199,0.5177567601,0.0398100056,0.0972354859,0.4376426637,0.1970631927,0.4261662662,-0.0823322833,0.0689360127,-0.1690145433,0.0005423338,-0.1711448282,0.107939221,-0.0178557411,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{"html_url":"https:\/\/github.com\/huggingface\/datasets\/issues\/2888","title":"v1.11.1 release date","comments":"Hi ! Probably 1.12 on monday :)\r\n","body":"Hello, i need to use latest features in one of my packages but there have been no new datasets release since 2 months ago.\r\n\r\nWhen do you plan to publush v1.11.1 release?","comment_length":7,"text":"v1.11.1 release date\nHello, i need to use latest features in one of my packages but there have been no new datasets release since 2 months ago.\r\n\r\nWhen do you plan to publush v1.11.1 release?\nHi ! Probably 1.12 on monday :)\r\n","embeddings":[-0.3028725386,-0.1802771538,-0.2127547413,-0.0963228866,0.0652879179,-0.1868623346,0.0738429576,0.3128882051,-0.2114172131,0.4218455255,0.2351345271,0.0822070688,-0.0895844847,0.4837630689,-0.1932857186,-0.2654596269,0.0769366696,0.0144002829,-0.1551253647,0.0024792501,-0.1483270228,0.119360745,-0.3253111243,0.0831760392,0.0989715233,-0.064572975,-0.3526091278,-0.1743175536,-0.5363405347,-0.3933886588,0.461257726,0.1939074099,0.2547554672,0.2986641824,-0.000103304,-0.4014018774,0.5556403995,0.2845861912,-0.2422691882,-0.2193116248,-0.3339813054,-0.5906572938,-0.1154627725,0.0800851658,-0.2921097875,-0.1461395621,0.082330212,-0.0849997699,-0.13677302,-0.1171071827,0.2824233174,0.0947450623,0.1915313601,-0.3989170194,0.284394294,-0.0203845855,-0.2789727747,-0.3155975044,0.5697530508,0.1857773513,0.1898058355,0.326002121,0.2075845897,-0.1623990536,0.1387228072,0.1931113005,0.1122244373,-0.32729882,-0.0393120684,0.1010244489,0.9985591769,-0.0018671668,-0.2434326708,0.2223907411,-0.0925616995,-0.3847976029,0.1220049784,-0.0718759969,0.1220379025,0.1305280179,0.0270749014,-0.3837861121,-0.1746051461,0.2439880967,0.0338811204,0.3361899853,-0.1041846797,-0.0660860538,0.1154744327,-0.2463802546,0.0967559218,-0.0215033758,-0.1613555253,0.2227307111,0.0464089848,-0.4094306231,0.1858401746,-0.0288592987,0.1305842102,0.3231448531,-0.1524199545,-0.1115325615,-0.1302562058,-0.0906753838,0.5744841099,0.0935288742,0.377846837,0.2240085304,0.3023318052,0.1646759659,0.210433796,-0.0148354508,0.131557256,0.2080590278,-0.1345603913,0.092921719,0.2413697988,-0.5841081142,0.3566501141,0.0036136017,0.0791958421,-0.3253816366,-0.3280638158,-0.1975245178,-0.0646178052,0.1883967668,-0.1564619541,-0.1827139109,0.1210180596,-0.2046548128,-0.3577520847,0.0363553949,-0.2325457633,-0.0549016744,0.1167488173,-0.2526122332,-0.1254964024,0.0305748545,-0.04656725,0.0622468628,0.0610297844,0.1928608567,-0.2869565487,0.2402708977,-0.1486318558,0.2325121164,-0.3784801364,0.3326986134,-0.1420154572,0.2017378062,-0.0192655772,-0.2063460052,-0.235487327,0.3137744367,-0.1372713745,-0.3029890358,0.19962731,0.3279361129,-0.3900982738,-0.1182468757,-0.1860611141,0.2779344022,-0.1112236157,-0.2089126557,0.0612550527,-0.0017352636,-0.2293154597,0.0255627967,-0.3211435378,-0.2947054803,0.1805807501,-0.0630620569,0.0061964095,-0.2280418724,0.1311280131,-0.185826987,0.4691230059,-0.3665311038,-0.5793189406,0.4294295013,-0.1866938472,-0.4467723966,0.4338716865,0.2241641581,0.3097352386,-0.1575234383,-0.2617045343,0.2637113929,-0.1526574194,-0.320348084,-0.2554035187,-0.3677665293,-0.0720299929,0.1507190168,0.1602411419,0.2366000116,0.2579503655,0.2858259678,0.1898807436,0.1925684959,0.1466663629,0.0294717029,0.5004534125,-0.10350997,0.2177486867,-0.249899298,-0.2581174374,-0.0229802001,-0.0547287352,0.2486825585,0.5317671299,-0.2098047286,-0.168516919,-0.1740678996,0.2359928489,-0.0324108824,0.1631854326,0.1089351401,-0.0164421871,0.1266786158,-0.3025694489,0.0803501308,0.1472560316,0.0244728141,-0.075657174,0.1248692945,-0.2761505842,0.0051354663,0.2405391037,-0.0050578136,0.0540098287,-0.2141433656,0.1412835717,0.0534515046,-0.0641530901,0.3371422291,0.6113381386,0.1324200183,0.3750956953,0.1302689165,0.219974041,0.0903351158,-0.0883826166,0.2412555814,-0.269962877,0.312500447,0.238511622,-0.2063528448,-0.1451906115,0.118214719,0.1887120157,-0.0891980454,-0.0327405259,-0.3398402631,0.2254811525,-0.1658332348,-0.2750526965,-0.0365721621,-0.2551523447,0.3512178659,0.3392684758,-0.1608422101,-0.1638178527,0.0186253563,0.0652871504,-0.0031810594,0.2478724569,0.0328481123,-0.1552314162,0.3118087649,0.1120094359,-0.0968455672,0.0139505379,0.0497668646,0.0382369012,0.0392541103,-0.3878073692,-0.221236378,0.0975770727,0.1155316979,0.0994631052,0.1521302015,0.1214670911,0.2327489555,0.2580001354,-0.344601959,-0.3168298304,-0.2266455889,0.1146346107,-0.1705584228,0.1445552707,-0.1704138368,0.0943085477,0.0777231157,0.2056838423,0.1746924818,-0.0169805512,0.7022944689,-0.2754337788,0.4286308587,-0.22941567,-0.0400346629,-0.287625581,0.3418615162,-0.1031061858,-0.2282226235,0.195717901,0.0163822714,0.3134722412,-0.4504329264,-0.4006796479,0.0965960771,-0.190621376,-0.0871734247,0.1440135986,-0.3507573307,-0.0002354266,0.2681168616,0.020206226,-0.1884005666,-0.1797890365,-0.1004347429,-0.1091894209,0.0140760578,-0.2644398212,-0.5476667881,-0.23607862,-0.032877408,0.0195357129,0.1396642774,0.0098538799,-0.2097719312,-0.0314573459,-0.0043667466,-0.025507886,-0.3325499892,-0.13492091,-0.1378961504,0.1646966338,-0.1210952327,-0.3690144718,0.0233245771,0.2133821547,-0.1906823069,0.0343830734,-0.608182013,-0.2255241424,-0.07357122,0.0902312696,0.271065414,0.0482119732,0.3331973553,0.0043859896,-0.2407487333,-0.0966798887,-0.289496839,-0.0617358088,0.2790717185,0.3425323367,0.1480440795,0.2153837532,0.0382699557,0.4080422521,0.0405399092,-0.0831079558,0.1420852691,0.0724038258,0.1340251565,-0.0351542011,-0.1449554712,0.1592445076,0.0636743233,0.211825773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{"html_url":"https:\/\/github.com\/huggingface\/datasets\/issues\/2888","title":"v1.11.1 release date","comments":"@albertvillanova i think this issue is still valid and should not be closed till `>1.11.0` is published :)","body":"Hello, i need to use latest features in one of my packages but there have been no new datasets release since 2 months ago.\r\n\r\nWhen do you plan to publush v1.11.1 release?","comment_length":18,"text":"v1.11.1 release date\nHello, i need to use latest features in one of my packages but there have been no new datasets release since 2 months ago.\r\n\r\nWhen do you plan to publush v1.11.1 release?\n@albertvillanova i think this issue is still valid and should not be closed till `>1.11.0` is published :)","embeddings":[-0.2534325421,-0.1435918212,-0.1170087606,0.1250979602,-0.0803622156,-0.2069433331,0.3182261586,0.2595030665,-0.2662116587,0.2432924211,0.4498234391,-0.0775993839,-0.0092685595,0.3775973022,-0.3972175717,-0.0890159234,0.0787209198,0.1089016348,-0.0526827313,0.0449188799,-0.2720680833,0.068657726,-0.3964475691,0.1608792841,0.0663995072,-0.0716037601,-0.1215934381,-0.254347086,-0.6671610475,-0.5313281417,0.3133550286,0.2034503967,0.0760271847,0.271275878,-0.0001110468,-0.423866421,0.5205672979,0.1486699134,-0.2614662647,-0.1513033807,-0.416443944,-0.5353802443,-0.1008592322,-0.0137669845,-0.3072520196,-0.1077144369,0.0117483158,-0.1943197548,-0.0628163069,-0.0156020885,0.2480115443,0.1673386395,0.4019794762,-0.3542853892,0.1863051802,-0.1159243658,-0.3703640699,-0.2499655932,0.5717850924,0.2568038106,0.2268878967,0.384983182,0.1912631989,-0.1922257096,0.1507626772,-0.0097723147,0.2396764457,-0.266032964,0.0415400378,0.0777787417,0.9955607653,0.0203635413,-0.3793464899,0.2034740597,-0.0927332118,-0.2712348402,0.2412474453,0.027283011,-0.0292447396,0.149242878,0.0873354375,-0.4584193528,-0.1516828388,0.1602145284,-0.0272049885,0.459104538,-0.0887170136,0.1067651212,-0.0215255283,-0.3015371263,0.2346984148,0.0200975835,0.0850308686,0.156445384,0.0514521152,-0.4402125776,0.1060789451,-0.0172465947,0.1498136967,0.1924144477,-0.2606184483,-0.249166131,-0.0464439057,0.0133378366,0.6594331861,0.0745627508,0.3405662179,0.188009277,0.4329450428,0.2551412284,0.0644607022,0.0997430384,0.0618424639,-0.0808662996,-0.1455136389,0.2052437365,0.4584421515,-0.5718432069,0.3661362231,0.1454990655,0.0115315011,-0.073997356,-0.3209313154,-0.1508467942,-0.0373871252,0.3257052004,0.0893606171,-0.1983124912,0.1348611414,-0.1805576384,-0.1951919943,-0.1088341549,-0.200981006,-0.0183628146,-0.031561859,-0.3116750121,-0.2775349915,0.1134495363,-0.1254057586,-0.0434127748,0.0564601496,0.1302085966,-0.2246665657,0.4118489623,-0.2466320097,0.082844384,-0.3096274436,0.2402789593,-0.0819456652,0.3500746489,-0.0424564332,-0.1780534983,-0.3391320705,0.2919041216,-0.1666521877,-0.0776967406,0.2701286674,0.1953231692,-0.2030190676,-0.2382987589,-0.0494303294,0.1722414643,-0.2394903451,-0.256931901,-0.0695379525,0.0432331599,-0.1193417981,0.1540283114,-0.2632963657,-0.2000599653,0.1197276041,0.0051401095,-0.1842305511,-0.3186813593,0.1262588203,-0.2455536276,0.3016958833,-0.3433002532,-0.639559865,0.3815340102,-0.284142822,-0.3894364536,0.4114672244,0.1741337925,0.2954373658,-0.3212958872,-0.1409738809,-0.0816188827,-0.1616334766,-0.3559683263,-0.4418994188,-0.4019602537,0.0429010168,0.0181122757,0.2204887122,0.1503366083,0.0952749476,0.4235877395,0.0749824941,0.2797828615,0.2078803927,0.0765660107,0.482803762,-0.1286068708,0.0529898442,-0.1006552279,-0.2767008245,0.0938937142,-0.1435723305,0.1591186672,0.4384330511,-0.3189628124,-0.2085864544,-0.0561050922,0.3260715008,-0.0709523261,0.0817816257,0.0915738642,-0.2478206754,0.1673493236,-0.4060981572,0.1368636489,-0.0098665319,-0.07066378,-0.088441737,0.0452465005,-0.2908729911,0.0994255021,0.1788440347,0.0838302895,0.1694515795,-0.2390569299,0.1745637655,-0.0815123394,-0.0723995641,0.3089856207,0.3561557233,0.1862898916,0.2350969911,0.2040317804,0.0746104494,0.1710554659,-0.0586112514,0.2303554267,-0.2253153324,0.3460877836,0.3549923301,-0.2632007301,-0.0564746819,0.144726038,0.1307961047,-0.2599101663,-0.0981654152,-0.2556478679,0.0817662179,-0.221117571,-0.2914198935,0.2252827138,-0.2763938606,0.2965317667,0.5968988538,0.0184195228,-0.047829736,0.0809677541,0.2573701739,-0.0809906572,0.1679659784,-0.0462063886,0.0884929076,0.2539899945,-0.021018533,0.0049677342,0.1221921593,0.0425285064,0.1027600989,0.0455321558,-0.4042115211,-0.0299377795,0.0530678742,0.0633863062,0.115278326,0.1767398119,0.1918154806,0.2266006619,0.0650810748,-0.3978857994,-0.2835028172,-0.0675668642,0.0291238204,-0.0194902401,0.0619625486,0.0534454733,0.1382157803,0.1468727291,0.0316265896,0.1826277375,-0.1548491269,0.7969358563,-0.3889087141,0.4114349484,-0.156639576,-0.1199053377,-0.3110970855,0.2345053405,-0.0124809062,-0.2776047289,0.2567169964,0.0537328571,0.2090214789,-0.6258595586,-0.4577414989,0.0528508835,-0.144126296,0.0452978648,0.0343037061,-0.2054453045,-0.107023187,0.2207032591,0.0384894237,-0.0587673448,-0.2087223232,-0.1666585952,-0.0068196445,0.0721502379,-0.2570260763,-0.6848427057,-0.1318627,-0.3363646269,-0.0185509361,-0.1078772321,0.1569403857,-0.1232450008,-0.051425416,-0.1049095914,0.0217202213,-0.3342834115,-0.0948758051,-0.048731003,0.0945054814,-0.0082828654,-0.4569739103,0.1993883997,0.1774878055,-0.2025116682,0.1531127393,-0.5314667225,-0.2725237608,0.206520468,-0.043862544,0.1573558748,-0.1776329577,0.5536837578,-0.1086382791,-0.2538419664,-0.1345501691,-0.2866043448,0.0720614642,0.1712462157,0.3246344626,0.409180969,0.2624562085,0.0242744964,0.3892090321,0.1903090924,0.0267370865,0.2341599315,0.1795129925,0.1924058646,-0.0937660858,-0.2098539323,0.0676550344,0.1260128468,0.161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{"html_url":"https:\/\/github.com\/huggingface\/datasets\/issues\/2885","title":"Adding an Elastic Search index to a Dataset","comments":"Hi, is this bug deterministic in your poetry env ? I mean, does it always stop at 90% or is it random ?\r\n\r\nAlso, can you try using another version of Elasticsearch ? Maybe there's an issue with the one of you poetry env","body":"## Describe the bug\r\nWhen trying to index documents from the squad dataset, the connection to ElasticSearch seems to break:\r\n\r\nReusing dataset squad (\/Users\/andreasmotz\/.cache\/huggingface\/datasets\/squad\/plain_text\/1.0.0\/d6ec3ceb99ca480ce37cdd35555d6cb2511d223b9150cce08a837ef62ffea453)\r\n 90%|\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2589 | 9501\/10570 [00:01<00:00, 6335.61docs\/s]\r\n\r\nNo error is thrown, but the indexing breaks ~90%.\r\n\r\n## Steps to reproduce the bug\r\n```python\r\n# Sample code to reproduce the bug\r\nfrom datasets import load_dataset\r\nfrom elasticsearch import Elasticsearch\r\nes = Elasticsearch()\r\nsquad = load_dataset('squad', split='validation')\r\nindex_name = \"corpus\"\r\nes_config = {\r\n \"settings\": {\r\n \"number_of_shards\": 1,\r\n \"analysis\": {\"analyzer\": {\"stop_standard\": {\"type\": \"standard\", \" stopwords\": \"_english_\"}}},\r\n },\r\n \"mappings\": {\r\n \"properties\": {\r\n \"idx\" : {\"type\" : \"keyword\"},\r\n \"title\" : {\"type\" : \"keyword\"},\r\n \"text\": {\r\n \"type\": \"text\",\r\n \"analyzer\": \"standard\",\r\n \"similarity\": \"BM25\"\r\n },\r\n }\r\n },\r\n}\r\nclass IndexBuilder:\r\n \"\"\"\r\n Elastic search indexing of a corpus\r\n \"\"\"\r\n def __init__(\r\n self,\r\n *args,\r\n #corpus : None,\r\n dataset : squad,\r\n index_name = str,\r\n query = str,\r\n config = dict,\r\n **kwargs,\r\n ):\r\n #instantiate HuggingFace dataset\r\n self.dataset = dataset\r\n #instantiate ElasticSearch config\r\n self.config = config\r\n self.es = Elasticsearch()\r\n self.index_name = index_name\r\n self.query = query\r\n def elastic_index(self):\r\n print(self.es.info)\r\n self.es.indices.delete(index=self.index_name, ignore=[400, 404])\r\n search_index = self.dataset.add_elasticsearch_index(column='context', host='localhost', port='9200', es_index_name=self.index_name, es_index_config=self.config)\r\n return search_index\r\n def exact_match_method(self, index):\r\n scores, retrieved_examples = index.get_nearest_examples('context', query=self.query, k=1)\r\n return scores, retrieved_examples\r\nif __name__ == \"__main__\":\r\n print(type(squad))\r\n Index = IndexBuilder(dataset=squad, index_name='corpus_index', query='Where was Chopin born?', config=es_config)\r\n search_index = Index.elastic_index()\r\n scores, examples = Index.exact_match_method(search_index)\r\n print(scores, examples)\r\n for name in squad.column_names:\r\n print(type(squad[name]))\r\n```\r\n\r\n## Environment info\r\nWe run the code in Poetry. This might be the issue, since the script runs successfully in our local environment.\r\n\r\nPoetry:\r\n- Python version: 3.8\r\n- PyArrow: 4.0.1\r\n- Elasticsearch: 7.13.4\r\n- datasets: 1.10.2\r\n\r\nLocal:\r\n- Python version: 3.8\r\n- PyArrow: 3.0.0\r\n- Elasticsearch: 7.7.1\r\n- datasets: 1.7.0\r\n","comment_length":44,"text":"Adding an Elastic Search index to a Dataset\n## Describe the bug\r\nWhen trying to index documents from the squad dataset, the connection to ElasticSearch seems to break:\r\n\r\nReusing dataset squad (\/Users\/andreasmotz\/.cache\/huggingface\/datasets\/squad\/plain_text\/1.0.0\/d6ec3ceb99ca480ce37cdd35555d6cb2511d223b9150cce08a837ef62ffea453)\r\n 90%|\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2589 | 9501\/10570 [00:01<00:00, 6335.61docs\/s]\r\n\r\nNo error is thrown, but the indexing breaks ~90%.\r\n\r\n## Steps to reproduce the bug\r\n```python\r\n# Sample code to reproduce the bug\r\nfrom datasets import load_dataset\r\nfrom elasticsearch import Elasticsearch\r\nes = Elasticsearch()\r\nsquad = load_dataset('squad', split='validation')\r\nindex_name = \"corpus\"\r\nes_config = {\r\n \"settings\": {\r\n \"number_of_shards\": 1,\r\n \"analysis\": {\"analyzer\": {\"stop_standard\": {\"type\": \"standard\", \" stopwords\": \"_english_\"}}},\r\n },\r\n \"mappings\": {\r\n \"properties\": {\r\n \"idx\" : {\"type\" : \"keyword\"},\r\n \"title\" : {\"type\" : \"keyword\"},\r\n \"text\": {\r\n \"type\": \"text\",\r\n \"analyzer\": \"standard\",\r\n \"similarity\": \"BM25\"\r\n },\r\n }\r\n },\r\n}\r\nclass IndexBuilder:\r\n \"\"\"\r\n Elastic search indexing of a corpus\r\n \"\"\"\r\n def __init__(\r\n self,\r\n *args,\r\n #corpus : None,\r\n dataset : squad,\r\n index_name = str,\r\n query = str,\r\n config = dict,\r\n **kwargs,\r\n ):\r\n #instantiate HuggingFace dataset\r\n self.dataset = dataset\r\n #instantiate ElasticSearch config\r\n self.config = config\r\n self.es = Elasticsearch()\r\n self.index_name = index_name\r\n self.query = query\r\n def elastic_index(self):\r\n print(self.es.info)\r\n self.es.indices.delete(index=self.index_name, ignore=[400, 404])\r\n search_index = self.dataset.add_elasticsearch_index(column='context', host='localhost', port='9200', es_index_name=self.index_name, es_index_config=self.config)\r\n return search_index\r\n def exact_match_method(self, index):\r\n scores, retrieved_examples = index.get_nearest_examples('context', query=self.query, k=1)\r\n return scores, retrieved_examples\r\nif __name__ == \"__main__\":\r\n print(type(squad))\r\n Index = IndexBuilder(dataset=squad, index_name='corpus_index', query='Where was Chopin born?', config=es_config)\r\n search_index = Index.elastic_index()\r\n scores, examples = Index.exact_match_method(search_index)\r\n print(scores, examples)\r\n for name in squad.column_names:\r\n print(type(squad[name]))\r\n```\r\n\r\n## Environment info\r\nWe run the code in Poetry. This might be the issue, since the script runs successfully in our local environment.\r\n\r\nPoetry:\r\n- Python version: 3.8\r\n- PyArrow: 4.0.1\r\n- Elasticsearch: 7.13.4\r\n- datasets: 1.10.2\r\n\r\nLocal:\r\n- Python version: 3.8\r\n- PyArrow: 3.0.0\r\n- Elasticsearch: 7.7.1\r\n- datasets: 1.7.0\r\n\nHi, is this bug deterministic in your poetry env ? I mean, does it always stop at 90% or is it random ?\r\n\r\nAlso, can you try using another version of Elasticsearch ? Maybe there's an issue with the one of you poetry 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{"html_url":"https:\/\/github.com\/huggingface\/datasets\/issues\/2882","title":"`load_dataset('docred')` results in a `NonMatchingChecksumError` ","comments":"Hi @tmpr, thanks for reporting.\r\n\r\nTwo weeks ago (23th Aug), the host of the source `docred` dataset updated one of the files (`dev.json`): you can see it [here](https:\/\/drive.google.com\/drive\/folders\/1c5-0YwnoJx8NS6CV2f-NoTHR__BdkNqw).\r\n\r\nTherefore, the checksum needs to be updated.\r\n\r\nNormally, in the meantime, you could avoid the error by passing `ignore_verifications=True` to `load_dataset`. However, as the old link points to a non-existing file, the link must be updated too.\r\n\r\nI'm fixing all this.\r\n\r\n","body":"## Describe the bug\r\nI get consistent `NonMatchingChecksumError: Checksums didn't match for dataset source files` errors when trying to execute `datasets.load_dataset('docred')`.\r\n\r\n## Steps to reproduce the bug\r\nIt is quasi only this code:\r\n```python\r\nimport datasets\r\ndata = datasets.load_dataset('docred')\r\n```\r\n\r\n## Expected results\r\nThe DocRED dataset should be loaded without any problems.\r\n\r\n## Actual results\r\n```\r\nNonMatchingChecksumError Traceback (most recent call last)\r\n in \r\n----> 1 d = datasets.load_dataset('docred')\r\n\r\n~\/anaconda3\/lib\/python3.8\/site-packages\/datasets\/load.py in load_dataset(path, name, data_dir, data_files, split, cache_dir, features, download_config, download_mode, ignore_verifications, keep_in_memory, save_infos, script_version, use_auth_token, task, streaming, **config_kwargs)\r\n 845 \r\n 846 # Download and prepare data\r\n--> 847 builder_instance.download_and_prepare(\r\n 848 download_config=download_config,\r\n 849 download_mode=download_mode,\r\n\r\n~\/anaconda3\/lib\/python3.8\/site-packages\/datasets\/builder.py in download_and_prepare(self, download_config, download_mode, ignore_verifications, try_from_hf_gcs, dl_manager, base_path, use_auth_token, **download_and_prepare_kwargs)\r\n 613 logger.warning(\"HF google storage unreachable. Downloading and preparing it from source\")\r\n 614 if not downloaded_from_gcs:\r\n--> 615 self._download_and_prepare(\r\n 616 dl_manager=dl_manager, verify_infos=verify_infos, **download_and_prepare_kwargs\r\n 617 )\r\n\r\n~\/anaconda3\/lib\/python3.8\/site-packages\/datasets\/builder.py in _download_and_prepare(self, dl_manager, verify_infos, **prepare_split_kwargs)\r\n 673 # Checksums verification\r\n 674 if verify_infos:\r\n--> 675 verify_checksums(\r\n 676 self.info.download_checksums, dl_manager.get_recorded_sizes_checksums(), \"dataset source files\"\r\n 677 )\r\n\r\n~\/anaconda3\/lib\/python3.8\/site-packages\/datasets\/utils\/info_utils.py in verify_checksums(expected_checksums, recorded_checksums, verification_name)\r\n 38 if len(bad_urls) > 0:\r\n 39 error_msg = \"Checksums didn't match\" + for_verification_name + \":\\n\"\r\n---> 40 raise NonMatchingChecksumError(error_msg + str(bad_urls))\r\n 41 logger.info(\"All the checksums matched successfully\" + for_verification_name)\r\n 42 \r\n\r\nNonMatchingChecksumError: Checksums didn't match for dataset source files:\r\n['https:\/\/drive.google.com\/uc?export=download&id=1fDmfUUo5G7gfaoqWWvK81u08m71TK2g7']\r\n```\r\n\r\n## Environment info\r\n- `datasets` version: 1.11.0\r\n- Platform: Linux-5.11.0-7633-generic-x86_64-with-glibc2.10\r\n- Python version: 3.8.5\r\n- PyArrow version: 5.0.0\r\n\r\nThis error also happened on my Windows-partition, after freshly installing python 3.9 and `datasets`.\r\n\r\n## Remarks\r\n\r\n- I have already called `rm -rf \/home\/\/.cache\/huggingface`, i.e., I have tried clearing the cache.\r\n- The problem does not exist for other datasets, i.e., it seems to be DocRED-specific.","comment_length":69,"text":"`load_dataset('docred')` results in a `NonMatchingChecksumError` \n## Describe the bug\r\nI get consistent `NonMatchingChecksumError: Checksums didn't match for dataset source files` errors when trying to execute `datasets.load_dataset('docred')`.\r\n\r\n## Steps to reproduce the bug\r\nIt is quasi only this code:\r\n```python\r\nimport datasets\r\ndata = datasets.load_dataset('docred')\r\n```\r\n\r\n## Expected results\r\nThe DocRED dataset should be loaded without any problems.\r\n\r\n## Actual results\r\n```\r\nNonMatchingChecksumError Traceback (most recent call last)\r\n in \r\n----> 1 d = datasets.load_dataset('docred')\r\n\r\n~\/anaconda3\/lib\/python3.8\/site-packages\/datasets\/load.py in load_dataset(path, name, data_dir, data_files, split, cache_dir, features, download_config, download_mode, ignore_verifications, keep_in_memory, save_infos, script_version, use_auth_token, task, streaming, **config_kwargs)\r\n 845 \r\n 846 # Download and prepare data\r\n--> 847 builder_instance.download_and_prepare(\r\n 848 download_config=download_config,\r\n 849 download_mode=download_mode,\r\n\r\n~\/anaconda3\/lib\/python3.8\/site-packages\/datasets\/builder.py in download_and_prepare(self, download_config, download_mode, ignore_verifications, try_from_hf_gcs, dl_manager, base_path, use_auth_token, **download_and_prepare_kwargs)\r\n 613 logger.warning(\"HF google storage unreachable. Downloading and preparing it from source\")\r\n 614 if not downloaded_from_gcs:\r\n--> 615 self._download_and_prepare(\r\n 616 dl_manager=dl_manager, verify_infos=verify_infos, **download_and_prepare_kwargs\r\n 617 )\r\n\r\n~\/anaconda3\/lib\/python3.8\/site-packages\/datasets\/builder.py in _download_and_prepare(self, dl_manager, verify_infos, **prepare_split_kwargs)\r\n 673 # Checksums verification\r\n 674 if verify_infos:\r\n--> 675 verify_checksums(\r\n 676 self.info.download_checksums, dl_manager.get_recorded_sizes_checksums(), \"dataset source files\"\r\n 677 )\r\n\r\n~\/anaconda3\/lib\/python3.8\/site-packages\/datasets\/utils\/info_utils.py in verify_checksums(expected_checksums, recorded_checksums, verification_name)\r\n 38 if len(bad_urls) > 0:\r\n 39 error_msg = \"Checksums didn't match\" + for_verification_name + \":\\n\"\r\n---> 40 raise NonMatchingChecksumError(error_msg + str(bad_urls))\r\n 41 logger.info(\"All the checksums matched successfully\" + for_verification_name)\r\n 42 \r\n\r\nNonMatchingChecksumError: Checksums didn't match for dataset source files:\r\n['https:\/\/drive.google.com\/uc?export=download&id=1fDmfUUo5G7gfaoqWWvK81u08m71TK2g7']\r\n```\r\n\r\n## Environment info\r\n- `datasets` version: 1.11.0\r\n- Platform: Linux-5.11.0-7633-generic-x86_64-with-glibc2.10\r\n- Python version: 3.8.5\r\n- PyArrow version: 5.0.0\r\n\r\nThis error also happened on my Windows-partition, after freshly installing python 3.9 and `datasets`.\r\n\r\n## Remarks\r\n\r\n- I have already called `rm -rf \/home\/\/.cache\/huggingface`, i.e., I have tried clearing the cache.\r\n- The problem does not exist for other datasets, i.e., it seems to be DocRED-specific.\nHi @tmpr, thanks for reporting.\r\n\r\nTwo weeks ago (23th Aug), the host of the source `docred` dataset updated one of the files (`dev.json`): you can see it [here](https:\/\/drive.google.com\/drive\/folders\/1c5-0YwnoJx8NS6CV2f-NoTHR__BdkNqw).\r\n\r\nTherefore, the checksum needs to be updated.\r\n\r\nNormally, in the meantime, you could avoid the error by passing `ignore_verifications=True` to `load_dataset`. However, as the old link points to a non-existing file, the link must be updated too.\r\n\r\nI'm fixing all this.\r\n\r\n","embeddings":[-0.2659113109,0.3422692716,0.0355651341,0.3098572195,0.214197889,0.0404327884,0.3646671176,0.4201550186,0.3040324748,0.0715031847,-0.2768665254,0.190118432,0.1306955665,-0.1951108724,-0.1612918228,0.3339123726,0.1246936545,0.1053829789,-0.2242237628,-0.1252798289,-0.3106808662,0.1985796541,-0.2184446454,-0.2843059599,-0.0529530197,0.1835617423,0.1532600224,0.2324340791,-0.120700039,-0.3587095439,0.2788212895,0.2785292566,0.173633635,0.4296127558,-0.0001249282,0.1207987443,0.2817707658,-0.0242785923,-0.501280129,-0.2013867348,-0.563354671,-0.3742280006,-0.0070420792,-0.1583375037,-0.0344875716,0.2363334298,-0.0177811794,-0.2750721872,0.0001792605,0.3736942112,0.1168587953,0.3918669224,0.1610017419,0.0871690363,0.3812574446,0.0504982956,-0.0901036188,0.4119758606,0.219390288,-0.0385965593,-0.2582117915,0.1683268249,-0.4234545529,0.3069981039,0.232055366,0.0618822426,0.0648707673,-0.1373666972,0.253241241,0.2994743288,0.4981358647,-0.4146616459,-0.3278160989,-0.2285789698,-0.298303932,-0.3188637793,0.3791346848,0.1398549974,-0.1967773736,0.0336276442,-0.3680784702,0.1499671191,0.0037718869,0.1940130293,0.2422606051,0.0545322224,0.0316536799,-0.0187701844,0.0221102182,0.0298728757,0.3937070668,-0.5399644375,-0.2046732903,0.2048074454,-0.5590269566,0.0730827823,0.0080572087,0.2777058184,0.352638036,0.4925115705,0.08901117,0.2042775005,-0.0375440791,0.188513726,0.1643131375,0.2119750381,-0.0647413805,0.4100411534,0.2708570957,0.3656848669,-0.1347787827,0.0432133712,-0.0054317582,-0.1764253378,0.6712611914,-0.1259947717,0.3084286749,-0.4890398979,-0.4388926029,0.271324724,0.0082045635,-0.0947970897,0.0869470164,0.322021246,-0.226584658,0.1516590118,0.1470181793,0.1476904005,-0.2125054002,-0.0541327372,-0.187337622,-0.0764364675,-0.0596771426,0.1976072788,0.2942281067,-0.1821978688,0.2553656697,0.1083121598,0.2190237492,-0.1960808337,0.2999760807,-0.1955657154,-0.0533633046,0.3180696368,-0.0322253928,0.0961275995,0.2476602346,-0.1812210828,-0.1439391971,0.1828757226,-0.2557730675,-0.2137421519,-0.1269117147,0.1511545181,-0.3514274061,0.0082077142,-0.2378200293,-0.400021553,0.4063311517,-0.3223438859,-0.0154792899,-0.3542110622,-0.2637994885,-0.353944093,0.2389694899,0.4025940299,0.0419260897,0.0426536016,-0.0905655473,-0.0114815729,0.294190079,0.183512345,0.0093251551,0.1056279317,-0.4124949574,-0.0635623261,0.2606512606,-0.3542023897,-0.6170941591,0.1036195084,0.1277518421,0.6295824647,0.0476492308,0.0750021562,0.1831660569,-0.0502420217,0.2216965407,0.1532353014,-0.038785696,0.1244795322,-0.252807796,-0.0325587615,-0.0434744433,0.1044865996,0.0201719031,0.2498004735,0.2889224589,-0.1661153138,0.2637803555,-0.0953656435,-0.1257363111,0.2321082801,0.2991938293,0.046871759,0.0639734343,-0.0167484935,-0.7693794966,0.3423186839,-0.1505941451,-0.0280080661,-0.0733518675,-0.0444171354,-0.1803081781,-0.1141534746,-0.3073681295,-0.0912047699,-0.0125983637,0.3906361461,0.2074201256,0.0201071296,0.0379508324,0.4137744009,-0.3047952354,0.1248284727,-0.3346244991,0.4118489027,0.0743400902,-0.1029386371,0.1318240315,-0.0030252386,0.1328218132,-0.0562920161,-0.2745426893,0.4387036562,0.3811519444,0.1363846362,-0.1962373555,0.3414201438,-0.0699199662,-0.1395473629,-0.012621915,0.4393106699,0.1203998923,-0.1690554321,-0.1533752978,0.3473964334,-0.0006330401,0.1946744621,0.0062628747,0.0575816073,0.2450569719,-0.1167023629,-0.1461344063,-0.261870563,0.3650653958,0.1296010762,0.2266256809,0.196940884,-0.1572066098,-0.0258856118,0.4515280128,-0.0735068321,-0.0205244031,0.1288514733,0.0474654101,-0.0436953641,0.0753767639,0.2370779216,0.5027197599,0.0668875426,-0.04627854,0.1425257325,-0.1718104333,-0.0678285286,0.0968001559,-0.0407392457,0.1989537925,0.6168555617,0.1380470097,-0.1526708454,-0.2818833888,-0.0774433017,-0.1217622161,0.3029148579,-0.4903008938,0.0769678801,-0.1072904691,0.0858299136,-0.3731725514,-0.1268986762,-0.1855977178,-0.2465110421,-0.2533238232,0.6255096793,0.0291794557,0.1770886779,-0.543838203,0.0261698551,-0.0482735336,-0.3658800423,0.0178647786,0.2034313083,-0.2111107856,-0.154736951,0.5788052082,-0.0910895392,0.3742569685,-0.4110652506,-0.0268509947,-0.5500833392,-0.2233636677,0.0945177004,0.0162294693,0.2236746848,0.171183303,0.0406548828,-0.0192004945,-0.2501042485,0.2409217954,-0.0442294776,-0.2682688534,0.3236261308,-0.1510273069,-0.0955459252,0.1709357947,-0.1985139698,-0.0594486669,-0.3050559163,-0.1297381818,0.2319205552,0.2133797705,0.1936335266,-0.0064717545,0.0152918147,0.1389674693,0.337371707,-0.1682054251,-0.6134564281,0.4448585808,-0.0378274098,-0.220205918,-0.1443342119,-0.1069903895,0.1174569875,0.3974053264,-0.5095450878,0.0167492125,-0.288091749,0.0945630446,0.0950631127,-0.0240606703,0.2160272449,0.145353362,0.0829630345,-0.2499599308,-0.2719066143,0.1908720434,-0.1372274011,0.1996578574,0.0226447172,0.3025070429,-0.2673497498,0.5462415814,0.4274444282,0.0061363615,0.2280853242,0.0467436984,0.526879847,-0.1603343338,-0.3179928064,-0.0785081387,-0.2098642141,-0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{"html_url":"https:\/\/github.com\/huggingface\/datasets\/issues\/2879","title":"In v1.4.1, all TIMIT train transcripts are \"Would such an act of refusal be useful?\"","comments":"Hi @rcgale, thanks for reporting.\r\n\r\nPlease note that this bug was fixed on `datasets` version 1.5.0: https:\/\/github.com\/huggingface\/datasets\/commit\/a23c73e526e1c30263834164f16f1fdf76722c8c#diff-f12a7a42d4673bb6c2ca5a40c92c29eb4fe3475908c84fd4ce4fad5dc2514878\r\n\r\nIf you update `datasets` version, that should work.\r\n\r\nOn the other hand, would it be possible for @patrickvonplaten to update the [blog post](https:\/\/huggingface.co\/blog\/fine-tune-wav2vec2-english) with the correct version of `datasets`?","body":"## Describe the bug\r\nUsing version 1.4.1 of `datasets`, TIMIT transcripts are all the same.\r\n\r\n## Steps to reproduce the bug\r\nI was following this tutorial\r\n- https:\/\/huggingface.co\/blog\/fine-tune-wav2vec2-english\r\n\r\nBut here's a distilled repro:\r\n```python\r\n!pip install datasets==1.4.1\r\nfrom datasets import load_dataset\r\ntimit = load_dataset(\"timit_asr\", cache_dir=\".\/temp\")\r\nunique_transcripts = set(timit[\"train\"][\"text\"])\r\nprint(unique_transcripts)\r\nassert len(unique_transcripts) > 1\r\n```\r\n## Expected results\r\nExpected the correct TIMIT data. Or an error saying that this version of `datasets` can't produce it.\r\n\r\n## Actual results\r\nEvery train transcript was \"Would such an act of refusal be useful?\" Every test transcript was \"The bungalow was pleasantly situated near the shore.\"\r\n\r\n## Environment info\r\n- `datasets` version: 1.4.1\r\n- Platform: Darwin-18.7.0-x86_64-i386-64bit\r\n- Python version: 3.7.9\r\n- PyTorch version (GPU?): 1.9.0 (False)\r\n- Tensorflow version (GPU?): not installed (NA)\r\n- Using GPU in script?: tried both\r\n- Using distributed or parallel set-up in script?: no\r\n- \r\n\r\n","comment_length":46,"text":"In v1.4.1, all TIMIT train transcripts are \"Would such an act of refusal be useful?\"\n## Describe the bug\r\nUsing version 1.4.1 of `datasets`, TIMIT transcripts are all the same.\r\n\r\n## Steps to reproduce the bug\r\nI was following this tutorial\r\n- https:\/\/huggingface.co\/blog\/fine-tune-wav2vec2-english\r\n\r\nBut here's a distilled repro:\r\n```python\r\n!pip install datasets==1.4.1\r\nfrom datasets import load_dataset\r\ntimit = load_dataset(\"timit_asr\", cache_dir=\".\/temp\")\r\nunique_transcripts = set(timit[\"train\"][\"text\"])\r\nprint(unique_transcripts)\r\nassert len(unique_transcripts) > 1\r\n```\r\n## Expected results\r\nExpected the correct TIMIT data. Or an error saying that this version of `datasets` can't produce it.\r\n\r\n## Actual results\r\nEvery train transcript was \"Would such an act of refusal be useful?\" Every test transcript was \"The bungalow was pleasantly situated near the shore.\"\r\n\r\n## Environment info\r\n- `datasets` version: 1.4.1\r\n- Platform: Darwin-18.7.0-x86_64-i386-64bit\r\n- Python version: 3.7.9\r\n- PyTorch version (GPU?): 1.9.0 (False)\r\n- Tensorflow version (GPU?): not installed (NA)\r\n- Using GPU in script?: tried both\r\n- Using distributed or parallel set-up in script?: no\r\n- \r\n\r\n\nHi @rcgale, thanks for reporting.\r\n\r\nPlease note that this bug was fixed on `datasets` version 1.5.0: https:\/\/github.com\/huggingface\/datasets\/commit\/a23c73e526e1c30263834164f16f1fdf76722c8c#diff-f12a7a42d4673bb6c2ca5a40c92c29eb4fe3475908c84fd4ce4fad5dc2514878\r\n\r\nIf you update `datasets` version, that should work.\r\n\r\nOn the other hand, would it be possible for @patrickvonplaten to update the [blog post](https:\/\/huggingface.co\/blog\/fine-tune-wav2vec2-english) with the correct version of `datasets`?","embeddings":[0.124852851,-0.0535938032,0.0293307602,0.0739796385,0.2047281116,-0.0245766733,0.2489377707,0.4213005006,-0.4746908545,0.1751622707,0.1361838877,0.5513864756,-0.3347690701,-0.0850955397,0.0344839208,0.1865644157,-0.0921123177,0.1384560466,-0.2678906322,-0.2130741179,0.0398925841,0.3963992298,-0.1676281989,-0.0685962737,-0.3741768599,0.244877398,0.2273771465,-0.2314912826,0.1054832488,-0.4600336254,0.2924744487,0.1486980319,0.0194595307,0.3702304661,-0.0001191299,0.0063226023,0.2757813334,0.0352541432,-0.2775231004,-0.4479430318,-0.0650265291,0.1085864976,0.0530338921,0.1475536525,-0.4656309485,-0.1793126911,0.2337317616,-0.3409471512,0.0292004794,0.4485591054,0.0685843602,-0.0634173304,-0.2496642619,-0.0824669749,0.3689354062,0.0125433672,-0.0625187159,0.0592703931,-0.0683355555,0.2411289513,0.2196747363,0.3755480051,-0.0313242152,0.0129956314,-0.1954175979,-0.0935640186,-0.2540271878,-0.3487864733,0.0747668445,0.2519178092,0.7106900215,-0.2958623171,-0.3215118349,-0.2683330774,0.3369950652,-0.0542203225,0.2911206484,0.0254176874,-0.2944933772,0.1627251506,-0.5629917383,0.0742394626,-0.3041745424,0.1429014206,-0.0974289998,-0.359634608,-0.143771261,0.1845409572,-0.1975932419,0.1163858026,0.114155367,0.1884384304,-0.0396124236,-0.0923857763,-0.3206233084,-0.0985560194,0.0559260733,-0.0815377459,0.0717144236,0.3172878623,0.2842969596,0.0675945058,-0.4174665213,-0.135505408,0.0322321728,0.1481688768,0.1823079735,-0.1778396666,0.101101391,-0.0720301494,-0.1533745825,0.0139923282,0.0189836212,-0.0720990822,-0.1062536985,-0.0153032569,0.3571920395,-0.1175772175,-0.5631766319,0.1525668353,-0.5025030375,0.2135901749,-0.0520560816,0.1947724819,-0.2154174149,0.1350183487,0.2971945703,0.0870628357,0.02487945,-0.3115249574,-0.0998023972,-0.1971572787,0.1828780323,0.0968628898,0.2440094203,-0.2103739977,0.2123047858,0.257135421,0.2615037858,-0.1538840383,-0.0926653668,-0.0326226279,0.2529811561,0.2668441832,-0.1248196512,0.3939130902,-0.0565123409,0.1157927513,0.106670782,0.2571235001,-0.0651218817,-0.0061710193,0.5623431206,0.060588941,-0.1836533099,-0.3006524444,-0.1071216986,0.3333047032,0.0442795344,-0.0668319687,0.2504209578,-0.2751781344,-0.2271512002,0.0158824958,0.1358926445,0.1566538513,-0.5853290558,0.0243286006,0.1945606619,0.0684714913,0.3222911954,0.3032742143,0.1651955247,0.1811709255,-0.0816664621,0.1170219034,-0.219933033,-0.3883649707,-0.2060429454,0.2092838287,-0.2708128095,0.264259249,-0.0832697079,-0.2192167342,0.0802669078,0.0991331413,0.3640693426,-0.1891600937,0.24668172,-0.1056700721,-0.3831144273,-0.1445571929,0.1893382818,0.1755265892,-0.0177115817,0.0532647818,-0.2982362509,-0.0164561383,0.4027053714,0.0394103304,-0.0291296821,-0.025731707,0.3644500375,-0.1775300503,0.263861388,-0.1554233581,0.2872300744,0.0272912737,0.1604733467,0.2198717296,0.5499910116,-0.1693307608,-0.1779452264,-0.0762331262,-0.0728189349,-0.0683157369,0.0582602769,0.1486589164,-0.1489184052,0.3247836828,-0.0285170637,0.4712553024,-0.4881338477,-0.0153098824,-0.0879812539,-0.1088415384,0.3033908308,-0.0987990201,0.1165293604,0.2411418706,0.0172993075,0.0872968137,-0.110142529,0.3636822999,0.2269500643,0.1219172627,-0.3682011068,-0.4027226269,0.2316687405,-0.5078880787,-0.4552857876,0.5677568913,-0.0276648402,0.2008169293,-0.1534452736,0.060441643,0.0807491466,0.0479999073,0.0348503962,0.0360783227,-0.019409189,-0.1079213247,-0.470849663,-0.1607666463,0.2607947588,-0.2098269463,0.1590214819,-0.1161290258,-0.320378989,0.1368902475,0.315872401,-0.0502400734,0.0395470038,0.2744301558,-0.1326723844,0.209983319,0.0375697017,0.1719760001,0.2373924702,0.2009201497,0.0807271153,-0.0535479188,-0.0781926885,-0.2605938315,0.2923918068,0.1945406348,-0.3651409447,0.3840377629,0.2203695476,0.0489813201,-0.4372256994,-0.0049824319,-0.1904428899,0.2824560404,-0.449403286,-0.1749291867,-0.3977611363,0.1078346595,-0.1361607611,-0.0218015406,0.0710958019,0.0436779261,0.3717918992,-0.0027077466,-0.0859973133,0.2743452489,0.1501078755,0.1915652007,-0.110422954,0.1713414043,-0.0096689994,0.1939062178,-0.4710183442,0.0452458076,-0.1606466472,-0.2644656301,0.1504383832,-0.1925743371,-0.1099141166,-0.4143466055,-0.071546413,0.1478731036,-0.1916618943,0.4232137799,0.2326952517,-0.0426136926,-0.1860425919,-0.194430843,-0.1887598336,-0.0310719088,-0.0671303943,0.0714476034,-0.1346394867,0.0415853858,-0.256131798,-0.8888216615,0.1003023162,-0.2204675525,0.7185165882,0.0599351153,-0.0460496917,0.5412366986,0.2822042704,0.2012919486,0.0792539567,0.1863065064,-0.3713881075,-0.1230798736,0.0792584717,0.0293458365,-0.4573093057,-0.298314631,0.1936385185,0.1548020095,0.2074109167,-0.4953384995,0.3497347236,-0.1089945436,-0.1306295842,0.0102862855,-0.1989106387,0.2906790078,-0.2038048357,-0.1159159765,-0.2554523647,0.1175914109,0.3896949589,0.1017955169,0.112711817,0.3398635685,0.2160488367,0.2003065348,0.3506759107,0.7158643007,0.0828187764,0.039017871,-0.1280344874,0.4244537055,-0.0728549138,-0.052832868,0.1600767821,0.0590854175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{"html_url":"https:\/\/github.com\/huggingface\/datasets\/issues\/2879","title":"In v1.4.1, all TIMIT train transcripts are \"Would such an act of refusal be useful?\"","comments":"I just proposed a change in the blog post.\r\n\r\nI had assumed there was a data format change that broke a previous version of the code, since presumably @patrickvonplaten tested the tutorial with the version they explicitly referenced. But that fix you linked suggests a problem in the code, which surprised me.\r\n\r\nI still wonder, though, is there a way for downloads to be invalidated server-side? If the client can announce its version during a download request, perhaps the server could reject known incompatibilities? It would save much valuable time if `datasets` raised an informative error on a known problem (\"Error: the requested data set requires `datasets>=1.5.0`.\"). This kind of API versioning is a prudent move anyhow, as there will surely come a time when you'll need to make a breaking change to data.","body":"## Describe the bug\r\nUsing version 1.4.1 of `datasets`, TIMIT transcripts are all the same.\r\n\r\n## Steps to reproduce the bug\r\nI was following this tutorial\r\n- https:\/\/huggingface.co\/blog\/fine-tune-wav2vec2-english\r\n\r\nBut here's a distilled repro:\r\n```python\r\n!pip install datasets==1.4.1\r\nfrom datasets import load_dataset\r\ntimit = load_dataset(\"timit_asr\", cache_dir=\".\/temp\")\r\nunique_transcripts = set(timit[\"train\"][\"text\"])\r\nprint(unique_transcripts)\r\nassert len(unique_transcripts) > 1\r\n```\r\n## Expected results\r\nExpected the correct TIMIT data. Or an error saying that this version of `datasets` can't produce it.\r\n\r\n## Actual results\r\nEvery train transcript was \"Would such an act of refusal be useful?\" Every test transcript was \"The bungalow was pleasantly situated near the shore.\"\r\n\r\n## Environment info\r\n- `datasets` version: 1.4.1\r\n- Platform: Darwin-18.7.0-x86_64-i386-64bit\r\n- Python version: 3.7.9\r\n- PyTorch version (GPU?): 1.9.0 (False)\r\n- Tensorflow version (GPU?): not installed (NA)\r\n- Using GPU in script?: tried both\r\n- Using distributed or parallel set-up in script?: no\r\n- \r\n\r\n","comment_length":134,"text":"In v1.4.1, all TIMIT train transcripts are \"Would such an act of refusal be useful?\"\n## Describe the bug\r\nUsing version 1.4.1 of `datasets`, TIMIT transcripts are all the same.\r\n\r\n## Steps to reproduce the bug\r\nI was following this tutorial\r\n- https:\/\/huggingface.co\/blog\/fine-tune-wav2vec2-english\r\n\r\nBut here's a distilled repro:\r\n```python\r\n!pip install datasets==1.4.1\r\nfrom datasets import load_dataset\r\ntimit = load_dataset(\"timit_asr\", cache_dir=\".\/temp\")\r\nunique_transcripts = set(timit[\"train\"][\"text\"])\r\nprint(unique_transcripts)\r\nassert len(unique_transcripts) > 1\r\n```\r\n## Expected results\r\nExpected the correct TIMIT data. Or an error saying that this version of `datasets` can't produce it.\r\n\r\n## Actual results\r\nEvery train transcript was \"Would such an act of refusal be useful?\" Every test transcript was \"The bungalow was pleasantly situated near the shore.\"\r\n\r\n## Environment info\r\n- `datasets` version: 1.4.1\r\n- Platform: Darwin-18.7.0-x86_64-i386-64bit\r\n- Python version: 3.7.9\r\n- PyTorch version (GPU?): 1.9.0 (False)\r\n- Tensorflow version (GPU?): not installed (NA)\r\n- Using GPU in script?: tried both\r\n- Using distributed or parallel set-up in script?: no\r\n- \r\n\r\n\nI just proposed a change in the blog post.\r\n\r\nI had assumed there was a data format change that broke a previous version of the code, since presumably @patrickvonplaten tested the tutorial with the version they explicitly referenced. But that fix you linked suggests a problem in the code, which surprised me.\r\n\r\nI still wonder, though, is there a way for downloads to be invalidated server-side? If the client can announce its version during a download request, perhaps the server could reject known incompatibilities? It would save much valuable time if `datasets` raised an informative error on a known problem (\"Error: the requested data set requires `datasets>=1.5.0`.\"). This kind of API versioning is a prudent move anyhow, as there will surely come a time when you'll need to make a breaking change to 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{"html_url":"https:\/\/github.com\/huggingface\/datasets\/issues\/2879","title":"In v1.4.1, all TIMIT train transcripts are \"Would such an act of refusal be useful?\"","comments":"Also, thank you for a quick and helpful reply!","body":"## Describe the bug\r\nUsing version 1.4.1 of `datasets`, TIMIT transcripts are all the same.\r\n\r\n## Steps to reproduce the bug\r\nI was following this tutorial\r\n- https:\/\/huggingface.co\/blog\/fine-tune-wav2vec2-english\r\n\r\nBut here's a distilled repro:\r\n```python\r\n!pip install datasets==1.4.1\r\nfrom datasets import load_dataset\r\ntimit = load_dataset(\"timit_asr\", cache_dir=\".\/temp\")\r\nunique_transcripts = set(timit[\"train\"][\"text\"])\r\nprint(unique_transcripts)\r\nassert len(unique_transcripts) > 1\r\n```\r\n## Expected results\r\nExpected the correct TIMIT data. Or an error saying that this version of `datasets` can't produce it.\r\n\r\n## Actual results\r\nEvery train transcript was \"Would such an act of refusal be useful?\" Every test transcript was \"The bungalow was pleasantly situated near the shore.\"\r\n\r\n## Environment info\r\n- `datasets` version: 1.4.1\r\n- Platform: Darwin-18.7.0-x86_64-i386-64bit\r\n- Python version: 3.7.9\r\n- PyTorch version (GPU?): 1.9.0 (False)\r\n- Tensorflow version (GPU?): not installed (NA)\r\n- Using GPU in script?: tried both\r\n- Using distributed or parallel set-up in script?: no\r\n- \r\n\r\n","comment_length":9,"text":"In v1.4.1, all TIMIT train transcripts are \"Would such an act of refusal be useful?\"\n## Describe the bug\r\nUsing version 1.4.1 of `datasets`, TIMIT transcripts are all the same.\r\n\r\n## Steps to reproduce the bug\r\nI was following this tutorial\r\n- https:\/\/huggingface.co\/blog\/fine-tune-wav2vec2-english\r\n\r\nBut here's a distilled repro:\r\n```python\r\n!pip install datasets==1.4.1\r\nfrom datasets import load_dataset\r\ntimit = load_dataset(\"timit_asr\", cache_dir=\".\/temp\")\r\nunique_transcripts = set(timit[\"train\"][\"text\"])\r\nprint(unique_transcripts)\r\nassert len(unique_transcripts) > 1\r\n```\r\n## Expected results\r\nExpected the correct TIMIT data. Or an error saying that this version of `datasets` can't produce it.\r\n\r\n## Actual results\r\nEvery train transcript was \"Would such an act of refusal be useful?\" Every test transcript was \"The bungalow was pleasantly situated near the shore.\"\r\n\r\n## Environment info\r\n- `datasets` version: 1.4.1\r\n- Platform: Darwin-18.7.0-x86_64-i386-64bit\r\n- Python version: 3.7.9\r\n- PyTorch version (GPU?): 1.9.0 (False)\r\n- Tensorflow version (GPU?): not installed (NA)\r\n- Using GPU in script?: tried both\r\n- Using distributed or parallel set-up in script?: no\r\n- \r\n\r\n\nAlso, thank you for a quick and helpful reply!","embeddings":[0.1843589097,-0.0476369262,0.0212153625,0.1307302862,0.1825789064,-0.0150703816,0.222696349,0.4102399647,-0.4767715335,0.1482010931,0.202155903,0.528619945,-0.3603378236,-0.1392592937,0.0697514489,0.1514802277,-0.0296482705,0.1163090765,-0.2856072187,-0.2430297881,0.0460388102,0.3836102486,-0.181789279,-0.0110180005,-0.3692496121,0.2394319922,0.2274626493,-0.1810913682,0.1650811285,-0.439029336,0.2945018709,0.0983762294,0.0515014045,0.3712564111,-0.0001205389,-0.0167328734,0.2610318065,0.0087951152,-0.2081606984,-0.3689297736,-0.1382844448,0.1195933074,0.0133538358,0.12241216,-0.4130392373,-0.2065744102,0.229093805,-0.3684004843,0.0639212877,0.4605449438,0.0608402193,-0.139359951,-0.2579361498,-0.0793896466,0.3681408465,-0.0151606631,-0.0316133946,0.0299798381,-0.0864327401,0.2322303355,0.2330624908,0.3767850101,-0.0501611307,0.0072294213,-0.2207338065,-0.0934685469,-0.2900455594,-0.3867571652,0.0662252977,0.191508621,0.7059831023,-0.3136530221,-0.2619046569,-0.2177537978,0.3014097512,-0.0254760459,0.2628924251,0.0440048538,-0.2687057555,0.1821450889,-0.5459147692,0.1098416448,-0.3146458268,0.1415527016,-0.0640842691,-0.3725824058,-0.0981010497,0.2094152719,-0.2525474727,0.1218328848,0.1371880621,0.2098523825,-0.0407567285,-0.088758558,-0.3333061934,-0.0827903822,0.0905025452,-0.055269625,0.0512395315,0.2592660189,0.323356241,0.0425352305,-0.3752723634,-0.1163501963,0.0656884834,0.1570367366,0.0961320028,-0.1948441863,0.118978247,-0.1403936744,-0.1913475394,0.040307723,-0.0055863252,-0.091643773,-0.0389522314,-0.0102081764,0.3480204046,-0.1361118853,-0.5908363461,0.1955148876,-0.5599816442,0.2329349965,-0.0647064596,0.2073190659,-0.187874347,0.1397468895,0.3171749115,0.0926715434,-0.0059219012,-0.3403543532,-0.0963281542,-0.1902716309,0.1971511841,0.0616736077,0.2055280805,-0.1423812211,0.2137727439,0.2923474014,0.2695505917,-0.1473722309,-0.0583484769,-0.0180806592,0.219842881,0.2483473718,-0.1362519711,0.4016667604,-0.0248719845,0.1940863729,0.1011622474,0.2526570261,-0.1005024016,-0.0175146516,0.5515517592,0.0515450686,-0.179110989,-0.2624935806,-0.0348146893,0.3113076985,0.0846941844,-0.1433292776,0.2118196636,-0.2840275764,-0.1747703999,-0.003525354,0.0937831625,0.1310754865,-0.5885374546,0.0558972545,0.228044942,0.1303474754,0.3747636676,0.2952876389,0.1747010648,0.2124052495,-0.0759727508,0.1145521328,-0.1961544603,-0.4299621284,-0.214312613,0.1697297394,-0.2615546882,0.2817715108,-0.0361457802,-0.2367807031,0.0825164616,0.075459443,0.409607172,-0.2213379294,0.1860471368,-0.0808987841,-0.3983337283,-0.1363315433,0.2186726034,0.1355755478,-0.0341216251,0.0313469507,-0.2578755319,-0.0243165027,0.3640209436,0.0280066933,-0.004362273,-0.0479702353,0.3482239544,-0.2454301864,0.3254101574,-0.0976360664,0.3093805313,0.0151669383,0.1946799606,0.1750653833,0.6044173241,-0.1565310061,-0.170942992,-0.0799789056,-0.0831656903,-0.0937429518,0.0424369089,0.1493053585,-0.1577068269,0.3208678961,-0.0529016294,0.496171236,-0.4533744752,-0.0638343394,-0.0522209033,-0.1226176918,0.2798168063,-0.0861343965,0.0933275223,0.2543967366,0.0215519834,0.0870476365,-0.0931508169,0.3177514076,0.2538267672,0.1660675555,-0.3391086459,-0.3895223737,0.2347876579,-0.5418210626,-0.418391794,0.6494663954,0.0136631234,0.2044710368,-0.1246106178,0.0206717309,0.0992714986,0.0414252579,0.0030764376,0.0376889668,-0.0036091113,-0.0781878829,-0.4513742328,-0.0986297056,0.2685841024,-0.2420790792,0.1837171465,-0.1044279113,-0.3128755689,0.1960173547,0.3068779707,-0.0273673404,0.0484327525,0.2494100779,-0.1171611398,0.1943250597,0.0596161298,0.2010252476,0.235914126,0.1829334795,0.0925755948,-0.0770207793,-0.0987522975,-0.221516192,0.2697022557,0.1580257267,-0.3681939244,0.4219260216,0.2311407924,0.0666007549,-0.3795827329,0.0026456879,-0.2107334882,0.3031838238,-0.4800261259,-0.1368284374,-0.3495121598,0.1713373214,-0.1380009055,-0.0323438756,0.1311494857,0.0800623074,0.370326221,-0.0135218948,-0.1168963611,0.2293738723,0.1091258749,0.2434615046,-0.0670005679,0.2318317592,0.0222459622,0.2099670172,-0.4693998694,0.015322485,-0.1299001575,-0.2572244108,0.1167961061,-0.1661652029,-0.0994341001,-0.3659780025,-0.0571099631,0.151061967,-0.1844133586,0.4728702605,0.2280860841,-0.0554880984,-0.1941481084,-0.2376766503,-0.2153888792,0.0371617377,-0.0857695267,0.097694777,-0.1236358881,-0.0152868517,-0.2216340005,-0.9135431647,0.0924015716,-0.2011582255,0.6722809076,0.0548606068,-0.0086461287,0.4621789753,0.2635646164,0.1870574206,0.138909936,0.2119944692,-0.3575545549,-0.106063135,0.0823722705,0.0353392884,-0.4551185369,-0.3046335578,0.1577614248,0.1924328506,0.2105116844,-0.4597027302,0.4100583196,-0.0954082683,-0.1507000029,-0.0038921644,-0.2178034633,0.3120282888,-0.1800539792,-0.0859067366,-0.2840114534,0.1318731755,0.3946647942,0.1762969643,0.1079193801,0.3068368733,0.1843467206,0.1532683223,0.3919531703,0.7317343354,0.1164943278,0.0385542214,-0.1042457595,0.3726763427,-0.0699758604,-0.0704685152,0.1748739481,0.0528061725,-0.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{"html_url":"https:\/\/github.com\/huggingface\/datasets\/issues\/2871","title":"datasets.config.PYARROW_VERSION has no attribute 'major'","comments":"I have changed line 288 to `if int(datasets.config.PYARROW_VERSION.split(\".\")[0]) < 3:` just to get around it.","body":"In the test_dataset_common.py script, line 288-289\r\n\r\n```\r\nif datasets.config.PYARROW_VERSION.major < 3:\r\n packaged_datasets = [pd for pd in packaged_datasets if pd[\"dataset_name\"] != \"parquet\"]\r\n```\r\n\r\nwhich throws the error below. `datasets.config.PYARROW_VERSION` itself return the string '4.0.1'. I have tested this on both datasets.__version_=='1.11.0' and '1.9.0'. I am using Mac OS.\r\n\r\n```\r\nimport datasets\r\ndatasets.config.PYARROW_VERSION.major\r\n---------------------------------------------------------------------------\r\nAttributeError Traceback (most recent call last)\r\n\/var\/folders\/1f\/0wqmlgp90qjd5mpj53fnjq440000gn\/T\/ipykernel_73361\/2547517336.py in \r\n 1 import datasets\r\n----> 2 datasets.config.PYARROW_VERSION.major\r\n\r\nAttributeError: 'str' object has no attribute 'major'\r\n```\r\n\r\n## Environment info\r\n- `datasets` version: 1.11.0\r\n- Platform: Darwin-20.6.0-x86_64-i386-64bit\r\n- Python version: 3.7.11\r\n- PyArrow version: 4.0.1\r\n","comment_length":15,"text":"datasets.config.PYARROW_VERSION has no attribute 'major'\nIn the test_dataset_common.py script, line 288-289\r\n\r\n```\r\nif datasets.config.PYARROW_VERSION.major < 3:\r\n packaged_datasets = [pd for pd in packaged_datasets if pd[\"dataset_name\"] != \"parquet\"]\r\n```\r\n\r\nwhich throws the error below. `datasets.config.PYARROW_VERSION` itself return the string '4.0.1'. I have tested this on both datasets.__version_=='1.11.0' and '1.9.0'. I am using Mac OS.\r\n\r\n```\r\nimport datasets\r\ndatasets.config.PYARROW_VERSION.major\r\n---------------------------------------------------------------------------\r\nAttributeError Traceback (most recent call last)\r\n\/var\/folders\/1f\/0wqmlgp90qjd5mpj53fnjq440000gn\/T\/ipykernel_73361\/2547517336.py in \r\n 1 import datasets\r\n----> 2 datasets.config.PYARROW_VERSION.major\r\n\r\nAttributeError: 'str' object has no attribute 'major'\r\n```\r\n\r\n## Environment info\r\n- `datasets` version: 1.11.0\r\n- Platform: Darwin-20.6.0-x86_64-i386-64bit\r\n- Python version: 3.7.11\r\n- PyArrow version: 4.0.1\r\n\nI have changed line 288 to `if int(datasets.config.PYARROW_VERSION.split(\".\")[0]) < 3:` just to get around it.","embeddings":[-0.4137373269,0.0904145688,0.0879315063,0.069035016,0.284868449,0.1465740502,0.1234253496,0.3872671723,-0.1835217625,0.1793182641,0.3690841198,0.3502765,-0.2362233698,0.1078671366,-0.2026903927,0.0031209812,0.0010433139,0.2549073994,0.1760860234,-0.0239745975,-0.1098649725,0.1690122485,-0.2097337544,0.2731696069,-0.2981304824,0.0923148543,0.0032627089,-0.0122110816,-0.1285185814,-0.5752780437,0.2998543382,-0.1617958844,0.2332223952,0.391263485,-0.000127086,0.0779013038,0.3378002942,-0.0440747142,-0.2238939106,-0.4914156199,-0.085651435,0.0122515438,0.3829289079,-0.0937147513,0.0937459767,-0.6206139922,0.0335906781,0.0384339988,-0.1429862529,0.3645019233,0.127537936,0.0344468243,0.1980867237,-0.0754810423,0.2223289162,0.083245784,-0.0282249879,0.5229272842,0.1158733368,-0.1320393234,0.233240068,-0.102022633,0.0575134605,-0.0043903464,0.0711091682,0.1914277673,0.2576049566,-0.1857423782,0.2388329953,0.0887792706,0.5562196374,-0.6069078445,-0.4294553101,0.0136653185,0.174752906,-0.2191064954,0.2185028791,0.2610872686,-0.1244944483,0.1687700599,-0.0923660845,-0.1329844445,-0.0807051584,-0.0779537112,-0.433144331,0.3678042889,0.1932168156,0.1645069569,-0.0400829166,-0.04866606,-0.0215911791,-0.0890382826,0.1259018928,-0.0294876229,-0.2407489419,-0.0118983239,-0.0643955991,0.1943915635,0.2201173902,0.1590977758,0.0649064705,-0.234773919,0.2536158264,0.0735231191,0.0475472286,0.0977316201,-0.0197430961,0.146631524,0.0936663523,-0.0873535872,0.0256926231,-0.0208997894,-0.0806714743,-0.0734729469,0.3739596009,0.0080980845,0.7578920722,-0.3194511235,-0.2312780768,0.1659606099,-0.3343805671,-0.184103936,-0.2247074097,0.2836925983,0.0676930696,0.3056817055,-0.1982494593,0.3802600801,-0.1360655129,-0.1857322007,-0.0950188115,0.1552126408,0.0275490843,-0.1461474597,0.0764700472,-0.2719792128,-0.0986580551,0.3349583149,0.0094830692,0.1213422492,-0.0362757482,-0.0120166596,-0.1315119714,0.4732261598,-0.2890482843,0.2727305889,0.2008104473,-0.4936734736,-0.3582565486,0.2217560709,-0.1869192868,-0.431489408,-0.2999134958,0.1262726486,-0.1483470798,-0.2103191763,-0.0671084151,-0.0523799025,0.1624187529,-0.1384892911,0.0297997743,-0.651668191,0.1586196274,-0.3059960306,0.2577728629,0.161972031,-0.6419220567,0.0579073988,0.1789008826,-0.1889813095,0.1506783217,-0.1405111104,0.0306624472,-0.1734109372,0.074796848,0.0583436862,0.3201945126,-0.2379271239,-0.3039623797,0.1230635121,0.0718067214,0.0206128471,0.2803038359,-0.3171919584,-0.0282199513,0.1652483642,-0.1928093135,0.1018786877,0.0079200668,0.0062634307,-0.1814045608,-0.0036330107,0.4458725452,0.2917095721,0.1499163508,-0.2299220115,-0.0262244828,-0.1855148673,0.1473240703,-0.0819691345,0.0311352387,-0.0489151254,0.3686681986,-0.2390524894,-0.0064461622,-0.2394002527,-0.2902263105,0.2341018915,0.0907700509,0.0002655792,-0.3193365932,-0.219354555,-0.2210743129,0.0846685618,-0.128561303,-0.011503078,-0.0059905695,0.3605172932,0.0959836021,0.0846642852,-0.3152723312,0.1812611818,0.0422432944,0.1910732836,-0.0209711008,0.4087104499,-0.4075936377,-0.3384674788,0.0850214958,0.0640254617,0.1028376147,0.002861667,0.0299142897,0.3328282237,0.0242639706,0.0389278978,0.0299662594,0.2096638978,-0.0284052063,-0.2111189663,-0.1741658747,0.3066881597,-0.0031621177,0.3704423904,0.0565479696,0.4024959803,0.5626875758,0.3306960762,-0.0120106991,0.0881720185,-0.1884381175,-0.0055437423,0.1211171523,-0.1257602423,0.0624467172,-0.1393158436,0.1693660468,-0.0199615061,-0.5397575498,0.0718814433,0.3745397031,-0.0722794607,0.1365482211,0.1192966476,-0.22845155,0.0563648492,0.0482595488,0.0757280812,0.249298811,0.1221992299,-0.1398308128,0.1419418901,-0.3319807649,0.1508725435,0.1715394557,0.053618446,0.1923853457,0.1373240501,0.2172163874,0.0542263351,-0.0518643856,-0.4222477078,0.1137933508,0.4095291495,-0.4994865358,0.2577461302,-0.2975929677,0.2889801562,-0.0827534124,-0.1969304681,-0.4268323183,-0.2678608894,0.0104828775,0.0935454518,0.1283202618,0.1767116934,-0.3569593728,-0.1166164875,0.0463055074,-0.5190931559,-0.252766639,-0.2070385814,-0.4546803832,0.0009253586,0.2450136989,-0.2954372466,0.0536534749,-0.2570302188,-0.2196664661,-0.2246728688,-0.1574143022,0.0125030046,-0.269426465,0.3128561378,0.2743914127,0.0396343656,-0.0582421124,-0.0491061211,0.3296924531,-0.3633875251,-0.2326972187,0.1670558602,-0.0441559106,-0.018140588,-0.0896702781,-0.5271107554,-0.2856226861,-0.174629271,-0.0225634612,0.0724860951,0.1668928266,-0.0621382482,0.2801332772,-0.0787437782,-0.1362159103,0.0422251076,0.0579393916,0.0014274698,0.2768362463,0.0047421837,-0.2914088666,0.010116118,-0.0066247168,0.1385644227,0.0028628155,-0.2945348024,-0.1095552146,0.0360653736,0.5227450132,-0.2845128775,0.0628193095,0.3160390556,0.2878689766,-0.0280942582,0.0421851873,-0.1316174716,-0.1662545651,0.1143491268,0.1350112706,0.1534068286,0.1728003323,-0.0685666278,0.6282868385,-0.0858015046,-0.2661091983,0.19313097,-0.3469312489,0.3226404786,0.0537074953,-0.1815225929,-0.0795435756,0.02120829,0.000880775,0.2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{"html_url":"https:\/\/github.com\/huggingface\/datasets\/issues\/2871","title":"datasets.config.PYARROW_VERSION has no attribute 'major'","comments":"Hi @bwang482,\r\n\r\nI'm sorry but I'm not able to reproduce your bug.\r\n\r\nPlease note that in our current master branch, we made a commit (d03223d4d64b89e76b48b00602aba5aa2f817f1e) that simultaneously modified:\r\n- test_dataset_common.py: https:\/\/github.com\/huggingface\/datasets\/commit\/d03223d4d64b89e76b48b00602aba5aa2f817f1e#diff-a1bc225bd9a5bade373d1f140e24d09cbbdc97971c2f73bb627daaa803ada002L289 that introduces the usage of `datasets.config.PYARROW_VERSION.major`\r\n- but also changed config.py: https:\/\/github.com\/huggingface\/datasets\/commit\/d03223d4d64b89e76b48b00602aba5aa2f817f1e#diff-e021fcfc41811fb970fab889b8d245e68382bca8208e63eaafc9a396a336f8f2L40, so that `datasets.config.PYARROW_VERSION.major` exists\r\n","body":"In the test_dataset_common.py script, line 288-289\r\n\r\n```\r\nif datasets.config.PYARROW_VERSION.major < 3:\r\n packaged_datasets = [pd for pd in packaged_datasets if pd[\"dataset_name\"] != \"parquet\"]\r\n```\r\n\r\nwhich throws the error below. `datasets.config.PYARROW_VERSION` itself return the string '4.0.1'. I have tested this on both datasets.__version_=='1.11.0' and '1.9.0'. I am using Mac OS.\r\n\r\n```\r\nimport datasets\r\ndatasets.config.PYARROW_VERSION.major\r\n---------------------------------------------------------------------------\r\nAttributeError Traceback (most recent call last)\r\n\/var\/folders\/1f\/0wqmlgp90qjd5mpj53fnjq440000gn\/T\/ipykernel_73361\/2547517336.py in \r\n 1 import datasets\r\n----> 2 datasets.config.PYARROW_VERSION.major\r\n\r\nAttributeError: 'str' object has no attribute 'major'\r\n```\r\n\r\n## Environment info\r\n- `datasets` version: 1.11.0\r\n- Platform: Darwin-20.6.0-x86_64-i386-64bit\r\n- Python version: 3.7.11\r\n- PyArrow version: 4.0.1\r\n","comment_length":47,"text":"datasets.config.PYARROW_VERSION has no attribute 'major'\nIn the test_dataset_common.py script, line 288-289\r\n\r\n```\r\nif datasets.config.PYARROW_VERSION.major < 3:\r\n packaged_datasets = [pd for pd in packaged_datasets if pd[\"dataset_name\"] != \"parquet\"]\r\n```\r\n\r\nwhich throws the error below. `datasets.config.PYARROW_VERSION` itself return the string '4.0.1'. I have tested this on both datasets.__version_=='1.11.0' and '1.9.0'. I am using Mac OS.\r\n\r\n```\r\nimport datasets\r\ndatasets.config.PYARROW_VERSION.major\r\n---------------------------------------------------------------------------\r\nAttributeError Traceback (most recent call last)\r\n\/var\/folders\/1f\/0wqmlgp90qjd5mpj53fnjq440000gn\/T\/ipykernel_73361\/2547517336.py in \r\n 1 import datasets\r\n----> 2 datasets.config.PYARROW_VERSION.major\r\n\r\nAttributeError: 'str' object has no attribute 'major'\r\n```\r\n\r\n## Environment info\r\n- `datasets` version: 1.11.0\r\n- Platform: Darwin-20.6.0-x86_64-i386-64bit\r\n- Python version: 3.7.11\r\n- PyArrow version: 4.0.1\r\n\nHi @bwang482,\r\n\r\nI'm sorry but I'm not able to reproduce your bug.\r\n\r\nPlease note that in our current master branch, we made a commit (d03223d4d64b89e76b48b00602aba5aa2f817f1e) that simultaneously modified:\r\n- test_dataset_common.py: https:\/\/github.com\/huggingface\/datasets\/commit\/d03223d4d64b89e76b48b00602aba5aa2f817f1e#diff-a1bc225bd9a5bade373d1f140e24d09cbbdc97971c2f73bb627daaa803ada002L289 that introduces the usage of `datasets.config.PYARROW_VERSION.major`\r\n- but also changed config.py: https:\/\/github.com\/huggingface\/datasets\/commit\/d03223d4d64b89e76b48b00602aba5aa2f817f1e#diff-e021fcfc41811fb970fab889b8d245e68382bca8208e63eaafc9a396a336f8f2L40, so that `datasets.config.PYARROW_VERSION.major` exists\r\n","embeddings":[-0.3742081225,0.1478579193,0.0884070471,0.0458860211,0.2804539502,0.1281492561,0.1531104892,0.3776514232,-0.2427559048,0.2011216879,0.3052013814,0.4177031815,-0.1865011901,0.1149607599,-0.1479020417,0.0099561084,0.0530921482,0.2520797253,0.1291816086,0.0060467068,-0.1062645391,0.1722441018,-0.1942494661,0.2815794349,-0.2942166626,0.1177279726,-0.0419812873,-0.0479392149,-0.1290827245,-0.5930629969,0.3376209438,-0.1589786112,0.2437154949,0.3399909437,-0.0001267467,0.1114126891,0.4063085616,-0.0081883511,-0.2549678385,-0.5021974444,-0.1217937693,-0.0536484718,0.3664952815,-0.0817655995,0.102711834,-0.7000570297,0.013414585,0.049935773,-0.1659477353,0.329718858,0.1360016465,0.0637335405,0.2616915703,-0.097520113,0.2251864374,0.0621270798,-0.0079679107,0.4649414122,0.0723097548,-0.1101893187,0.2356003225,-0.0848251805,0.0687128976,-0.0513685308,0.0992346108,0.1825542748,0.2629990876,-0.1627095938,0.1989027411,0.0318586826,0.5882601738,-0.626342833,-0.4298146665,-0.0176614802,0.220206961,-0.2607834935,0.2262852788,0.2429365963,-0.10094136,0.1628725231,-0.0570024997,-0.1206212416,-0.063535206,-0.0164811891,-0.4222541451,0.3365793526,0.1881286055,0.140308246,-0.1132394746,-0.009329834,0.0025072652,-0.1097331718,0.10199292,-0.078015849,-0.2746736109,-0.0144315083,-0.0775343105,0.1633089334,0.1757595986,0.208408609,0.0030351472,-0.2792110145,0.2704052031,0.0860595629,0.1035962552,0.1269080788,-0.0204388909,0.1562927812,0.1005842909,-0.1070765704,0.0690039098,-0.0031423518,-0.0581314154,-0.0568243526,0.3815536797,0.0283413101,0.742669642,-0.2355119437,-0.2066710442,0.1739302725,-0.3724194169,-0.179325074,-0.2660050988,0.2187235057,0.0305577051,0.3607175052,-0.1849215776,0.3320734203,-0.0956049785,-0.2356070876,-0.113595061,0.1457725167,0.0022741908,-0.1549615115,0.0268715788,-0.3101926744,-0.0878965631,0.3905238211,0.0014736976,0.1260318905,-0.1415989846,-0.0541735664,-0.1536239386,0.4754760563,-0.2805734873,0.2540762722,0.1305656582,-0.4681425095,-0.3429700136,0.2116095126,-0.2033770382,-0.4039486051,-0.4072811902,0.1281087101,-0.1272283196,-0.1809758395,-0.1497177035,-0.0052619977,0.2306341529,-0.1509694159,-0.0146371135,-0.6668522954,0.1032236964,-0.319904536,0.2467015982,0.1543766707,-0.6335021853,0.0087504741,0.1704790741,-0.1900213808,0.165815562,-0.1197979152,0.005515072,-0.1398759037,0.0290386174,0.0476634167,0.3468930423,-0.1955857724,-0.3685946465,0.0728684291,0.058083415,-0.0002380217,0.2594134212,-0.3139027059,0.0474226773,0.1808189899,-0.1679071486,0.0811104253,0.001909597,0.0160693731,-0.1740115881,-0.0421568789,0.3828534484,0.2783960104,0.1499212235,-0.1601757854,0.0293607879,-0.2005846798,0.1286726147,-0.0868610367,0.0851052552,0.0264915284,0.4046365619,-0.1430395395,0.033946842,-0.2175856382,-0.3197008371,0.1867123246,0.0698746145,0.0265328661,-0.3560784161,-0.1848592609,-0.1795900613,0.1088649258,-0.1084898785,-0.0283278916,0.0058021918,0.3832255006,0.0711459517,0.1115474254,-0.2974824309,0.2475375384,0.0242408346,0.1781353056,-0.0212112162,0.4727578163,-0.4082576334,-0.3153122663,0.0902254656,0.0457065813,0.102861464,0.0391666442,-0.0002053427,0.2879419625,-0.0028909601,0.0584653802,-0.0276767816,0.2250142992,0.0138650322,-0.1725012511,-0.192661047,0.2565892935,0.0011452289,0.4009828568,0.1184246242,0.3715967536,0.5518648624,0.3002988398,-0.0234602094,0.09365049,-0.1508531719,-0.0168528631,0.1085570678,-0.0839205682,0.0393525958,-0.1069295108,0.2100666016,-0.0077000619,-0.5035149455,0.0840201378,0.3833167255,-0.0595607199,0.1452025324,0.1436303854,-0.2609054446,0.0484301373,0.0339332297,0.1345786303,0.2741984427,0.1518546194,-0.1246563643,0.1309271455,-0.3188162744,0.164799884,0.1828395575,0.1089477018,0.1759979427,0.1831200421,0.2829288542,0.086618349,-0.0567072183,-0.3240150213,0.1387683749,0.3523797393,-0.4344475269,0.2530074716,-0.3444136083,0.250282228,-0.1669087112,-0.1862104386,-0.4147616029,-0.2429396659,-0.0304380916,0.0963686109,0.1005030423,0.2397005558,-0.3122081459,-0.1296312362,0.0462817326,-0.3988128304,-0.2186687887,-0.1758570671,-0.4486293495,-0.019090917,0.2013365775,-0.3951259255,0.039651487,-0.2199624628,-0.2137857676,-0.3060598373,-0.1851968169,0.0833885372,-0.2393283546,0.2992102802,0.2597303987,-0.038242083,-0.0473477803,-0.0988906175,0.3235451281,-0.3321583569,-0.2847580612,0.1006459966,-0.0942858085,0.0356610715,-0.0603935421,-0.4970048368,-0.3575113714,-0.1462482959,0.0124236261,0.041099906,0.1555687338,-0.0624720603,0.2309518009,-0.1249248758,-0.1489116102,-0.0105736004,-0.0276867412,0.0039672754,0.2373251915,0.0062804506,-0.2969486415,-0.0511556789,0.0064248098,0.0896267369,0.0367034785,-0.3144406974,-0.1439506412,0.0547291487,0.5389251113,-0.3207369447,0.0192000363,0.2970771492,0.2694778144,-0.0367961302,0.0630886257,-0.1787241399,-0.1892798841,0.1446789801,0.1415685862,0.1744132191,0.2410916984,-0.0849480182,0.5790010095,-0.0703439489,-0.298630327,0.2510004938,-0.29209131,0.3411943913,0.0630541816,-0.1798293442,-0.0886980519,-0.0483298227,0.01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{"html_url":"https:\/\/github.com\/huggingface\/datasets\/issues\/2871","title":"datasets.config.PYARROW_VERSION has no attribute 'major'","comments":"Sorted. Thanks!","body":"In the test_dataset_common.py script, line 288-289\r\n\r\n```\r\nif datasets.config.PYARROW_VERSION.major < 3:\r\n packaged_datasets = [pd for pd in packaged_datasets if pd[\"dataset_name\"] != \"parquet\"]\r\n```\r\n\r\nwhich throws the error below. `datasets.config.PYARROW_VERSION` itself return the string '4.0.1'. I have tested this on both datasets.__version_=='1.11.0' and '1.9.0'. I am using Mac OS.\r\n\r\n```\r\nimport datasets\r\ndatasets.config.PYARROW_VERSION.major\r\n---------------------------------------------------------------------------\r\nAttributeError Traceback (most recent call last)\r\n\/var\/folders\/1f\/0wqmlgp90qjd5mpj53fnjq440000gn\/T\/ipykernel_73361\/2547517336.py in \r\n 1 import datasets\r\n----> 2 datasets.config.PYARROW_VERSION.major\r\n\r\nAttributeError: 'str' object has no attribute 'major'\r\n```\r\n\r\n## Environment info\r\n- `datasets` version: 1.11.0\r\n- Platform: Darwin-20.6.0-x86_64-i386-64bit\r\n- Python version: 3.7.11\r\n- PyArrow version: 4.0.1\r\n","comment_length":2,"text":"datasets.config.PYARROW_VERSION has no attribute 'major'\nIn the test_dataset_common.py script, line 288-289\r\n\r\n```\r\nif datasets.config.PYARROW_VERSION.major < 3:\r\n packaged_datasets = [pd for pd in packaged_datasets if pd[\"dataset_name\"] != \"parquet\"]\r\n```\r\n\r\nwhich throws the error below. `datasets.config.PYARROW_VERSION` itself return the string '4.0.1'. I have tested this on both datasets.__version_=='1.11.0' and '1.9.0'. I am using Mac OS.\r\n\r\n```\r\nimport datasets\r\ndatasets.config.PYARROW_VERSION.major\r\n---------------------------------------------------------------------------\r\nAttributeError Traceback (most recent call last)\r\n\/var\/folders\/1f\/0wqmlgp90qjd5mpj53fnjq440000gn\/T\/ipykernel_73361\/2547517336.py in \r\n 1 import datasets\r\n----> 2 datasets.config.PYARROW_VERSION.major\r\n\r\nAttributeError: 'str' object has no attribute 'major'\r\n```\r\n\r\n## Environment info\r\n- `datasets` version: 1.11.0\r\n- Platform: Darwin-20.6.0-x86_64-i386-64bit\r\n- Python version: 3.7.11\r\n- PyArrow version: 4.0.1\r\n\nSorted. Thanks!","embeddings":[-0.3823499978,0.0788534731,0.0835613832,0.0859657899,0.254578352,0.1588205546,0.1258136332,0.378461957,-0.1942313612,0.1517066956,0.3442723751,0.3460409343,-0.2396473587,0.1160570979,-0.1717098057,-0.0130585758,0.0182114933,0.2685298622,0.1562249959,-0.0352682173,-0.0831688866,0.1723991781,-0.221207574,0.2769206166,-0.290784806,0.1055777818,-0.034875419,-0.0441389605,-0.1249132976,-0.5678355694,0.3373684287,-0.1683481783,0.259375453,0.3960514963,-0.0001278901,0.0832418501,0.355887711,-0.0235939845,-0.2130245715,-0.4989036024,-0.1226171777,-0.0021165852,0.3659736514,-0.0908794627,0.0975263193,-0.645819068,0.0478101447,0.0595673434,-0.1573934406,0.3680754006,0.1251091063,0.040394716,0.2063285261,-0.0739030614,0.2163523734,0.092064105,-0.0217345729,0.534501493,0.1071522757,-0.1533436328,0.2649391592,-0.1108957157,0.0430092327,-0.0005163433,0.0668540001,0.1680730581,0.2481788546,-0.173890233,0.2404453605,0.0941150188,0.6074262857,-0.5957273245,-0.4052781761,0.0457946397,0.190304175,-0.2164417058,0.2071692944,0.2712188065,-0.1031831801,0.1470091492,-0.0970233083,-0.1404293329,-0.0962809622,-0.0484454148,-0.4191841185,0.3295250833,0.1847844124,0.1627088934,-0.0797763839,-0.0509254895,0.0048299716,-0.1046580449,0.1460140944,-0.0359727815,-0.2669477761,-0.0234076288,-0.0792864859,0.217488572,0.217696771,0.1644843668,0.0548282899,-0.2486527711,0.2624913454,0.0772697702,0.0593933463,0.1027095169,0.0099531254,0.1515246481,0.0868951976,-0.0923113823,0.0064102691,0.0057760188,-0.0834435672,-0.0731900558,0.4025044441,-0.0256594121,0.7480243444,-0.2999703586,-0.2222675681,0.1814114749,-0.3430295587,-0.2135788947,-0.2431992441,0.2692058682,0.0672401413,0.310472101,-0.2242441922,0.3650549948,-0.1089137867,-0.199377805,-0.0976025686,0.1639591455,0.0397986546,-0.1415777802,0.0776998177,-0.2863925397,-0.0622021332,0.3482494056,-0.0095234104,0.1233666539,-0.0444290265,-0.0483978391,-0.1564430743,0.4665051401,-0.2961569726,0.2662153244,0.1866068244,-0.4694723487,-0.3469584286,0.228558898,-0.2082118392,-0.4199297726,-0.3010490537,0.1158056408,-0.1309493929,-0.2246068269,-0.1018703878,-0.0314013213,0.1599574685,-0.1390951127,0.0193729699,-0.6764339805,0.1813001037,-0.3093072474,0.2377792299,0.1493155062,-0.64209795,0.0522496849,0.1860803813,-0.1793422401,0.1692923605,-0.1044029519,0.0251485948,-0.1446892172,0.074836567,0.0642969012,0.3613587618,-0.2439746708,-0.3121292293,0.0909591764,0.1042780653,0.0162541959,0.2491032332,-0.3086020947,-0.0181004889,0.1892558783,-0.1845408976,0.0676994994,0.013640088,0.0026977183,-0.1708292961,0.0054476419,0.438311249,0.2977380157,0.1350409687,-0.2180688083,-0.0289862547,-0.2037833929,0.1285439283,-0.0818165392,0.0136220716,-0.0355041698,0.407566458,-0.211303249,-0.0053179162,-0.21974428,-0.2898447216,0.2070418745,0.0864594579,0.017564917,-0.310914129,-0.1828339547,-0.2235690951,0.092757836,-0.1389970034,-0.0187330078,-0.008984318,0.3708506823,0.0991881639,0.0983951762,-0.3148404062,0.1957128495,0.0238641016,0.1941994578,0.0052944184,0.4397611022,-0.4184099436,-0.3409918547,0.0828048363,0.0324428193,0.1147675738,-0.0007691359,0.0294004232,0.3283750117,0.0075974376,0.0259973463,0.0376454331,0.2178497612,-0.0152834198,-0.2490369678,-0.1754437536,0.2634848952,-0.0124529032,0.3771674037,0.0714703649,0.367174536,0.5579252839,0.311139673,-0.0011530713,0.081929028,-0.1928504854,0.0089487033,0.1333372742,-0.0902685896,0.0926108137,-0.1520457119,0.1808014959,-0.0404996648,-0.5255686641,0.0326813981,0.378573209,-0.0664351285,0.1505247355,0.1364178061,-0.2508790195,0.0396844186,0.0591744334,0.0680330917,0.2792570293,0.1244287118,-0.1432555616,0.1382844001,-0.3376169503,0.1645191908,0.1703767031,0.0325545408,0.1882993728,0.1368130594,0.2204149663,0.053874325,-0.0650231689,-0.3855646849,0.1114446223,0.4056575298,-0.4613749981,0.2288228422,-0.2899155617,0.3022912443,-0.0999656469,-0.1894143373,-0.4212338924,-0.2496228665,-0.0001924169,0.090336822,0.1711301655,0.1835670471,-0.3250343204,-0.1124665737,0.0609607361,-0.4758637249,-0.2720769644,-0.1949386746,-0.4537057579,-0.0113761248,0.2226081789,-0.3075243831,0.0475775488,-0.2794205546,-0.222134456,-0.2541949451,-0.156630829,0.0200997163,-0.2725733817,0.3184693456,0.2527645528,0.0669448599,-0.0692736134,-0.0613622926,0.3347893357,-0.3718841076,-0.2399427444,0.1490340978,-0.0621652342,-0.0054474128,-0.0345259421,-0.5232025981,-0.3153493404,-0.1216146052,0.0009835535,0.0737264007,0.1543922126,-0.0844632015,0.2646680176,-0.0991962701,-0.1470929235,0.0729259551,0.0268436,-0.0291636419,0.2536785007,0.0024928947,-0.2990637422,-0.015819408,0.006221428,0.1566198468,0.0125369066,-0.2939115465,-0.1314835399,0.0421486981,0.5094447136,-0.2864129841,0.0493873321,0.3148921132,0.2830879986,-0.0151688168,0.0654160902,-0.1438310146,-0.1625919193,0.1416301429,0.1366788298,0.1592234224,0.1884554476,-0.0858525932,0.6183635592,-0.066043742,-0.27083534,0.2227285802,-0.349478066,0.3271769881,0.0535552427,-0.1810140163,-0.0944271237,-0.0012391821,0.0283627696,0.2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{"html_url":"https:\/\/github.com\/huggingface\/datasets\/issues\/2871","title":"datasets.config.PYARROW_VERSION has no attribute 'major'","comments":"Reopening this. Although the `test_dataset_common.py` script works fine now.\r\n\r\nHas this got something to do with my pull request not passing `ci\/circleci: run_dataset_script_tests_pyarrow` tests?\r\n\r\nhttps:\/\/github.com\/huggingface\/datasets\/pull\/2873","body":"In the test_dataset_common.py script, line 288-289\r\n\r\n```\r\nif datasets.config.PYARROW_VERSION.major < 3:\r\n packaged_datasets = [pd for pd in packaged_datasets if pd[\"dataset_name\"] != \"parquet\"]\r\n```\r\n\r\nwhich throws the error below. `datasets.config.PYARROW_VERSION` itself return the string '4.0.1'. I have tested this on both datasets.__version_=='1.11.0' and '1.9.0'. I am using Mac OS.\r\n\r\n```\r\nimport datasets\r\ndatasets.config.PYARROW_VERSION.major\r\n---------------------------------------------------------------------------\r\nAttributeError Traceback (most recent call last)\r\n\/var\/folders\/1f\/0wqmlgp90qjd5mpj53fnjq440000gn\/T\/ipykernel_73361\/2547517336.py in \r\n 1 import datasets\r\n----> 2 datasets.config.PYARROW_VERSION.major\r\n\r\nAttributeError: 'str' object has no attribute 'major'\r\n```\r\n\r\n## Environment info\r\n- `datasets` version: 1.11.0\r\n- Platform: Darwin-20.6.0-x86_64-i386-64bit\r\n- Python version: 3.7.11\r\n- PyArrow version: 4.0.1\r\n","comment_length":25,"text":"datasets.config.PYARROW_VERSION has no attribute 'major'\nIn the test_dataset_common.py script, line 288-289\r\n\r\n```\r\nif datasets.config.PYARROW_VERSION.major < 3:\r\n packaged_datasets = [pd for pd in packaged_datasets if pd[\"dataset_name\"] != \"parquet\"]\r\n```\r\n\r\nwhich throws the error below. `datasets.config.PYARROW_VERSION` itself return the string '4.0.1'. I have tested this on both datasets.__version_=='1.11.0' and '1.9.0'. I am using Mac OS.\r\n\r\n```\r\nimport datasets\r\ndatasets.config.PYARROW_VERSION.major\r\n---------------------------------------------------------------------------\r\nAttributeError Traceback (most recent call last)\r\n\/var\/folders\/1f\/0wqmlgp90qjd5mpj53fnjq440000gn\/T\/ipykernel_73361\/2547517336.py in \r\n 1 import datasets\r\n----> 2 datasets.config.PYARROW_VERSION.major\r\n\r\nAttributeError: 'str' object has no attribute 'major'\r\n```\r\n\r\n## Environment info\r\n- `datasets` version: 1.11.0\r\n- Platform: Darwin-20.6.0-x86_64-i386-64bit\r\n- Python version: 3.7.11\r\n- PyArrow version: 4.0.1\r\n\nReopening this. Although the `test_dataset_common.py` script works fine now.\r\n\r\nHas this got something to do with my pull request not passing `ci\/circleci: run_dataset_script_tests_pyarrow` tests?\r\n\r\nhttps:\/\/github.com\/huggingface\/datasets\/pull\/2873","embeddings":[-0.3413378894,0.2006237954,0.049372945,0.0571891814,0.1107941419,0.0854446366,0.2353403866,0.2816726863,-0.1760852039,0.1386214346,0.438138634,0.3304321766,-0.1294845939,0.297735095,-0.1947026998,0.0704638883,0.0133854281,0.1427540034,0.3433145285,0.0562438518,-0.1254506111,0.239258036,-0.1946723908,0.1283952147,-0.1329981238,0.0372122005,-0.1708889008,0.0408576243,-0.1387818605,-0.4680930376,0.4385558963,0.0902260914,0.1819554418,0.6217026114,-0.0001299516,0.1380508989,0.4102708101,-0.0885429606,-0.2731285393,-0.5470474362,0.0774918571,-0.0126978597,0.3988084793,-0.1040790603,0.0965566337,-0.3847824633,-0.0448813178,0.1092322022,-0.067902863,0.3774314225,0.0892633647,0.2561304271,0.0892846957,0.0130179506,0.2664367259,0.3202229738,-0.1790551394,0.4346646965,0.163472265,-0.1368527114,0.18223162,-0.0818933249,-0.0010008782,0.0381129086,0.058766406,0.0650137067,-0.0054221773,-0.2756882608,0.2095608413,-0.0046765376,0.4738269448,-0.5655617118,-0.4999672771,0.0325702988,0.1310693324,-0.2759967446,0.1989559233,0.1092876121,-0.2475142032,0.1120673195,-0.1768936068,-0.1210495085,-0.0797204599,-0.0597053356,-0.452073127,0.1955433339,0.1415925175,0.1933697611,0.0005672796,-0.0080453893,0.0825758576,-0.1077340245,0.1832358539,-0.0202006698,-0.2383793592,-0.0761251375,0.0723483413,0.3277744949,0.2713033557,0.4195830226,0.0403551459,-0.1306673586,0.174266085,0.0946188346,0.0676531792,0.2015321553,-0.0325329341,0.1100755706,0.26883623,0.0022651241,0.0251369309,-0.0340531245,-0.0820883214,-0.118339479,0.2201674134,0.0307946801,0.8329048157,-0.3100616932,-0.264413327,0.0647196248,-0.4131411612,-0.1456345022,-0.1949632913,0.2120336592,0.1063020974,0.3991495371,-0.054112941,0.3935264349,-0.0928063095,-0.0335153863,-0.1254985034,0.0960216671,0.0741094276,-0.0745831132,0.2243506163,-0.3418166041,-0.0214521512,0.2785852551,0.2317495495,0.1688358784,0.0847869664,0.1707445085,-0.0158238746,0.5882755518,-0.1170846,0.3030574322,0.1808865517,-0.4662673175,-0.3240087628,0.2942477167,-0.3417190313,-0.3119837642,-0.1544470787,0.0395626612,-0.3309824169,-0.2279408276,-0.2230107188,-0.199565202,0.230595544,-0.09554369,0.0377825759,-0.6431552768,0.1101010367,-0.2451968342,0.2660878897,0.2522025704,-0.4532644153,0.0551288687,-0.0058161258,0.0077627655,0.1686531156,0.108184278,-0.0699362084,-0.1368115842,-0.0259974599,0.0800719559,0.4520925283,-0.4171760976,-0.3087326586,0.2504969537,-0.0502103381,0.0525274239,0.2544584572,-0.4099239409,0.0670625865,0.1091564149,-0.1023410782,-0.0559723116,-0.013213655,0.1008469164,-0.1170869917,-0.0518476814,0.4205104113,0.2592461705,0.0945970491,-0.1817267984,-0.0615676045,-0.3520296216,0.2080374062,-0.058173541,-0.00003425,-0.0472278409,0.4154104888,-0.3017407358,0.0376632102,-0.0054553566,-0.2810015082,0.2353983223,0.1397245526,0.1832517534,-0.4007819295,-0.2460963428,-0.2957423627,0.1120926812,-0.1815855503,-0.0250835977,-0.0734229907,0.3676602542,0.0943571031,0.0908512399,-0.2459866107,0.1231615022,-0.0549701601,0.121638678,-0.0157055464,0.36100775,-0.39767465,-0.3114204407,0.1486184299,0.0152983777,0.1552105695,-0.0953354239,0.0492075197,0.1977923065,0.018292509,-0.0615071394,0.0249138251,0.2371177673,0.0974489674,-0.1439389586,-0.1320542544,0.2738496065,-0.0527514368,0.2288922668,0.0089537343,0.4483506978,0.433740288,0.4135373831,-0.0052913735,0.133137241,-0.0789800286,0.0464649163,-0.1026584506,0.0235418081,0.0623203032,-0.1579686403,0.2385576516,-0.0083107622,-0.4083735943,0.1789080948,0.4755972028,0.0045280191,0.0512079373,0.1263181567,-0.0464047939,0.0135155227,0.1427060068,0.1196604073,0.3690716922,0.1315706223,-0.1269986928,0.1280188709,-0.3089667261,-0.000240603,0.0881366134,0.0855403543,-0.0177239105,0.1066989228,0.2934764922,0.0094751427,-0.1714618802,-0.5069288611,0.1386284977,0.3523923159,-0.5544535518,0.2957420945,-0.2599960566,0.2412185669,-0.0563951358,-0.099517554,-0.3512249887,-0.3400330842,-0.0426338091,0.1322477013,0.1855418086,0.1987326741,-0.3156895339,0.085814856,0.0278856121,-0.5474075079,-0.3988513947,-0.2250475734,-0.3432812393,-0.0082413657,0.335031271,-0.3440192044,0.1043744832,-0.3536006212,-0.0615599714,-0.3151578307,-0.3276818395,0.0971032903,-0.3790067136,0.3400279582,0.264110893,0.1390078962,-0.0936909392,-0.0775850788,0.2825623751,-0.3451506793,-0.3498209715,0.0558034815,-0.0393706858,-0.081658788,-0.2297518551,-0.4395770431,-0.1756117046,-0.1256152838,0.1797543317,0.1345760971,0.0894499645,0.1602129638,0.2617307603,-0.1119602174,0.0015942445,-0.080045335,0.0416262671,-0.1853195578,0.311385572,-0.1229889318,-0.2905154526,0.0768143982,0.0901876315,0.2567841113,0.0905639157,-0.3195661008,-0.2088564485,0.0071465177,0.5721033216,-0.1528172642,0.0589576438,0.3950414658,0.1801723242,0.033580374,0.0303174295,-0.2007779479,-0.0994261503,0.1576837599,0.0561942831,0.1956194937,0.2270442694,-0.0645815507,0.7777855992,-0.0912658498,-0.247752741,0.3013986051,-0.3482937813,0.4125493765,0.092459954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{"html_url":"https:\/\/github.com\/huggingface\/datasets\/issues\/2871","title":"datasets.config.PYARROW_VERSION has no attribute 'major'","comments":"Hi @bwang482,\r\n\r\nIf you click on `Details` (on the right of your non passing CI test names: `ci\/circleci: run_dataset_script_tests_pyarrow`), you can have more information about the non-passing tests.\r\n\r\nFor example, for [\"ci\/circleci: run_dataset_script_tests_pyarrow_1\" details](https:\/\/circleci.com\/gh\/huggingface\/datasets\/46324?utm_campaign=vcs-integration-link&utm_medium=referral&utm_source=github-build-link), you can see that the only non-passing test has to do with the dataset card (missing information in the `README.md` file): `test_changed_dataset_card`\r\n```\r\n=========================== short test summary info ============================\r\nFAILED tests\/test_dataset_cards.py::test_changed_dataset_card[swedish_medical_ner]\r\n= 1 failed, 3214 passed, 2874 skipped, 2 xfailed, 1 xpassed, 15 warnings in 175.59s (0:02:55) =\r\n```\r\n\r\nTherefore, your PR non-passing test has nothing to do with this issue.","body":"In the test_dataset_common.py script, line 288-289\r\n\r\n```\r\nif datasets.config.PYARROW_VERSION.major < 3:\r\n packaged_datasets = [pd for pd in packaged_datasets if pd[\"dataset_name\"] != \"parquet\"]\r\n```\r\n\r\nwhich throws the error below. `datasets.config.PYARROW_VERSION` itself return the string '4.0.1'. I have tested this on both datasets.__version_=='1.11.0' and '1.9.0'. I am using Mac OS.\r\n\r\n```\r\nimport datasets\r\ndatasets.config.PYARROW_VERSION.major\r\n---------------------------------------------------------------------------\r\nAttributeError Traceback (most recent call last)\r\n\/var\/folders\/1f\/0wqmlgp90qjd5mpj53fnjq440000gn\/T\/ipykernel_73361\/2547517336.py in \r\n 1 import datasets\r\n----> 2 datasets.config.PYARROW_VERSION.major\r\n\r\nAttributeError: 'str' object has no attribute 'major'\r\n```\r\n\r\n## Environment info\r\n- `datasets` version: 1.11.0\r\n- Platform: Darwin-20.6.0-x86_64-i386-64bit\r\n- Python version: 3.7.11\r\n- PyArrow version: 4.0.1\r\n","comment_length":95,"text":"datasets.config.PYARROW_VERSION has no attribute 'major'\nIn the test_dataset_common.py script, line 288-289\r\n\r\n```\r\nif datasets.config.PYARROW_VERSION.major < 3:\r\n packaged_datasets = [pd for pd in packaged_datasets if pd[\"dataset_name\"] != \"parquet\"]\r\n```\r\n\r\nwhich throws the error below. `datasets.config.PYARROW_VERSION` itself return the string '4.0.1'. I have tested this on both datasets.__version_=='1.11.0' and '1.9.0'. I am using Mac OS.\r\n\r\n```\r\nimport datasets\r\ndatasets.config.PYARROW_VERSION.major\r\n---------------------------------------------------------------------------\r\nAttributeError Traceback (most recent call last)\r\n\/var\/folders\/1f\/0wqmlgp90qjd5mpj53fnjq440000gn\/T\/ipykernel_73361\/2547517336.py in \r\n 1 import datasets\r\n----> 2 datasets.config.PYARROW_VERSION.major\r\n\r\nAttributeError: 'str' object has no attribute 'major'\r\n```\r\n\r\n## Environment info\r\n- `datasets` version: 1.11.0\r\n- Platform: Darwin-20.6.0-x86_64-i386-64bit\r\n- Python version: 3.7.11\r\n- PyArrow version: 4.0.1\r\n\nHi @bwang482,\r\n\r\nIf you click on `Details` (on the right of your non passing CI test names: `ci\/circleci: run_dataset_script_tests_pyarrow`), you can have more information about the non-passing tests.\r\n\r\nFor example, for [\"ci\/circleci: run_dataset_script_tests_pyarrow_1\" details](https:\/\/circleci.com\/gh\/huggingface\/datasets\/46324?utm_campaign=vcs-integration-link&utm_medium=referral&utm_source=github-build-link), you can see that the only non-passing test has to do with the dataset card (missing information in the `README.md` file): `test_changed_dataset_card`\r\n```\r\n=========================== short test summary info ============================\r\nFAILED tests\/test_dataset_cards.py::test_changed_dataset_card[swedish_medical_ner]\r\n= 1 failed, 3214 passed, 2874 skipped, 2 xfailed, 1 xpassed, 15 warnings in 175.59s (0:02:55) =\r\n```\r\n\r\nTherefore, your PR non-passing test has nothing to do with this issue.","embeddings":[-0.4502635896,0.1362894326,0.0727608278,-0.0077378419,0.210440293,0.1455685943,0.1347785145,0.3891866505,-0.2612731457,0.1966487318,0.2423591018,0.4409922659,-0.174534291,0.1955467612,-0.2465572804,0.0201375429,-0.0359608233,0.1991022974,0.1349838227,0.0418360345,-0.1486246139,0.1625906974,-0.2162213922,0.2647956908,-0.1537034959,-0.0562001877,-0.0889388993,-0.0443796068,-0.1304095089,-0.543780148,0.374103874,-0.0596004017,0.1746742874,0.5324847698,-0.0001234938,0.1340290904,0.3579029739,-0.099743627,-0.3201577663,-0.4985058904,0.0303555876,0.0250716098,0.3309290409,-0.1497163624,0.0457028411,-0.5905317068,-0.0560617782,0.011204035,-0.0264583249,0.4921305478,0.1557275653,0.1648605168,0.0755554661,-0.137879923,0.210254997,0.089855887,-0.0760812536,0.4706763327,0.1216390133,-0.0717137381,0.1600072682,-0.1023208573,-0.0217084177,-0.0166188478,-0.0197366606,0.0890951231,0.2701399326,-0.2540642619,0.2758241594,0.0213556439,0.6139847636,-0.6099302769,-0.402813077,0.0584969297,0.1510484666,-0.1977946013,0.2043727338,0.1740199775,-0.2345986068,0.1370747983,-0.1940619051,-0.0381158032,-0.0790201724,-0.1012421995,-0.3796566129,0.2510077655,0.1984764487,0.1712024063,-0.0156713389,-0.1160794795,0.0763115138,-0.1649231166,0.170402199,0.0014240057,-0.2107874453,-0.129683286,-0.0683172196,0.3605410159,0.2446825057,0.2437425703,0.0395206213,-0.2193709165,0.2899466455,0.1493432522,-0.0002065366,0.060830459,0.0282580554,0.1525740027,0.1156841591,-0.0743039697,0.0116414698,0.0125757102,-0.0356712267,-0.1347546428,0.2806890607,-0.0175586436,0.747718215,-0.386092782,-0.3294264078,0.1127082333,-0.3191033304,-0.1121121868,-0.1496730894,0.3698425591,0.0801952556,0.3064732254,-0.2025549859,0.3027328849,-0.148769632,-0.1783850491,-0.0953058898,0.0878898203,-0.0219249018,-0.1331822574,0.1687428951,-0.2979690433,-0.0465056896,0.3469100296,0.1025219187,0.1879935563,0.0652616769,-0.0026953628,-0.0544139817,0.4611659646,-0.2218509614,0.2516638935,0.1780836433,-0.5087434649,-0.3634715378,0.2743989229,-0.2164251655,-0.3534741104,-0.2432028204,0.151281327,-0.0920187533,-0.2586004138,-0.0489280932,-0.0613507144,0.1802086085,-0.1575635076,-0.0042943428,-0.6692387462,0.1291497648,-0.3172443509,0.2979747355,0.2355631441,-0.5403274298,0.0511988066,0.1171005592,-0.1538690478,0.2208563685,-0.0480547622,0.0285186488,-0.1684476435,0.008382176,0.0004034062,0.3619309366,-0.3157839477,-0.2808323205,0.2037823498,0.1373576522,-0.0054161474,0.258110404,-0.3559542,0.0676776916,0.1332530528,-0.2449017912,0.0707520768,-0.0634788871,0.0394995026,-0.1724347621,0.0111094378,0.4310903251,0.2942523658,0.0776945651,-0.1869189143,0.0541623756,-0.2536583543,0.1724235415,-0.153491497,0.0145558426,-0.095210731,0.4228506982,-0.3071483076,0.0091015995,-0.0820851251,-0.3720258772,0.214207381,0.1426869631,0.0427836291,-0.3331008852,-0.2545279264,-0.3418053687,0.0929807276,-0.1178567037,-0.0309027005,0.0290229879,0.3834606111,0.0836455449,0.0666082948,-0.1785346568,0.1259467155,0.0109810112,0.1843898743,-0.021914199,0.4052859545,-0.3848957419,-0.3287788928,0.1164525375,0.0147691462,0.2110700905,-0.0747004673,-0.0244301055,0.3569452167,0.0668140128,0.0324969031,0.0375453681,0.2812748551,0.0095077008,-0.2744845748,-0.2255578786,0.2845185697,-0.0563144833,0.335890919,-0.0477804728,0.4245945215,0.4690216482,0.2440936267,0.0434890911,0.1207157895,-0.0876624584,0.0307110269,0.0175956078,-0.1027696207,0.1239546984,-0.0975145176,0.2429556996,0.0073814266,-0.4534864426,0.0181120038,0.3326230049,-0.1036967561,0.079722181,0.1088910624,-0.1348168701,0.0713322908,0.1310128421,0.1286019683,0.2671003044,0.1545218527,-0.0664283633,0.1459353566,-0.3957768083,0.0493688993,0.1474568546,0.046951104,0.0843830109,0.1178493723,0.2030754983,0.0692258701,-0.1964526772,-0.4386540353,0.1690077484,0.3975126445,-0.4556099176,0.2525030673,-0.2593023777,0.2879627645,-0.1762989759,-0.1013129577,-0.2812756002,-0.2871729434,0.0298087746,0.1613865793,0.1280528903,0.2844814956,-0.3949874341,0.1308537275,0.0604371689,-0.5389617682,-0.3587738574,-0.1788505465,-0.4358465075,0.0564847142,0.2930062115,-0.2936736047,0.1870015562,-0.3385720551,-0.1041571423,-0.2780030966,-0.2546269596,0.1000234187,-0.2732539177,0.4224722683,0.2331346571,0.0653007999,-0.0509411618,-0.1394465566,0.2985119224,-0.5006918907,-0.2382101864,0.1075783893,-0.0609142967,-0.0281601492,-0.0992578343,-0.6026321054,-0.2700941563,-0.1767314076,-0.0738294199,0.1012948826,0.1171792299,0.0153186703,0.1794993281,-0.0414384827,-0.1064003855,0.040731471,0.0227799695,-0.0935673937,0.2638745904,-0.1412262917,-0.247062102,0.0030329677,0.0593990013,0.3075082004,0.0263136849,-0.3157670796,-0.1785085499,0.0870808735,0.5713326931,-0.213754639,-0.0088340752,0.2017015219,0.3253875971,-0.0461313352,0.0438692309,-0.1561382264,-0.2257906049,0.0831051841,0.1174168065,0.1173686087,0.2478049397,-0.0001471604,0.7000100017,0.0901702568,-0.2647122443,0.2456341684,-0.290442735,0.3646113276,0.0215920843,-0.2104302496,-0.0293033496,-0.0032851116,-0.08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{"html_url":"https:\/\/github.com\/huggingface\/datasets\/issues\/2869","title":"TypeError: 'NoneType' object is not callable","comments":"Hi, @Chenfei-Kang.\r\n\r\nI'm sorry, but I'm not able to reproduce your bug:\r\n```python\r\nfrom datasets import load_dataset\r\n\r\nds = load_dataset(\"glue\", 'cola')\r\nds\r\n```\r\n```\r\nDatasetDict({\r\n train: Dataset({\r\n features: ['sentence', 'label', 'idx'],\r\n num_rows: 8551\r\n })\r\n validation: Dataset({\r\n features: ['sentence', 'label', 'idx'],\r\n num_rows: 1043\r\n })\r\n test: Dataset({\r\n features: ['sentence', 'label', 'idx'],\r\n num_rows: 1063\r\n })\r\n})\r\n```\r\n\r\nCould you please give more details and environment info (platform, PyArrow version)?","body":"## Describe the bug\r\n\r\nTypeError: 'NoneType' object is not callable\r\n## Steps to reproduce the bug\r\n```python\r\nfrom datasets import load_dataset, load_metric\r\ndataset = datasets.load_dataset(\"glue\", 'cola')\r\n```\r\n\r\n## Expected results\r\nA clear and concise description of the expected results.\r\n\r\n## Actual results\r\nSpecify the actual results or traceback.\r\n\r\n## Environment info\r\n\r\n- `datasets` version: 1.11.0\r\n- Platform:\r\n- Python version: 3.7\r\n- PyArrow version:\r\n","comment_length":66,"text":"TypeError: 'NoneType' object is not callable\n## Describe the bug\r\n\r\nTypeError: 'NoneType' object is not callable\r\n## Steps to reproduce the bug\r\n```python\r\nfrom datasets import load_dataset, load_metric\r\ndataset = datasets.load_dataset(\"glue\", 'cola')\r\n```\r\n\r\n## Expected results\r\nA clear and concise description of the expected results.\r\n\r\n## Actual results\r\nSpecify the actual results or traceback.\r\n\r\n## Environment info\r\n\r\n- `datasets` version: 1.11.0\r\n- Platform:\r\n- Python version: 3.7\r\n- PyArrow version:\r\n\nHi, @Chenfei-Kang.\r\n\r\nI'm sorry, but I'm not able to reproduce your bug:\r\n```python\r\nfrom datasets import load_dataset\r\n\r\nds = load_dataset(\"glue\", 'cola')\r\nds\r\n```\r\n```\r\nDatasetDict({\r\n train: Dataset({\r\n features: ['sentence', 'label', 'idx'],\r\n num_rows: 8551\r\n })\r\n validation: Dataset({\r\n features: ['sentence', 'label', 'idx'],\r\n num_rows: 1043\r\n })\r\n test: Dataset({\r\n features: ['sentence', 'label', 'idx'],\r\n num_rows: 1063\r\n })\r\n})\r\n```\r\n\r\nCould you please give more details and environment info (platform, PyArrow version)?","embeddings":[-0.0594903305,-0.1199711189,0.0148641206,0.2438831329,0.4732634127,-0.0013416682,0.4414617419,0.1253976971,-0.0142953452,0.2675631642,-0.1799774915,0.4658890367,-0.1191558838,0.1054266095,0.0933514312,-0.1634460539,-0.1493485421,0.1789052486,-0.0950517431,0.0041558379,-0.2951413095,-0.1227453202,-0.2647020519,0.1601062417,-0.4545831382,-0.2473363578,-0.1237274557,-0.0289071202,-0.2947974801,-0.3072052002,0.3905930817,0.045531936,0.0802016407,0.573389709,-0.0001046333,0.085742943,0.5220624804,0.0795245767,-0.1019385606,-0.4897305667,-0.1364545226,-0.1668332815,0.2139172852,-0.393879205,-0.174881503,-0.0434178077,0.0156303495,0.1047198623,0.3135638833,0.4849516451,0.3089385629,0.4509330392,0.0464522541,-0.2673178315,0.2966395915,0.1515992731,-0.0532518923,0.3601016104,-0.0297630094,-0.1026376411,0.1846834719,0.1567065567,-0.2669480145,-0.0967591628,0.1903866827,0.0687865466,-0.0415391438,-0.290898174,-0.0751495287,0.4143870771,0.3387809098,-0.4593112171,-0.2788108885,0.1180645153,0.2122831494,-0.2495187223,0.2602464557,0.0856177285,-0.0128446911,0.1975728273,-0.0867947638,0.2796069086,-0.2793028951,0.3049078584,-0.164794296,0.1444952488,-0.0630867183,0.0345241651,0.2060508877,-0.1919636279,0.059791442,-0.1310273856,0.0356741138,0.1007023752,-0.243607074,-0.1369945854,0.1091515124,-0.0550249554,0.1305607557,-0.2993285656,0.1565279961,-0.0825746059,-0.0047007087,0.4082542658,0.183615461,0.3742778003,0.2353915423,0.146733135,0.2031041682,0.1250560284,-0.2914271057,0.0851671919,-0.1916304976,-0.0795564353,0.3350311518,0.141778037,0.4543803036,0.004213843,-0.4538616538,-0.0484698229,-0.4231696129,0.0795800835,0.2269208282,0.4578568935,-0.1490905881,0.111972034,0.0448685922,0.1539013535,-0.1707451195,-0.2831352949,-0.2869074643,0.4119275212,-0.3327414989,-0.2005894333,0.1725695282,0.0631759688,-0.0440938734,-0.0142761068,-0.124532178,0.0147927273,0.0312171597,-0.4864667356,-0.1069177911,0.1802367717,0.0366909429,-0.1434192806,0.2177432328,-0.4385626912,0.0038601621,0.2104463279,-0.1390264481,-0.2205248028,-0.0644018948,0.2921482325,-0.3297555149,-0.1617497504,-0.1220805645,0.1581521481,0.158894524,0.0152816763,0.0386043973,-0.0845265612,-0.0218608137,-0.3067058027,0.0268780459,0.3902682364,-0.3743338883,-0.2277735025,-0.1163482741,-0.0573342443,0.3309760094,-0.0552941114,-0.0139303515,0.2842520177,-0.0162361618,0.1270191222,0.6166725755,-0.3811469376,-0.3189837933,-0.0871518478,-0.0525565632,-0.0323508754,-0.0000088416,-0.1415092349,0.0952094793,0.1851929128,0.4305967093,0.1825711876,0.0108494731,-0.0068763741,-0.2708131671,-0.2111279964,0.1911941022,0.0895309374,0.1389753819,-0.0566433519,-0.0780088603,-0.24225308,0.0179314129,-0.1191949025,-0.1323612928,-0.0616726913,0.5806082487,-0.0895553157,-0.1220896021,-0.5331310034,-0.2384607494,0.1451272964,-0.0384060368,0.4400658906,-0.1647625715,-0.2132972479,-0.4087546468,0.0467493199,-0.0534322485,-0.0422442332,0.2887170017,0.0327202827,-0.162372008,0.1476718634,-0.093931675,0.0690900609,-0.1891066879,0.1402491182,0.1150690317,0.3248268962,-0.052656617,-0.4338489175,0.0160555448,0.2450485379,0.4278887212,0.0615985915,-0.0518304221,0.3103211224,0.0966809914,-0.2166442722,-0.332929194,-0.0375642255,0.1061588004,-0.3284879327,-0.1916384846,-0.02861006,0.1814015508,-0.0039089932,0.0155871715,0.5066564679,0.073046267,0.0751549006,-0.0834585428,0.1962543428,-0.0521300845,-0.1202241629,-0.1189233959,-0.0935665593,0.0853727981,-0.0516477935,0.1604620069,0.0839664191,-0.1486921012,-0.0939611942,0.6276284456,0.066133365,0.4066179395,-0.1278576404,-0.253084898,0.1469340771,0.0753541663,0.0935161412,0.3560036421,0.309184581,-0.1741830856,-0.012002632,-0.1699138284,0.0200339202,0.0936975554,-0.0300577246,0.3375006616,-0.0191667695,0.326565057,0.0025228276,-0.3129945993,0.1154984683,0.0383162536,0.1894647479,-0.3931485116,0.1806956828,-0.3155397177,-0.1539050788,0.0592735447,-0.1398235261,-0.0421994813,-0.2707495987,-0.085526906,0.1815255284,0.0589705072,0.264362663,0.1597475857,-0.0327072591,0.0847381577,-0.0655731633,-0.102235347,-0.2201068252,-0.1743910462,0.1428499222,0.1274092495,0.2078600228,0.2995347381,-0.1425538957,0.0480940603,0.0138055841,-0.3602250516,0.0857338384,-0.1215342283,0.4401930273,0.3425597847,0.0728836656,-0.2368693203,0.0173933227,0.3619660437,-0.3473314047,-0.108051084,0.3983776271,-0.1979978234,0.0502504855,-0.2161714584,-0.3248876333,-0.510535121,-0.318955183,0.1311067194,-0.0294710808,0.0872791484,0.3062438071,0.2281634808,0.450894624,-0.052571062,0.0395837203,-0.1739951223,-0.0077805598,0.3531797528,-0.0148417056,-0.3720351756,0.0079913763,-0.1651002616,0.1313911527,-0.0117581366,-0.1790904701,-0.5396494269,-0.1400610059,0.288399756,0.0229437444,-0.0957489237,0.3742546439,0.0539732315,-0.1510512084,-0.1974764913,-0.082263872,0.0317742862,0.3172571063,0.0219645612,-0.069488965,0.2908799648,0.0552209876,0.2811395824,-0.113323316,-0.2513748407,0.4198065102,-0.2915056348,0.2461425811,-0.0716580451,-0.4766027927,-0.0776230395,-0.05550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{"html_url":"https:\/\/github.com\/huggingface\/datasets\/issues\/2869","title":"TypeError: 'NoneType' object is not callable","comments":"> Hi, @Chenfei-Kang.\r\n> \r\n> I'm sorry, but I'm not able to reproduce your bug:\r\n> \r\n> ```python\r\n> from datasets import load_dataset\r\n> \r\n> ds = load_dataset(\"glue\", 'cola')\r\n> ds\r\n> ```\r\n> \r\n> ```\r\n> DatasetDict({\r\n> train: Dataset({\r\n> features: ['sentence', 'label', 'idx'],\r\n> num_rows: 8551\r\n> })\r\n> validation: Dataset({\r\n> features: ['sentence', 'label', 'idx'],\r\n> num_rows: 1043\r\n> })\r\n> test: Dataset({\r\n> features: ['sentence', 'label', 'idx'],\r\n> num_rows: 1063\r\n> })\r\n> })\r\n> ```\r\n> \r\n> Could you please give more details and environment info (platform, PyArrow version)?\r\n\r\nSorry to reply you so late.\r\nplatform: pycharm 2021 + anaconda with python 3.7\r\nPyArrow version: 5.0.0\r\nhuggingface-hub: 0.0.16\r\ndatasets: 1.9.0\r\n","body":"## Describe the bug\r\n\r\nTypeError: 'NoneType' object is not callable\r\n## Steps to reproduce the bug\r\n```python\r\nfrom datasets import load_dataset, load_metric\r\ndataset = datasets.load_dataset(\"glue\", 'cola')\r\n```\r\n\r\n## Expected results\r\nA clear and concise description of the expected results.\r\n\r\n## Actual results\r\nSpecify the actual results or traceback.\r\n\r\n## Environment info\r\n\r\n- `datasets` version: 1.11.0\r\n- Platform:\r\n- Python version: 3.7\r\n- PyArrow version:\r\n","comment_length":116,"text":"TypeError: 'NoneType' object is not callable\n## Describe the bug\r\n\r\nTypeError: 'NoneType' object is not callable\r\n## Steps to reproduce the bug\r\n```python\r\nfrom datasets import load_dataset, load_metric\r\ndataset = datasets.load_dataset(\"glue\", 'cola')\r\n```\r\n\r\n## Expected results\r\nA clear and concise description of the expected results.\r\n\r\n## Actual results\r\nSpecify the actual results or traceback.\r\n\r\n## Environment info\r\n\r\n- `datasets` version: 1.11.0\r\n- Platform:\r\n- Python version: 3.7\r\n- PyArrow version:\r\n\n> Hi, @Chenfei-Kang.\r\n> \r\n> I'm sorry, but I'm not able to reproduce your bug:\r\n> \r\n> ```python\r\n> from datasets import load_dataset\r\n> \r\n> ds = load_dataset(\"glue\", 'cola')\r\n> ds\r\n> ```\r\n> \r\n> ```\r\n> DatasetDict({\r\n> train: Dataset({\r\n> features: ['sentence', 'label', 'idx'],\r\n> num_rows: 8551\r\n> })\r\n> validation: Dataset({\r\n> features: ['sentence', 'label', 'idx'],\r\n> num_rows: 1043\r\n> })\r\n> test: Dataset({\r\n> features: ['sentence', 'label', 'idx'],\r\n> num_rows: 1063\r\n> })\r\n> })\r\n> ```\r\n> \r\n> Could you please give more details and environment info (platform, PyArrow version)?\r\n\r\nSorry to reply you so late.\r\nplatform: pycharm 2021 + anaconda with python 3.7\r\nPyArrow version: 5.0.0\r\nhuggingface-hub: 0.0.16\r\ndatasets: 1.9.0\r\n","embeddings":[-0.0170860495,-0.3240526319,0.062121436,0.2103672326,0.3840116858,-0.0155181745,0.415794313,0.1785911471,0.1879396886,0.3116176724,-0.2562711835,0.2617366314,-0.0900785625,0.1555979401,0.134646982,-0.1783233583,-0.1780708879,0.2362426817,-0.1429384202,-0.0610416606,-0.307266444,0.016021125,-0.2765933871,0.1563270539,-0.3710355163,-0.2232295871,-0.0727495775,-0.0337860063,-0.3516702652,-0.3736093938,0.3502697647,-0.0149404071,0.1077515855,0.3936695755,-0.000109273,0.039748542,0.5531897545,0.0916725695,-0.1215192825,-0.486445725,0.0033005883,-0.153592959,0.2640893161,-0.2971876264,-0.2491431981,-0.1678692102,-0.0046053282,0.1546724737,0.3977606893,0.5068286061,0.2735918462,0.5179857016,0.1557451636,-0.3057215512,0.2476199716,0.1295629144,-0.0830335468,0.3645498157,0.0868025422,0.0324823782,0.2476591766,0.22567074,-0.2615945935,-0.1020329148,0.2481998503,0.0714492425,0.0565817356,-0.3359338641,-0.0874417275,0.3458363712,0.2176503092,-0.4307984114,-0.2740110457,-0.0156725049,0.1589335203,-0.2778740823,0.2152642608,0.1117749512,-0.0614302717,0.2610106766,-0.0477596298,0.2916436493,-0.2724869251,0.2264740765,0.0332289934,0.0998286605,-0.0974352807,0.0864828005,0.3184731901,-0.1978602111,-0.0712477863,-0.0744847059,0.0765407234,0.1997876465,-0.2625720799,-0.1569988281,0.0412150547,-0.0527565517,0.2478982806,-0.0623121895,0.010698121,-0.0784372389,0.0866332129,0.3945228755,0.1457946301,0.3695580363,0.2746926844,0.0596430153,0.147126019,0.2683903873,-0.1179920584,0.0550732911,-0.1812906712,-0.0505757257,0.2737024724,0.1010781154,0.4157111049,-0.0123857539,-0.3835292757,-0.0340619497,-0.249388665,0.2141660452,0.2170223594,0.4983271658,-0.1098527387,-0.006581971,0.1920767128,0.1782709658,-0.1211596727,-0.1304862946,-0.2357181609,0.34223032,-0.3215116858,-0.0617322512,0.2265961319,0.0308971405,0.0039272024,-0.0047287471,0.0682608932,-0.0418036729,-0.0186945554,-0.3479027152,-0.0821475908,0.2535481453,0.032342352,-0.0685248151,0.2264001071,-0.410043478,-0.0981175676,0.1309780478,-0.0746355355,-0.1795981675,-0.2304216176,0.2343917638,-0.2560875714,-0.0213031564,-0.0934348702,0.0212540068,0.1447509378,0.1013658345,0.1272418797,-0.0190788247,0.0178890768,-0.2180711776,0.1380720735,0.3654916883,-0.100190863,-0.3205661774,0.0321522094,-0.0025296691,0.1171867996,-0.0173707716,0.0019335701,0.2421703041,-0.0305478908,0.1668857634,0.4985628724,-0.62414819,-0.3442799449,-0.2747382522,-0.0806370825,-0.0161460973,0.0393288732,-0.2286882102,0.1914647818,0.2106938213,0.4453007579,0.1527808011,0.1522087157,0.0167638846,-0.2598265111,-0.2841210067,-0.1492304802,0.1216640398,0.0572228469,-0.0611823201,-0.1246782616,-0.3315826058,0.0439961776,-0.1464243233,-0.0947787762,-0.0285352468,0.4802513719,-0.0267939679,-0.0334615521,-0.5103312731,-0.3110117316,0.1444107145,-0.0827431381,0.4963111877,-0.2935872972,-0.2401018441,-0.3797984123,0.0216788147,-0.0260307416,-0.1073646471,0.2608529925,-0.0292999409,-0.0793902054,0.2039854974,-0.148156628,0.3061811626,-0.0629678369,0.3361934423,-0.0899120644,0.2520775199,-0.0519085974,-0.4535894096,0.0178125389,0.3152451813,0.407485038,0.0184305087,0.0400744975,0.2059644312,0.120073542,-0.2516606152,-0.3879702389,-0.2059795707,0.0908953026,-0.2052687109,-0.1253317297,-0.1193971634,0.1875582188,0.043237146,0.0816024616,0.4845341146,0.0002889726,0.1447360814,-0.0010887757,0.2192268968,0.0464254729,-0.2223508209,-0.1943704933,-0.1116545573,0.1098374575,0.0302241202,0.2480703294,0.0179758705,-0.1454162598,0.0168016292,0.6618235111,0.0420250148,0.258257091,-0.1182069257,-0.3660911322,0.2513479888,0.1739427298,0.087606214,0.3652841747,0.2524144948,-0.2956422269,0.0845419914,-0.1635932028,0.0505567454,0.0976488069,-0.0679601654,0.3684989512,-0.0912532359,0.3075386584,0.0185329355,-0.3090053499,0.0744575188,-0.0793073252,0.1062876061,-0.3757323921,0.1150494143,-0.2569516301,-0.1478515416,-0.0088819563,-0.133373037,0.0266572647,-0.1974048913,-0.0598685481,0.3584897518,0.0893847048,0.282640487,0.2069196403,0.0192367453,-0.0590613931,-0.1030487418,-0.1551313996,-0.108617492,-0.11700131,0.0823388919,0.0588655174,0.1915499568,0.3331587911,-0.3143273294,0.0909607261,0.1370236278,-0.3026359379,0.0858223662,-0.1740436107,0.2888210118,0.2684489191,0.1589440256,-0.1759042591,-0.0624604672,0.4340486825,-0.3824962378,-0.192428574,0.2827731073,-0.1414361447,-0.0550831221,-0.2623379529,-0.2640984952,-0.5197219253,-0.2946316004,0.2550328076,0.0227000788,0.0717564151,0.3472696841,0.234048292,0.452318579,-0.3834219277,-0.0229388513,-0.1441199481,0.0412894003,0.2728778422,-0.0820257813,-0.4259680212,-0.0077680945,-0.0855097622,0.2539603114,-0.1286979169,-0.0568948649,-0.4479478002,-0.1266304255,0.3238252997,0.0086144321,-0.1531503648,0.6029988527,0.0156400762,-0.1078105569,-0.1583341658,-0.1767390072,0.1045932546,0.1382757425,0.0056557627,-0.0700706616,0.2453286648,-0.0265153348,0.351564467,-0.0049929833,-0.2051898241,0.3471206427,-0.304443121,0.3146957457,-0.1218410507,-0.5708717108,-0.0727783665,-0.02652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{"html_url":"https:\/\/github.com\/huggingface\/datasets\/issues\/2869","title":"TypeError: 'NoneType' object is not callable","comments":"- For the platform, we need to know the operating system of your machine. Could you please run the command `datasets-cli env` and copy-and-paste its output below?\r\n- In relation with the error, you just gave us the error type and message (`TypeError: 'NoneType' object is not callable`). Could you please copy-paste the complete stack trace, so that we know exactly which part of the code threw the error?","body":"## Describe the bug\r\n\r\nTypeError: 'NoneType' object is not callable\r\n## Steps to reproduce the bug\r\n```python\r\nfrom datasets import load_dataset, load_metric\r\ndataset = datasets.load_dataset(\"glue\", 'cola')\r\n```\r\n\r\n## Expected results\r\nA clear and concise description of the expected results.\r\n\r\n## Actual results\r\nSpecify the actual results or traceback.\r\n\r\n## Environment info\r\n\r\n- `datasets` version: 1.11.0\r\n- Platform:\r\n- Python version: 3.7\r\n- PyArrow version:\r\n","comment_length":69,"text":"TypeError: 'NoneType' object is not callable\n## Describe the bug\r\n\r\nTypeError: 'NoneType' object is not callable\r\n## Steps to reproduce the bug\r\n```python\r\nfrom datasets import load_dataset, load_metric\r\ndataset = datasets.load_dataset(\"glue\", 'cola')\r\n```\r\n\r\n## Expected results\r\nA clear and concise description of the expected results.\r\n\r\n## Actual results\r\nSpecify the actual results or traceback.\r\n\r\n## Environment info\r\n\r\n- `datasets` version: 1.11.0\r\n- Platform:\r\n- Python version: 3.7\r\n- PyArrow version:\r\n\n- For the platform, we need to know the operating system of your machine. Could you please run the command `datasets-cli env` and copy-and-paste its output below?\r\n- In relation with the error, you just gave us the error type and message (`TypeError: 'NoneType' object is not callable`). Could you please copy-paste the complete stack trace, so that we know exactly which part of the code threw the error?","embeddings":[-0.2277715802,-0.3193501234,0.0008596373,0.3918522596,0.3677237034,0.0557456017,0.3176339269,0.1545283645,0.1733444631,0.2960280478,-0.1426855326,0.4552958608,-0.0776570067,0.2298437357,0.0493332893,-0.1651720107,-0.2105034888,0.2611335516,-0.1191705689,0.0262278002,-0.5092200041,-0.0741478875,-0.1640852094,0.1349925548,-0.237754494,-0.2719812989,-0.1240942478,0.0408258066,-0.1921427697,-0.2838498354,0.3645710647,-0.0801881999,0.2122620046,0.5988925099,-0.0001008489,-0.1514540613,0.435205698,0.0475999489,-0.1254921108,-0.3523042202,-0.2946639061,-0.3847207725,0.18985717,-0.3827019334,-0.0743031427,-0.0082017854,0.0286093634,-0.2426373065,0.3481251895,0.3484524488,0.3506994545,0.415902108,0.0833576843,-0.2823812366,0.1884763539,0.0775724575,-0.1048533171,0.2966803014,0.0623726882,-0.0164799206,0.3145338893,0.1602044255,-0.2830132842,-0.0542992242,0.2551215291,0.0645872205,-0.0053246845,-0.3559243679,0.0310364459,0.3066703379,0.4195721149,-0.3946249187,-0.1872214228,0.0607764684,0.1299087554,-0.2234973758,0.1539727449,-0.0115599288,-0.0064773369,0.0658862591,-0.0040367073,0.1557925344,-0.236804232,0.2183835655,-0.1664240062,0.0901961327,-0.2121003419,0.107063584,0.0670970306,-0.1992394775,0.0452290624,-0.1839859337,0.0351355337,0.1096044704,-0.221126616,-0.1452792883,0.1299011409,0.1111679673,0.1270107031,-0.2455778271,0.1168725938,-0.0483121127,-0.0236970503,0.4071713388,0.239894405,0.2160654962,0.197072491,0.1496332735,0.2160288244,0.0441346094,-0.1853791922,-0.0519746169,-0.1255763024,-0.1014563665,0.2814617455,0.1962061971,0.5696896911,-0.0633770376,-0.4451116025,-0.0369891338,-0.207089901,0.0923674777,0.2541407347,0.3783914447,-0.1840863526,0.1531827301,0.2149179727,0.0695832223,-0.1426405609,-0.2327828705,-0.2402686328,0.3336758912,-0.2726967335,-0.232448265,0.0732233152,0.0800087824,-0.1016519293,0.0515346751,-0.0466053039,0.0736266375,0.1052564383,-0.382973969,-0.1715993732,0.1613502353,0.1342369616,-0.0109220734,0.3575491905,-0.413230598,-0.1455421895,0.1080859974,-0.3179209232,-0.0927535221,-0.0903914943,0.3212557733,-0.2362898439,-0.079209581,-0.1951911002,-0.0047342735,0.0772167519,-0.2021209151,0.0243879221,-0.1612926275,0.0099704852,-0.4124752581,0.0396714061,0.5434519649,-0.3702253997,-0.131931603,-0.3560011387,-0.0526704639,0.3650825024,-0.1617242545,0.0869184732,0.2049263865,-0.1619556099,-0.0783798695,0.5953560472,-0.3626013696,-0.1942619234,0.0556299053,-0.079251498,-0.0580402203,-0.067448847,-0.0140706375,0.0950176865,0.1139309034,0.4095413983,0.2211544514,-0.0633310452,-0.027870506,-0.1950395554,-0.1733900309,0.1649025828,0.0918372497,0.1436722875,0.1626258194,0.0630830303,-0.2037843466,0.0184938218,-0.0397915058,-0.09291742,-0.0652414113,0.6565243602,-0.0872236714,-0.0788739324,-0.5945641994,-0.3253455162,0.1684785038,-0.0677354112,0.2807234824,-0.1447329372,-0.1707682014,-0.3664011657,0.0677765012,-0.0498286709,0.0480884425,0.34265396,0.1271567047,-0.1998722106,0.0451182313,-0.1443472803,0.1779200137,-0.1498600394,0.1019958928,-0.0057357312,0.3032817245,-0.0105569726,-0.3550938368,0.0437213145,0.2375442684,0.5392077565,0.0301111341,-0.0714498162,0.2961714268,0.0839738399,-0.1378158778,-0.1490661353,-0.0828541517,0.1191909611,-0.2615092099,0.0761332735,0.113510862,0.341347903,-0.0806762725,0.1379154474,0.550291419,0.070542492,0.218250677,-0.0425794087,0.2359925807,-0.0458750986,-0.0404749922,-0.09132009,-0.1391232908,0.1310136467,0.0969333351,0.1783950478,-0.0640903935,-0.1166954562,0.0628789961,0.5675632358,0.0985855833,0.3846487105,-0.0710379705,-0.3002262712,0.1548450738,0.0890177488,0.2822793424,0.4458229542,0.3082370758,-0.1348168105,0.0028449628,-0.105473116,0.0909257606,0.069577001,-0.008273948,0.2112074643,-0.0183985941,0.1782516539,0.0799446777,-0.1778929234,-0.0452206172,-0.2235442847,0.2644327581,-0.3588179946,0.1916420162,-0.2675382495,-0.1693612486,0.0416815057,-0.0662412271,-0.1583892852,-0.343996793,-0.2322412729,0.0256404057,0.1133051515,0.1315740049,0.114424713,0.1559722424,0.0080657853,0.0855939835,-0.1656484157,-0.1026751921,-0.1289932728,0.1639403254,0.1211261749,0.2124070823,0.4851669073,-0.2281131297,0.2450820208,-0.0264441986,-0.227994293,0.0638851374,-0.0960038304,0.4924280643,0.4533854723,0.170710057,-0.0828904957,-0.0167101845,0.3197818995,-0.3656996191,0.0297314506,0.2110722065,-0.2212308943,0.1077203006,-0.1586056501,-0.1741414219,-0.5684420466,-0.4111053944,0.1035611331,0.1680119336,0.1657065153,0.1624201983,0.1247417033,0.5334134102,0.1375749111,0.1933974028,-0.195846498,-0.0487356074,0.2836108506,-0.122860238,-0.2586844563,0.1470841169,-0.1050434485,0.2065625638,0.0155344047,-0.1752419621,-0.5393431783,-0.1731456071,0.1546034515,-0.0829334036,0.0919005796,0.2602021098,0.1155401021,-0.1390545517,-0.2339776456,-0.1428654939,-0.1661490351,0.2557976246,0.0672933385,0.0401755236,0.3397756815,-0.178367734,0.1294477135,0.0065674935,-0.2346490473,0.3460306227,-0.2629454136,0.3489436209,-0.1248321906,-0.5024338961,-0.1444402039,0.0058216783,-0.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{"html_url":"https:\/\/github.com\/huggingface\/datasets\/issues\/2869","title":"TypeError: 'NoneType' object is not callable","comments":"> * For the platform, we need to know the operating system of your machine. Could you please run the command `datasets-cli env` and copy-and-paste its output below?\r\n> * In relation with the error, you just gave us the error type and message (`TypeError: 'NoneType' object is not callable`). Could you please copy-paste the complete stack trace, so that we know exactly which part of the code threw the error?\r\n\r\n1. For the platform, here are the output:\r\n - datasets` version: 1.11.0\r\n - Platform: Windows-10-10.0.19041-SP0\r\n - Python version: 3.7.10\r\n - PyArrow version: 5.0.0\r\n2. For the code and error\uff1a\r\n ```python\r\n from datasets import load_dataset, load_metric\r\n dataset = load_dataset(\"glue\", \"cola\")\r\n ```\r\n ```python\r\n Traceback (most recent call last):\r\n ....\r\n ....\r\n File \"my_file.py\", line 2, in \r\n dataset = load_dataset(\"glue\", \"cola\")\r\n File \"My environments\\lib\\site-packages\\datasets\\load.py\", line 830, in load_dataset\r\n **config_kwargs,\r\n File \"My environments\\lib\\site-packages\\datasets\\load.py\", line 710, in load_dataset_builder\r\n **config_kwargs,\r\n TypeError: 'NoneType' object is not callable\r\n ```\r\n Thank you!","body":"## Describe the bug\r\n\r\nTypeError: 'NoneType' object is not callable\r\n## Steps to reproduce the bug\r\n```python\r\nfrom datasets import load_dataset, load_metric\r\ndataset = datasets.load_dataset(\"glue\", 'cola')\r\n```\r\n\r\n## Expected results\r\nA clear and concise description of the expected results.\r\n\r\n## Actual results\r\nSpecify the actual results or traceback.\r\n\r\n## Environment info\r\n\r\n- `datasets` version: 1.11.0\r\n- Platform:\r\n- Python version: 3.7\r\n- PyArrow version:\r\n","comment_length":154,"text":"TypeError: 'NoneType' object is not callable\n## Describe the bug\r\n\r\nTypeError: 'NoneType' object is not callable\r\n## Steps to reproduce the bug\r\n```python\r\nfrom datasets import load_dataset, load_metric\r\ndataset = datasets.load_dataset(\"glue\", 'cola')\r\n```\r\n\r\n## Expected results\r\nA clear and concise description of the expected results.\r\n\r\n## Actual results\r\nSpecify the actual results or traceback.\r\n\r\n## Environment info\r\n\r\n- `datasets` version: 1.11.0\r\n- Platform:\r\n- Python version: 3.7\r\n- PyArrow version:\r\n\n> * For the platform, we need to know the operating system of your machine. Could you please run the command `datasets-cli env` and copy-and-paste its output below?\r\n> * In relation with the error, you just gave us the error type and message (`TypeError: 'NoneType' object is not callable`). Could you please copy-paste the complete stack trace, so that we know exactly which part of the code threw the error?\r\n\r\n1. For the platform, here are the output:\r\n - datasets` version: 1.11.0\r\n - Platform: Windows-10-10.0.19041-SP0\r\n - Python version: 3.7.10\r\n - PyArrow version: 5.0.0\r\n2. For the code and error\uff1a\r\n ```python\r\n from datasets import load_dataset, load_metric\r\n dataset = load_dataset(\"glue\", \"cola\")\r\n ```\r\n ```python\r\n Traceback (most recent call last):\r\n ....\r\n ....\r\n File \"my_file.py\", line 2, in \r\n dataset = load_dataset(\"glue\", \"cola\")\r\n File \"My environments\\lib\\site-packages\\datasets\\load.py\", line 830, in load_dataset\r\n **config_kwargs,\r\n File \"My environments\\lib\\site-packages\\datasets\\load.py\", line 710, in load_dataset_builder\r\n **config_kwargs,\r\n TypeError: 'NoneType' object is not callable\r\n ```\r\n Thank you!","embeddings":[-0.2333926857,-0.2121910155,0.0151841119,0.3540363908,0.431312561,0.0565467253,0.4275258183,0.1668837517,0.1619586647,0.2543876767,-0.1491201818,0.4838864207,-0.032627441,0.2008449435,0.1351143867,-0.1875769347,-0.1780486703,0.1836340725,-0.2075739056,0.0554907173,-0.530339241,-0.0694576576,-0.131100297,0.1142207906,-0.165304631,-0.2441832423,-0.1580201536,0.0987920165,-0.1795856655,-0.3501777351,0.4064702392,-0.1164285839,0.2196373641,0.562065959,-0.0001015193,-0.0459203348,0.4622377753,0.0741885006,-0.1826767772,-0.3177576065,-0.2952965498,-0.3818572462,0.1700716317,-0.3912402391,-0.0481698103,-0.0371986367,-0.0039750608,-0.2519207001,0.2980602384,0.3795799315,0.328802675,0.4538740516,0.0911629125,-0.2488764822,0.1904570758,0.0910028517,-0.0621268973,0.3023785055,0.0538949445,-0.0567670763,0.2113882601,0.1328090131,-0.3628790081,-0.0556369275,0.2719312906,0.0631388128,0.0336346067,-0.3873294294,0.0748671964,0.3000453711,0.4482415617,-0.3896304667,-0.2469754517,0.0235931426,0.1223488674,-0.1994509548,0.2123644501,0.0509505086,0.0008063291,0.0616286434,0.0196442436,0.1935778111,-0.1930177808,0.2497536093,-0.1554912627,0.1598448157,-0.1672741175,0.1182282194,0.0708542541,-0.2033922374,0.049795296,-0.2369977683,0.007765641,0.1136214361,-0.2622630298,-0.0578037463,0.1595869213,0.1328142434,0.0924718305,-0.2031244338,0.0840387568,-0.0385783762,0.0005321783,0.3734622598,0.2657110691,0.2719011307,0.2630656064,0.1053493395,0.3113079071,0.0968508199,-0.1035405621,0.0171599817,-0.1741403788,-0.1962881684,0.2919623256,0.27147156,0.5614536405,-0.0071394164,-0.4003361762,-0.112911351,-0.1762718856,0.0414780118,0.1914937347,0.3488394022,-0.1659963131,0.1930406094,0.2003636211,0.1248331815,-0.2139800191,-0.2602629662,-0.2342291027,0.277436316,-0.2562621832,-0.1992109269,0.1157746315,-0.0126312999,-0.0741725713,0.0112992479,-0.1771455258,0.1546337306,0.1403729916,-0.3297455609,-0.1945308298,0.1805401146,0.0513517223,0.0468542874,0.3396641314,-0.3767827153,-0.1449093074,0.0857277736,-0.2972788513,-0.1406729817,-0.1555437893,0.3137725294,-0.203810975,-0.0596022606,-0.2211033255,-0.0271769408,0.1290350109,-0.1592079401,0.0224947892,-0.1568120122,-0.0343936272,-0.4068643153,0.0919899419,0.6147862673,-0.4071787894,-0.1555482447,-0.3654867709,-0.1499285698,0.3206854463,-0.2192046493,0.0420476161,0.205342114,-0.1884394288,-0.0670418441,0.5587269068,-0.4109594524,-0.2658071816,0.071330905,-0.1287924349,-0.0868593082,0.0406506956,-0.0299981683,0.0722305849,0.1137080416,0.4100970328,0.275131911,0.0011142003,0.0079012448,-0.1864448488,-0.2357116938,0.1991765499,0.0649753883,0.1384467632,0.1023558527,0.1406214237,-0.1971790642,0.0246631335,-0.0487639643,-0.0327245519,-0.057789661,0.5558087826,-0.0468545221,-0.044843737,-0.5518350005,-0.4227520227,0.1897544414,-0.0799524039,0.3541664779,-0.1335036159,-0.1872806996,-0.3264653981,0.0583028831,-0.1389628798,-0.0200660415,0.3176531494,0.1545748264,-0.1688733399,0.0288684927,-0.0564694032,0.2119377255,-0.0756313056,0.0736123845,-0.14293015,0.3080175817,-0.057636708,-0.3339879513,0.0377683677,0.2538016438,0.5117610097,-0.0004488377,-0.0633197874,0.3244804144,0.0866748691,-0.0496543907,-0.1650345176,-0.0999800116,0.1509415507,-0.2289367616,0.0971660241,0.0872650221,0.3168590069,-0.0662048236,0.0954951942,0.4775231183,0.0889250115,0.2869647741,-0.0390479192,0.2417568564,-0.0411586203,-0.0287830383,-0.0849919841,-0.1743532121,0.0761678517,0.1894836426,0.2709217072,-0.0320920832,-0.0377041772,0.0921959504,0.5580118299,0.0443215631,0.3538882732,-0.0804426596,-0.3818507195,0.0884140804,0.1868627965,0.2703929543,0.4774660766,0.3163056076,-0.1177275926,-0.0135746626,-0.0687375143,0.068971917,0.0899053961,0.0000686368,0.2182746232,0.0326346494,0.2071536779,0.0608280376,-0.2113714963,-0.0529055037,-0.1733764559,0.2231471837,-0.4077748656,0.1184415296,-0.3216187358,-0.1545066088,-0.0203860439,0.0009128465,-0.1665667146,-0.3700627089,-0.2923040688,0.0090758018,0.0586140528,0.1355309337,0.0729432553,0.0848084092,0.0303801391,0.0038109233,-0.1432915479,-0.1506637037,-0.197236672,0.1309271753,0.1724003851,0.2035219669,0.4579099715,-0.1723938286,0.1496831626,-0.0668234155,-0.2592738867,0.0691069737,-0.0358921215,0.5192118287,0.4751577079,0.1759489179,0.0373363346,-0.0723712072,0.3917131424,-0.337565273,-0.0363603607,0.2813171744,-0.2262491435,0.1912381798,-0.1627744734,-0.2709549367,-0.5850613117,-0.4314647317,0.0900498033,0.1581933945,0.1574169397,0.2290733755,0.1376304924,0.4989827573,0.0554460995,0.173627466,-0.1574210674,-0.0517017953,0.2869262695,-0.128268525,-0.3188622594,0.0627037063,-0.1610585004,0.2123719454,0.0095064696,-0.2091803849,-0.5421624184,-0.1937738359,0.248898387,-0.0088543072,0.1406644732,0.2820861042,0.1652371287,-0.171896413,-0.2008978277,-0.1451961696,-0.0958748311,0.2647386193,0.003278581,0.1268489063,0.3415829241,-0.1613709927,0.1591750383,0.0212520342,-0.1993087977,0.414999783,-0.2640288472,0.3553538322,-0.1553532481,-0.501110673,-0.1315999925,-0.034155868,0.0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{"html_url":"https:\/\/github.com\/huggingface\/datasets\/issues\/2869","title":"TypeError: 'NoneType' object is not callable","comments":"For that environment, I am sorry but I can't reproduce the bug: I can load the dataset without any problem.","body":"## Describe the bug\r\n\r\nTypeError: 'NoneType' object is not callable\r\n## Steps to reproduce the bug\r\n```python\r\nfrom datasets import load_dataset, load_metric\r\ndataset = datasets.load_dataset(\"glue\", 'cola')\r\n```\r\n\r\n## Expected results\r\nA clear and concise description of the expected results.\r\n\r\n## Actual results\r\nSpecify the actual results or traceback.\r\n\r\n## Environment info\r\n\r\n- `datasets` version: 1.11.0\r\n- Platform:\r\n- Python version: 3.7\r\n- PyArrow version:\r\n","comment_length":20,"text":"TypeError: 'NoneType' object is not callable\n## Describe the bug\r\n\r\nTypeError: 'NoneType' object is not callable\r\n## Steps to reproduce the bug\r\n```python\r\nfrom datasets import load_dataset, load_metric\r\ndataset = datasets.load_dataset(\"glue\", 'cola')\r\n```\r\n\r\n## Expected results\r\nA clear and concise description of the expected results.\r\n\r\n## Actual results\r\nSpecify the actual results or traceback.\r\n\r\n## Environment info\r\n\r\n- `datasets` version: 1.11.0\r\n- Platform:\r\n- Python version: 3.7\r\n- PyArrow version:\r\n\nFor that environment, I am sorry but I can't reproduce the bug: I can load the dataset without any problem.","embeddings":[-0.2608940005,-0.2007066011,0.0660000592,0.400195688,0.4645994604,0.0115246382,0.3240968585,0.0941318348,0.1896000355,0.3001818657,-0.2849094868,0.4667589068,-0.0366437919,0.1668581367,0.0777649358,-0.1253727525,-0.2606472373,0.2636680901,-0.1777470112,0.0613667145,-0.5233634114,0.0231986102,-0.2373635471,0.0858903304,-0.2768001556,-0.1533993483,-0.0281501114,0.079713583,-0.2462176234,-0.2805505991,0.4611611068,-0.0159628261,0.1640476584,0.5104134083,-0.0001023479,-0.0184572823,0.5836995244,0.0530032516,-0.0761517882,-0.4211900532,-0.2255626768,-0.3977999389,0.2870038748,-0.3548148572,-0.1275422126,0.0055780676,0.0405133776,-0.1819047779,0.262139827,0.3204336464,0.3242153823,0.5296897888,0.0114422301,-0.3599714339,0.1965120882,0.0808303058,-0.0636900738,0.3710546196,0.0265845973,-0.0474929884,0.2439645529,0.0870305225,-0.3353538513,0.0527608916,0.2847363651,0.0382548533,0.0095302081,-0.270170182,0.0964757726,0.3694062233,0.3855828047,-0.382620275,-0.1514433473,0.1750092059,0.2239649594,-0.2032708824,0.211649254,0.0468775555,0.0163049828,0.0907445624,-0.0130718844,0.1712200791,-0.2445577234,0.2749375999,-0.2020063251,0.1065498963,-0.1935495585,0.1447966695,0.0849972516,-0.1188601851,0.0763338953,-0.1345976889,-0.0619969517,0.0890901908,-0.2083802223,0.0349296518,0.1152055487,0.1535798907,0.1636114269,-0.2843680978,0.1018852517,-0.0238335412,0.0397984199,0.4501174688,0.2414609641,0.1897560805,0.2724782526,0.088692598,0.2660125494,0.066787757,-0.2417135537,-0.0120890485,-0.1543584764,-0.0610723533,0.2427943051,0.1411847323,0.5124645233,0.0189152192,-0.4501633942,-0.1742377281,-0.172133401,0.0913005546,0.2716411948,0.4277633727,-0.1526402384,0.0820783153,0.1332417876,0.1743281931,-0.1884514242,-0.2918041646,-0.3183253706,0.3539678454,-0.2528480887,-0.2349549532,0.1911386847,0.0579657778,-0.0449788868,0.0134671535,-0.0958473086,0.1027007028,0.0886686146,-0.2908604145,-0.2310647517,0.3132710755,0.2443055063,-0.0628327206,0.2800607383,-0.4668283463,-0.091596745,0.2292028666,-0.2898909748,-0.1671735048,-0.0732437372,0.2667024732,-0.3390546739,-0.0697349459,-0.2441662848,0.0466724299,0.0489577651,-0.1566343755,-0.1024087444,-0.0962615982,-0.112656869,-0.4234251976,0.0446183719,0.5165780783,-0.3751866817,-0.1992407143,-0.3078093231,-0.1091819853,0.352284044,-0.147301212,0.0145670017,0.0629001632,-0.1706566364,-0.0006857364,0.6008648276,-0.3847208321,-0.2815137208,0.0563009419,-0.0371869579,-0.1165539101,-0.0517222472,-0.0470438264,0.0718874335,0.135642916,0.4128215313,0.2446535677,0.0493588299,-0.0324675739,-0.2109587342,-0.2381093949,0.1280453652,0.151658386,0.1272704005,0.1630408317,0.0712505579,-0.2506299913,-0.0557164401,-0.0061886362,-0.1022448465,-0.0301638599,0.5365030169,-0.1599618644,-0.0399641804,-0.532881856,-0.3950176537,0.2330813259,-0.0779283196,0.2541694939,-0.105489023,-0.1700357646,-0.352982074,0.1019510254,-0.0170240067,0.0607618615,0.2853280008,0.0909614265,-0.2011494935,0.1454881579,-0.0809026584,0.1637727916,-0.0873494446,0.0512318462,0.0335052349,0.291667223,0.0747782141,-0.3356175423,0.0352951288,0.1897852123,0.4574481249,-0.0104598496,-0.0672703311,0.2640901804,0.070892185,-0.0788056329,-0.2070860565,-0.05381817,0.0879925042,-0.313064307,-0.0303661674,0.0379851758,0.3187099099,-0.0943738967,0.0625385866,0.6038931608,0.0668431222,0.1744620055,-0.0730603263,0.2714183331,0.0653238744,-0.0385558642,-0.149077177,-0.1286280155,0.0459849499,0.1176183671,0.2486371994,0.0061038891,-0.0880215764,-0.0231885035,0.5769147873,0.0913832113,0.3607913256,-0.1375178844,-0.4204133153,0.1137951836,0.0370061696,0.2525543571,0.5032669902,0.332315892,-0.1068636402,0.023629915,-0.0571720079,0.0404726267,0.1032921076,0.0494471341,0.2328974009,-0.0000240203,0.3057212234,-0.0209420472,-0.3462260365,0.1613915414,-0.053847719,0.1842062026,-0.3760870993,0.1845387518,-0.2747030258,-0.076757893,0.1425513774,-0.1027247906,-0.125005424,-0.3286290169,-0.2664072216,0.1081375405,0.1210905612,0.1938206255,0.1422476321,0.0763199255,0.0087453714,-0.0255289134,-0.1826611906,-0.1326549202,-0.1483921856,0.0880739987,0.1653355807,0.181941092,0.4119828641,-0.1496375948,0.2284882814,-0.0511268564,-0.245598793,0.100177817,-0.0573291741,0.5299291015,0.4086084068,0.0602920428,-0.1410644501,0.1004512459,0.4188910425,-0.4658019245,0.0264790654,0.363583684,-0.3070645034,0.0837264657,-0.2518776655,-0.1060993373,-0.5478299856,-0.4280380607,0.0683310926,0.0958201066,0.0449768677,0.172417596,0.1191176772,0.444096446,0.112392433,0.0157600436,-0.2330423892,-0.0854077637,0.3311132789,-0.1693256646,-0.3608907759,0.2042488754,-0.1323478222,0.2548744977,0.0285864808,-0.1609783471,-0.5243912339,-0.1556174606,0.2053533643,-0.005188724,-0.0110916914,0.2912253737,0.0402853899,-0.1367866844,-0.2726752162,-0.1413018703,-0.0488153882,0.2352696806,-0.0683882236,0.0471838973,0.2825388908,-0.1223018318,0.1436379254,-0.1094394699,-0.1853011698,0.4059675932,-0.3138792515,0.3742715716,-0.1514657587,-0.4665456712,-0.2108906806,0.0278709959,0.04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{"html_url":"https:\/\/github.com\/huggingface\/datasets\/issues\/2869","title":"TypeError: 'NoneType' object is not callable","comments":"One naive question: do you have internet access from the machine where you execute the code?","body":"## Describe the bug\r\n\r\nTypeError: 'NoneType' object is not callable\r\n## Steps to reproduce the bug\r\n```python\r\nfrom datasets import load_dataset, load_metric\r\ndataset = datasets.load_dataset(\"glue\", 'cola')\r\n```\r\n\r\n## Expected results\r\nA clear and concise description of the expected results.\r\n\r\n## Actual results\r\nSpecify the actual results or traceback.\r\n\r\n## Environment info\r\n\r\n- `datasets` version: 1.11.0\r\n- Platform:\r\n- Python version: 3.7\r\n- PyArrow version:\r\n","comment_length":16,"text":"TypeError: 'NoneType' object is not callable\n## Describe the bug\r\n\r\nTypeError: 'NoneType' object is not callable\r\n## Steps to reproduce the bug\r\n```python\r\nfrom datasets import load_dataset, load_metric\r\ndataset = datasets.load_dataset(\"glue\", 'cola')\r\n```\r\n\r\n## Expected results\r\nA clear and concise description of the expected results.\r\n\r\n## Actual results\r\nSpecify the actual results or traceback.\r\n\r\n## Environment info\r\n\r\n- `datasets` version: 1.11.0\r\n- Platform:\r\n- Python version: 3.7\r\n- PyArrow version:\r\n\nOne naive question: do you have internet access from the machine where you execute the code?","embeddings":[-0.2466751933,-0.2166070193,-0.0038837458,0.3586860299,0.4009475708,0.035536956,0.302520901,0.1521895081,0.1650357544,0.3566101193,-0.1779196858,0.4452134073,-0.0382566825,0.1990447938,0.0816193596,-0.0899617374,-0.2033715993,0.2447062582,-0.1842225194,0.010521763,-0.472876668,-0.0185202286,-0.1821441352,0.0199327096,-0.2480738759,-0.2002862543,-0.1163984835,0.0645010024,-0.2148991227,-0.2530295849,0.3632104993,0.0450640544,0.112367928,0.5467213392,-0.0001019842,-0.0862105861,0.4975403249,0.0762401372,-0.1133384928,-0.2227149755,-0.2704135478,-0.3431518078,0.1823247373,-0.4260804653,-0.0916544795,0.028890904,0.0123812426,-0.2481914461,0.3053150177,0.3072139323,0.339708358,0.4426216781,-0.0190175083,-0.3322893977,0.2162288725,0.0421040244,-0.0323108882,0.362215519,0.0633602291,-0.0537314415,0.1767412126,0.1824111044,-0.2867747545,-0.0029550304,0.2504888773,0.0876018703,-0.0245356578,-0.2595610619,0.1022056341,0.2668823004,0.2856886089,-0.4520843923,-0.1245521009,0.1114801839,0.1043578833,-0.2367105186,0.1430763751,-0.0036112065,-0.0197007451,0.1060361415,-0.0351593755,0.133691445,-0.231664747,0.2396647781,-0.209117204,0.1492936164,-0.1894275993,0.0967105776,0.0752299801,-0.0852814317,0.0532211438,-0.1713415682,0.021075597,0.0105691114,-0.1507948637,-0.0472187474,0.1830180287,0.1402942985,0.1534052491,-0.254579097,0.1078871489,-0.0058753905,0.1186242029,0.4159373343,0.154383108,0.2415641695,0.2150634825,0.0880800635,0.2187133878,0.1302518398,-0.2568061352,-0.0115337186,-0.0923354477,-0.0363530554,0.3084871173,0.1605716944,0.4978731573,0.0148101076,-0.43729496,-0.0482422002,-0.2314789593,0.1063998938,0.2851623595,0.3828612864,-0.2197893709,0.0280763004,0.1954128742,0.1463474184,-0.167907998,-0.2896850407,-0.3228367269,0.3451814651,-0.3253240883,-0.1996293813,0.1255358905,0.0956025869,-0.078586556,0.0480372384,-0.035712216,0.0553010367,0.0763480887,-0.2473653257,-0.1706801951,0.1856051236,0.2096802443,-0.1042110175,0.3234429955,-0.4836687446,-0.1027234495,0.1058211848,-0.2070778757,-0.0896052122,-0.0359199308,0.3180175722,-0.3513986468,-0.1008676514,-0.151536569,0.1008075848,0.0612458289,-0.1549359858,0.0268286895,-0.0838796198,-0.0460206904,-0.443877846,0.0987593383,0.5773766041,-0.3923660815,-0.1583535075,-0.3149743676,-0.0410091579,0.3569568694,-0.2316169292,0.1328972578,0.1612990648,-0.1505951583,0.0307376627,0.5494579673,-0.3594247699,-0.298289597,-0.0194503367,-0.1195071116,-0.0845604688,-0.0080690281,-0.1045471951,0.0758646503,0.1502961367,0.4117963314,0.2450776398,-0.0244919565,-0.0403468348,-0.2356397361,-0.2162138522,0.0301526804,0.107242316,0.1855029613,0.160957098,0.1086669937,-0.2254877985,0.0150828492,-0.0165604558,-0.0678272471,-0.0664632171,0.5562682748,-0.1198019162,-0.1030108109,-0.548601687,-0.3364979625,0.2137777209,-0.0644096881,0.2583818138,-0.1622745097,-0.1771243662,-0.4169964492,0.1173746884,-0.0591691211,0.0415956117,0.3479426801,0.1584892571,-0.1316381693,0.1201645657,-0.038923569,0.1554773301,-0.0975586995,0.0615419187,0.002867274,0.3624258637,0.0288160499,-0.3402500749,0.0125437025,0.2390864491,0.492028594,0.0251288041,-0.0366603509,0.3425714076,0.0153975571,-0.0589240976,-0.0713611618,0.0129818767,0.0355378203,-0.3102554679,0.0903075784,0.0740944669,0.274332881,-0.0501489863,0.0455586836,0.5526733398,0.0639084801,0.1631180197,-0.0774783418,0.2747522891,-0.1043333039,-0.0652063265,-0.1591193974,-0.1482455432,0.1165027544,0.0529977977,0.1941261292,-0.0445721783,-0.102411747,0.0711867437,0.6148303747,0.07447882,0.410061866,-0.1350679696,-0.3626484573,0.1363148987,0.0632819161,0.2627360523,0.4373947978,0.3606951535,-0.0552522913,0.0725060552,-0.0956002772,0.0463584624,0.0818468109,0.0780013651,0.1710066646,-0.0235593766,0.2648818195,0.0000485086,-0.302197367,0.0393880606,-0.2115725577,0.1999685317,-0.3442276716,0.2703234255,-0.2761572301,-0.1083705425,0.0665704906,-0.1341651529,-0.1119984984,-0.3972260058,-0.189691484,0.0495734848,0.0846979097,0.1596637964,0.1331482828,0.1780963689,0.0435850658,-0.0506396033,-0.2169364691,-0.0929441899,-0.1491832733,0.124519974,0.1127889976,0.2195498496,0.4563970566,-0.2264528573,0.2653894424,0.0405263342,-0.2521411479,0.1091040745,-0.1113009229,0.5382031798,0.4217015207,0.0849712491,-0.1375489533,0.0479233935,0.4025655687,-0.3987693489,0.0345607921,0.2760668993,-0.1733168364,0.0993671566,-0.2214981169,-0.1091302112,-0.4937289059,-0.430516243,0.0535355024,0.0940166637,0.0907848328,0.1661356688,0.1647697985,0.5161489248,0.0903599858,0.0853421465,-0.2309448719,-0.0928328782,0.2652721107,-0.1356292665,-0.3170882165,0.1164372712,-0.1327296942,0.1882433891,0.0346632898,-0.2173748314,-0.5696399808,-0.1761069298,0.2179042101,-0.0343912281,0.0933210403,0.26999861,0.0949118435,-0.1569222659,-0.2845886648,-0.1625180691,-0.09149611,0.1541405916,-0.0318299644,-0.0249708015,0.2786458135,-0.1161085069,0.1320557296,-0.0665605962,-0.2075443119,0.3825691044,-0.3480911255,0.3984667063,-0.0996405408,-0.5200996399,-0.1736067683,0.0353287831,-0.0609325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{"html_url":"https:\/\/github.com\/huggingface\/datasets\/issues\/2869","title":"TypeError: 'NoneType' object is not callable","comments":"> For that environment, I am sorry but I can't reproduce the bug: I can load the dataset without any problem.\r\n\r\nBut I can download other task dataset such as `dataset = load_dataset('squad')`. I don't know what went wrong. Thank you so much!","body":"## Describe the bug\r\n\r\nTypeError: 'NoneType' object is not callable\r\n## Steps to reproduce the bug\r\n```python\r\nfrom datasets import load_dataset, load_metric\r\ndataset = datasets.load_dataset(\"glue\", 'cola')\r\n```\r\n\r\n## Expected results\r\nA clear and concise description of the expected results.\r\n\r\n## Actual results\r\nSpecify the actual results or traceback.\r\n\r\n## Environment info\r\n\r\n- `datasets` version: 1.11.0\r\n- Platform:\r\n- Python version: 3.7\r\n- PyArrow version:\r\n","comment_length":43,"text":"TypeError: 'NoneType' object is not callable\n## Describe the bug\r\n\r\nTypeError: 'NoneType' object is not callable\r\n## Steps to reproduce the bug\r\n```python\r\nfrom datasets import load_dataset, load_metric\r\ndataset = datasets.load_dataset(\"glue\", 'cola')\r\n```\r\n\r\n## Expected results\r\nA clear and concise description of the expected results.\r\n\r\n## Actual results\r\nSpecify the actual results or traceback.\r\n\r\n## Environment info\r\n\r\n- `datasets` version: 1.11.0\r\n- Platform:\r\n- Python version: 3.7\r\n- PyArrow version:\r\n\n> For that environment, I am sorry but I can't reproduce the bug: I can load the dataset without any problem.\r\n\r\nBut I can download other task dataset such as `dataset = load_dataset('squad')`. I don't know what went wrong. Thank you so much!","embeddings":[-0.2479815185,-0.3217719793,0.0763500705,0.4701036215,0.4953816831,0.0185086466,0.3540442288,0.0614947863,0.2402586043,0.3128617108,-0.276409179,0.4540319443,-0.0626192093,0.1033334509,0.1556583196,-0.1557852626,-0.2255954146,0.1679677218,-0.0779853985,0.1020552889,-0.4996086359,-0.0293463878,-0.2632772624,0.1951634139,-0.1559653133,-0.1863708049,-0.0737683624,0.0807150304,-0.1478742063,-0.2537325919,0.5681656003,-0.1083382666,0.2122284323,0.5568339229,-0.0001079977,0.0223296881,0.5475671887,0.0222912077,-0.181262657,-0.5533769727,-0.1755931079,-0.4152663052,0.3201848865,-0.3743572831,-0.1145967916,0.07909403,0.1459887475,-0.1117727757,0.3590354919,0.4540637732,0.2855804861,0.7018626928,-0.0197485741,-0.342117399,0.1333463937,0.0541447364,-0.1285513192,0.2871590555,0.1371662915,-0.0082335789,0.4334596992,-0.0373312868,-0.3705081344,0.090445593,0.3338054717,0.0173116531,0.0456819162,-0.4084408581,0.0989959762,0.3526202738,0.429132849,-0.2789865732,-0.1445523649,0.1062167063,0.2393290848,-0.061995618,0.1765795946,0.0757426322,-0.0298103616,0.0413528346,0.0463871136,0.1718294621,-0.2779862285,0.2125990689,-0.1284300834,-0.0353670754,-0.1932722479,0.2176218927,0.038036257,-0.0971409827,0.0310842935,-0.1765830368,0.0018496352,0.1451958716,-0.3074558675,-0.0370389931,0.1003599092,0.0206406731,0.2188021392,-0.2397266477,0.2141465396,-0.0814069584,0.0014896236,0.3541437387,0.346270442,0.2386882752,0.2158100009,0.1408458501,0.1517539918,0.022729395,-0.1789033264,0.0621397234,-0.2835336924,-0.0460709594,0.2485666424,0.2519290149,0.62191993,0.0226177946,-0.5257906914,-0.1946161389,-0.0697027519,0.0744651854,0.1489258707,0.4044239819,-0.2522181571,0.0734108984,0.0253312159,0.2898434997,-0.1949325502,-0.3826381862,-0.250428021,0.1526836008,-0.268599242,-0.270571053,0.24026227,0.0925443023,0.0312479846,0.0430606492,-0.2270833105,0.0859438777,0.1672883034,-0.3710010648,-0.3183163404,0.203129068,0.2981905341,0.013275166,0.2797108889,-0.3238164485,-0.0456261188,0.2975198925,-0.3877721429,-0.1205919012,-0.1263929754,0.1935602129,-0.3629306555,-0.1565003395,-0.5364289284,0.0805210099,0.0401180312,-0.0892851651,-0.0573687814,-0.1425406635,-0.0697560161,-0.3583665192,-0.0551820286,0.4761920869,-0.2147590965,-0.2288669497,-0.3011833727,-0.2443191111,0.3596111238,-0.1455561668,-0.0476030186,0.2456554323,-0.1385620087,0.0195395816,0.642037034,-0.5051142573,-0.2810824811,0.1532612592,-0.1181739345,-0.076440759,-0.0949228108,-0.0446148701,0.2863956094,0.0758701265,0.3716195226,0.3349784911,0.0008509188,-0.0305648036,-0.131107524,-0.2105619609,0.2909449637,0.1949024647,0.2361101955,0.2211598307,0.0545335151,-0.1998473704,-0.0358049236,0.0527693219,-0.0813176557,-0.0201197267,0.4355514646,-0.0436968356,-0.0257663354,-0.5290393233,-0.5686805844,0.1899500042,-0.0841612145,0.1826199889,-0.115729481,-0.0673960596,-0.3040428758,0.042624291,-0.078021042,-0.0217457879,0.182378903,0.0794131309,-0.2668655217,0.0558810793,-0.0770036429,0.1909017116,-0.1526100785,0.095781371,0.1212609336,0.2611952722,0.1016982198,-0.3987404406,-0.0017724648,0.1362133324,0.4781362414,0.0183626059,-0.0826574564,0.1729604006,0.0458101444,-0.1020453051,-0.2171081603,-0.1762046367,0.0575558543,-0.3366027176,-0.0447619446,0.1347156912,0.3460848331,-0.160481751,0.0836437568,0.4571962357,-0.0167928878,0.3421861231,-0.1437793821,0.2406296581,0.0525164865,-0.0606726483,-0.1331075728,0.1804715544,0.1290700138,0.0913747177,0.2827326059,-0.0355854295,-0.2706822753,0.0046682684,0.6162089109,0.0299597997,0.3194147646,-0.0831516907,-0.33788234,0.1462022662,0.0000325413,0.0418264307,0.5353927612,0.2632282078,-0.1103599295,-0.0164734125,0.0007065669,0.0515690558,0.0333776362,0.069090277,0.2856582701,0.0461304933,0.2759691477,0.0386635847,-0.2005325556,0.1444482505,-0.0084361024,0.1951348335,-0.3969622254,0.1878786534,-0.1423569769,-0.1165724248,-0.0304603409,0.1370230019,-0.1042248011,-0.1990451217,-0.3134959638,0.1589778066,0.2137130797,0.1265260726,0.1616361737,0.0088405497,-0.0113126803,-0.0408857763,-0.1634947658,-0.1458583921,-0.1861838251,0.0462163053,0.1946974099,0.0735260546,0.3495370448,-0.1900552809,0.1245678738,-0.1000485346,-0.1240719408,0.0189164113,-0.0774424672,0.5409654975,0.2683835626,0.2594893575,-0.162481755,0.1194115579,0.3791869879,-0.4782688916,-0.0512078144,0.3330359757,-0.334943682,0.0967640132,-0.2121215761,-0.3186377287,-0.5942174196,-0.3938197494,0.2054920793,-0.0129524982,0.069686912,0.3293658495,0.1026407331,0.4716491699,0.2177550197,0.0916105509,-0.1844632626,-0.0342024155,0.2794252634,-0.1346675754,-0.3884647787,0.1575419456,-0.0971030593,0.2852425575,0.0799094364,-0.1245648041,-0.2630615234,-0.0669115633,0.197574988,-0.1246708706,-0.1207072735,0.3231537342,-0.0845761523,-0.0469946526,-0.2115352452,-0.1213957146,-0.0482246727,0.3319038451,-0.0071725766,0.0945167914,0.3415936828,-0.1624031961,0.3309932351,-0.0868292972,-0.0393967107,0.4093084633,-0.3032317758,0.2776997089,-0.0965931192,-0.4811325371,0.0073162122,0.0563486181,0.06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{"html_url":"https:\/\/github.com\/huggingface\/datasets\/issues\/2866","title":"\"counter\" dataset raises an error in normal mode, but not in streaming mode","comments":"Hi @severo, thanks for reporting.\r\n\r\nJust note that currently not all canonical datasets support streaming mode: this is one case!\r\n\r\nAll datasets that use `pathlib` joins (using `\/`) instead of `os.path.join` (as in this dataset) do not support streaming mode yet.","body":"## Describe the bug\r\n\r\n`counter` dataset raises an error on `load_dataset()`, but simply returns an empty iterator in streaming mode.\r\n\r\n## Steps to reproduce the bug\r\n\r\n```python\r\n>>> import datasets as ds\r\n>>> a = ds.load_dataset('counter', split=\"train\", streaming=False)\r\nUsing custom data configuration default\r\nDownloading and preparing dataset counter\/default (download: 1.29 MiB, generated: 2.48 MiB, post-processed: Unknown size, total: 3.77 MiB) to \/home\/slesage\/.cache\/huggingface\/datasets\/counter\/default\/1.0.0\/9f84962fa0f35bec5a34fe0bdff8681838d497008c457f7856c48654476ec0e9...\r\nTraceback (most recent call last):\r\n File \"\/home\/slesage\/hf\/datasets-preview-backend\/.venv\/lib\/python3.8\/site-packages\/datasets\/builder.py\", line 726, in _download_and_prepare\r\n self._prepare_split(split_generator, **prepare_split_kwargs)\r\n File \"\/home\/slesage\/hf\/datasets-preview-backend\/.venv\/lib\/python3.8\/site-packages\/datasets\/builder.py\", line 1124, in _prepare_split\r\n for key, record in utils.tqdm(\r\n File \"\/home\/slesage\/hf\/datasets-preview-backend\/.venv\/lib\/python3.8\/site-packages\/tqdm\/std.py\", line 1185, in __iter__\r\n for obj in iterable:\r\n File \"\/home\/slesage\/.cache\/huggingface\/modules\/datasets_modules\/datasets\/counter\/9f84962fa0f35bec5a34fe0bdff8681838d497008c457f7856c48654476ec0e9\/counter.py\", line 161, in _generate_examples\r\n with derived_file.open(encoding=\"utf-8\") as f:\r\n File \"\/home\/slesage\/.pyenv\/versions\/3.8.11\/lib\/python3.8\/pathlib.py\", line 1222, in open\r\n return io.open(self, mode, buffering, encoding, errors, newline,\r\n File \"\/home\/slesage\/.pyenv\/versions\/3.8.11\/lib\/python3.8\/pathlib.py\", line 1078, in _opener\r\n return self._accessor.open(self, flags, mode)\r\nFileNotFoundError: [Errno 2] No such file or directory: '\/home\/slesage\/.cache\/huggingface\/datasets\/downloads\/extracted\/b57aa6db5601a738e57b95c1fd8cced54ff28fc540efcdaf0f6c4f1bb5dfe211\/COUNTER\/0032p.xml'\r\n\r\nDuring handling of the above exception, another exception occurred:\r\n\r\nTraceback (most recent call last):\r\n File \"\", line 1, in \r\n File \"\/home\/slesage\/hf\/datasets-preview-backend\/.venv\/lib\/python3.8\/site-packages\/datasets\/load.py\", line 1112, in load_dataset\r\n builder_instance.download_and_prepare(\r\n File \"\/home\/slesage\/hf\/datasets-preview-backend\/.venv\/lib\/python3.8\/site-packages\/datasets\/builder.py\", line 636, in download_and_prepare\r\n self._download_and_prepare(\r\n File \"\/home\/slesage\/hf\/datasets-preview-backend\/.venv\/lib\/python3.8\/site-packages\/datasets\/builder.py\", line 728, in _download_and_prepare\r\n raise OSError(\r\nOSError: Cannot find data file.\r\nOriginal error:\r\n[Errno 2] No such file or directory: '\/home\/slesage\/.cache\/huggingface\/datasets\/downloads\/extracted\/b57aa6db5601a738e57b95c1fd8cced54ff28fc540efcdaf0f6c4f1bb5dfe211\/COUNTER\/0032p.xml'\r\n```\r\n\r\n```python\r\n>>> import datasets as ds\r\n>>> b = ds.load_dataset('counter', split=\"train\", streaming=True)\r\nUsing custom data configuration default\r\n>>> list(b)\r\n[]\r\n```\r\n\r\n## Expected results\r\n\r\nAn exception should be raised in streaming mode\r\n\r\n## Actual results\r\n\r\nNo exception is raised in streaming mode: there is no way to tell if something has broken or if the dataset is simply empty.\r\n\r\n## Environment info\r\n\r\n- `datasets` version: 1.11.1.dev0\r\n- Platform: Linux-5.11.0-1016-aws-x86_64-with-glibc2.29\r\n- Python version: 3.8.11\r\n- PyArrow version: 4.0.1\r\n","comment_length":41,"text":"\"counter\" dataset raises an error in normal mode, but not in streaming mode\n## Describe the bug\r\n\r\n`counter` dataset raises an error on `load_dataset()`, but simply returns an empty iterator in streaming mode.\r\n\r\n## Steps to reproduce the bug\r\n\r\n```python\r\n>>> import datasets as ds\r\n>>> a = ds.load_dataset('counter', split=\"train\", streaming=False)\r\nUsing custom data configuration default\r\nDownloading and preparing dataset counter\/default (download: 1.29 MiB, generated: 2.48 MiB, post-processed: Unknown size, total: 3.77 MiB) to \/home\/slesage\/.cache\/huggingface\/datasets\/counter\/default\/1.0.0\/9f84962fa0f35bec5a34fe0bdff8681838d497008c457f7856c48654476ec0e9...\r\nTraceback (most recent call last):\r\n File \"\/home\/slesage\/hf\/datasets-preview-backend\/.venv\/lib\/python3.8\/site-packages\/datasets\/builder.py\", line 726, in _download_and_prepare\r\n self._prepare_split(split_generator, **prepare_split_kwargs)\r\n File \"\/home\/slesage\/hf\/datasets-preview-backend\/.venv\/lib\/python3.8\/site-packages\/datasets\/builder.py\", line 1124, in _prepare_split\r\n for key, record in utils.tqdm(\r\n File \"\/home\/slesage\/hf\/datasets-preview-backend\/.venv\/lib\/python3.8\/site-packages\/tqdm\/std.py\", line 1185, in __iter__\r\n for obj in iterable:\r\n File \"\/home\/slesage\/.cache\/huggingface\/modules\/datasets_modules\/datasets\/counter\/9f84962fa0f35bec5a34fe0bdff8681838d497008c457f7856c48654476ec0e9\/counter.py\", line 161, in _generate_examples\r\n with derived_file.open(encoding=\"utf-8\") as f:\r\n File \"\/home\/slesage\/.pyenv\/versions\/3.8.11\/lib\/python3.8\/pathlib.py\", line 1222, in open\r\n return io.open(self, mode, buffering, encoding, errors, newline,\r\n File \"\/home\/slesage\/.pyenv\/versions\/3.8.11\/lib\/python3.8\/pathlib.py\", line 1078, in _opener\r\n return self._accessor.open(self, flags, mode)\r\nFileNotFoundError: [Errno 2] No such file or directory: '\/home\/slesage\/.cache\/huggingface\/datasets\/downloads\/extracted\/b57aa6db5601a738e57b95c1fd8cced54ff28fc540efcdaf0f6c4f1bb5dfe211\/COUNTER\/0032p.xml'\r\n\r\nDuring handling of the above exception, another exception occurred:\r\n\r\nTraceback (most recent call last):\r\n File \"\", line 1, in \r\n File \"\/home\/slesage\/hf\/datasets-preview-backend\/.venv\/lib\/python3.8\/site-packages\/datasets\/load.py\", line 1112, in load_dataset\r\n builder_instance.download_and_prepare(\r\n File \"\/home\/slesage\/hf\/datasets-preview-backend\/.venv\/lib\/python3.8\/site-packages\/datasets\/builder.py\", line 636, in download_and_prepare\r\n self._download_and_prepare(\r\n File \"\/home\/slesage\/hf\/datasets-preview-backend\/.venv\/lib\/python3.8\/site-packages\/datasets\/builder.py\", line 728, in _download_and_prepare\r\n raise OSError(\r\nOSError: Cannot find data file.\r\nOriginal error:\r\n[Errno 2] No such file or directory: '\/home\/slesage\/.cache\/huggingface\/datasets\/downloads\/extracted\/b57aa6db5601a738e57b95c1fd8cced54ff28fc540efcdaf0f6c4f1bb5dfe211\/COUNTER\/0032p.xml'\r\n```\r\n\r\n```python\r\n>>> import datasets as ds\r\n>>> b = ds.load_dataset('counter', split=\"train\", streaming=True)\r\nUsing custom data configuration default\r\n>>> list(b)\r\n[]\r\n```\r\n\r\n## Expected results\r\n\r\nAn exception should be raised in streaming mode\r\n\r\n## Actual results\r\n\r\nNo exception is raised in streaming mode: there is no way to tell if something has broken or if the dataset is simply empty.\r\n\r\n## Environment info\r\n\r\n- `datasets` version: 1.11.1.dev0\r\n- Platform: Linux-5.11.0-1016-aws-x86_64-with-glibc2.29\r\n- Python version: 3.8.11\r\n- PyArrow version: 4.0.1\r\n\nHi @severo, thanks for reporting.\r\n\r\nJust note that currently not all canonical datasets support streaming mode: this is one case!\r\n\r\nAll datasets that use `pathlib` joins (using `\/`) instead of `os.path.join` (as in this dataset) do not support streaming mode yet.","embeddings":[-0.4171527922,-0.2417904586,0.0004065955,0.1936673522,0.187378943,0.0572046041,0.5056346059,0.1059950739,0.1942841411,0.1353349984,0.0698306859,0.1297213137,-0.2097095996,0.3238820434,0.0893105865,0.0787798092,0.0210811216,0.1588886231,0.0335764661,0.0774051622,-0.2157031149,0.2028147876,-0.3865993321,-0.3339452446,-0.1323988438,0.1463262439,0.1100258455,0.1485300958,0.1753474176,-0.6307143569,0.2604337335,0.0775746107,0.2229160666,0.7022691965,-0.0001109225,0.1807845384,0.4209491313,-0.138756454,-0.5919362307,-0.2953981757,-0.3683612645,-0.3184562027,0.1747525334,-0.1460207105,-0.1155984327,0.0416297689,-0.2645150125,-0.4016982019,0.2196300626,0.44546175,0.2117786556,0.4712955952,-0.1509929299,-0.1280123889,0.1274099499,0.0086588459,-0.1527633816,0.2987458408,0.0759947449,0.2464787662,-0.030185163,0.400646776,-0.0477484316,0.1930941492,0.2172417343,-0.0403118059,-0.0638400242,-0.2730312347,0.077968359,0.2267186195,0.3892460763,-0.3428210616,-0.1372255683,-0.4340849519,-0.0550197884,-0.4057786763,0.2461039424,-0.0120554632,-0.2402932793,0.0848756358,-0.3027442396,0.1529166549,0.0393640399,-0.1008400992,-0.0583141893,0.1315543056,-0.0259356182,0.1768467724,-0.0182867814,-0.0394550189,0.1186181977,-0.2750140131,-0.0770448223,0.1979830861,-0.5796865821,0.0617473088,0.3563539386,-0.483120203,0.2248359621,0.0766406581,0.3723810911,0.0808451474,0.1294206977,-0.0054175756,0.3990353644,0.2017412037,0.1839399487,-0.2446381003,0.1665526032,0.3514225185,-0.0046332767,-0.3092014194,0.0455646105,-0.0928959474,0.2623339891,0.0135595212,0.3132420182,0.0522785522,-0.3759042025,0.1984404474,-0.2598961294,0.0621640086,0.3526681066,0.2470078766,0.31240955,0.331681639,-0.1649695039,0.2566638589,-0.170162797,-0.4501440227,-0.2399167717,-0.3115689754,0.0298984032,0.0585324802,0.2430290878,-0.4115727246,0.2127870023,0.1143840179,0.2314704955,-0.1508325785,0.2350654602,-0.3559949696,0.2714752853,0.4170607924,0.2132242918,0.30559659,0.2130860984,-0.078121908,0.0865608603,0.1292979866,-0.0135739716,-0.3794960678,0.1188295856,0.241816327,-0.1854702979,0.0091848904,-0.1626182795,0.3421088755,0.0492594019,-0.490398556,0.1006153971,-0.1393199116,-0.1788205355,-0.1388723552,0.3767809272,0.4191721678,-0.3163481355,-0.0631755143,-0.2223777026,-0.1933458447,0.3068187237,0.0627034158,-0.1867482066,0.0545980632,-0.198907733,-0.0324022733,0.1652611792,-0.133823514,-0.4505629539,0.3839800358,-0.1003188565,0.4216839075,0.003368129,0.0348957814,0.0462126397,-0.1179660112,0.2082362026,-0.0482203104,-0.3429863751,0.1301704794,-0.2063286006,0.0962786973,0.1745322049,0.0263372958,0.1080550551,0.145677641,0.1164149791,-0.0307607576,0.1964359283,-0.0440106578,0.0053364923,0.2909404337,0.0424242578,-0.0588180684,0.000367176,-0.1810717881,-0.4702015221,0.3554164469,0.2276427895,0.0265536476,0.0678977966,0.0377307907,-0.3046529889,0.141935274,-0.2711749077,-0.4771591425,0.1949529499,0.3907288015,-0.1118490696,0.0888262317,-0.277343154,0.3020512462,-0.1696492136,-0.1002230644,-0.1528523117,0.1263132989,0.1340726018,-0.3565647304,-0.0337362103,0.0897880867,0.2519123256,-0.1344926953,-0.234999463,0.4969182014,0.1069902405,0.0699909925,-0.0781737417,-0.173212111,0.1972211748,-0.1314566433,-0.2321656495,0.4724216461,0.3397763968,-0.2434855402,-0.1393338144,0.186793372,-0.2051788419,0.2564637661,-0.0694206432,0.0912937298,0.328330636,0.3197959363,-0.0928376839,-0.0212596133,0.2077185959,-0.1777403057,0.1031239405,-0.1610922515,-0.2532593608,-0.0428625979,0.1946246028,0.1955534518,0.0406559408,0.0529748946,-0.3236100376,-0.2086987048,0.3706597984,0.0880206153,0.4839422405,0.0791207775,0.2721221745,0.0922638103,-0.0106658787,-0.1263325214,0.2971056998,0.0752388388,0.0984970778,0.3603755832,0.2497458905,-0.1204286814,-0.4016779959,-0.2251661271,0.1358991563,0.133663699,-0.3052351177,0.0297692902,-0.1282226741,-0.2748440802,0.1133073941,-0.4021544755,-0.2225447446,-0.3337677121,-0.2058949023,0.3451238573,-0.1469643116,0.1498992443,-0.180475831,-0.0882678479,0.308257699,-0.0031163315,-0.2322359681,-0.1135618612,-0.2594828606,0.03214081,0.0412808843,-0.3234791756,0.434748143,-0.1234986335,-0.1801847816,-0.1512944698,-0.0745449811,0.1740905792,0.0435857289,0.2001399845,0.2743394077,0.2197588384,0.039221812,0.0045702313,0.4241863489,-0.2919014394,0.0146440258,0.3762074709,0.2012005895,0.2666827142,-0.2297265977,-0.2490313053,-0.0779714212,-0.4310797453,-0.174230665,-0.0227724351,0.0273636784,0.4507275522,0.262483865,0.3891989887,0.3127643466,-0.0566316359,-0.3348741829,-0.3475183249,0.1322237253,-0.1974763721,-0.4097188413,0.1144938767,0.0011452077,0.1664847434,0.2781507671,-0.3012993634,0.0803638548,-0.0408363193,0.1188741624,-0.05930195,-0.1120122075,0.1432749033,-0.06137814,-0.1019272059,-0.3338415623,-0.1850921214,-0.1276152432,-0.1529603153,0.2324113101,0.0381841213,0.5950309038,0.207781747,0.5280631185,0.4836455286,0.0581972674,0.6157116294,-0.2377375066,0.2161288112,-0.3673551977,-0.3503070772,0.2225523591,-0.0085880933,-0.1320838332,0.208188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{"html_url":"https:\/\/github.com\/huggingface\/datasets\/issues\/2866","title":"\"counter\" dataset raises an error in normal mode, but not in streaming mode","comments":"OK. Do you think it's possible to detect this, and raise an exception (maybe `NotImplementedError`, or a specific `StreamingError`)?","body":"## Describe the bug\r\n\r\n`counter` dataset raises an error on `load_dataset()`, but simply returns an empty iterator in streaming mode.\r\n\r\n## Steps to reproduce the bug\r\n\r\n```python\r\n>>> import datasets as ds\r\n>>> a = ds.load_dataset('counter', split=\"train\", streaming=False)\r\nUsing custom data configuration default\r\nDownloading and preparing dataset counter\/default (download: 1.29 MiB, generated: 2.48 MiB, post-processed: Unknown size, total: 3.77 MiB) to \/home\/slesage\/.cache\/huggingface\/datasets\/counter\/default\/1.0.0\/9f84962fa0f35bec5a34fe0bdff8681838d497008c457f7856c48654476ec0e9...\r\nTraceback (most recent call last):\r\n File \"\/home\/slesage\/hf\/datasets-preview-backend\/.venv\/lib\/python3.8\/site-packages\/datasets\/builder.py\", line 726, in _download_and_prepare\r\n self._prepare_split(split_generator, **prepare_split_kwargs)\r\n File \"\/home\/slesage\/hf\/datasets-preview-backend\/.venv\/lib\/python3.8\/site-packages\/datasets\/builder.py\", line 1124, in _prepare_split\r\n for key, record in utils.tqdm(\r\n File \"\/home\/slesage\/hf\/datasets-preview-backend\/.venv\/lib\/python3.8\/site-packages\/tqdm\/std.py\", line 1185, in __iter__\r\n for obj in iterable:\r\n File \"\/home\/slesage\/.cache\/huggingface\/modules\/datasets_modules\/datasets\/counter\/9f84962fa0f35bec5a34fe0bdff8681838d497008c457f7856c48654476ec0e9\/counter.py\", line 161, in _generate_examples\r\n with derived_file.open(encoding=\"utf-8\") as f:\r\n File \"\/home\/slesage\/.pyenv\/versions\/3.8.11\/lib\/python3.8\/pathlib.py\", line 1222, in open\r\n return io.open(self, mode, buffering, encoding, errors, newline,\r\n File \"\/home\/slesage\/.pyenv\/versions\/3.8.11\/lib\/python3.8\/pathlib.py\", line 1078, in _opener\r\n return self._accessor.open(self, flags, mode)\r\nFileNotFoundError: [Errno 2] No such file or directory: '\/home\/slesage\/.cache\/huggingface\/datasets\/downloads\/extracted\/b57aa6db5601a738e57b95c1fd8cced54ff28fc540efcdaf0f6c4f1bb5dfe211\/COUNTER\/0032p.xml'\r\n\r\nDuring handling of the above exception, another exception occurred:\r\n\r\nTraceback (most recent call last):\r\n File \"\", line 1, in \r\n File \"\/home\/slesage\/hf\/datasets-preview-backend\/.venv\/lib\/python3.8\/site-packages\/datasets\/load.py\", line 1112, in load_dataset\r\n builder_instance.download_and_prepare(\r\n File \"\/home\/slesage\/hf\/datasets-preview-backend\/.venv\/lib\/python3.8\/site-packages\/datasets\/builder.py\", line 636, in download_and_prepare\r\n self._download_and_prepare(\r\n File \"\/home\/slesage\/hf\/datasets-preview-backend\/.venv\/lib\/python3.8\/site-packages\/datasets\/builder.py\", line 728, in _download_and_prepare\r\n raise OSError(\r\nOSError: Cannot find data file.\r\nOriginal error:\r\n[Errno 2] No such file or directory: '\/home\/slesage\/.cache\/huggingface\/datasets\/downloads\/extracted\/b57aa6db5601a738e57b95c1fd8cced54ff28fc540efcdaf0f6c4f1bb5dfe211\/COUNTER\/0032p.xml'\r\n```\r\n\r\n```python\r\n>>> import datasets as ds\r\n>>> b = ds.load_dataset('counter', split=\"train\", streaming=True)\r\nUsing custom data configuration default\r\n>>> list(b)\r\n[]\r\n```\r\n\r\n## Expected results\r\n\r\nAn exception should be raised in streaming mode\r\n\r\n## Actual results\r\n\r\nNo exception is raised in streaming mode: there is no way to tell if something has broken or if the dataset is simply empty.\r\n\r\n## Environment info\r\n\r\n- `datasets` version: 1.11.1.dev0\r\n- Platform: Linux-5.11.0-1016-aws-x86_64-with-glibc2.29\r\n- Python version: 3.8.11\r\n- PyArrow version: 4.0.1\r\n","comment_length":19,"text":"\"counter\" dataset raises an error in normal mode, but not in streaming mode\n## Describe the bug\r\n\r\n`counter` dataset raises an error on `load_dataset()`, but simply returns an empty iterator in streaming mode.\r\n\r\n## Steps to reproduce the bug\r\n\r\n```python\r\n>>> import datasets as ds\r\n>>> a = ds.load_dataset('counter', split=\"train\", streaming=False)\r\nUsing custom data configuration default\r\nDownloading and preparing dataset counter\/default (download: 1.29 MiB, generated: 2.48 MiB, post-processed: Unknown size, total: 3.77 MiB) to \/home\/slesage\/.cache\/huggingface\/datasets\/counter\/default\/1.0.0\/9f84962fa0f35bec5a34fe0bdff8681838d497008c457f7856c48654476ec0e9...\r\nTraceback (most recent call last):\r\n File \"\/home\/slesage\/hf\/datasets-preview-backend\/.venv\/lib\/python3.8\/site-packages\/datasets\/builder.py\", line 726, in _download_and_prepare\r\n self._prepare_split(split_generator, **prepare_split_kwargs)\r\n File \"\/home\/slesage\/hf\/datasets-preview-backend\/.venv\/lib\/python3.8\/site-packages\/datasets\/builder.py\", line 1124, in _prepare_split\r\n for key, record in utils.tqdm(\r\n File \"\/home\/slesage\/hf\/datasets-preview-backend\/.venv\/lib\/python3.8\/site-packages\/tqdm\/std.py\", line 1185, in __iter__\r\n for obj in iterable:\r\n File \"\/home\/slesage\/.cache\/huggingface\/modules\/datasets_modules\/datasets\/counter\/9f84962fa0f35bec5a34fe0bdff8681838d497008c457f7856c48654476ec0e9\/counter.py\", line 161, in _generate_examples\r\n with derived_file.open(encoding=\"utf-8\") as f:\r\n File \"\/home\/slesage\/.pyenv\/versions\/3.8.11\/lib\/python3.8\/pathlib.py\", line 1222, in open\r\n return io.open(self, mode, buffering, encoding, errors, newline,\r\n File \"\/home\/slesage\/.pyenv\/versions\/3.8.11\/lib\/python3.8\/pathlib.py\", line 1078, in _opener\r\n return self._accessor.open(self, flags, mode)\r\nFileNotFoundError: [Errno 2] No such file or directory: '\/home\/slesage\/.cache\/huggingface\/datasets\/downloads\/extracted\/b57aa6db5601a738e57b95c1fd8cced54ff28fc540efcdaf0f6c4f1bb5dfe211\/COUNTER\/0032p.xml'\r\n\r\nDuring handling of the above exception, another exception occurred:\r\n\r\nTraceback (most recent call last):\r\n File \"\", line 1, in \r\n File \"\/home\/slesage\/hf\/datasets-preview-backend\/.venv\/lib\/python3.8\/site-packages\/datasets\/load.py\", line 1112, in load_dataset\r\n builder_instance.download_and_prepare(\r\n File \"\/home\/slesage\/hf\/datasets-preview-backend\/.venv\/lib\/python3.8\/site-packages\/datasets\/builder.py\", line 636, in download_and_prepare\r\n self._download_and_prepare(\r\n File \"\/home\/slesage\/hf\/datasets-preview-backend\/.venv\/lib\/python3.8\/site-packages\/datasets\/builder.py\", line 728, in _download_and_prepare\r\n raise OSError(\r\nOSError: Cannot find data file.\r\nOriginal error:\r\n[Errno 2] No such file or directory: '\/home\/slesage\/.cache\/huggingface\/datasets\/downloads\/extracted\/b57aa6db5601a738e57b95c1fd8cced54ff28fc540efcdaf0f6c4f1bb5dfe211\/COUNTER\/0032p.xml'\r\n```\r\n\r\n```python\r\n>>> import datasets as ds\r\n>>> b = ds.load_dataset('counter', split=\"train\", streaming=True)\r\nUsing custom data configuration default\r\n>>> list(b)\r\n[]\r\n```\r\n\r\n## Expected results\r\n\r\nAn exception should be raised in streaming mode\r\n\r\n## Actual results\r\n\r\nNo exception is raised in streaming mode: there is no way to tell if something has broken or if the dataset is simply empty.\r\n\r\n## Environment info\r\n\r\n- `datasets` version: 1.11.1.dev0\r\n- Platform: Linux-5.11.0-1016-aws-x86_64-with-glibc2.29\r\n- Python version: 3.8.11\r\n- PyArrow version: 4.0.1\r\n\nOK. Do you think it's possible to detect this, and raise an exception (maybe `NotImplementedError`, or a specific `StreamingError`)?","embeddings":[-0.4171527922,-0.2417904586,0.0004065955,0.1936673522,0.187378943,0.0572046041,0.5056346059,0.1059950739,0.1942841411,0.1353349984,0.0698306859,0.1297213137,-0.2097095996,0.3238820434,0.0893105865,0.0787798092,0.0210811216,0.1588886231,0.0335764661,0.0774051622,-0.2157031149,0.2028147876,-0.3865993321,-0.3339452446,-0.1323988438,0.1463262439,0.1100258455,0.1485300958,0.1753474176,-0.6307143569,0.2604337335,0.0775746107,0.2229160666,0.7022691965,-0.0001109225,0.1807845384,0.4209491313,-0.138756454,-0.5919362307,-0.2953981757,-0.3683612645,-0.3184562027,0.1747525334,-0.1460207105,-0.1155984327,0.0416297689,-0.2645150125,-0.4016982019,0.2196300626,0.44546175,0.2117786556,0.4712955952,-0.1509929299,-0.1280123889,0.1274099499,0.0086588459,-0.1527633816,0.2987458408,0.0759947449,0.2464787662,-0.030185163,0.400646776,-0.0477484316,0.1930941492,0.2172417343,-0.0403118059,-0.0638400242,-0.2730312347,0.077968359,0.2267186195,0.3892460763,-0.3428210616,-0.1372255683,-0.4340849519,-0.0550197884,-0.4057786763,0.2461039424,-0.0120554632,-0.2402932793,0.0848756358,-0.3027442396,0.1529166549,0.0393640399,-0.1008400992,-0.0583141893,0.1315543056,-0.0259356182,0.1768467724,-0.0182867814,-0.0394550189,0.1186181977,-0.2750140131,-0.0770448223,0.1979830861,-0.5796865821,0.0617473088,0.3563539386,-0.483120203,0.2248359621,0.0766406581,0.3723810911,0.0808451474,0.1294206977,-0.0054175756,0.3990353644,0.2017412037,0.1839399487,-0.2446381003,0.1665526032,0.3514225185,-0.0046332767,-0.3092014194,0.0455646105,-0.0928959474,0.2623339891,0.0135595212,0.3132420182,0.0522785522,-0.3759042025,0.1984404474,-0.2598961294,0.0621640086,0.3526681066,0.2470078766,0.31240955,0.331681639,-0.1649695039,0.2566638589,-0.170162797,-0.4501440227,-0.2399167717,-0.3115689754,0.0298984032,0.0585324802,0.2430290878,-0.4115727246,0.2127870023,0.1143840179,0.2314704955,-0.1508325785,0.2350654602,-0.3559949696,0.2714752853,0.4170607924,0.2132242918,0.30559659,0.2130860984,-0.078121908,0.0865608603,0.1292979866,-0.0135739716,-0.3794960678,0.1188295856,0.241816327,-0.1854702979,0.0091848904,-0.1626182795,0.3421088755,0.0492594019,-0.490398556,0.1006153971,-0.1393199116,-0.1788205355,-0.1388723552,0.3767809272,0.4191721678,-0.3163481355,-0.0631755143,-0.2223777026,-0.1933458447,0.3068187237,0.0627034158,-0.1867482066,0.0545980632,-0.198907733,-0.0324022733,0.1652611792,-0.133823514,-0.4505629539,0.3839800358,-0.1003188565,0.4216839075,0.003368129,0.0348957814,0.0462126397,-0.1179660112,0.2082362026,-0.0482203104,-0.3429863751,0.1301704794,-0.2063286006,0.0962786973,0.1745322049,0.0263372958,0.1080550551,0.145677641,0.1164149791,-0.0307607576,0.1964359283,-0.0440106578,0.0053364923,0.2909404337,0.0424242578,-0.0588180684,0.000367176,-0.1810717881,-0.4702015221,0.3554164469,0.2276427895,0.0265536476,0.0678977966,0.0377307907,-0.3046529889,0.141935274,-0.2711749077,-0.4771591425,0.1949529499,0.3907288015,-0.1118490696,0.0888262317,-0.277343154,0.3020512462,-0.1696492136,-0.1002230644,-0.1528523117,0.1263132989,0.1340726018,-0.3565647304,-0.0337362103,0.0897880867,0.2519123256,-0.1344926953,-0.234999463,0.4969182014,0.1069902405,0.0699909925,-0.0781737417,-0.173212111,0.1972211748,-0.1314566433,-0.2321656495,0.4724216461,0.3397763968,-0.2434855402,-0.1393338144,0.186793372,-0.2051788419,0.2564637661,-0.0694206432,0.0912937298,0.328330636,0.3197959363,-0.0928376839,-0.0212596133,0.2077185959,-0.1777403057,0.1031239405,-0.1610922515,-0.2532593608,-0.0428625979,0.1946246028,0.1955534518,0.0406559408,0.0529748946,-0.3236100376,-0.2086987048,0.3706597984,0.0880206153,0.4839422405,0.0791207775,0.2721221745,0.0922638103,-0.0106658787,-0.1263325214,0.2971056998,0.0752388388,0.0984970778,0.3603755832,0.2497458905,-0.1204286814,-0.4016779959,-0.2251661271,0.1358991563,0.133663699,-0.3052351177,0.0297692902,-0.1282226741,-0.2748440802,0.1133073941,-0.4021544755,-0.2225447446,-0.3337677121,-0.2058949023,0.3451238573,-0.1469643116,0.1498992443,-0.180475831,-0.0882678479,0.308257699,-0.0031163315,-0.2322359681,-0.1135618612,-0.2594828606,0.03214081,0.0412808843,-0.3234791756,0.434748143,-0.1234986335,-0.1801847816,-0.1512944698,-0.0745449811,0.1740905792,0.0435857289,0.2001399845,0.2743394077,0.2197588384,0.039221812,0.0045702313,0.4241863489,-0.2919014394,0.0146440258,0.3762074709,0.2012005895,0.2666827142,-0.2297265977,-0.2490313053,-0.0779714212,-0.4310797453,-0.174230665,-0.0227724351,0.0273636784,0.4507275522,0.262483865,0.3891989887,0.3127643466,-0.0566316359,-0.3348741829,-0.3475183249,0.1322237253,-0.1974763721,-0.4097188413,0.1144938767,0.0011452077,0.1664847434,0.2781507671,-0.3012993634,0.0803638548,-0.0408363193,0.1188741624,-0.05930195,-0.1120122075,0.1432749033,-0.06137814,-0.1019272059,-0.3338415623,-0.1850921214,-0.1276152432,-0.1529603153,0.2324113101,0.0381841213,0.5950309038,0.207781747,0.5280631185,0.4836455286,0.0581972674,0.6157116294,-0.2377375066,0.2161288112,-0.3673551977,-0.3503070772,0.2225523591,-0.0085880933,-0.13208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{"html_url":"https:\/\/github.com\/huggingface\/datasets\/issues\/2866","title":"\"counter\" dataset raises an error in normal mode, but not in streaming mode","comments":"We should definitely support datasets using `pathlib` in streaming mode...\r\n\r\nFor non-supported datasets in streaming mode, we have already a request of raising an error\/warning: see #2654.","body":"## Describe the bug\r\n\r\n`counter` dataset raises an error on `load_dataset()`, but simply returns an empty iterator in streaming mode.\r\n\r\n## Steps to reproduce the bug\r\n\r\n```python\r\n>>> import datasets as ds\r\n>>> a = ds.load_dataset('counter', split=\"train\", streaming=False)\r\nUsing custom data configuration default\r\nDownloading and preparing dataset counter\/default (download: 1.29 MiB, generated: 2.48 MiB, post-processed: Unknown size, total: 3.77 MiB) to \/home\/slesage\/.cache\/huggingface\/datasets\/counter\/default\/1.0.0\/9f84962fa0f35bec5a34fe0bdff8681838d497008c457f7856c48654476ec0e9...\r\nTraceback (most recent call last):\r\n File \"\/home\/slesage\/hf\/datasets-preview-backend\/.venv\/lib\/python3.8\/site-packages\/datasets\/builder.py\", line 726, in _download_and_prepare\r\n self._prepare_split(split_generator, **prepare_split_kwargs)\r\n File \"\/home\/slesage\/hf\/datasets-preview-backend\/.venv\/lib\/python3.8\/site-packages\/datasets\/builder.py\", line 1124, in _prepare_split\r\n for key, record in utils.tqdm(\r\n File \"\/home\/slesage\/hf\/datasets-preview-backend\/.venv\/lib\/python3.8\/site-packages\/tqdm\/std.py\", line 1185, in __iter__\r\n for obj in iterable:\r\n File \"\/home\/slesage\/.cache\/huggingface\/modules\/datasets_modules\/datasets\/counter\/9f84962fa0f35bec5a34fe0bdff8681838d497008c457f7856c48654476ec0e9\/counter.py\", line 161, in _generate_examples\r\n with derived_file.open(encoding=\"utf-8\") as f:\r\n File \"\/home\/slesage\/.pyenv\/versions\/3.8.11\/lib\/python3.8\/pathlib.py\", line 1222, in open\r\n return io.open(self, mode, buffering, encoding, errors, newline,\r\n File \"\/home\/slesage\/.pyenv\/versions\/3.8.11\/lib\/python3.8\/pathlib.py\", line 1078, in _opener\r\n return self._accessor.open(self, flags, mode)\r\nFileNotFoundError: [Errno 2] No such file or directory: '\/home\/slesage\/.cache\/huggingface\/datasets\/downloads\/extracted\/b57aa6db5601a738e57b95c1fd8cced54ff28fc540efcdaf0f6c4f1bb5dfe211\/COUNTER\/0032p.xml'\r\n\r\nDuring handling of the above exception, another exception occurred:\r\n\r\nTraceback (most recent call last):\r\n File \"\", line 1, in \r\n File \"\/home\/slesage\/hf\/datasets-preview-backend\/.venv\/lib\/python3.8\/site-packages\/datasets\/load.py\", line 1112, in load_dataset\r\n builder_instance.download_and_prepare(\r\n File \"\/home\/slesage\/hf\/datasets-preview-backend\/.venv\/lib\/python3.8\/site-packages\/datasets\/builder.py\", line 636, in download_and_prepare\r\n self._download_and_prepare(\r\n File \"\/home\/slesage\/hf\/datasets-preview-backend\/.venv\/lib\/python3.8\/site-packages\/datasets\/builder.py\", line 728, in _download_and_prepare\r\n raise OSError(\r\nOSError: Cannot find data file.\r\nOriginal error:\r\n[Errno 2] No such file or directory: '\/home\/slesage\/.cache\/huggingface\/datasets\/downloads\/extracted\/b57aa6db5601a738e57b95c1fd8cced54ff28fc540efcdaf0f6c4f1bb5dfe211\/COUNTER\/0032p.xml'\r\n```\r\n\r\n```python\r\n>>> import datasets as ds\r\n>>> b = ds.load_dataset('counter', split=\"train\", streaming=True)\r\nUsing custom data configuration default\r\n>>> list(b)\r\n[]\r\n```\r\n\r\n## Expected results\r\n\r\nAn exception should be raised in streaming mode\r\n\r\n## Actual results\r\n\r\nNo exception is raised in streaming mode: there is no way to tell if something has broken or if the dataset is simply empty.\r\n\r\n## Environment info\r\n\r\n- `datasets` version: 1.11.1.dev0\r\n- Platform: Linux-5.11.0-1016-aws-x86_64-with-glibc2.29\r\n- Python version: 3.8.11\r\n- PyArrow version: 4.0.1\r\n","comment_length":27,"text":"\"counter\" dataset raises an error in normal mode, but not in streaming mode\n## Describe the bug\r\n\r\n`counter` dataset raises an error on `load_dataset()`, but simply returns an empty iterator in streaming mode.\r\n\r\n## Steps to reproduce the bug\r\n\r\n```python\r\n>>> import datasets as ds\r\n>>> a = ds.load_dataset('counter', split=\"train\", streaming=False)\r\nUsing custom data configuration default\r\nDownloading and preparing dataset counter\/default (download: 1.29 MiB, generated: 2.48 MiB, post-processed: Unknown size, total: 3.77 MiB) to \/home\/slesage\/.cache\/huggingface\/datasets\/counter\/default\/1.0.0\/9f84962fa0f35bec5a34fe0bdff8681838d497008c457f7856c48654476ec0e9...\r\nTraceback (most recent call last):\r\n File \"\/home\/slesage\/hf\/datasets-preview-backend\/.venv\/lib\/python3.8\/site-packages\/datasets\/builder.py\", line 726, in _download_and_prepare\r\n self._prepare_split(split_generator, **prepare_split_kwargs)\r\n File \"\/home\/slesage\/hf\/datasets-preview-backend\/.venv\/lib\/python3.8\/site-packages\/datasets\/builder.py\", line 1124, in _prepare_split\r\n for key, record in utils.tqdm(\r\n File \"\/home\/slesage\/hf\/datasets-preview-backend\/.venv\/lib\/python3.8\/site-packages\/tqdm\/std.py\", line 1185, in __iter__\r\n for obj in iterable:\r\n File \"\/home\/slesage\/.cache\/huggingface\/modules\/datasets_modules\/datasets\/counter\/9f84962fa0f35bec5a34fe0bdff8681838d497008c457f7856c48654476ec0e9\/counter.py\", line 161, in _generate_examples\r\n with derived_file.open(encoding=\"utf-8\") as f:\r\n File \"\/home\/slesage\/.pyenv\/versions\/3.8.11\/lib\/python3.8\/pathlib.py\", line 1222, in open\r\n return io.open(self, mode, buffering, encoding, errors, newline,\r\n File \"\/home\/slesage\/.pyenv\/versions\/3.8.11\/lib\/python3.8\/pathlib.py\", line 1078, in _opener\r\n return self._accessor.open(self, flags, mode)\r\nFileNotFoundError: [Errno 2] No such file or directory: '\/home\/slesage\/.cache\/huggingface\/datasets\/downloads\/extracted\/b57aa6db5601a738e57b95c1fd8cced54ff28fc540efcdaf0f6c4f1bb5dfe211\/COUNTER\/0032p.xml'\r\n\r\nDuring handling of the above exception, another exception occurred:\r\n\r\nTraceback (most recent call last):\r\n File \"\", line 1, in \r\n File \"\/home\/slesage\/hf\/datasets-preview-backend\/.venv\/lib\/python3.8\/site-packages\/datasets\/load.py\", line 1112, in load_dataset\r\n builder_instance.download_and_prepare(\r\n File \"\/home\/slesage\/hf\/datasets-preview-backend\/.venv\/lib\/python3.8\/site-packages\/datasets\/builder.py\", line 636, in download_and_prepare\r\n self._download_and_prepare(\r\n File \"\/home\/slesage\/hf\/datasets-preview-backend\/.venv\/lib\/python3.8\/site-packages\/datasets\/builder.py\", line 728, in _download_and_prepare\r\n raise OSError(\r\nOSError: Cannot find data file.\r\nOriginal error:\r\n[Errno 2] No such file or directory: '\/home\/slesage\/.cache\/huggingface\/datasets\/downloads\/extracted\/b57aa6db5601a738e57b95c1fd8cced54ff28fc540efcdaf0f6c4f1bb5dfe211\/COUNTER\/0032p.xml'\r\n```\r\n\r\n```python\r\n>>> import datasets as ds\r\n>>> b = ds.load_dataset('counter', split=\"train\", streaming=True)\r\nUsing custom data configuration default\r\n>>> list(b)\r\n[]\r\n```\r\n\r\n## Expected results\r\n\r\nAn exception should be raised in streaming mode\r\n\r\n## Actual results\r\n\r\nNo exception is raised in streaming mode: there is no way to tell if something has broken or if the dataset is simply empty.\r\n\r\n## Environment info\r\n\r\n- `datasets` version: 1.11.1.dev0\r\n- Platform: Linux-5.11.0-1016-aws-x86_64-with-glibc2.29\r\n- Python version: 3.8.11\r\n- PyArrow version: 4.0.1\r\n\nWe should definitely support datasets using `pathlib` in streaming mode...\r\n\r\nFor non-supported datasets in streaming mode, we have already a request of raising an error\/warning: see #2654.","embeddings":[-0.4171527922,-0.2417904586,0.0004065955,0.1936673522,0.187378943,0.0572046041,0.5056346059,0.1059950739,0.1942841411,0.1353349984,0.0698306859,0.1297213137,-0.2097095996,0.3238820434,0.0893105865,0.0787798092,0.0210811216,0.1588886231,0.0335764661,0.0774051622,-0.2157031149,0.2028147876,-0.3865993321,-0.3339452446,-0.1323988438,0.1463262439,0.1100258455,0.1485300958,0.1753474176,-0.6307143569,0.2604337335,0.0775746107,0.2229160666,0.7022691965,-0.0001109225,0.1807845384,0.4209491313,-0.138756454,-0.5919362307,-0.2953981757,-0.3683612645,-0.3184562027,0.1747525334,-0.1460207105,-0.1155984327,0.0416297689,-0.2645150125,-0.4016982019,0.2196300626,0.44546175,0.2117786556,0.4712955952,-0.1509929299,-0.1280123889,0.1274099499,0.0086588459,-0.1527633816,0.2987458408,0.0759947449,0.2464787662,-0.030185163,0.400646776,-0.0477484316,0.1930941492,0.2172417343,-0.0403118059,-0.0638400242,-0.2730312347,0.077968359,0.2267186195,0.3892460763,-0.3428210616,-0.1372255683,-0.4340849519,-0.0550197884,-0.4057786763,0.2461039424,-0.0120554632,-0.2402932793,0.0848756358,-0.3027442396,0.1529166549,0.0393640399,-0.1008400992,-0.0583141893,0.1315543056,-0.0259356182,0.1768467724,-0.0182867814,-0.0394550189,0.1186181977,-0.2750140131,-0.0770448223,0.1979830861,-0.5796865821,0.0617473088,0.3563539386,-0.483120203,0.2248359621,0.0766406581,0.3723810911,0.0808451474,0.1294206977,-0.0054175756,0.3990353644,0.2017412037,0.1839399487,-0.2446381003,0.1665526032,0.3514225185,-0.0046332767,-0.3092014194,0.0455646105,-0.0928959474,0.2623339891,0.0135595212,0.3132420182,0.0522785522,-0.3759042025,0.1984404474,-0.2598961294,0.0621640086,0.3526681066,0.2470078766,0.31240955,0.331681639,-0.1649695039,0.2566638589,-0.170162797,-0.4501440227,-0.2399167717,-0.3115689754,0.0298984032,0.0585324802,0.2430290878,-0.4115727246,0.2127870023,0.1143840179,0.2314704955,-0.1508325785,0.2350654602,-0.3559949696,0.2714752853,0.4170607924,0.2132242918,0.30559659,0.2130860984,-0.078121908,0.0865608603,0.1292979866,-0.0135739716,-0.3794960678,0.1188295856,0.241816327,-0.1854702979,0.0091848904,-0.1626182795,0.3421088755,0.0492594019,-0.490398556,0.1006153971,-0.1393199116,-0.1788205355,-0.1388723552,0.3767809272,0.4191721678,-0.3163481355,-0.0631755143,-0.2223777026,-0.1933458447,0.3068187237,0.0627034158,-0.1867482066,0.0545980632,-0.198907733,-0.0324022733,0.1652611792,-0.133823514,-0.4505629539,0.3839800358,-0.1003188565,0.4216839075,0.003368129,0.0348957814,0.0462126397,-0.1179660112,0.2082362026,-0.0482203104,-0.3429863751,0.1301704794,-0.2063286006,0.0962786973,0.1745322049,0.0263372958,0.1080550551,0.145677641,0.1164149791,-0.0307607576,0.1964359283,-0.0440106578,0.0053364923,0.2909404337,0.0424242578,-0.0588180684,0.000367176,-0.1810717881,-0.4702015221,0.3554164469,0.2276427895,0.0265536476,0.0678977966,0.0377307907,-0.3046529889,0.141935274,-0.2711749077,-0.4771591425,0.1949529499,0.3907288015,-0.1118490696,0.0888262317,-0.277343154,0.3020512462,-0.1696492136,-0.1002230644,-0.1528523117,0.1263132989,0.1340726018,-0.3565647304,-0.0337362103,0.0897880867,0.2519123256,-0.1344926953,-0.234999463,0.4969182014,0.1069902405,0.0699909925,-0.0781737417,-0.173212111,0.1972211748,-0.1314566433,-0.2321656495,0.4724216461,0.3397763968,-0.2434855402,-0.1393338144,0.186793372,-0.2051788419,0.2564637661,-0.0694206432,0.0912937298,0.328330636,0.3197959363,-0.0928376839,-0.0212596133,0.2077185959,-0.1777403057,0.1031239405,-0.1610922515,-0.2532593608,-0.0428625979,0.1946246028,0.1955534518,0.0406559408,0.0529748946,-0.3236100376,-0.2086987048,0.3706597984,0.0880206153,0.4839422405,0.0791207775,0.2721221745,0.0922638103,-0.0106658787,-0.1263325214,0.2971056998,0.0752388388,0.0984970778,0.3603755832,0.2497458905,-0.1204286814,-0.4016779959,-0.2251661271,0.1358991563,0.133663699,-0.3052351177,0.0297692902,-0.1282226741,-0.2748440802,0.1133073941,-0.4021544755,-0.2225447446,-0.3337677121,-0.2058949023,0.3451238573,-0.1469643116,0.1498992443,-0.180475831,-0.0882678479,0.308257699,-0.0031163315,-0.2322359681,-0.1135618612,-0.2594828606,0.03214081,0.0412808843,-0.3234791756,0.434748143,-0.1234986335,-0.1801847816,-0.1512944698,-0.0745449811,0.1740905792,0.0435857289,0.2001399845,0.2743394077,0.2197588384,0.039221812,0.0045702313,0.4241863489,-0.2919014394,0.0146440258,0.3762074709,0.2012005895,0.2666827142,-0.2297265977,-0.2490313053,-0.0779714212,-0.4310797453,-0.174230665,-0.0227724351,0.0273636784,0.4507275522,0.262483865,0.3891989887,0.3127643466,-0.0566316359,-0.3348741829,-0.3475183249,0.1322237253,-0.1974763721,-0.4097188413,0.1144938767,0.0011452077,0.1664847434,0.2781507671,-0.3012993634,0.0803638548,-0.0408363193,0.1188741624,-0.05930195,-0.1120122075,0.1432749033,-0.06137814,-0.1019272059,-0.3338415623,-0.1850921214,-0.1276152432,-0.1529603153,0.2324113101,0.0381841213,0.5950309038,0.207781747,0.5280631185,0.4836455286,0.0581972674,0.6157116294,-0.2377375066,0.2161288112,-0.3673551977,-0.3503070772,0.2225523591,-0.0085880933,-0.1320838332,0.2081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{"html_url":"https:\/\/github.com\/huggingface\/datasets\/issues\/2866","title":"\"counter\" dataset raises an error in normal mode, but not in streaming mode","comments":"Hi @severo, please note that \"counter\" dataset will be streamable (at least until it arrives at the missing file, error already in normal mode) once these PRs are merged:\r\n- #2874\r\n- #2876\r\n- #2880\r\n\r\nI have tested it. \ud83d\ude09 ","body":"## Describe the bug\r\n\r\n`counter` dataset raises an error on `load_dataset()`, but simply returns an empty iterator in streaming mode.\r\n\r\n## Steps to reproduce the bug\r\n\r\n```python\r\n>>> import datasets as ds\r\n>>> a = ds.load_dataset('counter', split=\"train\", streaming=False)\r\nUsing custom data configuration default\r\nDownloading and preparing dataset counter\/default (download: 1.29 MiB, generated: 2.48 MiB, post-processed: Unknown size, total: 3.77 MiB) to \/home\/slesage\/.cache\/huggingface\/datasets\/counter\/default\/1.0.0\/9f84962fa0f35bec5a34fe0bdff8681838d497008c457f7856c48654476ec0e9...\r\nTraceback (most recent call last):\r\n File \"\/home\/slesage\/hf\/datasets-preview-backend\/.venv\/lib\/python3.8\/site-packages\/datasets\/builder.py\", line 726, in _download_and_prepare\r\n self._prepare_split(split_generator, **prepare_split_kwargs)\r\n File \"\/home\/slesage\/hf\/datasets-preview-backend\/.venv\/lib\/python3.8\/site-packages\/datasets\/builder.py\", line 1124, in _prepare_split\r\n for key, record in utils.tqdm(\r\n File \"\/home\/slesage\/hf\/datasets-preview-backend\/.venv\/lib\/python3.8\/site-packages\/tqdm\/std.py\", line 1185, in __iter__\r\n for obj in iterable:\r\n File \"\/home\/slesage\/.cache\/huggingface\/modules\/datasets_modules\/datasets\/counter\/9f84962fa0f35bec5a34fe0bdff8681838d497008c457f7856c48654476ec0e9\/counter.py\", line 161, in _generate_examples\r\n with derived_file.open(encoding=\"utf-8\") as f:\r\n File \"\/home\/slesage\/.pyenv\/versions\/3.8.11\/lib\/python3.8\/pathlib.py\", line 1222, in open\r\n return io.open(self, mode, buffering, encoding, errors, newline,\r\n File \"\/home\/slesage\/.pyenv\/versions\/3.8.11\/lib\/python3.8\/pathlib.py\", line 1078, in _opener\r\n return self._accessor.open(self, flags, mode)\r\nFileNotFoundError: [Errno 2] No such file or directory: '\/home\/slesage\/.cache\/huggingface\/datasets\/downloads\/extracted\/b57aa6db5601a738e57b95c1fd8cced54ff28fc540efcdaf0f6c4f1bb5dfe211\/COUNTER\/0032p.xml'\r\n\r\nDuring handling of the above exception, another exception occurred:\r\n\r\nTraceback (most recent call last):\r\n File \"\", line 1, in \r\n File \"\/home\/slesage\/hf\/datasets-preview-backend\/.venv\/lib\/python3.8\/site-packages\/datasets\/load.py\", line 1112, in load_dataset\r\n builder_instance.download_and_prepare(\r\n File \"\/home\/slesage\/hf\/datasets-preview-backend\/.venv\/lib\/python3.8\/site-packages\/datasets\/builder.py\", line 636, in download_and_prepare\r\n self._download_and_prepare(\r\n File \"\/home\/slesage\/hf\/datasets-preview-backend\/.venv\/lib\/python3.8\/site-packages\/datasets\/builder.py\", line 728, in _download_and_prepare\r\n raise OSError(\r\nOSError: Cannot find data file.\r\nOriginal error:\r\n[Errno 2] No such file or directory: '\/home\/slesage\/.cache\/huggingface\/datasets\/downloads\/extracted\/b57aa6db5601a738e57b95c1fd8cced54ff28fc540efcdaf0f6c4f1bb5dfe211\/COUNTER\/0032p.xml'\r\n```\r\n\r\n```python\r\n>>> import datasets as ds\r\n>>> b = ds.load_dataset('counter', split=\"train\", streaming=True)\r\nUsing custom data configuration default\r\n>>> list(b)\r\n[]\r\n```\r\n\r\n## Expected results\r\n\r\nAn exception should be raised in streaming mode\r\n\r\n## Actual results\r\n\r\nNo exception is raised in streaming mode: there is no way to tell if something has broken or if the dataset is simply empty.\r\n\r\n## Environment info\r\n\r\n- `datasets` version: 1.11.1.dev0\r\n- Platform: Linux-5.11.0-1016-aws-x86_64-with-glibc2.29\r\n- Python version: 3.8.11\r\n- PyArrow version: 4.0.1\r\n","comment_length":40,"text":"\"counter\" dataset raises an error in normal mode, but not in streaming mode\n## Describe the bug\r\n\r\n`counter` dataset raises an error on `load_dataset()`, but simply returns an empty iterator in streaming mode.\r\n\r\n## Steps to reproduce the bug\r\n\r\n```python\r\n>>> import datasets as ds\r\n>>> a = ds.load_dataset('counter', split=\"train\", streaming=False)\r\nUsing custom data configuration default\r\nDownloading and preparing dataset counter\/default (download: 1.29 MiB, generated: 2.48 MiB, post-processed: Unknown size, total: 3.77 MiB) to \/home\/slesage\/.cache\/huggingface\/datasets\/counter\/default\/1.0.0\/9f84962fa0f35bec5a34fe0bdff8681838d497008c457f7856c48654476ec0e9...\r\nTraceback (most recent call last):\r\n File \"\/home\/slesage\/hf\/datasets-preview-backend\/.venv\/lib\/python3.8\/site-packages\/datasets\/builder.py\", line 726, in _download_and_prepare\r\n self._prepare_split(split_generator, **prepare_split_kwargs)\r\n File \"\/home\/slesage\/hf\/datasets-preview-backend\/.venv\/lib\/python3.8\/site-packages\/datasets\/builder.py\", line 1124, in _prepare_split\r\n for key, record in utils.tqdm(\r\n File \"\/home\/slesage\/hf\/datasets-preview-backend\/.venv\/lib\/python3.8\/site-packages\/tqdm\/std.py\", line 1185, in __iter__\r\n for obj in iterable:\r\n File \"\/home\/slesage\/.cache\/huggingface\/modules\/datasets_modules\/datasets\/counter\/9f84962fa0f35bec5a34fe0bdff8681838d497008c457f7856c48654476ec0e9\/counter.py\", line 161, in _generate_examples\r\n with derived_file.open(encoding=\"utf-8\") as f:\r\n File \"\/home\/slesage\/.pyenv\/versions\/3.8.11\/lib\/python3.8\/pathlib.py\", line 1222, in open\r\n return io.open(self, mode, buffering, encoding, errors, newline,\r\n File \"\/home\/slesage\/.pyenv\/versions\/3.8.11\/lib\/python3.8\/pathlib.py\", line 1078, in _opener\r\n return self._accessor.open(self, flags, mode)\r\nFileNotFoundError: [Errno 2] No such file or directory: '\/home\/slesage\/.cache\/huggingface\/datasets\/downloads\/extracted\/b57aa6db5601a738e57b95c1fd8cced54ff28fc540efcdaf0f6c4f1bb5dfe211\/COUNTER\/0032p.xml'\r\n\r\nDuring handling of the above exception, another exception occurred:\r\n\r\nTraceback (most recent call last):\r\n File \"\", line 1, in \r\n File \"\/home\/slesage\/hf\/datasets-preview-backend\/.venv\/lib\/python3.8\/site-packages\/datasets\/load.py\", line 1112, in load_dataset\r\n builder_instance.download_and_prepare(\r\n File \"\/home\/slesage\/hf\/datasets-preview-backend\/.venv\/lib\/python3.8\/site-packages\/datasets\/builder.py\", line 636, in download_and_prepare\r\n self._download_and_prepare(\r\n File \"\/home\/slesage\/hf\/datasets-preview-backend\/.venv\/lib\/python3.8\/site-packages\/datasets\/builder.py\", line 728, in _download_and_prepare\r\n raise OSError(\r\nOSError: Cannot find data file.\r\nOriginal error:\r\n[Errno 2] No such file or directory: '\/home\/slesage\/.cache\/huggingface\/datasets\/downloads\/extracted\/b57aa6db5601a738e57b95c1fd8cced54ff28fc540efcdaf0f6c4f1bb5dfe211\/COUNTER\/0032p.xml'\r\n```\r\n\r\n```python\r\n>>> import datasets as ds\r\n>>> b = ds.load_dataset('counter', split=\"train\", streaming=True)\r\nUsing custom data configuration default\r\n>>> list(b)\r\n[]\r\n```\r\n\r\n## Expected results\r\n\r\nAn exception should be raised in streaming mode\r\n\r\n## Actual results\r\n\r\nNo exception is raised in streaming mode: there is no way to tell if something has broken or if the dataset is simply empty.\r\n\r\n## Environment info\r\n\r\n- `datasets` version: 1.11.1.dev0\r\n- Platform: Linux-5.11.0-1016-aws-x86_64-with-glibc2.29\r\n- Python version: 3.8.11\r\n- PyArrow version: 4.0.1\r\n\nHi @severo, please note that \"counter\" dataset will be streamable (at least until it arrives at the missing file, error already in normal mode) once these PRs are merged:\r\n- #2874\r\n- #2876\r\n- #2880\r\n\r\nI have tested it. \ud83d\ude09 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{"html_url":"https:\/\/github.com\/huggingface\/datasets\/issues\/2866","title":"\"counter\" dataset raises an error in normal mode, but not in streaming mode","comments":"Now (on master), we get:\r\n\r\n```\r\nimport datasets as ds\r\nds.load_dataset('counter', split=\"train\", streaming=False)\r\n```\r\n\r\n```\r\nUsing custom data configuration default\r\nDownloading and preparing dataset counter\/default (download: 1.29 MiB, generated: 2.48 MiB, post-processed: Unknown size, total: 3.77 MiB) to \/home\/slesage\/.cache\/huggingface\/datasets\/counter\/default\/1.0.0\/9f84962fa0f35bec5a34fe0bdff8681838d497008c457f7856c48654476ec0e9...\r\nTraceback (most recent call last):\r\n File \"\/home\/slesage\/hf\/datasets\/src\/datasets\/builder.py\", line 726, in _download_and_prepare\r\n self._prepare_split(split_generator, **prepare_split_kwargs)\r\n File \"\/home\/slesage\/hf\/datasets\/src\/datasets\/builder.py\", line 1124, in _prepare_split\r\n for key, record in utils.tqdm(\r\n File \"\/home\/slesage\/hf\/datasets\/.venv\/lib\/python3.8\/site-packages\/tqdm\/std.py\", line 1185, in __iter__\r\n for obj in iterable:\r\n File \"\/home\/slesage\/.cache\/huggingface\/modules\/datasets_modules\/datasets\/counter\/9f84962fa0f35bec5a34fe0bdff8681838d497008c457f7856c48654476ec0e9\/counter.py\", line 161, in _generate_examples\r\n with derived_file.open(encoding=\"utf-8\") as f:\r\n File \"\/home\/slesage\/.pyenv\/versions\/3.8.11\/lib\/python3.8\/pathlib.py\", line 1222, in open\r\n return io.open(self, mode, buffering, encoding, errors, newline,\r\n File \"\/home\/slesage\/.pyenv\/versions\/3.8.11\/lib\/python3.8\/pathlib.py\", line 1078, in _opener\r\n return self._accessor.open(self, flags, mode)\r\nFileNotFoundError: [Errno 2] No such file or directory: '\/home\/slesage\/.cache\/huggingface\/datasets\/downloads\/extracted\/b57aa6db5601a738e57b95c1fd8cced54ff28fc540efcdaf0f6c4f1bb5dfe211\/COUNTER\/0032p.xml'\r\n\r\nDuring handling of the above exception, another exception occurred:\r\n\r\nTraceback (most recent call last):\r\n File \"\", line 1, in \r\n File \"\/home\/slesage\/hf\/datasets\/src\/datasets\/load.py\", line 1112, in load_dataset\r\n builder_instance.download_and_prepare(\r\n File \"\/home\/slesage\/hf\/datasets\/src\/datasets\/builder.py\", line 636, in download_and_prepare\r\n self._download_and_prepare(\r\n File \"\/home\/slesage\/hf\/datasets\/src\/datasets\/builder.py\", line 728, in _download_and_prepare\r\n raise OSError(\r\nOSError: Cannot find data file.\r\nOriginal error:\r\n[Errno 2] No such file or directory: '\/home\/slesage\/.cache\/huggingface\/datasets\/downloads\/extracted\/b57aa6db5601a738e57b95c1fd8cced54ff28fc540efcdaf0f6c4f1bb5dfe211\/COUNTER\/0032p.xml'\r\n```\r\n\r\nThe error is now the same with or without streaming. I close the issue, thanks @albertvillanova and @lhoestq!\r\n","body":"## Describe the bug\r\n\r\n`counter` dataset raises an error on `load_dataset()`, but simply returns an empty iterator in streaming mode.\r\n\r\n## Steps to reproduce the bug\r\n\r\n```python\r\n>>> import datasets as ds\r\n>>> a = ds.load_dataset('counter', split=\"train\", streaming=False)\r\nUsing custom data configuration default\r\nDownloading and preparing dataset counter\/default (download: 1.29 MiB, generated: 2.48 MiB, post-processed: Unknown size, total: 3.77 MiB) to \/home\/slesage\/.cache\/huggingface\/datasets\/counter\/default\/1.0.0\/9f84962fa0f35bec5a34fe0bdff8681838d497008c457f7856c48654476ec0e9...\r\nTraceback (most recent call last):\r\n File \"\/home\/slesage\/hf\/datasets-preview-backend\/.venv\/lib\/python3.8\/site-packages\/datasets\/builder.py\", line 726, in _download_and_prepare\r\n self._prepare_split(split_generator, **prepare_split_kwargs)\r\n File \"\/home\/slesage\/hf\/datasets-preview-backend\/.venv\/lib\/python3.8\/site-packages\/datasets\/builder.py\", line 1124, in _prepare_split\r\n for key, record in utils.tqdm(\r\n File \"\/home\/slesage\/hf\/datasets-preview-backend\/.venv\/lib\/python3.8\/site-packages\/tqdm\/std.py\", line 1185, in __iter__\r\n for obj in iterable:\r\n File \"\/home\/slesage\/.cache\/huggingface\/modules\/datasets_modules\/datasets\/counter\/9f84962fa0f35bec5a34fe0bdff8681838d497008c457f7856c48654476ec0e9\/counter.py\", line 161, in _generate_examples\r\n with derived_file.open(encoding=\"utf-8\") as f:\r\n File \"\/home\/slesage\/.pyenv\/versions\/3.8.11\/lib\/python3.8\/pathlib.py\", line 1222, in open\r\n return io.open(self, mode, buffering, encoding, errors, newline,\r\n File \"\/home\/slesage\/.pyenv\/versions\/3.8.11\/lib\/python3.8\/pathlib.py\", line 1078, in _opener\r\n return self._accessor.open(self, flags, mode)\r\nFileNotFoundError: [Errno 2] No such file or directory: '\/home\/slesage\/.cache\/huggingface\/datasets\/downloads\/extracted\/b57aa6db5601a738e57b95c1fd8cced54ff28fc540efcdaf0f6c4f1bb5dfe211\/COUNTER\/0032p.xml'\r\n\r\nDuring handling of the above exception, another exception occurred:\r\n\r\nTraceback (most recent call last):\r\n File \"\", line 1, in \r\n File \"\/home\/slesage\/hf\/datasets-preview-backend\/.venv\/lib\/python3.8\/site-packages\/datasets\/load.py\", line 1112, in load_dataset\r\n builder_instance.download_and_prepare(\r\n File \"\/home\/slesage\/hf\/datasets-preview-backend\/.venv\/lib\/python3.8\/site-packages\/datasets\/builder.py\", line 636, in download_and_prepare\r\n self._download_and_prepare(\r\n File \"\/home\/slesage\/hf\/datasets-preview-backend\/.venv\/lib\/python3.8\/site-packages\/datasets\/builder.py\", line 728, in _download_and_prepare\r\n raise OSError(\r\nOSError: Cannot find data file.\r\nOriginal error:\r\n[Errno 2] No such file or directory: '\/home\/slesage\/.cache\/huggingface\/datasets\/downloads\/extracted\/b57aa6db5601a738e57b95c1fd8cced54ff28fc540efcdaf0f6c4f1bb5dfe211\/COUNTER\/0032p.xml'\r\n```\r\n\r\n```python\r\n>>> import datasets as ds\r\n>>> b = ds.load_dataset('counter', split=\"train\", streaming=True)\r\nUsing custom data configuration default\r\n>>> list(b)\r\n[]\r\n```\r\n\r\n## Expected results\r\n\r\nAn exception should be raised in streaming mode\r\n\r\n## Actual results\r\n\r\nNo exception is raised in streaming mode: there is no way to tell if something has broken or if the dataset is simply empty.\r\n\r\n## Environment info\r\n\r\n- `datasets` version: 1.11.1.dev0\r\n- Platform: Linux-5.11.0-1016-aws-x86_64-with-glibc2.29\r\n- Python version: 3.8.11\r\n- PyArrow version: 4.0.1\r\n","comment_length":191,"text":"\"counter\" dataset raises an error in normal mode, but not in streaming mode\n## Describe the bug\r\n\r\n`counter` dataset raises an error on `load_dataset()`, but simply returns an empty iterator in streaming mode.\r\n\r\n## Steps to reproduce the bug\r\n\r\n```python\r\n>>> import datasets as ds\r\n>>> a = ds.load_dataset('counter', split=\"train\", streaming=False)\r\nUsing custom data configuration default\r\nDownloading and preparing dataset counter\/default (download: 1.29 MiB, generated: 2.48 MiB, post-processed: Unknown size, total: 3.77 MiB) to \/home\/slesage\/.cache\/huggingface\/datasets\/counter\/default\/1.0.0\/9f84962fa0f35bec5a34fe0bdff8681838d497008c457f7856c48654476ec0e9...\r\nTraceback (most recent call last):\r\n File \"\/home\/slesage\/hf\/datasets-preview-backend\/.venv\/lib\/python3.8\/site-packages\/datasets\/builder.py\", line 726, in _download_and_prepare\r\n self._prepare_split(split_generator, **prepare_split_kwargs)\r\n File \"\/home\/slesage\/hf\/datasets-preview-backend\/.venv\/lib\/python3.8\/site-packages\/datasets\/builder.py\", line 1124, in _prepare_split\r\n for key, record in utils.tqdm(\r\n File \"\/home\/slesage\/hf\/datasets-preview-backend\/.venv\/lib\/python3.8\/site-packages\/tqdm\/std.py\", line 1185, in __iter__\r\n for obj in iterable:\r\n File \"\/home\/slesage\/.cache\/huggingface\/modules\/datasets_modules\/datasets\/counter\/9f84962fa0f35bec5a34fe0bdff8681838d497008c457f7856c48654476ec0e9\/counter.py\", line 161, in _generate_examples\r\n with derived_file.open(encoding=\"utf-8\") as f:\r\n File \"\/home\/slesage\/.pyenv\/versions\/3.8.11\/lib\/python3.8\/pathlib.py\", line 1222, in open\r\n return io.open(self, mode, buffering, encoding, errors, newline,\r\n File \"\/home\/slesage\/.pyenv\/versions\/3.8.11\/lib\/python3.8\/pathlib.py\", line 1078, in _opener\r\n return self._accessor.open(self, flags, mode)\r\nFileNotFoundError: [Errno 2] No such file or directory: '\/home\/slesage\/.cache\/huggingface\/datasets\/downloads\/extracted\/b57aa6db5601a738e57b95c1fd8cced54ff28fc540efcdaf0f6c4f1bb5dfe211\/COUNTER\/0032p.xml'\r\n\r\nDuring handling of the above exception, another exception occurred:\r\n\r\nTraceback (most recent call last):\r\n File \"\", line 1, in \r\n File \"\/home\/slesage\/hf\/datasets-preview-backend\/.venv\/lib\/python3.8\/site-packages\/datasets\/load.py\", line 1112, in load_dataset\r\n builder_instance.download_and_prepare(\r\n File \"\/home\/slesage\/hf\/datasets-preview-backend\/.venv\/lib\/python3.8\/site-packages\/datasets\/builder.py\", line 636, in download_and_prepare\r\n self._download_and_prepare(\r\n File \"\/home\/slesage\/hf\/datasets-preview-backend\/.venv\/lib\/python3.8\/site-packages\/datasets\/builder.py\", line 728, in _download_and_prepare\r\n raise OSError(\r\nOSError: Cannot find data file.\r\nOriginal error:\r\n[Errno 2] No such file or directory: '\/home\/slesage\/.cache\/huggingface\/datasets\/downloads\/extracted\/b57aa6db5601a738e57b95c1fd8cced54ff28fc540efcdaf0f6c4f1bb5dfe211\/COUNTER\/0032p.xml'\r\n```\r\n\r\n```python\r\n>>> import datasets as ds\r\n>>> b = ds.load_dataset('counter', split=\"train\", streaming=True)\r\nUsing custom data configuration default\r\n>>> list(b)\r\n[]\r\n```\r\n\r\n## Expected results\r\n\r\nAn exception should be raised in streaming mode\r\n\r\n## Actual results\r\n\r\nNo exception is raised in streaming mode: there is no way to tell if something has broken or if the dataset is simply empty.\r\n\r\n## Environment info\r\n\r\n- `datasets` version: 1.11.1.dev0\r\n- Platform: Linux-5.11.0-1016-aws-x86_64-with-glibc2.29\r\n- Python version: 3.8.11\r\n- PyArrow version: 4.0.1\r\n\nNow (on master), we get:\r\n\r\n```\r\nimport datasets as ds\r\nds.load_dataset('counter', split=\"train\", streaming=False)\r\n```\r\n\r\n```\r\nUsing custom data configuration default\r\nDownloading and preparing dataset counter\/default (download: 1.29 MiB, generated: 2.48 MiB, post-processed: Unknown size, total: 3.77 MiB) to \/home\/slesage\/.cache\/huggingface\/datasets\/counter\/default\/1.0.0\/9f84962fa0f35bec5a34fe0bdff8681838d497008c457f7856c48654476ec0e9...\r\nTraceback (most recent call last):\r\n File \"\/home\/slesage\/hf\/datasets\/src\/datasets\/builder.py\", line 726, in _download_and_prepare\r\n self._prepare_split(split_generator, **prepare_split_kwargs)\r\n File \"\/home\/slesage\/hf\/datasets\/src\/datasets\/builder.py\", line 1124, in _prepare_split\r\n for key, record in utils.tqdm(\r\n File \"\/home\/slesage\/hf\/datasets\/.venv\/lib\/python3.8\/site-packages\/tqdm\/std.py\", line 1185, in __iter__\r\n for obj in iterable:\r\n File \"\/home\/slesage\/.cache\/huggingface\/modules\/datasets_modules\/datasets\/counter\/9f84962fa0f35bec5a34fe0bdff8681838d497008c457f7856c48654476ec0e9\/counter.py\", line 161, in _generate_examples\r\n with derived_file.open(encoding=\"utf-8\") as f:\r\n File \"\/home\/slesage\/.pyenv\/versions\/3.8.11\/lib\/python3.8\/pathlib.py\", line 1222, in open\r\n return io.open(self, mode, buffering, encoding, errors, newline,\r\n File \"\/home\/slesage\/.pyenv\/versions\/3.8.11\/lib\/python3.8\/pathlib.py\", line 1078, in _opener\r\n return self._accessor.open(self, flags, mode)\r\nFileNotFoundError: [Errno 2] No such file or directory: '\/home\/slesage\/.cache\/huggingface\/datasets\/downloads\/extracted\/b57aa6db5601a738e57b95c1fd8cced54ff28fc540efcdaf0f6c4f1bb5dfe211\/COUNTER\/0032p.xml'\r\n\r\nDuring handling of the above exception, another exception occurred:\r\n\r\nTraceback (most recent call last):\r\n File \"\", line 1, in \r\n File \"\/home\/slesage\/hf\/datasets\/src\/datasets\/load.py\", line 1112, in load_dataset\r\n builder_instance.download_and_prepare(\r\n File \"\/home\/slesage\/hf\/datasets\/src\/datasets\/builder.py\", line 636, in download_and_prepare\r\n self._download_and_prepare(\r\n File \"\/home\/slesage\/hf\/datasets\/src\/datasets\/builder.py\", line 728, in _download_and_prepare\r\n raise OSError(\r\nOSError: Cannot find data file.\r\nOriginal error:\r\n[Errno 2] No such file or directory: '\/home\/slesage\/.cache\/huggingface\/datasets\/downloads\/extracted\/b57aa6db5601a738e57b95c1fd8cced54ff28fc540efcdaf0f6c4f1bb5dfe211\/COUNTER\/0032p.xml'\r\n```\r\n\r\nThe error is now the same with or without streaming. I close the issue, thanks @albertvillanova and @lhoestq!\r\n","embeddings":[-0.4171527922,-0.2417904586,0.0004065955,0.1936673522,0.187378943,0.0572046041,0.5056346059,0.1059950739,0.1942841411,0.1353349984,0.0698306859,0.1297213137,-0.2097095996,0.3238820434,0.0893105865,0.0787798092,0.0210811216,0.1588886231,0.0335764661,0.0774051622,-0.2157031149,0.2028147876,-0.3865993321,-0.3339452446,-0.1323988438,0.1463262439,0.1100258455,0.1485300958,0.1753474176,-0.6307143569,0.2604337335,0.0775746107,0.2229160666,0.7022691965,-0.0001109225,0.1807845384,0.4209491313,-0.138756454,-0.5919362307,-0.2953981757,-0.3683612645,-0.3184562027,0.1747525334,-0.1460207105,-0.1155984327,0.0416297689,-0.2645150125,-0.4016982019,0.2196300626,0.44546175,0.2117786556,0.4712955952,-0.1509929299,-0.1280123889,0.1274099499,0.0086588459,-0.1527633816,0.2987458408,0.0759947449,0.2464787662,-0.030185163,0.400646776,-0.0477484316,0.1930941492,0.2172417343,-0.0403118059,-0.0638400242,-0.2730312347,0.077968359,0.2267186195,0.3892460763,-0.3428210616,-0.1372255683,-0.4340849519,-0.0550197884,-0.4057786763,0.2461039424,-0.0120554632,-0.2402932793,0.0848756358,-0.3027442396,0.1529166549,0.0393640399,-0.1008400992,-0.0583141893,0.1315543056,-0.0259356182,0.1768467724,-0.0182867814,-0.0394550189,0.1186181977,-0.2750140131,-0.0770448223,0.1979830861,-0.5796865821,0.0617473088,0.3563539386,-0.483120203,0.2248359621,0.0766406581,0.3723810911,0.0808451474,0.1294206977,-0.0054175756,0.3990353644,0.2017412037,0.1839399487,-0.2446381003,0.1665526032,0.3514225185,-0.0046332767,-0.3092014194,0.0455646105,-0.0928959474,0.2623339891,0.0135595212,0.3132420182,0.0522785522,-0.3759042025,0.1984404474,-0.2598961294,0.0621640086,0.3526681066,0.2470078766,0.31240955,0.331681639,-0.1649695039,0.2566638589,-0.170162797,-0.4501440227,-0.2399167717,-0.3115689754,0.0298984032,0.0585324802,0.2430290878,-0.4115727246,0.2127870023,0.1143840179,0.2314704955,-0.1508325785,0.2350654602,-0.3559949696,0.2714752853,0.4170607924,0.2132242918,0.30559659,0.2130860984,-0.078121908,0.0865608603,0.1292979866,-0.0135739716,-0.3794960678,0.1188295856,0.241816327,-0.1854702979,0.0091848904,-0.1626182795,0.3421088755,0.0492594019,-0.490398556,0.1006153971,-0.1393199116,-0.1788205355,-0.1388723552,0.3767809272,0.4191721678,-0.3163481355,-0.0631755143,-0.2223777026,-0.1933458447,0.3068187237,0.0627034158,-0.1867482066,0.0545980632,-0.198907733,-0.0324022733,0.1652611792,-0.133823514,-0.4505629539,0.3839800358,-0.1003188565,0.4216839075,0.003368129,0.0348957814,0.0462126397,-0.1179660112,0.2082362026,-0.0482203104,-0.3429863751,0.1301704794,-0.2063286006,0.0962786973,0.1745322049,0.0263372958,0.1080550551,0.145677641,0.1164149791,-0.0307607576,0.1964359283,-0.0440106578,0.0053364923,0.2909404337,0.0424242578,-0.0588180684,0.000367176,-0.1810717881,-0.4702015221,0.3554164469,0.2276427895,0.0265536476,0.0678977966,0.0377307907,-0.3046529889,0.141935274,-0.2711749077,-0.4771591425,0.1949529499,0.3907288015,-0.1118490696,0.0888262317,-0.277343154,0.3020512462,-0.1696492136,-0.1002230644,-0.1528523117,0.1263132989,0.1340726018,-0.3565647304,-0.0337362103,0.0897880867,0.2519123256,-0.1344926953,-0.234999463,0.4969182014,0.1069902405,0.0699909925,-0.0781737417,-0.173212111,0.1972211748,-0.1314566433,-0.2321656495,0.4724216461,0.3397763968,-0.2434855402,-0.1393338144,0.186793372,-0.2051788419,0.2564637661,-0.0694206432,0.0912937298,0.328330636,0.3197959363,-0.0928376839,-0.0212596133,0.2077185959,-0.1777403057,0.1031239405,-0.1610922515,-0.2532593608,-0.0428625979,0.1946246028,0.1955534518,0.0406559408,0.0529748946,-0.3236100376,-0.2086987048,0.3706597984,0.0880206153,0.4839422405,0.0791207775,0.2721221745,0.0922638103,-0.0106658787,-0.1263325214,0.2971056998,0.0752388388,0.0984970778,0.3603755832,0.2497458905,-0.1204286814,-0.4016779959,-0.2251661271,0.1358991563,0.133663699,-0.3052351177,0.0297692902,-0.1282226741,-0.2748440802,0.1133073941,-0.4021544755,-0.2225447446,-0.3337677121,-0.2058949023,0.3451238573,-0.1469643116,0.1498992443,-0.180475831,-0.0882678479,0.308257699,-0.0031163315,-0.2322359681,-0.1135618612,-0.2594828606,0.03214081,0.0412808843,-0.3234791756,0.434748143,-0.1234986335,-0.1801847816,-0.1512944698,-0.0745449811,0.1740905792,0.0435857289,0.2001399845,0.2743394077,0.2197588384,0.039221812,0.0045702313,0.4241863489,-0.2919014394,0.0146440258,0.3762074709,0.2012005895,0.2666827142,-0.2297265977,-0.2490313053,-0.0779714212,-0.4310797453,-0.174230665,-0.0227724351,0.0273636784,0.4507275522,0.262483865,0.3891989887,0.3127643466,-0.0566316359,-0.3348741829,-0.3475183249,0.1322237253,-0.1974763721,-0.4097188413,0.1144938767,0.0011452077,0.1664847434,0.2781507671,-0.3012993634,0.0803638548,-0.0408363193,0.1188741624,-0.05930195,-0.1120122075,0.1432749033,-0.06137814,-0.1019272059,-0.3338415623,-0.1850921214,-0.1276152432,-0.1529603153,0.2324113101,0.0381841213,0.5950309038,0.207781747,0.5280631185,0.4836455286,0.0581972674,0.6157116294,-0.2377375066,0.2161288112,-0.3673551977,-0.3503070772,0.2225523591,-0.0085880933,-0.1320838332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{"html_url":"https:\/\/github.com\/huggingface\/datasets\/issues\/2866","title":"\"counter\" dataset raises an error in normal mode, but not in streaming mode","comments":"Note that we might want to open an issue to fix the \"counter\" dataset by itself, but I let it up to you.","body":"## Describe the bug\r\n\r\n`counter` dataset raises an error on `load_dataset()`, but simply returns an empty iterator in streaming mode.\r\n\r\n## Steps to reproduce the bug\r\n\r\n```python\r\n>>> import datasets as ds\r\n>>> a = ds.load_dataset('counter', split=\"train\", streaming=False)\r\nUsing custom data configuration default\r\nDownloading and preparing dataset counter\/default (download: 1.29 MiB, generated: 2.48 MiB, post-processed: Unknown size, total: 3.77 MiB) to \/home\/slesage\/.cache\/huggingface\/datasets\/counter\/default\/1.0.0\/9f84962fa0f35bec5a34fe0bdff8681838d497008c457f7856c48654476ec0e9...\r\nTraceback (most recent call last):\r\n File \"\/home\/slesage\/hf\/datasets-preview-backend\/.venv\/lib\/python3.8\/site-packages\/datasets\/builder.py\", line 726, in _download_and_prepare\r\n self._prepare_split(split_generator, **prepare_split_kwargs)\r\n File \"\/home\/slesage\/hf\/datasets-preview-backend\/.venv\/lib\/python3.8\/site-packages\/datasets\/builder.py\", line 1124, in _prepare_split\r\n for key, record in utils.tqdm(\r\n File \"\/home\/slesage\/hf\/datasets-preview-backend\/.venv\/lib\/python3.8\/site-packages\/tqdm\/std.py\", line 1185, in __iter__\r\n for obj in iterable:\r\n File \"\/home\/slesage\/.cache\/huggingface\/modules\/datasets_modules\/datasets\/counter\/9f84962fa0f35bec5a34fe0bdff8681838d497008c457f7856c48654476ec0e9\/counter.py\", line 161, in _generate_examples\r\n with derived_file.open(encoding=\"utf-8\") as f:\r\n File \"\/home\/slesage\/.pyenv\/versions\/3.8.11\/lib\/python3.8\/pathlib.py\", line 1222, in open\r\n return io.open(self, mode, buffering, encoding, errors, newline,\r\n File \"\/home\/slesage\/.pyenv\/versions\/3.8.11\/lib\/python3.8\/pathlib.py\", line 1078, in _opener\r\n return self._accessor.open(self, flags, mode)\r\nFileNotFoundError: [Errno 2] No such file or directory: '\/home\/slesage\/.cache\/huggingface\/datasets\/downloads\/extracted\/b57aa6db5601a738e57b95c1fd8cced54ff28fc540efcdaf0f6c4f1bb5dfe211\/COUNTER\/0032p.xml'\r\n\r\nDuring handling of the above exception, another exception occurred:\r\n\r\nTraceback (most recent call last):\r\n File \"\", line 1, in \r\n File \"\/home\/slesage\/hf\/datasets-preview-backend\/.venv\/lib\/python3.8\/site-packages\/datasets\/load.py\", line 1112, in load_dataset\r\n builder_instance.download_and_prepare(\r\n File \"\/home\/slesage\/hf\/datasets-preview-backend\/.venv\/lib\/python3.8\/site-packages\/datasets\/builder.py\", line 636, in download_and_prepare\r\n self._download_and_prepare(\r\n File \"\/home\/slesage\/hf\/datasets-preview-backend\/.venv\/lib\/python3.8\/site-packages\/datasets\/builder.py\", line 728, in _download_and_prepare\r\n raise OSError(\r\nOSError: Cannot find data file.\r\nOriginal error:\r\n[Errno 2] No such file or directory: '\/home\/slesage\/.cache\/huggingface\/datasets\/downloads\/extracted\/b57aa6db5601a738e57b95c1fd8cced54ff28fc540efcdaf0f6c4f1bb5dfe211\/COUNTER\/0032p.xml'\r\n```\r\n\r\n```python\r\n>>> import datasets as ds\r\n>>> b = ds.load_dataset('counter', split=\"train\", streaming=True)\r\nUsing custom data configuration default\r\n>>> list(b)\r\n[]\r\n```\r\n\r\n## Expected results\r\n\r\nAn exception should be raised in streaming mode\r\n\r\n## Actual results\r\n\r\nNo exception is raised in streaming mode: there is no way to tell if something has broken or if the dataset is simply empty.\r\n\r\n## Environment info\r\n\r\n- `datasets` version: 1.11.1.dev0\r\n- Platform: Linux-5.11.0-1016-aws-x86_64-with-glibc2.29\r\n- Python version: 3.8.11\r\n- PyArrow version: 4.0.1\r\n","comment_length":23,"text":"\"counter\" dataset raises an error in normal mode, but not in streaming mode\n## Describe the bug\r\n\r\n`counter` dataset raises an error on `load_dataset()`, but simply returns an empty iterator in streaming mode.\r\n\r\n## Steps to reproduce the bug\r\n\r\n```python\r\n>>> import datasets as ds\r\n>>> a = ds.load_dataset('counter', split=\"train\", streaming=False)\r\nUsing custom data configuration default\r\nDownloading and preparing dataset counter\/default (download: 1.29 MiB, generated: 2.48 MiB, post-processed: Unknown size, total: 3.77 MiB) to \/home\/slesage\/.cache\/huggingface\/datasets\/counter\/default\/1.0.0\/9f84962fa0f35bec5a34fe0bdff8681838d497008c457f7856c48654476ec0e9...\r\nTraceback (most recent call last):\r\n File \"\/home\/slesage\/hf\/datasets-preview-backend\/.venv\/lib\/python3.8\/site-packages\/datasets\/builder.py\", line 726, in _download_and_prepare\r\n self._prepare_split(split_generator, **prepare_split_kwargs)\r\n File \"\/home\/slesage\/hf\/datasets-preview-backend\/.venv\/lib\/python3.8\/site-packages\/datasets\/builder.py\", line 1124, in _prepare_split\r\n for key, record in utils.tqdm(\r\n File \"\/home\/slesage\/hf\/datasets-preview-backend\/.venv\/lib\/python3.8\/site-packages\/tqdm\/std.py\", line 1185, in __iter__\r\n for obj in iterable:\r\n File \"\/home\/slesage\/.cache\/huggingface\/modules\/datasets_modules\/datasets\/counter\/9f84962fa0f35bec5a34fe0bdff8681838d497008c457f7856c48654476ec0e9\/counter.py\", line 161, in _generate_examples\r\n with derived_file.open(encoding=\"utf-8\") as f:\r\n File \"\/home\/slesage\/.pyenv\/versions\/3.8.11\/lib\/python3.8\/pathlib.py\", line 1222, in open\r\n return io.open(self, mode, buffering, encoding, errors, newline,\r\n File \"\/home\/slesage\/.pyenv\/versions\/3.8.11\/lib\/python3.8\/pathlib.py\", line 1078, in _opener\r\n return self._accessor.open(self, flags, mode)\r\nFileNotFoundError: [Errno 2] No such file or directory: '\/home\/slesage\/.cache\/huggingface\/datasets\/downloads\/extracted\/b57aa6db5601a738e57b95c1fd8cced54ff28fc540efcdaf0f6c4f1bb5dfe211\/COUNTER\/0032p.xml'\r\n\r\nDuring handling of the above exception, another exception occurred:\r\n\r\nTraceback (most recent call last):\r\n File \"\", line 1, in \r\n File \"\/home\/slesage\/hf\/datasets-preview-backend\/.venv\/lib\/python3.8\/site-packages\/datasets\/load.py\", line 1112, in load_dataset\r\n builder_instance.download_and_prepare(\r\n File \"\/home\/slesage\/hf\/datasets-preview-backend\/.venv\/lib\/python3.8\/site-packages\/datasets\/builder.py\", line 636, in download_and_prepare\r\n self._download_and_prepare(\r\n File \"\/home\/slesage\/hf\/datasets-preview-backend\/.venv\/lib\/python3.8\/site-packages\/datasets\/builder.py\", line 728, in _download_and_prepare\r\n raise OSError(\r\nOSError: Cannot find data file.\r\nOriginal error:\r\n[Errno 2] No such file or directory: '\/home\/slesage\/.cache\/huggingface\/datasets\/downloads\/extracted\/b57aa6db5601a738e57b95c1fd8cced54ff28fc540efcdaf0f6c4f1bb5dfe211\/COUNTER\/0032p.xml'\r\n```\r\n\r\n```python\r\n>>> import datasets as ds\r\n>>> b = ds.load_dataset('counter', split=\"train\", streaming=True)\r\nUsing custom data configuration default\r\n>>> list(b)\r\n[]\r\n```\r\n\r\n## Expected results\r\n\r\nAn exception should be raised in streaming mode\r\n\r\n## Actual results\r\n\r\nNo exception is raised in streaming mode: there is no way to tell if something has broken or if the dataset is simply empty.\r\n\r\n## Environment info\r\n\r\n- `datasets` version: 1.11.1.dev0\r\n- Platform: Linux-5.11.0-1016-aws-x86_64-with-glibc2.29\r\n- Python version: 3.8.11\r\n- PyArrow version: 4.0.1\r\n\nNote that we might want to open an issue to fix the \"counter\" dataset by itself, but I let it up to you.","embeddings":[-0.4171527922,-0.2417904586,0.0004065955,0.1936673522,0.187378943,0.0572046041,0.5056346059,0.1059950739,0.1942841411,0.1353349984,0.0698306859,0.1297213137,-0.2097095996,0.3238820434,0.0893105865,0.0787798092,0.0210811216,0.1588886231,0.0335764661,0.0774051622,-0.2157031149,0.2028147876,-0.3865993321,-0.3339452446,-0.1323988438,0.1463262439,0.1100258455,0.1485300958,0.1753474176,-0.6307143569,0.2604337335,0.0775746107,0.2229160666,0.7022691965,-0.0001109225,0.1807845384,0.4209491313,-0.138756454,-0.5919362307,-0.2953981757,-0.3683612645,-0.3184562027,0.1747525334,-0.1460207105,-0.1155984327,0.0416297689,-0.2645150125,-0.4016982019,0.2196300626,0.44546175,0.2117786556,0.4712955952,-0.1509929299,-0.1280123889,0.1274099499,0.0086588459,-0.1527633816,0.2987458408,0.0759947449,0.2464787662,-0.030185163,0.400646776,-0.0477484316,0.1930941492,0.2172417343,-0.0403118059,-0.0638400242,-0.2730312347,0.077968359,0.2267186195,0.3892460763,-0.3428210616,-0.1372255683,-0.4340849519,-0.0550197884,-0.4057786763,0.2461039424,-0.0120554632,-0.2402932793,0.0848756358,-0.3027442396,0.1529166549,0.0393640399,-0.1008400992,-0.0583141893,0.1315543056,-0.0259356182,0.1768467724,-0.0182867814,-0.0394550189,0.1186181977,-0.2750140131,-0.0770448223,0.1979830861,-0.5796865821,0.0617473088,0.3563539386,-0.483120203,0.2248359621,0.0766406581,0.3723810911,0.0808451474,0.1294206977,-0.0054175756,0.3990353644,0.2017412037,0.1839399487,-0.2446381003,0.1665526032,0.3514225185,-0.0046332767,-0.3092014194,0.0455646105,-0.0928959474,0.2623339891,0.0135595212,0.3132420182,0.0522785522,-0.3759042025,0.1984404474,-0.2598961294,0.0621640086,0.3526681066,0.2470078766,0.31240955,0.331681639,-0.1649695039,0.2566638589,-0.170162797,-0.4501440227,-0.2399167717,-0.3115689754,0.0298984032,0.0585324802,0.2430290878,-0.4115727246,0.2127870023,0.1143840179,0.2314704955,-0.1508325785,0.2350654602,-0.3559949696,0.2714752853,0.4170607924,0.2132242918,0.30559659,0.2130860984,-0.078121908,0.0865608603,0.1292979866,-0.0135739716,-0.3794960678,0.1188295856,0.241816327,-0.1854702979,0.0091848904,-0.1626182795,0.3421088755,0.0492594019,-0.490398556,0.1006153971,-0.1393199116,-0.1788205355,-0.1388723552,0.3767809272,0.4191721678,-0.3163481355,-0.0631755143,-0.2223777026,-0.1933458447,0.3068187237,0.0627034158,-0.1867482066,0.0545980632,-0.198907733,-0.0324022733,0.1652611792,-0.133823514,-0.4505629539,0.3839800358,-0.1003188565,0.4216839075,0.003368129,0.0348957814,0.0462126397,-0.1179660112,0.2082362026,-0.0482203104,-0.3429863751,0.1301704794,-0.2063286006,0.0962786973,0.1745322049,0.0263372958,0.1080550551,0.145677641,0.1164149791,-0.0307607576,0.1964359283,-0.0440106578,0.0053364923,0.2909404337,0.0424242578,-0.0588180684,0.000367176,-0.1810717881,-0.4702015221,0.3554164469,0.2276427895,0.0265536476,0.0678977966,0.0377307907,-0.3046529889,0.141935274,-0.2711749077,-0.4771591425,0.1949529499,0.3907288015,-0.1118490696,0.0888262317,-0.277343154,0.3020512462,-0.1696492136,-0.1002230644,-0.1528523117,0.1263132989,0.1340726018,-0.3565647304,-0.0337362103,0.0897880867,0.2519123256,-0.1344926953,-0.234999463,0.4969182014,0.1069902405,0.0699909925,-0.0781737417,-0.173212111,0.1972211748,-0.1314566433,-0.2321656495,0.4724216461,0.3397763968,-0.2434855402,-0.1393338144,0.186793372,-0.2051788419,0.2564637661,-0.0694206432,0.0912937298,0.328330636,0.3197959363,-0.0928376839,-0.0212596133,0.2077185959,-0.1777403057,0.1031239405,-0.1610922515,-0.2532593608,-0.0428625979,0.1946246028,0.1955534518,0.0406559408,0.0529748946,-0.3236100376,-0.2086987048,0.3706597984,0.0880206153,0.4839422405,0.0791207775,0.2721221745,0.0922638103,-0.0106658787,-0.1263325214,0.2971056998,0.0752388388,0.0984970778,0.3603755832,0.2497458905,-0.1204286814,-0.4016779959,-0.2251661271,0.1358991563,0.133663699,-0.3052351177,0.0297692902,-0.1282226741,-0.2748440802,0.1133073941,-0.4021544755,-0.2225447446,-0.3337677121,-0.2058949023,0.3451238573,-0.1469643116,0.1498992443,-0.180475831,-0.0882678479,0.308257699,-0.0031163315,-0.2322359681,-0.1135618612,-0.2594828606,0.03214081,0.0412808843,-0.3234791756,0.434748143,-0.1234986335,-0.1801847816,-0.1512944698,-0.0745449811,0.1740905792,0.0435857289,0.2001399845,0.2743394077,0.2197588384,0.039221812,0.0045702313,0.4241863489,-0.2919014394,0.0146440258,0.3762074709,0.2012005895,0.2666827142,-0.2297265977,-0.2490313053,-0.0779714212,-0.4310797453,-0.174230665,-0.0227724351,0.0273636784,0.4507275522,0.262483865,0.3891989887,0.3127643466,-0.0566316359,-0.3348741829,-0.3475183249,0.1322237253,-0.1974763721,-0.4097188413,0.1144938767,0.0011452077,0.1664847434,0.2781507671,-0.3012993634,0.0803638548,-0.0408363193,0.1188741624,-0.05930195,-0.1120122075,0.1432749033,-0.06137814,-0.1019272059,-0.3338415623,-0.1850921214,-0.1276152432,-0.1529603153,0.2324113101,0.0381841213,0.5950309038,0.207781747,0.5280631185,0.4836455286,0.0581972674,0.6157116294,-0.2377375066,0.2161288112,-0.3673551977,-0.3503070772,0.2225523591,-0.0085880933,-0.1320838332,0.208188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{"html_url":"https:\/\/github.com\/huggingface\/datasets\/issues\/2866","title":"\"counter\" dataset raises an error in normal mode, but not in streaming mode","comments":"Fixed here: https:\/\/github.com\/huggingface\/datasets\/pull\/2894. Thanks @albertvillanova ","body":"## Describe the bug\r\n\r\n`counter` dataset raises an error on `load_dataset()`, but simply returns an empty iterator in streaming mode.\r\n\r\n## Steps to reproduce the bug\r\n\r\n```python\r\n>>> import datasets as ds\r\n>>> a = ds.load_dataset('counter', split=\"train\", streaming=False)\r\nUsing custom data configuration default\r\nDownloading and preparing dataset counter\/default (download: 1.29 MiB, generated: 2.48 MiB, post-processed: Unknown size, total: 3.77 MiB) to \/home\/slesage\/.cache\/huggingface\/datasets\/counter\/default\/1.0.0\/9f84962fa0f35bec5a34fe0bdff8681838d497008c457f7856c48654476ec0e9...\r\nTraceback (most recent call last):\r\n File \"\/home\/slesage\/hf\/datasets-preview-backend\/.venv\/lib\/python3.8\/site-packages\/datasets\/builder.py\", line 726, in _download_and_prepare\r\n self._prepare_split(split_generator, **prepare_split_kwargs)\r\n File \"\/home\/slesage\/hf\/datasets-preview-backend\/.venv\/lib\/python3.8\/site-packages\/datasets\/builder.py\", line 1124, in _prepare_split\r\n for key, record in utils.tqdm(\r\n File \"\/home\/slesage\/hf\/datasets-preview-backend\/.venv\/lib\/python3.8\/site-packages\/tqdm\/std.py\", line 1185, in __iter__\r\n for obj in iterable:\r\n File \"\/home\/slesage\/.cache\/huggingface\/modules\/datasets_modules\/datasets\/counter\/9f84962fa0f35bec5a34fe0bdff8681838d497008c457f7856c48654476ec0e9\/counter.py\", line 161, in _generate_examples\r\n with derived_file.open(encoding=\"utf-8\") as f:\r\n File \"\/home\/slesage\/.pyenv\/versions\/3.8.11\/lib\/python3.8\/pathlib.py\", line 1222, in open\r\n return io.open(self, mode, buffering, encoding, errors, newline,\r\n File \"\/home\/slesage\/.pyenv\/versions\/3.8.11\/lib\/python3.8\/pathlib.py\", line 1078, in _opener\r\n return self._accessor.open(self, flags, mode)\r\nFileNotFoundError: [Errno 2] No such file or directory: '\/home\/slesage\/.cache\/huggingface\/datasets\/downloads\/extracted\/b57aa6db5601a738e57b95c1fd8cced54ff28fc540efcdaf0f6c4f1bb5dfe211\/COUNTER\/0032p.xml'\r\n\r\nDuring handling of the above exception, another exception occurred:\r\n\r\nTraceback (most recent call last):\r\n File \"\", line 1, in \r\n File \"\/home\/slesage\/hf\/datasets-preview-backend\/.venv\/lib\/python3.8\/site-packages\/datasets\/load.py\", line 1112, in load_dataset\r\n builder_instance.download_and_prepare(\r\n File \"\/home\/slesage\/hf\/datasets-preview-backend\/.venv\/lib\/python3.8\/site-packages\/datasets\/builder.py\", line 636, in download_and_prepare\r\n self._download_and_prepare(\r\n File \"\/home\/slesage\/hf\/datasets-preview-backend\/.venv\/lib\/python3.8\/site-packages\/datasets\/builder.py\", line 728, in _download_and_prepare\r\n raise OSError(\r\nOSError: Cannot find data file.\r\nOriginal error:\r\n[Errno 2] No such file or directory: '\/home\/slesage\/.cache\/huggingface\/datasets\/downloads\/extracted\/b57aa6db5601a738e57b95c1fd8cced54ff28fc540efcdaf0f6c4f1bb5dfe211\/COUNTER\/0032p.xml'\r\n```\r\n\r\n```python\r\n>>> import datasets as ds\r\n>>> b = ds.load_dataset('counter', split=\"train\", streaming=True)\r\nUsing custom data configuration default\r\n>>> list(b)\r\n[]\r\n```\r\n\r\n## Expected results\r\n\r\nAn exception should be raised in streaming mode\r\n\r\n## Actual results\r\n\r\nNo exception is raised in streaming mode: there is no way to tell if something has broken or if the dataset is simply empty.\r\n\r\n## Environment info\r\n\r\n- `datasets` version: 1.11.1.dev0\r\n- Platform: Linux-5.11.0-1016-aws-x86_64-with-glibc2.29\r\n- Python version: 3.8.11\r\n- PyArrow version: 4.0.1\r\n","comment_length":5,"text":"\"counter\" dataset raises an error in normal mode, but not in streaming mode\n## Describe the bug\r\n\r\n`counter` dataset raises an error on `load_dataset()`, but simply returns an empty iterator in streaming mode.\r\n\r\n## Steps to reproduce the bug\r\n\r\n```python\r\n>>> import datasets as ds\r\n>>> a = ds.load_dataset('counter', split=\"train\", streaming=False)\r\nUsing custom data configuration default\r\nDownloading and preparing dataset counter\/default (download: 1.29 MiB, generated: 2.48 MiB, post-processed: Unknown size, total: 3.77 MiB) to \/home\/slesage\/.cache\/huggingface\/datasets\/counter\/default\/1.0.0\/9f84962fa0f35bec5a34fe0bdff8681838d497008c457f7856c48654476ec0e9...\r\nTraceback (most recent call last):\r\n File \"\/home\/slesage\/hf\/datasets-preview-backend\/.venv\/lib\/python3.8\/site-packages\/datasets\/builder.py\", line 726, in _download_and_prepare\r\n self._prepare_split(split_generator, **prepare_split_kwargs)\r\n File \"\/home\/slesage\/hf\/datasets-preview-backend\/.venv\/lib\/python3.8\/site-packages\/datasets\/builder.py\", line 1124, in _prepare_split\r\n for key, record in utils.tqdm(\r\n File \"\/home\/slesage\/hf\/datasets-preview-backend\/.venv\/lib\/python3.8\/site-packages\/tqdm\/std.py\", line 1185, in __iter__\r\n for obj in iterable:\r\n File \"\/home\/slesage\/.cache\/huggingface\/modules\/datasets_modules\/datasets\/counter\/9f84962fa0f35bec5a34fe0bdff8681838d497008c457f7856c48654476ec0e9\/counter.py\", line 161, in _generate_examples\r\n with derived_file.open(encoding=\"utf-8\") as f:\r\n File \"\/home\/slesage\/.pyenv\/versions\/3.8.11\/lib\/python3.8\/pathlib.py\", line 1222, in open\r\n return io.open(self, mode, buffering, encoding, errors, newline,\r\n File \"\/home\/slesage\/.pyenv\/versions\/3.8.11\/lib\/python3.8\/pathlib.py\", line 1078, in _opener\r\n return self._accessor.open(self, flags, mode)\r\nFileNotFoundError: [Errno 2] No such file or directory: '\/home\/slesage\/.cache\/huggingface\/datasets\/downloads\/extracted\/b57aa6db5601a738e57b95c1fd8cced54ff28fc540efcdaf0f6c4f1bb5dfe211\/COUNTER\/0032p.xml'\r\n\r\nDuring handling of the above exception, another exception occurred:\r\n\r\nTraceback (most recent call last):\r\n File \"\", line 1, in \r\n File \"\/home\/slesage\/hf\/datasets-preview-backend\/.venv\/lib\/python3.8\/site-packages\/datasets\/load.py\", line 1112, in load_dataset\r\n builder_instance.download_and_prepare(\r\n File \"\/home\/slesage\/hf\/datasets-preview-backend\/.venv\/lib\/python3.8\/site-packages\/datasets\/builder.py\", line 636, in download_and_prepare\r\n self._download_and_prepare(\r\n File \"\/home\/slesage\/hf\/datasets-preview-backend\/.venv\/lib\/python3.8\/site-packages\/datasets\/builder.py\", line 728, in _download_and_prepare\r\n raise OSError(\r\nOSError: Cannot find data file.\r\nOriginal error:\r\n[Errno 2] No such file or directory: '\/home\/slesage\/.cache\/huggingface\/datasets\/downloads\/extracted\/b57aa6db5601a738e57b95c1fd8cced54ff28fc540efcdaf0f6c4f1bb5dfe211\/COUNTER\/0032p.xml'\r\n```\r\n\r\n```python\r\n>>> import datasets as ds\r\n>>> b = ds.load_dataset('counter', split=\"train\", streaming=True)\r\nUsing custom data configuration default\r\n>>> list(b)\r\n[]\r\n```\r\n\r\n## Expected results\r\n\r\nAn exception should be raised in streaming mode\r\n\r\n## Actual results\r\n\r\nNo exception is raised in streaming mode: there is no way to tell if something has broken or if the dataset is simply empty.\r\n\r\n## Environment info\r\n\r\n- `datasets` version: 1.11.1.dev0\r\n- Platform: Linux-5.11.0-1016-aws-x86_64-with-glibc2.29\r\n- Python version: 3.8.11\r\n- PyArrow version: 4.0.1\r\n\nFixed here: https:\/\/github.com\/huggingface\/datasets\/pull\/2894. Thanks @albertvillanova 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{"html_url":"https:\/\/github.com\/huggingface\/datasets\/issues\/2860","title":"Cannot download TOTTO dataset","comments":"Hola @mrm8488, thanks for reporting.\r\n\r\nApparently, the data source host changed their URL one week ago: https:\/\/github.com\/google-research-datasets\/ToTTo\/commit\/cebeb430ec2a97747e704d16a9354f7d9073ff8f\r\n\r\nI'm fixing it.","body":"Error: Couldn't find file at https:\/\/storage.googleapis.com\/totto\/totto_data.zip\r\n\r\n`datasets version: 1.11.0`\r\n# How to reproduce:\r\n\r\n```py\r\nfrom datasets import load_dataset\r\ndataset = load_dataset('totto')\r\n```\r\n\r\n\r\n","comment_length":20,"text":"Cannot download TOTTO dataset\nError: Couldn't find file at https:\/\/storage.googleapis.com\/totto\/totto_data.zip\r\n\r\n`datasets version: 1.11.0`\r\n# How to reproduce:\r\n\r\n```py\r\nfrom datasets import load_dataset\r\ndataset = load_dataset('totto')\r\n```\r\n\r\n\r\n\nHola @mrm8488, thanks for reporting.\r\n\r\nApparently, the data source host changed their URL one week ago: https:\/\/github.com\/google-research-datasets\/ToTTo\/commit\/cebeb430ec2a97747e704d16a9354f7d9073ff8f\r\n\r\nI'm fixing it.","embeddings":[-0.294410497,0.3900933862,-0.1317222118,0.0641968921,0.4353683293,0.1393284649,0.1679639369,0.5879850388,-0.0261453092,0.234341681,-0.1947766393,-0.0473091528,0.0594171956,0.318708539,0.0643599778,-0.3084107637,0.0652796775,-0.0130061815,-0.1395519972,0.0308540575,-0.0759798363,0.2782622278,0.0100256903,0.0654641986,0.0181091316,0.0936704874,-0.0290926713,-0.0979447067,-0.2686378956,-0.4347373247,0.415338546,0.0613755472,0.1697377264,0.3808855712,-0.0001073767,0.1472330391,0.3291011751,0.0086168032,-0.2500196695,-0.3531953394,-0.3288857937,-0.1141764298,-0.1965035796,-0.1480810791,-0.0830533355,0.2127362341,0.2038826346,-0.2607125342,0.0417160429,0.5573630333,0.2718278766,0.0907747969,0.3079364896,-0.3647713363,0.2657265365,0.1475394666,-0.0365158468,0.0705632791,0.0785895288,0.0108668972,0.3562817872,0.0460000634,-0.133997038,0.0743308142,-0.1553672999,-0.0840224102,-0.0924824327,-0.3424230218,0.2141403407,0.3073774278,0.5096561313,-0.2044652998,-0.3611649573,0.2267780751,-0.0598624535,-0.279548645,0.2698038518,0.1802222282,0.0900912583,0.1698183566,0.1732570976,-0.4855846167,-0.1890135705,0.0528651476,-0.4141792953,0.107604593,-0.1177637801,0.0048586847,-0.105921559,-0.1203932837,0.3016455472,-0.0547915176,-0.0375608727,0.2590126991,-0.2745456994,-0.1841468364,0.1035571247,0.0311484244,0.1920053959,0.0490685366,0.1362317801,-0.1017651632,-0.5062013865,-0.0666135922,0.2418693751,-0.1095379815,-0.000267334,0.3804118037,0.2949267328,0.2416359335,0.1484333426,0.0473140553,-0.1586337984,-0.358691752,0.0269331522,-0.113104403,0.4928361177,-0.2085420638,-0.3899927437,0.1616978347,-0.090081729,-0.1214746758,-0.0176121797,0.1517945826,-0.0244206693,0.0753581598,0.0918698311,0.1404550672,-0.008870828,-0.1377051771,-0.2661000192,0.2041446567,-0.0125199547,0.0713024735,0.2309996784,-0.3448672891,0.0952998549,-0.0258917771,-0.4624833763,0.0828349143,-0.1361531913,-0.0750162899,-0.2458771169,0.3632289171,0.2265905589,0.1045650393,0.0013822372,0.1134399772,-0.2949780524,0.2792558968,-0.3678761125,-0.0678893328,-0.1227772906,0.2941953242,-0.3227276504,-0.2466399968,-0.5071802735,-0.0339648984,-0.0525038354,-0.3056934178,-0.1234595478,-0.1745043397,-0.1805351675,-0.2515176237,0.1124921739,0.6013523936,-0.2063911855,0.020885611,-0.4196625054,-0.4201267958,0.1899637729,0.269320339,-0.0341406576,0.4477233887,-0.3025847673,0.0885180831,0.4392078221,-0.1254837364,-0.7618820667,-0.0033206183,-0.2596687675,-0.1898869723,0.1790016443,0.0790314004,0.2497510761,-0.1000057384,0.2460572869,0.1370749325,0.012917717,-0.0459471196,-0.2387001067,-0.0105643263,-0.0045891618,0.1839413047,0.1048776805,0.2050982267,0.1501675397,-0.0725682601,0.2357553095,0.1607331038,0.0994152948,0.3408914506,0.3313904405,-0.0265133232,-0.0199446026,-0.1835746318,-0.1992596537,0.1232289374,-0.2162778378,0.1084155291,-0.2086808383,-0.1304647177,-0.4467419088,0.0594626628,-0.0653845966,-0.093042925,0.1499165893,-0.0002756922,0.310736388,0.3321071863,-0.02035648,0.320682168,0.2677029073,0.1030120254,-0.1375443637,0.5665908456,-0.1339783072,0.0568048991,0.4536594152,-0.2412828058,0.236403808,-0.0816816241,-0.0623007454,0.1545941979,0.0604691543,0.3422275782,0.4771338403,0.2396415472,0.1721659005,-0.4354346693,0.2116640955,0.343268007,0.0783024356,0.1411449164,-0.1909071058,0.0213618409,0.1539851874,0.0350163281,-0.0176072661,0.2682003379,0.544712007,0.0763235763,0.0633015558,-0.0642063022,0.2679164112,0.5545850396,0.2224782109,-0.0677626878,-0.2419174314,0.1457102746,0.538705945,-0.0372180901,0.0527861379,0.2355953008,-0.1476681978,-0.0631909966,-0.1953097135,0.2097867578,0.1718096882,0.0596293472,0.1565221548,0.0106348377,-0.0777703449,-0.2667880654,0.0551331863,0.0725212246,0.1452946216,0.1418423057,0.1219759658,0.2061336339,-0.08200939,-0.3087290227,0.0309936032,0.3056665659,-0.1311020702,-0.0795388594,-0.2592677772,0.0049411356,-0.2732787728,-0.3159630299,-0.2113306969,-0.3952906728,0.0605420209,0.2657611072,0.0656001344,-0.0102982745,-0.0560371764,0.0340000875,0.2436152548,-0.3098428249,-0.1341092587,-0.150049597,-0.1041894853,0.170614779,0.2377597988,-0.3077156246,0.3982344866,-0.6368309259,0.1199382693,-0.5480054617,-0.234621942,-0.043117255,-0.1899068207,0.0942285731,0.175614506,0.4022015929,0.3427299261,0.2505168021,0.1937542409,-0.0247938484,-0.0510064363,0.0891098529,-0.1387198567,-0.0441237539,0.1827340573,-0.4613678753,-0.3021485507,-0.2280495763,0.0444356278,0.0629076734,0.2366307676,-0.2858436108,0.1709893048,0.0659947768,0.1328113973,-0.3080635071,-0.1222961098,-0.1796017885,0.4985628128,-0.2371805012,-0.2333060801,0.1059011444,0.130261451,0.4757525921,0.1674677283,-0.4497697651,0.179243952,-0.3459710777,0.1534799039,-0.0468465574,0.0758880824,0.3201242983,0.1453730166,-0.1137198061,-0.0569933727,-0.1610036939,-0.050038822,-0.3855092824,0.2330114543,-0.0367576629,0.2503889203,-0.1210072637,0.398199141,0.2551098466,0.07454741,0.3040669858,-0.1943264455,0.1306042373,-0.1279257536,-0.1889470071,-0.0984253138,0.1577033997,-0.116324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{"html_url":"https:\/\/github.com\/huggingface\/datasets\/issues\/2859","title":"Loading allenai\/c4 in streaming mode does too many HEAD requests","comments":"https:\/\/github.com\/huggingface\/datasets\/blob\/6c766f9115d686182d76b1b937cb27e099c45d68\/src\/datasets\/builder.py#L179-L186","body":"This does 60,000+ HEAD requests to get all the ETags of all the data files:\r\n```python\r\nfrom datasets import load_dataset\r\nload_dataset(\"allenai\/c4\", streaming=True)\r\n```\r\nIt makes loading the dataset completely impractical.\r\n\r\nThe ETags are used to compute the config id (it must depend on the data files being used).\r\nInstead of using the ETags, we could simply use the commit hash of the dataset repository on the hub, as well and the glob pattern used to resolve the files (here it's `*` by default, to load all the files of the repository)","comment_length":1,"text":"Loading allenai\/c4 in streaming mode does too many HEAD requests\nThis does 60,000+ HEAD requests to get all the ETags of all the data files:\r\n```python\r\nfrom datasets import load_dataset\r\nload_dataset(\"allenai\/c4\", streaming=True)\r\n```\r\nIt makes loading the dataset completely impractical.\r\n\r\nThe ETags are used to compute the config id (it must depend on the data files being used).\r\nInstead of using the ETags, we could simply use the commit hash of the dataset repository on the hub, as well and the glob pattern used to resolve the files (here it's `*` by default, to load all the files of the repository)\nhttps:\/\/github.com\/huggingface\/datasets\/blob\/6c766f9115d686182d76b1b937cb27e099c45d68\/src\/datasets\/builder.py#L179-L186","embeddings":[-0.5428535938,-0.0302166119,-0.1265247166,0.3286388516,0.256604135,-0.0104140276,0.197608754,0.3729439676,0.1844934821,0.2685716748,-0.1927886903,0.3850267828,0.0771837533,0.2340062857,-0.143449083,0.136008963,-0.1367563605,0.3763958514,0.1254129112,0.1078248546,-0.0619285703,0.0006759175,-0.0417456739,-0.0773263052,-0.2133178711,0.2759331763,0.1665377468,0.2257279307,-0.0134929046,-0.4966956973,0.240168795,0.2341412455,-0.0487481132,0.2447642982,-0.0001018645,0.1439338028,0.4033434391,-0.0089729438,-0.085589841,0.4268731475,-0.4157178402,-0.0739421472,0.0224096645,-0.3959259391,-0.2034465969,-0.2251017988,0.0632516742,-0.0980081558,0.2041195333,0.2827344537,0.260243088,0.4249944985,0.0377698317,-0.1970410794,0.2039705664,-0.316323489,0.0617868043,-0.056926813,0.0797685385,0.1593059301,-0.0834435821,0.2483358234,-0.0834733918,0.0802756175,0.2913605869,-0.1320183277,-0.0032377529,-0.2171800733,0.3939404786,0.2030942291,0.3443941772,-0.3286153674,-0.1332122386,-0.2533044219,0.0619671606,-0.1955298185,0.0476448163,0.1538827866,-0.1587007791,0.1192615852,-0.0326113962,-0.0544691943,-0.1602670252,-0.0494049191,-0.0776140019,0.089980714,-0.0840734094,0.1207940727,0.136279732,0.058017496,0.0522075854,-0.2175264806,-0.0846055672,-0.004862661,-0.3694218993,-0.0516511574,0.3653528988,0.4273298979,0.1478993595,0.1687032282,-0.1197926477,0.2871201038,0.0435164198,0.0685389638,0.0575072095,0.0231996793,-0.0176825654,-0.389597863,0.5508090258,0.0899150446,-0.1071902066,-0.2005494833,-0.0709187463,-0.1060602814,0.0840810388,-0.1210096851,0.1117649302,-0.2036800086,-0.0995911956,-0.1678549349,-0.0444756113,0.0720648617,0.1929066479,0.4125182927,-0.1070380881,0.2573559582,-0.2619225979,0.0917120874,-0.1934460849,-0.0925472528,-0.3160288334,-0.2928213477,-0.1694728285,0.0569894873,0.1707771719,-0.34016487,0.4697290957,-0.0191245824,0.3383053243,0.0339444056,0.0842757225,0.1081046611,-0.0809869394,0.4188500047,0.2263165563,-0.0787535161,-0.0641879365,-0.1923369914,-0.291814357,-0.1569433808,0.0312824622,-0.4784304202,0.1928419471,0.2877151072,-0.0427191779,0.1639586836,-0.3409174085,0.0171625558,0.0027538103,0.1268018633,-0.1082377136,-0.0361211002,0.1082094535,-0.2483724952,0.1618676782,0.4884290099,-0.0835868865,-0.0830648839,-0.039552927,-0.1286983341,-0.0772875845,0.3747945726,-0.3466023803,0.120490931,-0.0308591686,0.0409644805,0.032458812,-0.425350368,-0.6205683351,0.1140533686,0.0639335662,-0.0276992563,0.2386657745,0.3460126519,0.1039455682,-0.107216388,0.0222086739,0.1709899902,0.073792845,0.2256556302,-0.1190583929,-0.2345744073,-0.2999114394,0.3787722886,-0.1165996566,-0.1365634799,0.1347563714,-0.1876992285,0.2526454926,-0.3332684338,0.0380112678,-0.1584161669,0.0938546658,-0.0603501089,0.0127826966,0.2236943841,-0.4421748519,0.3059828579,0.2439787388,0.1666173935,-0.1472782493,-0.3505738974,0.0228462406,-0.0090298792,-0.3510281146,-0.0372455232,0.1835951656,0.0712855086,0.0677272379,-0.1504935324,-0.2270190865,0.2682373822,-0.3091340661,-0.0179539192,-0.4381204545,0.1312066019,0.0892122611,0.0468895361,0.2672764957,-0.1050158292,0.0074687083,0.0492864773,0.0621649623,0.1636259705,-0.0330553278,0.4530902505,0.0818722695,0.7110303044,0.1389819533,-0.2826481164,0.3203724325,0.2067766637,0.1654448509,-0.2394565195,-0.1613846868,0.4084748626,0.3778287172,0.3252526224,0.1585008949,-0.0329761878,0.410119772,-0.0721899271,-0.1317404956,-0.0330927819,-0.00823195,-0.0209802482,0.3309631944,-0.023005046,-0.2523918152,0.3234723508,0.3031021953,0.1111327931,-0.1492699087,-0.0652219951,-0.298992157,-0.1263385117,0.2080144435,-0.0855184197,0.2448243797,0.4307595193,-0.0929894522,0.1461498737,0.0120118773,-0.256077677,0.3098646998,0.0617690906,0.0520799011,0.0422569439,0.1629692465,-0.2966534495,-0.4100134969,-0.3106255233,-0.1572241336,0.1993097663,-0.2276444584,-0.0174489729,-0.0920694247,-0.3194893003,0.0963194221,-0.0115835099,0.0477257594,-0.2335455716,0.0685858503,0.2594055235,-0.2168204486,0.1381093711,-0.1156482473,0.2903750539,0.0892408416,-0.1132856086,-0.2681921422,-0.2419697493,-0.1095879748,0.1148864031,0.4646658599,0.0141319409,0.3488751948,-0.3224317729,0.1775819212,-0.3888134956,-0.1769560575,0.1015825272,-0.1227035671,0.0946206078,0.0673972368,0.1070112586,0.2800557911,-0.125384286,0.0769697279,-0.2286968827,-0.0885543525,0.1452503055,-0.154563725,-0.2497059852,0.0414588526,-0.3924611509,-0.0118674375,-0.3728982508,0.342957437,0.2925284505,0.2317622006,-0.0143444026,0.0886501297,0.003775317,0.0996953025,-0.221465528,-0.2427807301,-0.3542366624,0.1676867455,-0.1924644858,-0.1902693361,0.0533196107,0.1380496174,0.4981618226,-0.0522706732,-0.4013659656,-0.0050968095,-0.4799565375,0.4729455709,0.0018389857,0.1385525167,0.2309796959,0.1050632596,-0.2131042331,-0.003395986,0.0059769182,0.1519519687,-0.0271408688,-0.2601141036,-0.0332135223,0.1607500166,-0.14402548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{"html_url":"https:\/\/github.com\/huggingface\/datasets\/issues\/2945","title":"Protect master branch","comments":"Cool, I think we can do both :)","body":"After accidental merge commit (91c55355b634d0dc73350a7ddee1a6776dbbdd69) into `datasets` master branch, all commits present in the feature branch were permanently added to `datasets` master branch history, as e.g.:\r\n- 00cc036fea7c7745cfe722360036ed306796a3f2\r\n- 13ae8c98602bbad8197de3b9b425f4c78f582af1\r\n- ...\r\n\r\nI propose to protect our master branch, so that we avoid we can accidentally make this kind of mistakes in the future:\r\n- [x] For Pull Requests using GitHub, allow only squash merging, so that only a single commit per Pull Request is merged into the master branch\r\n - Currently, simple merge commits are already disabled\r\n - I propose to disable rebase merging as well\r\n- ~~Protect the master branch from direct pushes (to avoid accidentally pushing of merge commits)~~\r\n - ~~This protection would reject direct pushes to master branch~~\r\n - ~~If so, for each release (when we need to commit directly to the master branch), we should previously disable the protection and re-enable it again after the release~~\r\n- [x] Protect the master branch only from direct pushing of **merge commits**\r\n - GitHub offers the possibility to protect the master branch only from merge commits (which are the ones that introduce all the commits from the feature branch into the master branch).\r\n - No need to disable\/re-enable this protection on each release \r\n\r\nThis purpose of this Issue is to open a discussion about this problem and to agree in a solution.","comment_length":8,"text":"Protect master branch\nAfter accidental merge commit (91c55355b634d0dc73350a7ddee1a6776dbbdd69) into `datasets` master branch, all commits present in the feature branch were permanently added to `datasets` master branch history, as e.g.:\r\n- 00cc036fea7c7745cfe722360036ed306796a3f2\r\n- 13ae8c98602bbad8197de3b9b425f4c78f582af1\r\n- ...\r\n\r\nI propose to protect our master branch, so that we avoid we can accidentally make this kind of mistakes in the future:\r\n- [x] For Pull Requests using GitHub, allow only squash merging, so that only a single commit per Pull Request is merged into the master branch\r\n - Currently, simple merge commits are already disabled\r\n - I propose to disable rebase merging as well\r\n- ~~Protect the master branch from direct pushes (to avoid accidentally pushing of merge commits)~~\r\n - ~~This protection would reject direct pushes to master branch~~\r\n - ~~If so, for each release (when we need to commit directly to the master branch), we should previously disable the protection and re-enable it again after the release~~\r\n- [x] Protect the master branch only from direct pushing of **merge commits**\r\n - GitHub offers the possibility to protect the master branch only from merge commits (which are the ones that introduce all the commits from the feature branch into the master branch).\r\n - No need to disable\/re-enable this protection on each release \r\n\r\nThis purpose of this Issue is to open a discussion about this problem and to agree in a solution.\nCool, I think we can do both :)","embeddings":[-0.2390099317,-0.0966292247,-0.0759872422,-0.0984264985,-0.0993319154,-0.114640519,0.0078532966,0.239554435,-0.029255297,-0.0760675296,0.3045841157,-0.0697917044,-0.1556909084,0.2472542524,-0.0918809772,0.2528043091,0.1680651754,0.000099914,-0.099873215,0.0868200585,-0.0079817604,-0.1016465425,0.1242587939,0.0464673564,-0.2174060941,0.0417824537,0.0499481186,0.1391150504,-0.4184132516,-0.4594882727,0.1383522302,0.1707512289,0.0033366256,0.5652734041,-0.0001042576,0.0216173306,0.2390211523,0.0108919349,-0.1405786723,-0.1769673228,-0.5225075483,-0.4049667418,-0.1415199637,0.0287014097,0.0748746395,0.06865637,-0.0910198018,0.002452912,0.3400871456,0.0976495519,0.2408002317,-0.1941754073,0.1793913692,-0.0078934981,0.3311534822,0.5539640784,-0.0518133156,0.22529535,0.0045611048,-0.0102421539,0.1125852838,0.2086250186,-0.0837501734,-0.2613006532,0.2912853956,-0.1761568338,0.5298508406,-0.3999355435,-0.1809146106,-0.1078854129,0.0704470351,-0.201073736,-0.3955834508,-0.2669799924,-0.1179080084,-0.0097809853,0.0528227128,0.2314448655,-0.0157607514,0.0743468106,-0.3007844985,-0.3514812589,0.0444366112,0.0477003977,0.10768985,-0.1619907767,0.0425621495,-0.0867641419,0.3245029449,0.0032727672,-0.1678859442,-0.2417692542,-0.3024554849,-0.0731806457,-0.1582976431,-0.166840598,-0.2660009265,-0.3856663704,0.4048146307,0.3533258736,-0.360022366,-0.049508784,0.002726417,-0.0647690669,0.2959358394,0.0719453767,0.011164953,0.0792105719,0.5598432422,0.3058459461,0.0217627417,0.3349214792,0.3275439441,-0.0626471415,0.0689635947,0.5172939301,0.5336612463,-0.2621914148,0.0377011895,0.1680787653,-0.3323733509,-0.3190573454,0.0054372335,-0.0610207208,0.0830994621,0.0703226849,-0.1091590971,0.0525613427,0.1415696889,-0.0016033769,-0.3210552037,-0.1457319856,-0.3556134701,-0.0552582555,0.1311259568,-0.4680353403,0.0908330008,0.4519638121,-0.1013908461,-0.2686688304,0.0358462371,0.1716575772,0.0330468714,0.2908923328,-0.1433112174,-0.3825237751,0.2031245977,0.0965870917,-0.0456247479,0.2545187175,-0.1935967952,-0.2513652146,-0.1866476983,0.3089985549,-0.1121701747,0.3287965655,-0.548268497,0.0025912365,0.0102211721,0.3561609685,0.4972600937,0.385391444,0.2942132354,-0.1614470184,0.0424310081,0.1232627109,0.3384101689,-0.0511925742,-0.0100153778,-0.2738169432,0.1200275049,0.2083038092,-0.2946696877,-0.0103482995,-0.0507854298,-0.1232980564,0.0196767803,-0.048742909,-0.204722017,-0.0717563778,-0.4525829852,-0.0241770819,-0.0117112603,0.0191363525,0.1543500572,-0.3124671876,-0.0787971914,0.1779266298,-0.182664752,0.1171256378,-0.3127051294,-0.6241436601,-0.1159733236,-0.0647230893,0.1302614063,0.1962026656,0.3260959387,0.0762788281,0.0484594442,-0.1529644579,-0.0238246322,0.1797253937,0.3943619728,-0.1591625661,-0.2575499117,0.0904227868,-0.3276287019,0.0429003425,-0.0230592508,-0.0326709487,0.0328225382,-0.4201252162,0.2604811192,-0.0395855084,-0.089378275,-0.1092364043,0.2325994372,0.0994815007,-0.2429064363,-0.2273789346,-0.1815333217,0.2851662338,-0.242647782,0.2751372755,0.099704735,0.0815574303,-0.1571651399,-0.0062341182,-0.0228314847,0.3285116851,0.1736403704,-0.0701381266,-0.04674327,0.1801669151,-0.0945321396,0.1870126128,0.1769949794,0.4175725579,0.3424830437,-0.1347359419,-0.0105623221,-0.1067967415,-0.1460241973,0.022494385,-0.2684330642,0.2932992876,0.0991126224,-0.2107091695,-0.249318257,-0.0687692985,-0.1965132803,-0.265663594,-0.1541765183,0.0057348073,-0.1855473518,0.1418430507,-0.2901535332,0.2540666461,-0.2189239413,0.1275674105,0.0980846509,-0.201123789,-0.0243991297,0.0869311392,-0.0290957168,-0.057731878,0.100935325,0.3220874071,0.0902120993,0.183042407,-0.0160376783,0.0336617008,0.2433368862,-0.0592605695,0.362724781,0.1478046626,-0.0405387431,0.0476890579,0.3128943741,0.1199872941,0.0844289884,0.3880383074,-0.0403469466,0.1266814917,-0.2439862639,-0.0744038522,-0.2096193433,-0.5341181755,0.1785611957,-0.1322654784,-0.311395973,-0.0278486367,-0.053532742,0.1170226783,-0.4517551064,0.1408915371,0.2340205312,0.2509391308,-0.3069441617,-0.0698819757,0.1471458375,-0.0289604329,0.0071960338,0.1242603362,0.4291484952,-0.2824301422,0.4524761438,0.2389160544,-0.1856772155,-0.4897386432,-0.4018841088,0.0115547851,-0.1668282598,-0.1153612956,0.0861761943,-0.0717044175,0.1023176908,-0.401612252,-0.3951958418,0.0295078922,-0.3599407971,-0.1393553913,0.0470686667,0.1184691191,-0.2150190026,-0.0969456658,-0.2935731709,-0.2871156633,0.5013284087,-0.0580234416,0.0385896452,0.0112076271,-0.2732456625,-0.1094526052,0.1394193172,0.0409913287,-0.0648903027,-0.3088234365,-0.0299696531,0.0454311818,-0.0143893873,0.368963629,0.2126246095,-0.3675787747,0.0550749078,-0.350450635,-0.1397126317,-0.0122180469,0.4691388905,0.256077528,-0.1587453038,0.0662926659,0.0626281053,-0.1768465936,0.1262467802,-0.1262247413,0.1179754212,0.3565621078,0.3317129016,-0.0136891427,0.1207261309,0.0874625519,0.6525163054,0.3680907488,-0.0682478175,0.1437117904,0.2640963793,0.0372981504,0.0415912867,-0.2894337177,0.0679146126,-0.0695572942,-0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{"html_url":"https:\/\/github.com\/huggingface\/datasets\/issues\/2945","title":"Protect master branch","comments":"@lhoestq now the 2 are implemented.\r\n\r\nPlease note that for the the second protection, finally I have chosen to protect the master branch only from **merge commits** (see update comment above), so no need to disable\/re-enable the protection on each release (direct commits, different from merge commits, can be pushed to the remote master branch; and eventually reverted without messing up the repo history).","body":"After accidental merge commit (91c55355b634d0dc73350a7ddee1a6776dbbdd69) into `datasets` master branch, all commits present in the feature branch were permanently added to `datasets` master branch history, as e.g.:\r\n- 00cc036fea7c7745cfe722360036ed306796a3f2\r\n- 13ae8c98602bbad8197de3b9b425f4c78f582af1\r\n- ...\r\n\r\nI propose to protect our master branch, so that we avoid we can accidentally make this kind of mistakes in the future:\r\n- [x] For Pull Requests using GitHub, allow only squash merging, so that only a single commit per Pull Request is merged into the master branch\r\n - Currently, simple merge commits are already disabled\r\n - I propose to disable rebase merging as well\r\n- ~~Protect the master branch from direct pushes (to avoid accidentally pushing of merge commits)~~\r\n - ~~This protection would reject direct pushes to master branch~~\r\n - ~~If so, for each release (when we need to commit directly to the master branch), we should previously disable the protection and re-enable it again after the release~~\r\n- [x] Protect the master branch only from direct pushing of **merge commits**\r\n - GitHub offers the possibility to protect the master branch only from merge commits (which are the ones that introduce all the commits from the feature branch into the master branch).\r\n - No need to disable\/re-enable this protection on each release \r\n\r\nThis purpose of this Issue is to open a discussion about this problem and to agree in a solution.","comment_length":64,"text":"Protect master branch\nAfter accidental merge commit (91c55355b634d0dc73350a7ddee1a6776dbbdd69) into `datasets` master branch, all commits present in the feature branch were permanently added to `datasets` master branch history, as e.g.:\r\n- 00cc036fea7c7745cfe722360036ed306796a3f2\r\n- 13ae8c98602bbad8197de3b9b425f4c78f582af1\r\n- ...\r\n\r\nI propose to protect our master branch, so that we avoid we can accidentally make this kind of mistakes in the future:\r\n- [x] For Pull Requests using GitHub, allow only squash merging, so that only a single commit per Pull Request is merged into the master branch\r\n - Currently, simple merge commits are already disabled\r\n - I propose to disable rebase merging as well\r\n- ~~Protect the master branch from direct pushes (to avoid accidentally pushing of merge commits)~~\r\n - ~~This protection would reject direct pushes to master branch~~\r\n - ~~If so, for each release (when we need to commit directly to the master branch), we should previously disable the protection and re-enable it again after the release~~\r\n- [x] Protect the master branch only from direct pushing of **merge commits**\r\n - GitHub offers the possibility to protect the master branch only from merge commits (which are the ones that introduce all the commits from the feature branch into the master branch).\r\n - No need to disable\/re-enable this protection on each release \r\n\r\nThis purpose of this Issue is to open a discussion about this problem and to agree in a solution.\n@lhoestq now the 2 are implemented.\r\n\r\nPlease note that for the the second protection, finally I have chosen to protect the master branch only from **merge commits** (see update comment above), so no need to disable\/re-enable the protection on each release (direct commits, different from merge commits, can be pushed to the remote master branch; and eventually reverted without messing up the repo history).","embeddings":[-0.1553194672,-0.1002306268,-0.070321016,-0.0798171908,-0.1042534709,-0.1879874468,0.0104035577,0.2728635967,-0.0098489737,-0.0841399208,0.2926148772,-0.0778751224,-0.1324572563,0.2158903182,-0.0562292449,0.1705456823,0.2003232092,-0.0407981202,-0.0923330337,0.0279233586,0.0226084664,-0.0687029436,0.0832000151,0.0436301306,-0.1837819666,0.0054013738,-0.0148117775,0.1564962417,-0.4397666156,-0.4695702493,0.1452465504,0.2322402596,0.0251065325,0.570535183,-0.0001043637,-0.0003454557,0.1694866419,-0.0406010039,-0.1401022524,-0.2154636681,-0.4901072085,-0.4447958767,-0.07006336,-0.0083542913,0.1288067251,0.0528678969,-0.132499963,0.0238457564,0.3848451674,0.1055523977,0.2386109382,-0.1028517336,0.1812405735,0.0137694413,0.392008841,0.5474528074,-0.066634573,0.3031997979,0.0540179275,0.0006757497,0.0611058585,0.1741086096,-0.0823729113,-0.2866700888,0.2270194292,-0.1779912561,0.5181742311,-0.4243531227,-0.2415838391,-0.0831176639,0.0523793846,-0.1781465262,-0.4089041948,-0.2419367433,-0.1499476582,0.0076339091,0.0677802935,0.2468644679,0.039739579,0.0036496122,-0.3773649931,-0.2718090415,-0.004407926,0.0492040403,0.141940549,-0.1626293063,0.0664263666,-0.1270385236,0.2942230105,-0.0473279878,-0.1469833106,-0.3676346838,-0.2413333207,-0.0125874653,-0.1026133746,-0.1957468837,-0.2462065965,-0.3248277903,0.4251304567,0.3889683783,-0.3832727969,0.0137396995,-0.0304892361,-0.0810169131,0.2071851492,0.1178578511,-0.0104900496,0.0584886484,0.530713141,0.3144408166,0.0051584225,0.3255857527,0.3015701473,0.0030471815,0.0344459116,0.574118197,0.4289444685,-0.26292032,0.1276476383,0.1959735751,-0.3412007391,-0.3845081031,0.0711311549,-0.0861726329,0.1103260964,0.0271337908,-0.1147114784,0.0668015406,0.0451908335,-0.0380863659,-0.3079027534,-0.141485557,-0.3744001687,-0.0685305148,0.1860401928,-0.4571051598,0.1491677761,0.4325242639,-0.0680372268,-0.1992845535,0.0445522442,0.1292257011,0.0555874184,0.2961196899,-0.1537979692,-0.4526284337,0.2235967368,0.1313333064,-0.0668994114,0.1940043122,-0.1598993689,-0.2616221011,-0.1049757376,0.2977656722,-0.1482721567,0.3071561158,-0.5237665772,0.0363732092,-0.0044837268,0.3506707847,0.4910375476,0.3025480509,0.3280785084,-0.1685939282,0.0617566295,0.1903455257,0.3235116601,-0.0516864397,0.0623045377,-0.2877363563,0.1095039099,0.1450733244,-0.3121602237,0.0175557248,-0.0052360594,-0.1312822402,0.0107761566,-0.0714070052,-0.1119878516,-0.0989294425,-0.3666853309,0.0694030076,-0.0407375731,-0.1007770002,0.2079568803,-0.3201756477,-0.0947941095,0.2050814927,-0.1481231451,0.0208985135,-0.2919872105,-0.6061385274,-0.1636558473,-0.06343887,0.1249144152,0.2275642306,0.3607693315,-0.0050310493,-0.0283617247,-0.1714023799,-0.0093972106,0.1125069708,0.3421364129,-0.1496780068,-0.2415423691,0.1322465539,-0.3545884192,0.0911409035,-0.0785121024,-0.0097747799,0.0344485678,-0.3157884181,0.3412798941,-0.0440529883,-0.1208016798,-0.1141509414,0.2415172756,0.095951654,-0.1747335494,-0.2039210498,-0.2074069828,0.2601909339,-0.2408306301,0.3018657863,0.1300136894,0.1026374176,-0.1411807239,-0.0364533328,-0.0250960924,0.36193645,0.2363044769,-0.0899174288,-0.0591584407,0.1687609404,-0.0490626656,0.2035702467,0.2246947736,0.4252423346,0.3736152053,-0.1026815102,0.0626009405,-0.0917925984,-0.0787246898,0.0245947056,-0.3291273713,0.3280371726,0.073694557,-0.1394644678,-0.1740321219,-0.1033326834,-0.1740740985,-0.2202692628,-0.1707722992,-0.012951361,-0.1690982729,0.1943652332,-0.3365918398,0.2223692834,-0.2772080004,0.148579374,0.0150526064,-0.2516064942,-0.1024010181,0.0682980418,-0.0059674503,-0.1327149123,0.1241709664,0.3773829341,0.1278743744,0.1701000035,0.0042741634,0.0201513059,0.2572831213,-0.1164359227,0.353325963,0.1443069726,-0.1054452285,0.054064367,0.3364559114,0.1812186539,0.0643318817,0.346919328,-0.0033243245,0.0352581739,-0.2084068358,-0.0459848903,-0.2602106631,-0.5283415914,0.2192376256,-0.110637784,-0.3552163243,-0.0085041393,-0.0453661792,0.047418233,-0.5248722434,0.136029914,0.1835808307,0.2905260623,-0.3178029358,-0.1199780703,0.1263155937,-0.0225951802,0.0123441136,0.1285599768,0.3631602824,-0.2266809344,0.45945099,0.2389809936,-0.1695563644,-0.4726397991,-0.3434132934,0.0191301201,-0.2039363831,-0.1179748252,0.0098189935,-0.0989309773,0.0921538174,-0.3585401177,-0.3257124722,-0.018773742,-0.3730046451,-0.1600959152,0.0695955083,0.0628860295,-0.2346176803,-0.114040032,-0.386590004,-0.2821527123,0.4793556035,-0.0164330266,0.1156589612,-0.0431727543,-0.2923772335,-0.0459178463,0.0874827728,0.1366390288,-0.0000448525,-0.3609322906,0.0325667374,-0.0406603813,-0.0293471292,0.352609843,0.1854910851,-0.4302407205,0.0712517202,-0.3217604458,-0.1558190286,-0.0520843901,0.4864443243,0.3141199946,-0.0973737389,0.0710799471,0.1035510823,-0.1919001341,0.0550895296,-0.1816118211,0.1472238153,0.3454884887,0.2857186198,0.0378993787,0.1458674669,0.0254973993,0.7140879035,0.3778497577,-0.059657298,0.210337624,0.3106974065,-0.0277180839,0.0039902786,-0.2601569593,0.065046832,-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{"html_url":"https:\/\/github.com\/huggingface\/datasets\/issues\/2943","title":"Backwards compatibility broken for cached datasets that use `.filter()`","comments":"Hi ! I guess the caching mechanism should have considered the new `filter` to be different from the old one, and don't use cached results from the old `filter`.\r\nTo avoid other users from having this issue we could make the caching differentiate the two, what do you think ?","body":"## Describe the bug\r\nAfter upgrading to datasets `1.12.0`, some cached `.filter()` steps from `1.11.0` started failing with \r\n`ValueError: Keys mismatch: between {'indices': Value(dtype='uint64', id=None)} and {'file': Value(dtype='string', id=None), 'text': Value(dtype='string', id=None), 'speaker_id': Value(dtype='int64', id=None), 'chapter_id': Value(dtype='int64', id=None), 'id': Value(dtype='string', id=None)}`\r\n\r\nRelated feature: https:\/\/github.com\/huggingface\/datasets\/pull\/2836\r\n\r\n:question: This is probably a `wontfix` bug, since it can be solved by simply cleaning the related cache dirs, but the workaround could be useful for someone googling the error :) \r\n\r\n## Workaround\r\nRemove the cache for the given dataset, e.g. `rm -rf ~\/.cache\/huggingface\/datasets\/librispeech_asr`.\r\n\r\n## Steps to reproduce the bug\r\n1. Delete `~\/.cache\/huggingface\/datasets\/librispeech_asr` if it exists.\r\n\r\n2. `pip install datasets==1.11.0` and run the following snippet:\r\n\r\n```python\r\nfrom datasets import load_dataset\r\n\r\nids = [\"1272-141231-0000\"]\r\nds = load_dataset(\"patrickvonplaten\/librispeech_asr_dummy\", \"clean\", split=\"validation\")\r\nds = ds.filter(lambda x: x[\"id\"] in ids)\r\n```\r\n3. `pip install datasets==1.12.1` and re-run the code again\r\n\r\n## Expected results\r\nSame result as with the previous `datasets` version.\r\n\r\n## Actual results\r\n```bash\r\nReusing dataset librispeech_asr (.\/.cache\/huggingface\/datasets\/librispeech_asr\/clean\/2.1.0\/468ec03677f46a8714ac6b5b64dba02d246a228d92cbbad7f3dc190fa039eab1)\r\nLoading cached processed dataset at .\/.cache\/huggingface\/datasets\/librispeech_asr\/clean\/2.1.0\/468ec03677f46a8714ac6b5b64dba02d246a228d92cbbad7f3dc190fa039eab1\/cache-cd1c29844fdbc87a.arrow\r\nTraceback (most recent call last):\r\n File \".\/repos\/transformers\/src\/transformers\/models\/wav2vec2\/try_dataset.py\", line 5, in \r\n ds = ds.filter(lambda x: x[\"id\"] in ids)\r\n File \".\/envs\/transformers\/lib\/python3.8\/site-packages\/datasets\/arrow_dataset.py\", line 185, in wrapper\r\n out: Union[\"Dataset\", \"DatasetDict\"] = func(self, *args, **kwargs)\r\n File \".\/envs\/transformers\/lib\/python3.8\/site-packages\/datasets\/fingerprint.py\", line 398, in wrapper\r\n out = func(self, *args, **kwargs)\r\n File \".\/envs\/transformers\/lib\/python3.8\/site-packages\/datasets\/arrow_dataset.py\", line 2169, in filter\r\n indices = self.map(\r\n File \".\/envs\/transformers\/lib\/python3.8\/site-packages\/datasets\/arrow_dataset.py\", line 1686, in map\r\n return self._map_single(\r\n File \".\/envs\/transformers\/lib\/python3.8\/site-packages\/datasets\/arrow_dataset.py\", line 185, in wrapper\r\n out: Union[\"Dataset\", \"DatasetDict\"] = func(self, *args, **kwargs)\r\n File \".\/envs\/transformers\/lib\/python3.8\/site-packages\/datasets\/fingerprint.py\", line 398, in wrapper\r\n out = func(self, *args, **kwargs)\r\n File \".\/envs\/transformers\/lib\/python3.8\/site-packages\/datasets\/arrow_dataset.py\", line 1896, in _map_single\r\n return Dataset.from_file(cache_file_name, info=info, split=self.split)\r\n File \".\/envs\/transformers\/lib\/python3.8\/site-packages\/datasets\/arrow_dataset.py\", line 343, in from_file\r\n return cls(\r\n File \".\/envs\/transformers\/lib\/python3.8\/site-packages\/datasets\/arrow_dataset.py\", line 282, in __init__\r\n self.info.features = self.info.features.reorder_fields_as(inferred_features)\r\n File \".\/envs\/transformers\/lib\/python3.8\/site-packages\/datasets\/features.py\", line 1151, in reorder_fields_as\r\n return Features(recursive_reorder(self, other))\r\n File \".\/envs\/transformers\/lib\/python3.8\/site-packages\/datasets\/features.py\", line 1140, in recursive_reorder\r\n raise ValueError(f\"Keys mismatch: between {source} and {target}\" + stack_position)\r\nValueError: Keys mismatch: between {'indices': Value(dtype='uint64', id=None)} and {'file': Value(dtype='string', id=None), 'text': Value(dtype='string', id=None), 'speaker_id': Value(dtype='int64', id=None), 'chapter_id': Value(dtype='int64', id=None), 'id': Value(dtype='string', id=None)}\r\n\r\nProcess finished with exit code 1\r\n\r\n```\r\n\r\n## Environment info\r\n- `datasets` version: 1.12.1\r\n- Platform: Linux-5.11.0-34-generic-x86_64-with-glibc2.17\r\n- Python version: 3.8.10\r\n- PyArrow version: 5.0.0\r\n","comment_length":50,"text":"Backwards compatibility broken for cached datasets that use `.filter()`\n## Describe the bug\r\nAfter upgrading to datasets `1.12.0`, some cached `.filter()` steps from `1.11.0` started failing with \r\n`ValueError: Keys mismatch: between {'indices': Value(dtype='uint64', id=None)} and {'file': Value(dtype='string', id=None), 'text': Value(dtype='string', id=None), 'speaker_id': Value(dtype='int64', id=None), 'chapter_id': Value(dtype='int64', id=None), 'id': Value(dtype='string', id=None)}`\r\n\r\nRelated feature: https:\/\/github.com\/huggingface\/datasets\/pull\/2836\r\n\r\n:question: This is probably a `wontfix` bug, since it can be solved by simply cleaning the related cache dirs, but the workaround could be useful for someone googling the error :) \r\n\r\n## Workaround\r\nRemove the cache for the given dataset, e.g. `rm -rf ~\/.cache\/huggingface\/datasets\/librispeech_asr`.\r\n\r\n## Steps to reproduce the bug\r\n1. Delete `~\/.cache\/huggingface\/datasets\/librispeech_asr` if it exists.\r\n\r\n2. `pip install datasets==1.11.0` and run the following snippet:\r\n\r\n```python\r\nfrom datasets import load_dataset\r\n\r\nids = [\"1272-141231-0000\"]\r\nds = load_dataset(\"patrickvonplaten\/librispeech_asr_dummy\", \"clean\", split=\"validation\")\r\nds = ds.filter(lambda x: x[\"id\"] in ids)\r\n```\r\n3. `pip install datasets==1.12.1` and re-run the code again\r\n\r\n## Expected results\r\nSame result as with the previous `datasets` version.\r\n\r\n## Actual results\r\n```bash\r\nReusing dataset librispeech_asr (.\/.cache\/huggingface\/datasets\/librispeech_asr\/clean\/2.1.0\/468ec03677f46a8714ac6b5b64dba02d246a228d92cbbad7f3dc190fa039eab1)\r\nLoading cached processed dataset at .\/.cache\/huggingface\/datasets\/librispeech_asr\/clean\/2.1.0\/468ec03677f46a8714ac6b5b64dba02d246a228d92cbbad7f3dc190fa039eab1\/cache-cd1c29844fdbc87a.arrow\r\nTraceback (most recent call last):\r\n File \".\/repos\/transformers\/src\/transformers\/models\/wav2vec2\/try_dataset.py\", line 5, in \r\n ds = ds.filter(lambda x: x[\"id\"] in ids)\r\n File \".\/envs\/transformers\/lib\/python3.8\/site-packages\/datasets\/arrow_dataset.py\", line 185, in wrapper\r\n out: Union[\"Dataset\", \"DatasetDict\"] = func(self, *args, **kwargs)\r\n File \".\/envs\/transformers\/lib\/python3.8\/site-packages\/datasets\/fingerprint.py\", line 398, in wrapper\r\n out = func(self, *args, **kwargs)\r\n File \".\/envs\/transformers\/lib\/python3.8\/site-packages\/datasets\/arrow_dataset.py\", line 2169, in filter\r\n indices = self.map(\r\n File \".\/envs\/transformers\/lib\/python3.8\/site-packages\/datasets\/arrow_dataset.py\", line 1686, in map\r\n return self._map_single(\r\n File \".\/envs\/transformers\/lib\/python3.8\/site-packages\/datasets\/arrow_dataset.py\", line 185, in wrapper\r\n out: Union[\"Dataset\", \"DatasetDict\"] = func(self, *args, **kwargs)\r\n File \".\/envs\/transformers\/lib\/python3.8\/site-packages\/datasets\/fingerprint.py\", line 398, in wrapper\r\n out = func(self, *args, **kwargs)\r\n File \".\/envs\/transformers\/lib\/python3.8\/site-packages\/datasets\/arrow_dataset.py\", line 1896, in _map_single\r\n return Dataset.from_file(cache_file_name, info=info, split=self.split)\r\n File \".\/envs\/transformers\/lib\/python3.8\/site-packages\/datasets\/arrow_dataset.py\", line 343, in from_file\r\n return cls(\r\n File \".\/envs\/transformers\/lib\/python3.8\/site-packages\/datasets\/arrow_dataset.py\", line 282, in __init__\r\n self.info.features = self.info.features.reorder_fields_as(inferred_features)\r\n File \".\/envs\/transformers\/lib\/python3.8\/site-packages\/datasets\/features.py\", line 1151, in reorder_fields_as\r\n return Features(recursive_reorder(self, other))\r\n File \".\/envs\/transformers\/lib\/python3.8\/site-packages\/datasets\/features.py\", line 1140, in recursive_reorder\r\n raise ValueError(f\"Keys mismatch: between {source} and {target}\" + stack_position)\r\nValueError: Keys mismatch: between {'indices': Value(dtype='uint64', id=None)} and {'file': Value(dtype='string', id=None), 'text': Value(dtype='string', id=None), 'speaker_id': Value(dtype='int64', id=None), 'chapter_id': Value(dtype='int64', id=None), 'id': Value(dtype='string', id=None)}\r\n\r\nProcess finished with exit code 1\r\n\r\n```\r\n\r\n## Environment info\r\n- `datasets` version: 1.12.1\r\n- Platform: Linux-5.11.0-34-generic-x86_64-with-glibc2.17\r\n- Python version: 3.8.10\r\n- PyArrow version: 5.0.0\r\n\nHi ! I guess the caching mechanism should have considered the new `filter` to be different from the old one, and don't use cached results from the old `filter`.\r\nTo avoid other users from having this issue we could make the caching differentiate the two, what do you think ?","embeddings":[-0.3049958348,0.1243880838,-0.0465583764,0.2368971109,0.1517290324,-0.0666598156,-0.0069413707,0.3285393715,0.1737383604,0.0293814819,-0.2313861847,0.3581927419,-0.1503304988,0.2527370155,-0.2977862358,-0.1378120482,0.0444693454,0.0321247876,-0.0614953265,0.166412577,-0.1239175051,0.4988580644,-0.1957765073,0.0070795678,0.2221652865,0.0513210297,0.1091236398,0.138566196,0.0798620358,-0.3392693698,0.5981172919,0.1064069718,-0.0348691642,0.498096019,-0.0001213098,0.099867776,0.4398066998,-0.1073976383,-0.4579497874,-0.3686879575,-0.192854017,0.1319934726,0.2131137848,0.0811629742,-0.0715122968,0.1143416911,-0.303493619,-0.346046567,0.2488765419,0.1989328116,0.2267285585,0.2136144787,0.2657418847,0.066587165,0.170179069,0.0826525018,0.0259718653,-0.0490864292,0.2940285802,0.014261202,0.043685697,0.4806104004,-0.3283523321,-0.2146463245,0.3046185374,-0.2982533276,0.1030709222,-0.3957037926,0.2999309897,0.0781370997,0.1462642252,-0.255518198,-0.5773367882,-0.3483263254,-0.5121472478,-0.2868291736,0.4164735079,-0.216358915,-0.2009968162,0.2200830877,-0.3342647851,-0.0270038545,0.0934983194,0.0535101071,-0.0584819205,0.2941842079,-0.1318320185,-0.0048626242,0.0149830757,-0.132900551,0.1924084276,-0.2750048339,-0.1968749017,0.1725800484,-0.0155595224,-0.0546131916,0.0954222009,0.3323281705,0.140570581,0.0356287211,0.0192098524,0.0845401734,-0.0409641638,0.0536773168,0.1373275816,0.475378722,0.4901195765,0.2467906624,0.2727875113,0.2854887545,-0.0078853024,-0.0013800253,0.5281852484,-0.1666412055,0.0747071281,0.1517307162,0.196695298,-0.4048683643,-0.3602132797,0.1689626127,-0.0769995078,-0.1250401586,0.2207264006,0.0534940958,0.1005648673,0.2142349631,0.1320811808,0.0338568501,-0.1964725256,0.0612344779,-0.1725096703,-0.1739457697,0.0011616903,-0.1112991124,-0.0473052748,-0.89977777,0.1747701168,0.0482602492,0.0063488153,0.1058098748,-0.0697732121,0.207434997,0.1730166823,0.5229284167,-0.2213587761,0.0839003325,0.1671907902,-0.548327744,-0.1386865377,0.0125284484,-0.2415378094,-0.1112066656,0.2085879147,0.1735862345,-0.3296715915,0.0126515068,-0.17029953,0.4515431225,0.2592140436,-0.5466870666,-0.0779574588,-0.213380143,-0.4382343888,-0.1997140199,0.0971902013,0.4131370485,-0.5942543149,-0.3216674924,0.0216870401,0.0319067985,0.0399160981,-0.1229341999,-0.063439101,-0.2444722205,-0.1077689156,-0.1384182572,0.3606542647,-0.3195546865,-0.733892858,0.1537256837,0.2360213548,0.5342661738,-0.0709591061,-0.1374839246,0.2813950479,-0.2578290701,0.1459920853,0.0848445743,-0.1649033576,-0.0079940017,-0.3409692049,-0.2346946597,0.2238697708,-0.1502910852,0.0904174298,0.3348585963,0.0790057704,-0.2542206049,0.0502917804,0.0350500681,0.1049243882,0.1990925223,0.2453232259,0.2237652242,0.2863062918,-0.4365186989,-0.2801407278,0.2506181598,0.2141302824,-0.0600011088,-0.1524066925,-0.1982273012,-0.2678262889,-0.0884182155,-0.1589775681,-0.3044096231,0.0788738504,0.0225765873,0.3843636513,-0.0385034606,-0.1145259663,0.6218649745,0.3084535897,0.0944804996,-0.5049411654,0.229641512,-0.0548514128,0.0051691518,-0.0591857955,0.0377827175,0.2939595878,-0.0685256049,-0.2895757854,0.407875061,0.0392592028,0.084168233,-0.0276337974,0.1937460154,0.1773459613,0.2105839252,0.0639338642,0.2741822898,-0.0148837138,0.2087214142,-0.195456177,0.557248354,0.0293753464,0.3240505159,0.1208347678,0.148756206,0.3702490628,0.1305633932,-0.2189950645,-0.1370786875,0.3204495311,-0.4587228,0.0626674443,0.0123394234,-0.1297932267,0.3776263595,0.0716826022,0.0755119473,-0.0334524736,0.3119212389,-0.1593657285,-0.042190969,0.1813624054,0.4024861157,0.4014509916,0.2088322341,0.0507172868,0.0758817792,-0.1615392268,-0.0248774402,0.2082498521,0.1293462515,-0.1696399748,0.1535750628,0.3034210205,0.2732039392,-0.3061974645,0.0933934823,-0.2356239706,0.1195331365,-0.270581305,0.3616366684,-0.2810101509,-0.2280866951,0.076141715,-0.1678324938,-0.2615140676,-0.3875222206,0.089899376,0.3934392929,-0.086219281,0.2567750216,0.0147161586,0.2445412278,0.136167407,-0.1996820718,-0.4005717337,-0.2078815252,-0.3476482034,0.0053976052,0.2051323503,-0.1568491161,0.2970358729,-0.0763099566,-0.2066247612,-0.390294075,-0.9079098701,-0.0486286655,-0.123964861,0.6180298328,0.3176228106,-0.0916265026,-0.1352719367,-0.2327560335,0.1665794849,-0.0413150303,-0.4310828745,0.1568385363,0.0666227341,0.1682221591,-0.2003851682,-0.2270279974,-0.1135412008,-0.1458190233,0.0079398574,-0.1276904047,0.0849654675,0.3094039559,0.1123534068,-0.3165157139,-0.0872011557,0.1330857426,-0.3335401416,-0.0773374513,0.0739419162,-0.054162316,-0.2302434891,-0.034139622,-0.0319806486,0.0831893757,0.3736632466,-0.4069587886,-0.3791774213,-0.2713731527,-0.0048744264,0.2311773747,0.068360962,0.2319667786,0.1899949759,0.0391035639,-0.2794680893,-0.4342857301,-0.0886206329,-0.2365958691,0.1095489264,0.1482250243,0.6309939027,0.0345636867,0.4764270186,0.2947264612,0.0489611253,0.1398259848,0.1815548688,0.4972691238,-0.3258462548,-0.4095653892,-0.0467088632,-0.0461938493,0.0505124666,-0.1531914324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{"html_url":"https:\/\/github.com\/huggingface\/datasets\/issues\/2943","title":"Backwards compatibility broken for cached datasets that use `.filter()`","comments":"If it's easy enough to implement, then yes please \ud83d\ude04 But this issue can be low-priority, since I've only encountered it in a couple of `transformers` CI tests.","body":"## Describe the bug\r\nAfter upgrading to datasets `1.12.0`, some cached `.filter()` steps from `1.11.0` started failing with \r\n`ValueError: Keys mismatch: between {'indices': Value(dtype='uint64', id=None)} and {'file': Value(dtype='string', id=None), 'text': Value(dtype='string', id=None), 'speaker_id': Value(dtype='int64', id=None), 'chapter_id': Value(dtype='int64', id=None), 'id': Value(dtype='string', id=None)}`\r\n\r\nRelated feature: https:\/\/github.com\/huggingface\/datasets\/pull\/2836\r\n\r\n:question: This is probably a `wontfix` bug, since it can be solved by simply cleaning the related cache dirs, but the workaround could be useful for someone googling the error :) \r\n\r\n## Workaround\r\nRemove the cache for the given dataset, e.g. `rm -rf ~\/.cache\/huggingface\/datasets\/librispeech_asr`.\r\n\r\n## Steps to reproduce the bug\r\n1. Delete `~\/.cache\/huggingface\/datasets\/librispeech_asr` if it exists.\r\n\r\n2. `pip install datasets==1.11.0` and run the following snippet:\r\n\r\n```python\r\nfrom datasets import load_dataset\r\n\r\nids = [\"1272-141231-0000\"]\r\nds = load_dataset(\"patrickvonplaten\/librispeech_asr_dummy\", \"clean\", split=\"validation\")\r\nds = ds.filter(lambda x: x[\"id\"] in ids)\r\n```\r\n3. `pip install datasets==1.12.1` and re-run the code again\r\n\r\n## Expected results\r\nSame result as with the previous `datasets` version.\r\n\r\n## Actual results\r\n```bash\r\nReusing dataset librispeech_asr (.\/.cache\/huggingface\/datasets\/librispeech_asr\/clean\/2.1.0\/468ec03677f46a8714ac6b5b64dba02d246a228d92cbbad7f3dc190fa039eab1)\r\nLoading cached processed dataset at .\/.cache\/huggingface\/datasets\/librispeech_asr\/clean\/2.1.0\/468ec03677f46a8714ac6b5b64dba02d246a228d92cbbad7f3dc190fa039eab1\/cache-cd1c29844fdbc87a.arrow\r\nTraceback (most recent call last):\r\n File \".\/repos\/transformers\/src\/transformers\/models\/wav2vec2\/try_dataset.py\", line 5, in \r\n ds = ds.filter(lambda x: x[\"id\"] in ids)\r\n File \".\/envs\/transformers\/lib\/python3.8\/site-packages\/datasets\/arrow_dataset.py\", line 185, in wrapper\r\n out: Union[\"Dataset\", \"DatasetDict\"] = func(self, *args, **kwargs)\r\n File \".\/envs\/transformers\/lib\/python3.8\/site-packages\/datasets\/fingerprint.py\", line 398, in wrapper\r\n out = func(self, *args, **kwargs)\r\n File \".\/envs\/transformers\/lib\/python3.8\/site-packages\/datasets\/arrow_dataset.py\", line 2169, in filter\r\n indices = self.map(\r\n File \".\/envs\/transformers\/lib\/python3.8\/site-packages\/datasets\/arrow_dataset.py\", line 1686, in map\r\n return self._map_single(\r\n File \".\/envs\/transformers\/lib\/python3.8\/site-packages\/datasets\/arrow_dataset.py\", line 185, in wrapper\r\n out: Union[\"Dataset\", \"DatasetDict\"] = func(self, *args, **kwargs)\r\n File \".\/envs\/transformers\/lib\/python3.8\/site-packages\/datasets\/fingerprint.py\", line 398, in wrapper\r\n out = func(self, *args, **kwargs)\r\n File \".\/envs\/transformers\/lib\/python3.8\/site-packages\/datasets\/arrow_dataset.py\", line 1896, in _map_single\r\n return Dataset.from_file(cache_file_name, info=info, split=self.split)\r\n File \".\/envs\/transformers\/lib\/python3.8\/site-packages\/datasets\/arrow_dataset.py\", line 343, in from_file\r\n return cls(\r\n File \".\/envs\/transformers\/lib\/python3.8\/site-packages\/datasets\/arrow_dataset.py\", line 282, in __init__\r\n self.info.features = self.info.features.reorder_fields_as(inferred_features)\r\n File \".\/envs\/transformers\/lib\/python3.8\/site-packages\/datasets\/features.py\", line 1151, in reorder_fields_as\r\n return Features(recursive_reorder(self, other))\r\n File \".\/envs\/transformers\/lib\/python3.8\/site-packages\/datasets\/features.py\", line 1140, in recursive_reorder\r\n raise ValueError(f\"Keys mismatch: between {source} and {target}\" + stack_position)\r\nValueError: Keys mismatch: between {'indices': Value(dtype='uint64', id=None)} and {'file': Value(dtype='string', id=None), 'text': Value(dtype='string', id=None), 'speaker_id': Value(dtype='int64', id=None), 'chapter_id': Value(dtype='int64', id=None), 'id': Value(dtype='string', id=None)}\r\n\r\nProcess finished with exit code 1\r\n\r\n```\r\n\r\n## Environment info\r\n- `datasets` version: 1.12.1\r\n- Platform: Linux-5.11.0-34-generic-x86_64-with-glibc2.17\r\n- Python version: 3.8.10\r\n- PyArrow version: 5.0.0\r\n","comment_length":28,"text":"Backwards compatibility broken for cached datasets that use `.filter()`\n## Describe the bug\r\nAfter upgrading to datasets `1.12.0`, some cached `.filter()` steps from `1.11.0` started failing with \r\n`ValueError: Keys mismatch: between {'indices': Value(dtype='uint64', id=None)} and {'file': Value(dtype='string', id=None), 'text': Value(dtype='string', id=None), 'speaker_id': Value(dtype='int64', id=None), 'chapter_id': Value(dtype='int64', id=None), 'id': Value(dtype='string', id=None)}`\r\n\r\nRelated feature: https:\/\/github.com\/huggingface\/datasets\/pull\/2836\r\n\r\n:question: This is probably a `wontfix` bug, since it can be solved by simply cleaning the related cache dirs, but the workaround could be useful for someone googling the error :) \r\n\r\n## Workaround\r\nRemove the cache for the given dataset, e.g. `rm -rf ~\/.cache\/huggingface\/datasets\/librispeech_asr`.\r\n\r\n## Steps to reproduce the bug\r\n1. Delete `~\/.cache\/huggingface\/datasets\/librispeech_asr` if it exists.\r\n\r\n2. `pip install datasets==1.11.0` and run the following snippet:\r\n\r\n```python\r\nfrom datasets import load_dataset\r\n\r\nids = [\"1272-141231-0000\"]\r\nds = load_dataset(\"patrickvonplaten\/librispeech_asr_dummy\", \"clean\", split=\"validation\")\r\nds = ds.filter(lambda x: x[\"id\"] in ids)\r\n```\r\n3. `pip install datasets==1.12.1` and re-run the code again\r\n\r\n## Expected results\r\nSame result as with the previous `datasets` version.\r\n\r\n## Actual results\r\n```bash\r\nReusing dataset librispeech_asr (.\/.cache\/huggingface\/datasets\/librispeech_asr\/clean\/2.1.0\/468ec03677f46a8714ac6b5b64dba02d246a228d92cbbad7f3dc190fa039eab1)\r\nLoading cached processed dataset at .\/.cache\/huggingface\/datasets\/librispeech_asr\/clean\/2.1.0\/468ec03677f46a8714ac6b5b64dba02d246a228d92cbbad7f3dc190fa039eab1\/cache-cd1c29844fdbc87a.arrow\r\nTraceback (most recent call last):\r\n File \".\/repos\/transformers\/src\/transformers\/models\/wav2vec2\/try_dataset.py\", line 5, in \r\n ds = ds.filter(lambda x: x[\"id\"] in ids)\r\n File \".\/envs\/transformers\/lib\/python3.8\/site-packages\/datasets\/arrow_dataset.py\", line 185, in wrapper\r\n out: Union[\"Dataset\", \"DatasetDict\"] = func(self, *args, **kwargs)\r\n File \".\/envs\/transformers\/lib\/python3.8\/site-packages\/datasets\/fingerprint.py\", line 398, in wrapper\r\n out = func(self, *args, **kwargs)\r\n File \".\/envs\/transformers\/lib\/python3.8\/site-packages\/datasets\/arrow_dataset.py\", line 2169, in filter\r\n indices = self.map(\r\n File \".\/envs\/transformers\/lib\/python3.8\/site-packages\/datasets\/arrow_dataset.py\", line 1686, in map\r\n return self._map_single(\r\n File \".\/envs\/transformers\/lib\/python3.8\/site-packages\/datasets\/arrow_dataset.py\", line 185, in wrapper\r\n out: Union[\"Dataset\", \"DatasetDict\"] = func(self, *args, **kwargs)\r\n File \".\/envs\/transformers\/lib\/python3.8\/site-packages\/datasets\/fingerprint.py\", line 398, in wrapper\r\n out = func(self, *args, **kwargs)\r\n File \".\/envs\/transformers\/lib\/python3.8\/site-packages\/datasets\/arrow_dataset.py\", line 1896, in _map_single\r\n return Dataset.from_file(cache_file_name, info=info, split=self.split)\r\n File \".\/envs\/transformers\/lib\/python3.8\/site-packages\/datasets\/arrow_dataset.py\", line 343, in from_file\r\n return cls(\r\n File \".\/envs\/transformers\/lib\/python3.8\/site-packages\/datasets\/arrow_dataset.py\", line 282, in __init__\r\n self.info.features = self.info.features.reorder_fields_as(inferred_features)\r\n File \".\/envs\/transformers\/lib\/python3.8\/site-packages\/datasets\/features.py\", line 1151, in reorder_fields_as\r\n return Features(recursive_reorder(self, other))\r\n File \".\/envs\/transformers\/lib\/python3.8\/site-packages\/datasets\/features.py\", line 1140, in recursive_reorder\r\n raise ValueError(f\"Keys mismatch: between {source} and {target}\" + stack_position)\r\nValueError: Keys mismatch: between {'indices': Value(dtype='uint64', id=None)} and {'file': Value(dtype='string', id=None), 'text': Value(dtype='string', id=None), 'speaker_id': Value(dtype='int64', id=None), 'chapter_id': Value(dtype='int64', id=None), 'id': Value(dtype='string', id=None)}\r\n\r\nProcess finished with exit code 1\r\n\r\n```\r\n\r\n## Environment info\r\n- `datasets` version: 1.12.1\r\n- Platform: Linux-5.11.0-34-generic-x86_64-with-glibc2.17\r\n- Python version: 3.8.10\r\n- PyArrow version: 5.0.0\r\n\nIf it's easy enough to implement, then yes please \ud83d\ude04 But this issue can be low-priority, since I've only encountered it in a couple of `transformers` CI 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{"html_url":"https:\/\/github.com\/huggingface\/datasets\/issues\/2943","title":"Backwards compatibility broken for cached datasets that use `.filter()`","comments":"Well it can cause issue with anyone that updates `datasets` and re-run some code that uses filter, so I'm creating a PR","body":"## Describe the bug\r\nAfter upgrading to datasets `1.12.0`, some cached `.filter()` steps from `1.11.0` started failing with \r\n`ValueError: Keys mismatch: between {'indices': Value(dtype='uint64', id=None)} and {'file': Value(dtype='string', id=None), 'text': Value(dtype='string', id=None), 'speaker_id': Value(dtype='int64', id=None), 'chapter_id': Value(dtype='int64', id=None), 'id': Value(dtype='string', id=None)}`\r\n\r\nRelated feature: https:\/\/github.com\/huggingface\/datasets\/pull\/2836\r\n\r\n:question: This is probably a `wontfix` bug, since it can be solved by simply cleaning the related cache dirs, but the workaround could be useful for someone googling the error :) \r\n\r\n## Workaround\r\nRemove the cache for the given dataset, e.g. `rm -rf ~\/.cache\/huggingface\/datasets\/librispeech_asr`.\r\n\r\n## Steps to reproduce the bug\r\n1. Delete `~\/.cache\/huggingface\/datasets\/librispeech_asr` if it exists.\r\n\r\n2. `pip install datasets==1.11.0` and run the following snippet:\r\n\r\n```python\r\nfrom datasets import load_dataset\r\n\r\nids = [\"1272-141231-0000\"]\r\nds = load_dataset(\"patrickvonplaten\/librispeech_asr_dummy\", \"clean\", split=\"validation\")\r\nds = ds.filter(lambda x: x[\"id\"] in ids)\r\n```\r\n3. `pip install datasets==1.12.1` and re-run the code again\r\n\r\n## Expected results\r\nSame result as with the previous `datasets` version.\r\n\r\n## Actual results\r\n```bash\r\nReusing dataset librispeech_asr (.\/.cache\/huggingface\/datasets\/librispeech_asr\/clean\/2.1.0\/468ec03677f46a8714ac6b5b64dba02d246a228d92cbbad7f3dc190fa039eab1)\r\nLoading cached processed dataset at .\/.cache\/huggingface\/datasets\/librispeech_asr\/clean\/2.1.0\/468ec03677f46a8714ac6b5b64dba02d246a228d92cbbad7f3dc190fa039eab1\/cache-cd1c29844fdbc87a.arrow\r\nTraceback (most recent call last):\r\n File \".\/repos\/transformers\/src\/transformers\/models\/wav2vec2\/try_dataset.py\", line 5, in \r\n ds = ds.filter(lambda x: x[\"id\"] in ids)\r\n File \".\/envs\/transformers\/lib\/python3.8\/site-packages\/datasets\/arrow_dataset.py\", line 185, in wrapper\r\n out: Union[\"Dataset\", \"DatasetDict\"] = func(self, *args, **kwargs)\r\n File \".\/envs\/transformers\/lib\/python3.8\/site-packages\/datasets\/fingerprint.py\", line 398, in wrapper\r\n out = func(self, *args, **kwargs)\r\n File \".\/envs\/transformers\/lib\/python3.8\/site-packages\/datasets\/arrow_dataset.py\", line 2169, in filter\r\n indices = self.map(\r\n File \".\/envs\/transformers\/lib\/python3.8\/site-packages\/datasets\/arrow_dataset.py\", line 1686, in map\r\n return self._map_single(\r\n File \".\/envs\/transformers\/lib\/python3.8\/site-packages\/datasets\/arrow_dataset.py\", line 185, in wrapper\r\n out: Union[\"Dataset\", \"DatasetDict\"] = func(self, *args, **kwargs)\r\n File \".\/envs\/transformers\/lib\/python3.8\/site-packages\/datasets\/fingerprint.py\", line 398, in wrapper\r\n out = func(self, *args, **kwargs)\r\n File \".\/envs\/transformers\/lib\/python3.8\/site-packages\/datasets\/arrow_dataset.py\", line 1896, in _map_single\r\n return Dataset.from_file(cache_file_name, info=info, split=self.split)\r\n File \".\/envs\/transformers\/lib\/python3.8\/site-packages\/datasets\/arrow_dataset.py\", line 343, in from_file\r\n return cls(\r\n File \".\/envs\/transformers\/lib\/python3.8\/site-packages\/datasets\/arrow_dataset.py\", line 282, in __init__\r\n self.info.features = self.info.features.reorder_fields_as(inferred_features)\r\n File \".\/envs\/transformers\/lib\/python3.8\/site-packages\/datasets\/features.py\", line 1151, in reorder_fields_as\r\n return Features(recursive_reorder(self, other))\r\n File \".\/envs\/transformers\/lib\/python3.8\/site-packages\/datasets\/features.py\", line 1140, in recursive_reorder\r\n raise ValueError(f\"Keys mismatch: between {source} and {target}\" + stack_position)\r\nValueError: Keys mismatch: between {'indices': Value(dtype='uint64', id=None)} and {'file': Value(dtype='string', id=None), 'text': Value(dtype='string', id=None), 'speaker_id': Value(dtype='int64', id=None), 'chapter_id': Value(dtype='int64', id=None), 'id': Value(dtype='string', id=None)}\r\n\r\nProcess finished with exit code 1\r\n\r\n```\r\n\r\n## Environment info\r\n- `datasets` version: 1.12.1\r\n- Platform: Linux-5.11.0-34-generic-x86_64-with-glibc2.17\r\n- Python version: 3.8.10\r\n- PyArrow version: 5.0.0\r\n","comment_length":22,"text":"Backwards compatibility broken for cached datasets that use `.filter()`\n## Describe the bug\r\nAfter upgrading to datasets `1.12.0`, some cached `.filter()` steps from `1.11.0` started failing with \r\n`ValueError: Keys mismatch: between {'indices': Value(dtype='uint64', id=None)} and {'file': Value(dtype='string', id=None), 'text': Value(dtype='string', id=None), 'speaker_id': Value(dtype='int64', id=None), 'chapter_id': Value(dtype='int64', id=None), 'id': Value(dtype='string', id=None)}`\r\n\r\nRelated feature: https:\/\/github.com\/huggingface\/datasets\/pull\/2836\r\n\r\n:question: This is probably a `wontfix` bug, since it can be solved by simply cleaning the related cache dirs, but the workaround could be useful for someone googling the error :) \r\n\r\n## Workaround\r\nRemove the cache for the given dataset, e.g. `rm -rf ~\/.cache\/huggingface\/datasets\/librispeech_asr`.\r\n\r\n## Steps to reproduce the bug\r\n1. Delete `~\/.cache\/huggingface\/datasets\/librispeech_asr` if it exists.\r\n\r\n2. `pip install datasets==1.11.0` and run the following snippet:\r\n\r\n```python\r\nfrom datasets import load_dataset\r\n\r\nids = [\"1272-141231-0000\"]\r\nds = load_dataset(\"patrickvonplaten\/librispeech_asr_dummy\", \"clean\", split=\"validation\")\r\nds = ds.filter(lambda x: x[\"id\"] in ids)\r\n```\r\n3. `pip install datasets==1.12.1` and re-run the code again\r\n\r\n## Expected results\r\nSame result as with the previous `datasets` version.\r\n\r\n## Actual results\r\n```bash\r\nReusing dataset librispeech_asr (.\/.cache\/huggingface\/datasets\/librispeech_asr\/clean\/2.1.0\/468ec03677f46a8714ac6b5b64dba02d246a228d92cbbad7f3dc190fa039eab1)\r\nLoading cached processed dataset at .\/.cache\/huggingface\/datasets\/librispeech_asr\/clean\/2.1.0\/468ec03677f46a8714ac6b5b64dba02d246a228d92cbbad7f3dc190fa039eab1\/cache-cd1c29844fdbc87a.arrow\r\nTraceback (most recent call last):\r\n File \".\/repos\/transformers\/src\/transformers\/models\/wav2vec2\/try_dataset.py\", line 5, in \r\n ds = ds.filter(lambda x: x[\"id\"] in ids)\r\n File \".\/envs\/transformers\/lib\/python3.8\/site-packages\/datasets\/arrow_dataset.py\", line 185, in wrapper\r\n out: Union[\"Dataset\", \"DatasetDict\"] = func(self, *args, **kwargs)\r\n File \".\/envs\/transformers\/lib\/python3.8\/site-packages\/datasets\/fingerprint.py\", line 398, in wrapper\r\n out = func(self, *args, **kwargs)\r\n File \".\/envs\/transformers\/lib\/python3.8\/site-packages\/datasets\/arrow_dataset.py\", line 2169, in filter\r\n indices = self.map(\r\n File \".\/envs\/transformers\/lib\/python3.8\/site-packages\/datasets\/arrow_dataset.py\", line 1686, in map\r\n return self._map_single(\r\n File \".\/envs\/transformers\/lib\/python3.8\/site-packages\/datasets\/arrow_dataset.py\", line 185, in wrapper\r\n out: Union[\"Dataset\", \"DatasetDict\"] = func(self, *args, **kwargs)\r\n File \".\/envs\/transformers\/lib\/python3.8\/site-packages\/datasets\/fingerprint.py\", line 398, in wrapper\r\n out = func(self, *args, **kwargs)\r\n File \".\/envs\/transformers\/lib\/python3.8\/site-packages\/datasets\/arrow_dataset.py\", line 1896, in _map_single\r\n return Dataset.from_file(cache_file_name, info=info, split=self.split)\r\n File \".\/envs\/transformers\/lib\/python3.8\/site-packages\/datasets\/arrow_dataset.py\", line 343, in from_file\r\n return cls(\r\n File \".\/envs\/transformers\/lib\/python3.8\/site-packages\/datasets\/arrow_dataset.py\", line 282, in __init__\r\n self.info.features = self.info.features.reorder_fields_as(inferred_features)\r\n File \".\/envs\/transformers\/lib\/python3.8\/site-packages\/datasets\/features.py\", line 1151, in reorder_fields_as\r\n return Features(recursive_reorder(self, other))\r\n File \".\/envs\/transformers\/lib\/python3.8\/site-packages\/datasets\/features.py\", line 1140, in recursive_reorder\r\n raise ValueError(f\"Keys mismatch: between {source} and {target}\" + stack_position)\r\nValueError: Keys mismatch: between {'indices': Value(dtype='uint64', id=None)} and {'file': Value(dtype='string', id=None), 'text': Value(dtype='string', id=None), 'speaker_id': Value(dtype='int64', id=None), 'chapter_id': Value(dtype='int64', id=None), 'id': Value(dtype='string', id=None)}\r\n\r\nProcess finished with exit code 1\r\n\r\n```\r\n\r\n## Environment info\r\n- `datasets` version: 1.12.1\r\n- Platform: Linux-5.11.0-34-generic-x86_64-with-glibc2.17\r\n- Python version: 3.8.10\r\n- PyArrow version: 5.0.0\r\n\nWell it can cause issue with anyone that updates `datasets` and re-run some code that uses filter, so I'm creating a PR","embeddings":[-0.3049958348,0.1243880838,-0.0465583764,0.2368971109,0.1517290324,-0.0666598156,-0.0069413707,0.3285393715,0.1737383604,0.0293814819,-0.2313861847,0.3581927419,-0.1503304988,0.2527370155,-0.2977862358,-0.1378120482,0.0444693454,0.0321247876,-0.0614953265,0.166412577,-0.1239175051,0.4988580644,-0.1957765073,0.0070795678,0.2221652865,0.0513210297,0.1091236398,0.138566196,0.0798620358,-0.3392693698,0.5981172919,0.1064069718,-0.0348691642,0.498096019,-0.0001213098,0.099867776,0.4398066998,-0.1073976383,-0.4579497874,-0.3686879575,-0.192854017,0.1319934726,0.2131137848,0.0811629742,-0.0715122968,0.1143416911,-0.303493619,-0.346046567,0.2488765419,0.1989328116,0.2267285585,0.2136144787,0.2657418847,0.066587165,0.170179069,0.0826525018,0.0259718653,-0.0490864292,0.2940285802,0.014261202,0.043685697,0.4806104004,-0.3283523321,-0.2146463245,0.3046185374,-0.2982533276,0.1030709222,-0.3957037926,0.2999309897,0.0781370997,0.1462642252,-0.255518198,-0.5773367882,-0.3483263254,-0.5121472478,-0.2868291736,0.4164735079,-0.216358915,-0.2009968162,0.2200830877,-0.3342647851,-0.0270038545,0.0934983194,0.0535101071,-0.0584819205,0.2941842079,-0.1318320185,-0.0048626242,0.0149830757,-0.132900551,0.1924084276,-0.2750048339,-0.1968749017,0.1725800484,-0.0155595224,-0.0546131916,0.0954222009,0.3323281705,0.140570581,0.0356287211,0.0192098524,0.0845401734,-0.0409641638,0.0536773168,0.1373275816,0.475378722,0.4901195765,0.2467906624,0.2727875113,0.2854887545,-0.0078853024,-0.0013800253,0.5281852484,-0.1666412055,0.0747071281,0.1517307162,0.196695298,-0.4048683643,-0.3602132797,0.1689626127,-0.0769995078,-0.1250401586,0.2207264006,0.0534940958,0.1005648673,0.2142349631,0.1320811808,0.0338568501,-0.1964725256,0.0612344779,-0.1725096703,-0.1739457697,0.0011616903,-0.1112991124,-0.0473052748,-0.89977777,0.1747701168,0.0482602492,0.0063488153,0.1058098748,-0.0697732121,0.207434997,0.1730166823,0.5229284167,-0.2213587761,0.0839003325,0.1671907902,-0.548327744,-0.1386865377,0.0125284484,-0.2415378094,-0.1112066656,0.2085879147,0.1735862345,-0.3296715915,0.0126515068,-0.17029953,0.4515431225,0.2592140436,-0.5466870666,-0.0779574588,-0.213380143,-0.4382343888,-0.1997140199,0.0971902013,0.4131370485,-0.5942543149,-0.3216674924,0.0216870401,0.0319067985,0.0399160981,-0.1229341999,-0.063439101,-0.2444722205,-0.1077689156,-0.1384182572,0.3606542647,-0.3195546865,-0.733892858,0.1537256837,0.2360213548,0.5342661738,-0.0709591061,-0.1374839246,0.2813950479,-0.2578290701,0.1459920853,0.0848445743,-0.1649033576,-0.0079940017,-0.3409692049,-0.2346946597,0.2238697708,-0.1502910852,0.0904174298,0.3348585963,0.0790057704,-0.2542206049,0.0502917804,0.0350500681,0.1049243882,0.1990925223,0.2453232259,0.2237652242,0.2863062918,-0.4365186989,-0.2801407278,0.2506181598,0.2141302824,-0.0600011088,-0.1524066925,-0.1982273012,-0.2678262889,-0.0884182155,-0.1589775681,-0.3044096231,0.0788738504,0.0225765873,0.3843636513,-0.0385034606,-0.1145259663,0.6218649745,0.3084535897,0.0944804996,-0.5049411654,0.229641512,-0.0548514128,0.0051691518,-0.0591857955,0.0377827175,0.2939595878,-0.0685256049,-0.2895757854,0.407875061,0.0392592028,0.084168233,-0.0276337974,0.1937460154,0.1773459613,0.2105839252,0.0639338642,0.2741822898,-0.0148837138,0.2087214142,-0.195456177,0.557248354,0.0293753464,0.3240505159,0.1208347678,0.148756206,0.3702490628,0.1305633932,-0.2189950645,-0.1370786875,0.3204495311,-0.4587228,0.0626674443,0.0123394234,-0.1297932267,0.3776263595,0.0716826022,0.0755119473,-0.0334524736,0.3119212389,-0.1593657285,-0.042190969,0.1813624054,0.4024861157,0.4014509916,0.2088322341,0.0507172868,0.0758817792,-0.1615392268,-0.0248774402,0.2082498521,0.1293462515,-0.1696399748,0.1535750628,0.3034210205,0.2732039392,-0.3061974645,0.0933934823,-0.2356239706,0.1195331365,-0.270581305,0.3616366684,-0.2810101509,-0.2280866951,0.076141715,-0.1678324938,-0.2615140676,-0.3875222206,0.089899376,0.3934392929,-0.086219281,0.2567750216,0.0147161586,0.2445412278,0.136167407,-0.1996820718,-0.4005717337,-0.2078815252,-0.3476482034,0.0053976052,0.2051323503,-0.1568491161,0.2970358729,-0.0763099566,-0.2066247612,-0.390294075,-0.9079098701,-0.0486286655,-0.123964861,0.6180298328,0.3176228106,-0.0916265026,-0.1352719367,-0.2327560335,0.1665794849,-0.0413150303,-0.4310828745,0.1568385363,0.0666227341,0.1682221591,-0.2003851682,-0.2270279974,-0.1135412008,-0.1458190233,0.0079398574,-0.1276904047,0.0849654675,0.3094039559,0.1123534068,-0.3165157139,-0.0872011557,0.1330857426,-0.3335401416,-0.0773374513,0.0739419162,-0.054162316,-0.2302434891,-0.034139622,-0.0319806486,0.0831893757,0.3736632466,-0.4069587886,-0.3791774213,-0.2713731527,-0.0048744264,0.2311773747,0.068360962,0.2319667786,0.1899949759,0.0391035639,-0.2794680893,-0.4342857301,-0.0886206329,-0.2365958691,0.1095489264,0.1482250243,0.6309939027,0.0345636867,0.4764270186,0.2947264612,0.0489611253,0.1398259848,0.1815548688,0.4972691238,-0.3258462548,-0.4095653892,-0.0467088632,-0.0461938493,0.0505124666,-0.153191432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{"html_url":"https:\/\/github.com\/huggingface\/datasets\/issues\/2943","title":"Backwards compatibility broken for cached datasets that use `.filter()`","comments":"I just merged a fix, let me know if you're still having this kind of issues :)\r\n\r\nWe'll do a release soon to make this fix available","body":"## Describe the bug\r\nAfter upgrading to datasets `1.12.0`, some cached `.filter()` steps from `1.11.0` started failing with \r\n`ValueError: Keys mismatch: between {'indices': Value(dtype='uint64', id=None)} and {'file': Value(dtype='string', id=None), 'text': Value(dtype='string', id=None), 'speaker_id': Value(dtype='int64', id=None), 'chapter_id': Value(dtype='int64', id=None), 'id': Value(dtype='string', id=None)}`\r\n\r\nRelated feature: https:\/\/github.com\/huggingface\/datasets\/pull\/2836\r\n\r\n:question: This is probably a `wontfix` bug, since it can be solved by simply cleaning the related cache dirs, but the workaround could be useful for someone googling the error :) \r\n\r\n## Workaround\r\nRemove the cache for the given dataset, e.g. `rm -rf ~\/.cache\/huggingface\/datasets\/librispeech_asr`.\r\n\r\n## Steps to reproduce the bug\r\n1. Delete `~\/.cache\/huggingface\/datasets\/librispeech_asr` if it exists.\r\n\r\n2. `pip install datasets==1.11.0` and run the following snippet:\r\n\r\n```python\r\nfrom datasets import load_dataset\r\n\r\nids = [\"1272-141231-0000\"]\r\nds = load_dataset(\"patrickvonplaten\/librispeech_asr_dummy\", \"clean\", split=\"validation\")\r\nds = ds.filter(lambda x: x[\"id\"] in ids)\r\n```\r\n3. `pip install datasets==1.12.1` and re-run the code again\r\n\r\n## Expected results\r\nSame result as with the previous `datasets` version.\r\n\r\n## Actual results\r\n```bash\r\nReusing dataset librispeech_asr (.\/.cache\/huggingface\/datasets\/librispeech_asr\/clean\/2.1.0\/468ec03677f46a8714ac6b5b64dba02d246a228d92cbbad7f3dc190fa039eab1)\r\nLoading cached processed dataset at .\/.cache\/huggingface\/datasets\/librispeech_asr\/clean\/2.1.0\/468ec03677f46a8714ac6b5b64dba02d246a228d92cbbad7f3dc190fa039eab1\/cache-cd1c29844fdbc87a.arrow\r\nTraceback (most recent call last):\r\n File \".\/repos\/transformers\/src\/transformers\/models\/wav2vec2\/try_dataset.py\", line 5, in \r\n ds = ds.filter(lambda x: x[\"id\"] in ids)\r\n File \".\/envs\/transformers\/lib\/python3.8\/site-packages\/datasets\/arrow_dataset.py\", line 185, in wrapper\r\n out: Union[\"Dataset\", \"DatasetDict\"] = func(self, *args, **kwargs)\r\n File \".\/envs\/transformers\/lib\/python3.8\/site-packages\/datasets\/fingerprint.py\", line 398, in wrapper\r\n out = func(self, *args, **kwargs)\r\n File \".\/envs\/transformers\/lib\/python3.8\/site-packages\/datasets\/arrow_dataset.py\", line 2169, in filter\r\n indices = self.map(\r\n File \".\/envs\/transformers\/lib\/python3.8\/site-packages\/datasets\/arrow_dataset.py\", line 1686, in map\r\n return self._map_single(\r\n File \".\/envs\/transformers\/lib\/python3.8\/site-packages\/datasets\/arrow_dataset.py\", line 185, in wrapper\r\n out: Union[\"Dataset\", \"DatasetDict\"] = func(self, *args, **kwargs)\r\n File \".\/envs\/transformers\/lib\/python3.8\/site-packages\/datasets\/fingerprint.py\", line 398, in wrapper\r\n out = func(self, *args, **kwargs)\r\n File \".\/envs\/transformers\/lib\/python3.8\/site-packages\/datasets\/arrow_dataset.py\", line 1896, in _map_single\r\n return Dataset.from_file(cache_file_name, info=info, split=self.split)\r\n File \".\/envs\/transformers\/lib\/python3.8\/site-packages\/datasets\/arrow_dataset.py\", line 343, in from_file\r\n return cls(\r\n File \".\/envs\/transformers\/lib\/python3.8\/site-packages\/datasets\/arrow_dataset.py\", line 282, in __init__\r\n self.info.features = self.info.features.reorder_fields_as(inferred_features)\r\n File \".\/envs\/transformers\/lib\/python3.8\/site-packages\/datasets\/features.py\", line 1151, in reorder_fields_as\r\n return Features(recursive_reorder(self, other))\r\n File \".\/envs\/transformers\/lib\/python3.8\/site-packages\/datasets\/features.py\", line 1140, in recursive_reorder\r\n raise ValueError(f\"Keys mismatch: between {source} and {target}\" + stack_position)\r\nValueError: Keys mismatch: between {'indices': Value(dtype='uint64', id=None)} and {'file': Value(dtype='string', id=None), 'text': Value(dtype='string', id=None), 'speaker_id': Value(dtype='int64', id=None), 'chapter_id': Value(dtype='int64', id=None), 'id': Value(dtype='string', id=None)}\r\n\r\nProcess finished with exit code 1\r\n\r\n```\r\n\r\n## Environment info\r\n- `datasets` version: 1.12.1\r\n- Platform: Linux-5.11.0-34-generic-x86_64-with-glibc2.17\r\n- Python version: 3.8.10\r\n- PyArrow version: 5.0.0\r\n","comment_length":27,"text":"Backwards compatibility broken for cached datasets that use `.filter()`\n## Describe the bug\r\nAfter upgrading to datasets `1.12.0`, some cached `.filter()` steps from `1.11.0` started failing with \r\n`ValueError: Keys mismatch: between {'indices': Value(dtype='uint64', id=None)} and {'file': Value(dtype='string', id=None), 'text': Value(dtype='string', id=None), 'speaker_id': Value(dtype='int64', id=None), 'chapter_id': Value(dtype='int64', id=None), 'id': Value(dtype='string', id=None)}`\r\n\r\nRelated feature: https:\/\/github.com\/huggingface\/datasets\/pull\/2836\r\n\r\n:question: This is probably a `wontfix` bug, since it can be solved by simply cleaning the related cache dirs, but the workaround could be useful for someone googling the error :) \r\n\r\n## Workaround\r\nRemove the cache for the given dataset, e.g. `rm -rf ~\/.cache\/huggingface\/datasets\/librispeech_asr`.\r\n\r\n## Steps to reproduce the bug\r\n1. Delete `~\/.cache\/huggingface\/datasets\/librispeech_asr` if it exists.\r\n\r\n2. `pip install datasets==1.11.0` and run the following snippet:\r\n\r\n```python\r\nfrom datasets import load_dataset\r\n\r\nids = [\"1272-141231-0000\"]\r\nds = load_dataset(\"patrickvonplaten\/librispeech_asr_dummy\", \"clean\", split=\"validation\")\r\nds = ds.filter(lambda x: x[\"id\"] in ids)\r\n```\r\n3. `pip install datasets==1.12.1` and re-run the code again\r\n\r\n## Expected results\r\nSame result as with the previous `datasets` version.\r\n\r\n## Actual results\r\n```bash\r\nReusing dataset librispeech_asr (.\/.cache\/huggingface\/datasets\/librispeech_asr\/clean\/2.1.0\/468ec03677f46a8714ac6b5b64dba02d246a228d92cbbad7f3dc190fa039eab1)\r\nLoading cached processed dataset at .\/.cache\/huggingface\/datasets\/librispeech_asr\/clean\/2.1.0\/468ec03677f46a8714ac6b5b64dba02d246a228d92cbbad7f3dc190fa039eab1\/cache-cd1c29844fdbc87a.arrow\r\nTraceback (most recent call last):\r\n File \".\/repos\/transformers\/src\/transformers\/models\/wav2vec2\/try_dataset.py\", line 5, in \r\n ds = ds.filter(lambda x: x[\"id\"] in ids)\r\n File \".\/envs\/transformers\/lib\/python3.8\/site-packages\/datasets\/arrow_dataset.py\", line 185, in wrapper\r\n out: Union[\"Dataset\", \"DatasetDict\"] = func(self, *args, **kwargs)\r\n File \".\/envs\/transformers\/lib\/python3.8\/site-packages\/datasets\/fingerprint.py\", line 398, in wrapper\r\n out = func(self, *args, **kwargs)\r\n File \".\/envs\/transformers\/lib\/python3.8\/site-packages\/datasets\/arrow_dataset.py\", line 2169, in filter\r\n indices = self.map(\r\n File \".\/envs\/transformers\/lib\/python3.8\/site-packages\/datasets\/arrow_dataset.py\", line 1686, in map\r\n return self._map_single(\r\n File \".\/envs\/transformers\/lib\/python3.8\/site-packages\/datasets\/arrow_dataset.py\", line 185, in wrapper\r\n out: Union[\"Dataset\", \"DatasetDict\"] = func(self, *args, **kwargs)\r\n File \".\/envs\/transformers\/lib\/python3.8\/site-packages\/datasets\/fingerprint.py\", line 398, in wrapper\r\n out = func(self, *args, **kwargs)\r\n File \".\/envs\/transformers\/lib\/python3.8\/site-packages\/datasets\/arrow_dataset.py\", line 1896, in _map_single\r\n return Dataset.from_file(cache_file_name, info=info, split=self.split)\r\n File \".\/envs\/transformers\/lib\/python3.8\/site-packages\/datasets\/arrow_dataset.py\", line 343, in from_file\r\n return cls(\r\n File \".\/envs\/transformers\/lib\/python3.8\/site-packages\/datasets\/arrow_dataset.py\", line 282, in __init__\r\n self.info.features = self.info.features.reorder_fields_as(inferred_features)\r\n File \".\/envs\/transformers\/lib\/python3.8\/site-packages\/datasets\/features.py\", line 1151, in reorder_fields_as\r\n return Features(recursive_reorder(self, other))\r\n File \".\/envs\/transformers\/lib\/python3.8\/site-packages\/datasets\/features.py\", line 1140, in recursive_reorder\r\n raise ValueError(f\"Keys mismatch: between {source} and {target}\" + stack_position)\r\nValueError: Keys mismatch: between {'indices': Value(dtype='uint64', id=None)} and {'file': Value(dtype='string', id=None), 'text': Value(dtype='string', id=None), 'speaker_id': Value(dtype='int64', id=None), 'chapter_id': Value(dtype='int64', id=None), 'id': Value(dtype='string', id=None)}\r\n\r\nProcess finished with exit code 1\r\n\r\n```\r\n\r\n## Environment info\r\n- `datasets` version: 1.12.1\r\n- Platform: Linux-5.11.0-34-generic-x86_64-with-glibc2.17\r\n- Python version: 3.8.10\r\n- PyArrow version: 5.0.0\r\n\nI just merged a fix, let me know if you're still having this kind of issues :)\r\n\r\nWe'll do a release soon to make this fix 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{"html_url":"https:\/\/github.com\/huggingface\/datasets\/issues\/2943","title":"Backwards compatibility broken for cached datasets that use `.filter()`","comments":"Definitely works on several manual cases with our dummy datasets, thank you @lhoestq !","body":"## Describe the bug\r\nAfter upgrading to datasets `1.12.0`, some cached `.filter()` steps from `1.11.0` started failing with \r\n`ValueError: Keys mismatch: between {'indices': Value(dtype='uint64', id=None)} and {'file': Value(dtype='string', id=None), 'text': Value(dtype='string', id=None), 'speaker_id': Value(dtype='int64', id=None), 'chapter_id': Value(dtype='int64', id=None), 'id': Value(dtype='string', id=None)}`\r\n\r\nRelated feature: https:\/\/github.com\/huggingface\/datasets\/pull\/2836\r\n\r\n:question: This is probably a `wontfix` bug, since it can be solved by simply cleaning the related cache dirs, but the workaround could be useful for someone googling the error :) \r\n\r\n## Workaround\r\nRemove the cache for the given dataset, e.g. `rm -rf ~\/.cache\/huggingface\/datasets\/librispeech_asr`.\r\n\r\n## Steps to reproduce the bug\r\n1. Delete `~\/.cache\/huggingface\/datasets\/librispeech_asr` if it exists.\r\n\r\n2. `pip install datasets==1.11.0` and run the following snippet:\r\n\r\n```python\r\nfrom datasets import load_dataset\r\n\r\nids = [\"1272-141231-0000\"]\r\nds = load_dataset(\"patrickvonplaten\/librispeech_asr_dummy\", \"clean\", split=\"validation\")\r\nds = ds.filter(lambda x: x[\"id\"] in ids)\r\n```\r\n3. `pip install datasets==1.12.1` and re-run the code again\r\n\r\n## Expected results\r\nSame result as with the previous `datasets` version.\r\n\r\n## Actual results\r\n```bash\r\nReusing dataset librispeech_asr (.\/.cache\/huggingface\/datasets\/librispeech_asr\/clean\/2.1.0\/468ec03677f46a8714ac6b5b64dba02d246a228d92cbbad7f3dc190fa039eab1)\r\nLoading cached processed dataset at .\/.cache\/huggingface\/datasets\/librispeech_asr\/clean\/2.1.0\/468ec03677f46a8714ac6b5b64dba02d246a228d92cbbad7f3dc190fa039eab1\/cache-cd1c29844fdbc87a.arrow\r\nTraceback (most recent call last):\r\n File \".\/repos\/transformers\/src\/transformers\/models\/wav2vec2\/try_dataset.py\", line 5, in \r\n ds = ds.filter(lambda x: x[\"id\"] in ids)\r\n File \".\/envs\/transformers\/lib\/python3.8\/site-packages\/datasets\/arrow_dataset.py\", line 185, in wrapper\r\n out: Union[\"Dataset\", \"DatasetDict\"] = func(self, *args, **kwargs)\r\n File \".\/envs\/transformers\/lib\/python3.8\/site-packages\/datasets\/fingerprint.py\", line 398, in wrapper\r\n out = func(self, *args, **kwargs)\r\n File \".\/envs\/transformers\/lib\/python3.8\/site-packages\/datasets\/arrow_dataset.py\", line 2169, in filter\r\n indices = self.map(\r\n File \".\/envs\/transformers\/lib\/python3.8\/site-packages\/datasets\/arrow_dataset.py\", line 1686, in map\r\n return self._map_single(\r\n File \".\/envs\/transformers\/lib\/python3.8\/site-packages\/datasets\/arrow_dataset.py\", line 185, in wrapper\r\n out: Union[\"Dataset\", \"DatasetDict\"] = func(self, *args, **kwargs)\r\n File \".\/envs\/transformers\/lib\/python3.8\/site-packages\/datasets\/fingerprint.py\", line 398, in wrapper\r\n out = func(self, *args, **kwargs)\r\n File \".\/envs\/transformers\/lib\/python3.8\/site-packages\/datasets\/arrow_dataset.py\", line 1896, in _map_single\r\n return Dataset.from_file(cache_file_name, info=info, split=self.split)\r\n File \".\/envs\/transformers\/lib\/python3.8\/site-packages\/datasets\/arrow_dataset.py\", line 343, in from_file\r\n return cls(\r\n File \".\/envs\/transformers\/lib\/python3.8\/site-packages\/datasets\/arrow_dataset.py\", line 282, in __init__\r\n self.info.features = self.info.features.reorder_fields_as(inferred_features)\r\n File \".\/envs\/transformers\/lib\/python3.8\/site-packages\/datasets\/features.py\", line 1151, in reorder_fields_as\r\n return Features(recursive_reorder(self, other))\r\n File \".\/envs\/transformers\/lib\/python3.8\/site-packages\/datasets\/features.py\", line 1140, in recursive_reorder\r\n raise ValueError(f\"Keys mismatch: between {source} and {target}\" + stack_position)\r\nValueError: Keys mismatch: between {'indices': Value(dtype='uint64', id=None)} and {'file': Value(dtype='string', id=None), 'text': Value(dtype='string', id=None), 'speaker_id': Value(dtype='int64', id=None), 'chapter_id': Value(dtype='int64', id=None), 'id': Value(dtype='string', id=None)}\r\n\r\nProcess finished with exit code 1\r\n\r\n```\r\n\r\n## Environment info\r\n- `datasets` version: 1.12.1\r\n- Platform: Linux-5.11.0-34-generic-x86_64-with-glibc2.17\r\n- Python version: 3.8.10\r\n- PyArrow version: 5.0.0\r\n","comment_length":14,"text":"Backwards compatibility broken for cached datasets that use `.filter()`\n## Describe the bug\r\nAfter upgrading to datasets `1.12.0`, some cached `.filter()` steps from `1.11.0` started failing with \r\n`ValueError: Keys mismatch: between {'indices': Value(dtype='uint64', id=None)} and {'file': Value(dtype='string', id=None), 'text': Value(dtype='string', id=None), 'speaker_id': Value(dtype='int64', id=None), 'chapter_id': Value(dtype='int64', id=None), 'id': Value(dtype='string', id=None)}`\r\n\r\nRelated feature: https:\/\/github.com\/huggingface\/datasets\/pull\/2836\r\n\r\n:question: This is probably a `wontfix` bug, since it can be solved by simply cleaning the related cache dirs, but the workaround could be useful for someone googling the error :) \r\n\r\n## Workaround\r\nRemove the cache for the given dataset, e.g. `rm -rf ~\/.cache\/huggingface\/datasets\/librispeech_asr`.\r\n\r\n## Steps to reproduce the bug\r\n1. Delete `~\/.cache\/huggingface\/datasets\/librispeech_asr` if it exists.\r\n\r\n2. `pip install datasets==1.11.0` and run the following snippet:\r\n\r\n```python\r\nfrom datasets import load_dataset\r\n\r\nids = [\"1272-141231-0000\"]\r\nds = load_dataset(\"patrickvonplaten\/librispeech_asr_dummy\", \"clean\", split=\"validation\")\r\nds = ds.filter(lambda x: x[\"id\"] in ids)\r\n```\r\n3. `pip install datasets==1.12.1` and re-run the code again\r\n\r\n## Expected results\r\nSame result as with the previous `datasets` version.\r\n\r\n## Actual results\r\n```bash\r\nReusing dataset librispeech_asr (.\/.cache\/huggingface\/datasets\/librispeech_asr\/clean\/2.1.0\/468ec03677f46a8714ac6b5b64dba02d246a228d92cbbad7f3dc190fa039eab1)\r\nLoading cached processed dataset at .\/.cache\/huggingface\/datasets\/librispeech_asr\/clean\/2.1.0\/468ec03677f46a8714ac6b5b64dba02d246a228d92cbbad7f3dc190fa039eab1\/cache-cd1c29844fdbc87a.arrow\r\nTraceback (most recent call last):\r\n File \".\/repos\/transformers\/src\/transformers\/models\/wav2vec2\/try_dataset.py\", line 5, in \r\n ds = ds.filter(lambda x: x[\"id\"] in ids)\r\n File \".\/envs\/transformers\/lib\/python3.8\/site-packages\/datasets\/arrow_dataset.py\", line 185, in wrapper\r\n out: Union[\"Dataset\", \"DatasetDict\"] = func(self, *args, **kwargs)\r\n File \".\/envs\/transformers\/lib\/python3.8\/site-packages\/datasets\/fingerprint.py\", line 398, in wrapper\r\n out = func(self, *args, **kwargs)\r\n File \".\/envs\/transformers\/lib\/python3.8\/site-packages\/datasets\/arrow_dataset.py\", line 2169, in filter\r\n indices = self.map(\r\n File \".\/envs\/transformers\/lib\/python3.8\/site-packages\/datasets\/arrow_dataset.py\", line 1686, in map\r\n return self._map_single(\r\n File \".\/envs\/transformers\/lib\/python3.8\/site-packages\/datasets\/arrow_dataset.py\", line 185, in wrapper\r\n out: Union[\"Dataset\", \"DatasetDict\"] = func(self, *args, **kwargs)\r\n File \".\/envs\/transformers\/lib\/python3.8\/site-packages\/datasets\/fingerprint.py\", line 398, in wrapper\r\n out = func(self, *args, **kwargs)\r\n File \".\/envs\/transformers\/lib\/python3.8\/site-packages\/datasets\/arrow_dataset.py\", line 1896, in _map_single\r\n return Dataset.from_file(cache_file_name, info=info, split=self.split)\r\n File \".\/envs\/transformers\/lib\/python3.8\/site-packages\/datasets\/arrow_dataset.py\", line 343, in from_file\r\n return cls(\r\n File \".\/envs\/transformers\/lib\/python3.8\/site-packages\/datasets\/arrow_dataset.py\", line 282, in __init__\r\n self.info.features = self.info.features.reorder_fields_as(inferred_features)\r\n File \".\/envs\/transformers\/lib\/python3.8\/site-packages\/datasets\/features.py\", line 1151, in reorder_fields_as\r\n return Features(recursive_reorder(self, other))\r\n File \".\/envs\/transformers\/lib\/python3.8\/site-packages\/datasets\/features.py\", line 1140, in recursive_reorder\r\n raise ValueError(f\"Keys mismatch: between {source} and {target}\" + stack_position)\r\nValueError: Keys mismatch: between {'indices': Value(dtype='uint64', id=None)} and {'file': Value(dtype='string', id=None), 'text': Value(dtype='string', id=None), 'speaker_id': Value(dtype='int64', id=None), 'chapter_id': Value(dtype='int64', id=None), 'id': Value(dtype='string', id=None)}\r\n\r\nProcess finished with exit code 1\r\n\r\n```\r\n\r\n## Environment info\r\n- `datasets` version: 1.12.1\r\n- Platform: Linux-5.11.0-34-generic-x86_64-with-glibc2.17\r\n- Python version: 3.8.10\r\n- PyArrow version: 5.0.0\r\n\nDefinitely works on several manual cases with our dummy datasets, thank you @lhoestq !","embeddings":[-0.3049958348,0.1243880838,-0.0465583764,0.2368971109,0.1517290324,-0.0666598156,-0.0069413707,0.3285393715,0.1737383604,0.0293814819,-0.2313861847,0.3581927419,-0.1503304988,0.2527370155,-0.2977862358,-0.1378120482,0.0444693454,0.0321247876,-0.0614953265,0.166412577,-0.1239175051,0.4988580644,-0.1957765073,0.0070795678,0.2221652865,0.0513210297,0.1091236398,0.138566196,0.0798620358,-0.3392693698,0.5981172919,0.1064069718,-0.0348691642,0.498096019,-0.0001213098,0.099867776,0.4398066998,-0.1073976383,-0.4579497874,-0.3686879575,-0.192854017,0.1319934726,0.2131137848,0.0811629742,-0.0715122968,0.1143416911,-0.303493619,-0.346046567,0.2488765419,0.1989328116,0.2267285585,0.2136144787,0.2657418847,0.066587165,0.170179069,0.0826525018,0.0259718653,-0.0490864292,0.2940285802,0.014261202,0.043685697,0.4806104004,-0.3283523321,-0.2146463245,0.3046185374,-0.2982533276,0.1030709222,-0.3957037926,0.2999309897,0.0781370997,0.1462642252,-0.255518198,-0.5773367882,-0.3483263254,-0.5121472478,-0.2868291736,0.4164735079,-0.216358915,-0.2009968162,0.2200830877,-0.3342647851,-0.0270038545,0.0934983194,0.0535101071,-0.0584819205,0.2941842079,-0.1318320185,-0.0048626242,0.0149830757,-0.132900551,0.1924084276,-0.2750048339,-0.1968749017,0.1725800484,-0.0155595224,-0.0546131916,0.0954222009,0.3323281705,0.140570581,0.0356287211,0.0192098524,0.0845401734,-0.0409641638,0.0536773168,0.1373275816,0.475378722,0.4901195765,0.2467906624,0.2727875113,0.2854887545,-0.0078853024,-0.0013800253,0.5281852484,-0.1666412055,0.0747071281,0.1517307162,0.196695298,-0.4048683643,-0.3602132797,0.1689626127,-0.0769995078,-0.1250401586,0.2207264006,0.0534940958,0.1005648673,0.2142349631,0.1320811808,0.0338568501,-0.1964725256,0.0612344779,-0.1725096703,-0.1739457697,0.0011616903,-0.1112991124,-0.0473052748,-0.89977777,0.1747701168,0.0482602492,0.0063488153,0.1058098748,-0.0697732121,0.207434997,0.1730166823,0.5229284167,-0.2213587761,0.0839003325,0.1671907902,-0.548327744,-0.1386865377,0.0125284484,-0.2415378094,-0.1112066656,0.2085879147,0.1735862345,-0.3296715915,0.0126515068,-0.17029953,0.4515431225,0.2592140436,-0.5466870666,-0.0779574588,-0.213380143,-0.4382343888,-0.1997140199,0.0971902013,0.4131370485,-0.5942543149,-0.3216674924,0.0216870401,0.0319067985,0.0399160981,-0.1229341999,-0.063439101,-0.2444722205,-0.1077689156,-0.1384182572,0.3606542647,-0.3195546865,-0.733892858,0.1537256837,0.2360213548,0.5342661738,-0.0709591061,-0.1374839246,0.2813950479,-0.2578290701,0.1459920853,0.0848445743,-0.1649033576,-0.0079940017,-0.3409692049,-0.2346946597,0.2238697708,-0.1502910852,0.0904174298,0.3348585963,0.0790057704,-0.2542206049,0.0502917804,0.0350500681,0.1049243882,0.1990925223,0.2453232259,0.2237652242,0.2863062918,-0.4365186989,-0.2801407278,0.2506181598,0.2141302824,-0.0600011088,-0.1524066925,-0.1982273012,-0.2678262889,-0.0884182155,-0.1589775681,-0.3044096231,0.0788738504,0.0225765873,0.3843636513,-0.0385034606,-0.1145259663,0.6218649745,0.3084535897,0.0944804996,-0.5049411654,0.229641512,-0.0548514128,0.0051691518,-0.0591857955,0.0377827175,0.2939595878,-0.0685256049,-0.2895757854,0.407875061,0.0392592028,0.084168233,-0.0276337974,0.1937460154,0.1773459613,0.2105839252,0.0639338642,0.2741822898,-0.0148837138,0.2087214142,-0.195456177,0.557248354,0.0293753464,0.3240505159,0.1208347678,0.148756206,0.3702490628,0.1305633932,-0.2189950645,-0.1370786875,0.3204495311,-0.4587228,0.0626674443,0.0123394234,-0.1297932267,0.3776263595,0.0716826022,0.0755119473,-0.0334524736,0.3119212389,-0.1593657285,-0.042190969,0.1813624054,0.4024861157,0.4014509916,0.2088322341,0.0507172868,0.0758817792,-0.1615392268,-0.0248774402,0.2082498521,0.1293462515,-0.1696399748,0.1535750628,0.3034210205,0.2732039392,-0.3061974645,0.0933934823,-0.2356239706,0.1195331365,-0.270581305,0.3616366684,-0.2810101509,-0.2280866951,0.076141715,-0.1678324938,-0.2615140676,-0.3875222206,0.089899376,0.3934392929,-0.086219281,0.2567750216,0.0147161586,0.2445412278,0.136167407,-0.1996820718,-0.4005717337,-0.2078815252,-0.3476482034,0.0053976052,0.2051323503,-0.1568491161,0.2970358729,-0.0763099566,-0.2066247612,-0.390294075,-0.9079098701,-0.0486286655,-0.123964861,0.6180298328,0.3176228106,-0.0916265026,-0.1352719367,-0.2327560335,0.1665794849,-0.0413150303,-0.4310828745,0.1568385363,0.0666227341,0.1682221591,-0.2003851682,-0.2270279974,-0.1135412008,-0.1458190233,0.0079398574,-0.1276904047,0.0849654675,0.3094039559,0.1123534068,-0.3165157139,-0.0872011557,0.1330857426,-0.3335401416,-0.0773374513,0.0739419162,-0.054162316,-0.2302434891,-0.034139622,-0.0319806486,0.0831893757,0.3736632466,-0.4069587886,-0.3791774213,-0.2713731527,-0.0048744264,0.2311773747,0.068360962,0.2319667786,0.1899949759,0.0391035639,-0.2794680893,-0.4342857301,-0.0886206329,-0.2365958691,0.1095489264,0.1482250243,0.6309939027,0.0345636867,0.4764270186,0.2947264612,0.0489611253,0.1398259848,0.1815548688,0.4972691238,-0.3258462548,-0.4095653892,-0.0467088632,-0.0461938493,0.0505124666,-0.1531914324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{"html_url":"https:\/\/github.com\/huggingface\/datasets\/issues\/2943","title":"Backwards compatibility broken for cached datasets that use `.filter()`","comments":"Fixed by #2947.","body":"## Describe the bug\r\nAfter upgrading to datasets `1.12.0`, some cached `.filter()` steps from `1.11.0` started failing with \r\n`ValueError: Keys mismatch: between {'indices': Value(dtype='uint64', id=None)} and {'file': Value(dtype='string', id=None), 'text': Value(dtype='string', id=None), 'speaker_id': Value(dtype='int64', id=None), 'chapter_id': Value(dtype='int64', id=None), 'id': Value(dtype='string', id=None)}`\r\n\r\nRelated feature: https:\/\/github.com\/huggingface\/datasets\/pull\/2836\r\n\r\n:question: This is probably a `wontfix` bug, since it can be solved by simply cleaning the related cache dirs, but the workaround could be useful for someone googling the error :) \r\n\r\n## Workaround\r\nRemove the cache for the given dataset, e.g. `rm -rf ~\/.cache\/huggingface\/datasets\/librispeech_asr`.\r\n\r\n## Steps to reproduce the bug\r\n1. Delete `~\/.cache\/huggingface\/datasets\/librispeech_asr` if it exists.\r\n\r\n2. `pip install datasets==1.11.0` and run the following snippet:\r\n\r\n```python\r\nfrom datasets import load_dataset\r\n\r\nids = [\"1272-141231-0000\"]\r\nds = load_dataset(\"patrickvonplaten\/librispeech_asr_dummy\", \"clean\", split=\"validation\")\r\nds = ds.filter(lambda x: x[\"id\"] in ids)\r\n```\r\n3. `pip install datasets==1.12.1` and re-run the code again\r\n\r\n## Expected results\r\nSame result as with the previous `datasets` version.\r\n\r\n## Actual results\r\n```bash\r\nReusing dataset librispeech_asr (.\/.cache\/huggingface\/datasets\/librispeech_asr\/clean\/2.1.0\/468ec03677f46a8714ac6b5b64dba02d246a228d92cbbad7f3dc190fa039eab1)\r\nLoading cached processed dataset at .\/.cache\/huggingface\/datasets\/librispeech_asr\/clean\/2.1.0\/468ec03677f46a8714ac6b5b64dba02d246a228d92cbbad7f3dc190fa039eab1\/cache-cd1c29844fdbc87a.arrow\r\nTraceback (most recent call last):\r\n File \".\/repos\/transformers\/src\/transformers\/models\/wav2vec2\/try_dataset.py\", line 5, in \r\n ds = ds.filter(lambda x: x[\"id\"] in ids)\r\n File \".\/envs\/transformers\/lib\/python3.8\/site-packages\/datasets\/arrow_dataset.py\", line 185, in wrapper\r\n out: Union[\"Dataset\", \"DatasetDict\"] = func(self, *args, **kwargs)\r\n File \".\/envs\/transformers\/lib\/python3.8\/site-packages\/datasets\/fingerprint.py\", line 398, in wrapper\r\n out = func(self, *args, **kwargs)\r\n File \".\/envs\/transformers\/lib\/python3.8\/site-packages\/datasets\/arrow_dataset.py\", line 2169, in filter\r\n indices = self.map(\r\n File \".\/envs\/transformers\/lib\/python3.8\/site-packages\/datasets\/arrow_dataset.py\", line 1686, in map\r\n return self._map_single(\r\n File \".\/envs\/transformers\/lib\/python3.8\/site-packages\/datasets\/arrow_dataset.py\", line 185, in wrapper\r\n out: Union[\"Dataset\", \"DatasetDict\"] = func(self, *args, **kwargs)\r\n File \".\/envs\/transformers\/lib\/python3.8\/site-packages\/datasets\/fingerprint.py\", line 398, in wrapper\r\n out = func(self, *args, **kwargs)\r\n File \".\/envs\/transformers\/lib\/python3.8\/site-packages\/datasets\/arrow_dataset.py\", line 1896, in _map_single\r\n return Dataset.from_file(cache_file_name, info=info, split=self.split)\r\n File \".\/envs\/transformers\/lib\/python3.8\/site-packages\/datasets\/arrow_dataset.py\", line 343, in from_file\r\n return cls(\r\n File \".\/envs\/transformers\/lib\/python3.8\/site-packages\/datasets\/arrow_dataset.py\", line 282, in __init__\r\n self.info.features = self.info.features.reorder_fields_as(inferred_features)\r\n File \".\/envs\/transformers\/lib\/python3.8\/site-packages\/datasets\/features.py\", line 1151, in reorder_fields_as\r\n return Features(recursive_reorder(self, other))\r\n File \".\/envs\/transformers\/lib\/python3.8\/site-packages\/datasets\/features.py\", line 1140, in recursive_reorder\r\n raise ValueError(f\"Keys mismatch: between {source} and {target}\" + stack_position)\r\nValueError: Keys mismatch: between {'indices': Value(dtype='uint64', id=None)} and {'file': Value(dtype='string', id=None), 'text': Value(dtype='string', id=None), 'speaker_id': Value(dtype='int64', id=None), 'chapter_id': Value(dtype='int64', id=None), 'id': Value(dtype='string', id=None)}\r\n\r\nProcess finished with exit code 1\r\n\r\n```\r\n\r\n## Environment info\r\n- `datasets` version: 1.12.1\r\n- Platform: Linux-5.11.0-34-generic-x86_64-with-glibc2.17\r\n- Python version: 3.8.10\r\n- PyArrow version: 5.0.0\r\n","comment_length":3,"text":"Backwards compatibility broken for cached datasets that use `.filter()`\n## Describe the bug\r\nAfter upgrading to datasets `1.12.0`, some cached `.filter()` steps from `1.11.0` started failing with \r\n`ValueError: Keys mismatch: between {'indices': Value(dtype='uint64', id=None)} and {'file': Value(dtype='string', id=None), 'text': Value(dtype='string', id=None), 'speaker_id': Value(dtype='int64', id=None), 'chapter_id': Value(dtype='int64', id=None), 'id': Value(dtype='string', id=None)}`\r\n\r\nRelated feature: https:\/\/github.com\/huggingface\/datasets\/pull\/2836\r\n\r\n:question: This is probably a `wontfix` bug, since it can be solved by simply cleaning the related cache dirs, but the workaround could be useful for someone googling the error :) \r\n\r\n## Workaround\r\nRemove the cache for the given dataset, e.g. `rm -rf ~\/.cache\/huggingface\/datasets\/librispeech_asr`.\r\n\r\n## Steps to reproduce the bug\r\n1. Delete `~\/.cache\/huggingface\/datasets\/librispeech_asr` if it exists.\r\n\r\n2. `pip install datasets==1.11.0` and run the following snippet:\r\n\r\n```python\r\nfrom datasets import load_dataset\r\n\r\nids = [\"1272-141231-0000\"]\r\nds = load_dataset(\"patrickvonplaten\/librispeech_asr_dummy\", \"clean\", split=\"validation\")\r\nds = ds.filter(lambda x: x[\"id\"] in ids)\r\n```\r\n3. `pip install datasets==1.12.1` and re-run the code again\r\n\r\n## Expected results\r\nSame result as with the previous `datasets` version.\r\n\r\n## Actual results\r\n```bash\r\nReusing dataset librispeech_asr (.\/.cache\/huggingface\/datasets\/librispeech_asr\/clean\/2.1.0\/468ec03677f46a8714ac6b5b64dba02d246a228d92cbbad7f3dc190fa039eab1)\r\nLoading cached processed dataset at .\/.cache\/huggingface\/datasets\/librispeech_asr\/clean\/2.1.0\/468ec03677f46a8714ac6b5b64dba02d246a228d92cbbad7f3dc190fa039eab1\/cache-cd1c29844fdbc87a.arrow\r\nTraceback (most recent call last):\r\n File \".\/repos\/transformers\/src\/transformers\/models\/wav2vec2\/try_dataset.py\", line 5, in \r\n ds = ds.filter(lambda x: x[\"id\"] in ids)\r\n File \".\/envs\/transformers\/lib\/python3.8\/site-packages\/datasets\/arrow_dataset.py\", line 185, in wrapper\r\n out: Union[\"Dataset\", \"DatasetDict\"] = func(self, *args, **kwargs)\r\n File \".\/envs\/transformers\/lib\/python3.8\/site-packages\/datasets\/fingerprint.py\", line 398, in wrapper\r\n out = func(self, *args, **kwargs)\r\n File \".\/envs\/transformers\/lib\/python3.8\/site-packages\/datasets\/arrow_dataset.py\", line 2169, in filter\r\n indices = self.map(\r\n File \".\/envs\/transformers\/lib\/python3.8\/site-packages\/datasets\/arrow_dataset.py\", line 1686, in map\r\n return self._map_single(\r\n File \".\/envs\/transformers\/lib\/python3.8\/site-packages\/datasets\/arrow_dataset.py\", line 185, in wrapper\r\n out: Union[\"Dataset\", \"DatasetDict\"] = func(self, *args, **kwargs)\r\n File \".\/envs\/transformers\/lib\/python3.8\/site-packages\/datasets\/fingerprint.py\", line 398, in wrapper\r\n out = func(self, *args, **kwargs)\r\n File \".\/envs\/transformers\/lib\/python3.8\/site-packages\/datasets\/arrow_dataset.py\", line 1896, in _map_single\r\n return Dataset.from_file(cache_file_name, info=info, split=self.split)\r\n File \".\/envs\/transformers\/lib\/python3.8\/site-packages\/datasets\/arrow_dataset.py\", line 343, in from_file\r\n return cls(\r\n File \".\/envs\/transformers\/lib\/python3.8\/site-packages\/datasets\/arrow_dataset.py\", line 282, in __init__\r\n self.info.features = self.info.features.reorder_fields_as(inferred_features)\r\n File \".\/envs\/transformers\/lib\/python3.8\/site-packages\/datasets\/features.py\", line 1151, in reorder_fields_as\r\n return Features(recursive_reorder(self, other))\r\n File \".\/envs\/transformers\/lib\/python3.8\/site-packages\/datasets\/features.py\", line 1140, in recursive_reorder\r\n raise ValueError(f\"Keys mismatch: between {source} and {target}\" + stack_position)\r\nValueError: Keys mismatch: between {'indices': Value(dtype='uint64', id=None)} and {'file': Value(dtype='string', id=None), 'text': Value(dtype='string', id=None), 'speaker_id': Value(dtype='int64', id=None), 'chapter_id': Value(dtype='int64', id=None), 'id': Value(dtype='string', id=None)}\r\n\r\nProcess finished with exit code 1\r\n\r\n```\r\n\r\n## Environment info\r\n- `datasets` version: 1.12.1\r\n- Platform: Linux-5.11.0-34-generic-x86_64-with-glibc2.17\r\n- Python version: 3.8.10\r\n- PyArrow version: 5.0.0\r\n\nFixed by #2947.","embeddings":[-0.3049958348,0.1243880838,-0.0465583764,0.2368971109,0.1517290324,-0.0666598156,-0.0069413707,0.3285393715,0.1737383604,0.0293814819,-0.2313861847,0.3581927419,-0.1503304988,0.2527370155,-0.2977862358,-0.1378120482,0.0444693454,0.0321247876,-0.0614953265,0.166412577,-0.1239175051,0.4988580644,-0.1957765073,0.0070795678,0.2221652865,0.0513210297,0.1091236398,0.138566196,0.0798620358,-0.3392693698,0.5981172919,0.1064069718,-0.0348691642,0.498096019,-0.0001213098,0.099867776,0.4398066998,-0.1073976383,-0.4579497874,-0.3686879575,-0.192854017,0.1319934726,0.2131137848,0.0811629742,-0.0715122968,0.1143416911,-0.303493619,-0.346046567,0.2488765419,0.1989328116,0.2267285585,0.2136144787,0.2657418847,0.066587165,0.170179069,0.0826525018,0.0259718653,-0.0490864292,0.2940285802,0.014261202,0.043685697,0.4806104004,-0.3283523321,-0.2146463245,0.3046185374,-0.2982533276,0.1030709222,-0.3957037926,0.2999309897,0.0781370997,0.1462642252,-0.255518198,-0.5773367882,-0.3483263254,-0.5121472478,-0.2868291736,0.4164735079,-0.216358915,-0.2009968162,0.2200830877,-0.3342647851,-0.0270038545,0.0934983194,0.0535101071,-0.0584819205,0.2941842079,-0.1318320185,-0.0048626242,0.0149830757,-0.132900551,0.1924084276,-0.2750048339,-0.1968749017,0.1725800484,-0.0155595224,-0.0546131916,0.0954222009,0.3323281705,0.140570581,0.0356287211,0.0192098524,0.0845401734,-0.0409641638,0.0536773168,0.1373275816,0.475378722,0.4901195765,0.2467906624,0.2727875113,0.2854887545,-0.0078853024,-0.0013800253,0.5281852484,-0.1666412055,0.0747071281,0.1517307162,0.196695298,-0.4048683643,-0.3602132797,0.1689626127,-0.0769995078,-0.1250401586,0.2207264006,0.0534940958,0.1005648673,0.2142349631,0.1320811808,0.0338568501,-0.1964725256,0.0612344779,-0.1725096703,-0.1739457697,0.0011616903,-0.1112991124,-0.0473052748,-0.89977777,0.1747701168,0.0482602492,0.0063488153,0.1058098748,-0.0697732121,0.207434997,0.1730166823,0.5229284167,-0.2213587761,0.0839003325,0.1671907902,-0.548327744,-0.1386865377,0.0125284484,-0.2415378094,-0.1112066656,0.2085879147,0.1735862345,-0.3296715915,0.0126515068,-0.17029953,0.4515431225,0.2592140436,-0.5466870666,-0.0779574588,-0.213380143,-0.4382343888,-0.1997140199,0.0971902013,0.4131370485,-0.5942543149,-0.3216674924,0.0216870401,0.0319067985,0.0399160981,-0.1229341999,-0.063439101,-0.2444722205,-0.1077689156,-0.1384182572,0.3606542647,-0.3195546865,-0.733892858,0.1537256837,0.2360213548,0.5342661738,-0.0709591061,-0.1374839246,0.2813950479,-0.2578290701,0.1459920853,0.0848445743,-0.1649033576,-0.0079940017,-0.3409692049,-0.2346946597,0.2238697708,-0.1502910852,0.0904174298,0.3348585963,0.0790057704,-0.2542206049,0.0502917804,0.0350500681,0.1049243882,0.1990925223,0.2453232259,0.2237652242,0.2863062918,-0.4365186989,-0.2801407278,0.2506181598,0.2141302824,-0.0600011088,-0.1524066925,-0.1982273012,-0.2678262889,-0.0884182155,-0.1589775681,-0.3044096231,0.0788738504,0.0225765873,0.3843636513,-0.0385034606,-0.1145259663,0.6218649745,0.3084535897,0.0944804996,-0.5049411654,0.229641512,-0.0548514128,0.0051691518,-0.0591857955,0.0377827175,0.2939595878,-0.0685256049,-0.2895757854,0.407875061,0.0392592028,0.084168233,-0.0276337974,0.1937460154,0.1773459613,0.2105839252,0.0639338642,0.2741822898,-0.0148837138,0.2087214142,-0.195456177,0.557248354,0.0293753464,0.3240505159,0.1208347678,0.148756206,0.3702490628,0.1305633932,-0.2189950645,-0.1370786875,0.3204495311,-0.4587228,0.0626674443,0.0123394234,-0.1297932267,0.3776263595,0.0716826022,0.0755119473,-0.0334524736,0.3119212389,-0.1593657285,-0.042190969,0.1813624054,0.4024861157,0.4014509916,0.2088322341,0.0507172868,0.0758817792,-0.1615392268,-0.0248774402,0.2082498521,0.1293462515,-0.1696399748,0.1535750628,0.3034210205,0.2732039392,-0.3061974645,0.0933934823,-0.2356239706,0.1195331365,-0.270581305,0.3616366684,-0.2810101509,-0.2280866951,0.076141715,-0.1678324938,-0.2615140676,-0.3875222206,0.089899376,0.3934392929,-0.086219281,0.2567750216,0.0147161586,0.2445412278,0.136167407,-0.1996820718,-0.4005717337,-0.2078815252,-0.3476482034,0.0053976052,0.2051323503,-0.1568491161,0.2970358729,-0.0763099566,-0.2066247612,-0.390294075,-0.9079098701,-0.0486286655,-0.123964861,0.6180298328,0.3176228106,-0.0916265026,-0.1352719367,-0.2327560335,0.1665794849,-0.0413150303,-0.4310828745,0.1568385363,0.0666227341,0.1682221591,-0.2003851682,-0.2270279974,-0.1135412008,-0.1458190233,0.0079398574,-0.1276904047,0.0849654675,0.3094039559,0.1123534068,-0.3165157139,-0.0872011557,0.1330857426,-0.3335401416,-0.0773374513,0.0739419162,-0.054162316,-0.2302434891,-0.034139622,-0.0319806486,0.0831893757,0.3736632466,-0.4069587886,-0.3791774213,-0.2713731527,-0.0048744264,0.2311773747,0.068360962,0.2319667786,0.1899949759,0.0391035639,-0.2794680893,-0.4342857301,-0.0886206329,-0.2365958691,0.1095489264,0.1482250243,0.6309939027,0.0345636867,0.4764270186,0.2947264612,0.0489611253,0.1398259848,0.1815548688,0.4972691238,-0.3258462548,-0.4095653892,-0.0467088632,-0.0461938493,0.0505124666,-0.15319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{"html_url":"https:\/\/github.com\/huggingface\/datasets\/issues\/2941","title":"OSCAR unshuffled_original_ko: NonMatchingSplitsSizesError","comments":"I tried `unshuffled_original_da` and it is also not working","body":"## Describe the bug\r\n\r\nCannot download OSCAR `unshuffled_original_ko` due to `NonMatchingSplitsSizesError`.\r\n\r\n## Steps to reproduce the bug\r\n\r\n```python\r\n>>> dataset = datasets.load_dataset('oscar', 'unshuffled_original_ko')\r\nNonMatchingSplitsSizesError: [{'expected': SplitInfo(name='train', num_bytes=25292102197, num_examples=7345075, dataset_name='oscar'), 'recorded': SplitInfo(name='train', num_bytes=25284578514, num_examples=7344907, dataset_name='oscar')}]\r\n```\r\n\r\n## Expected results\r\n\r\nLoading is successful.\r\n\r\n## Actual results\r\n\r\nLoading throws above error.\r\n\r\n## Environment info\r\n\r\n- `datasets` version: 1.12.1\r\n- Platform: Linux-5.4.0-81-generic-x86_64-with-glibc2.29\r\n- Python version: 3.8.10\r\n- PyArrow version: 5.0.0\r\n","comment_length":9,"text":"OSCAR unshuffled_original_ko: NonMatchingSplitsSizesError\n## Describe the bug\r\n\r\nCannot download OSCAR `unshuffled_original_ko` due to `NonMatchingSplitsSizesError`.\r\n\r\n## Steps to reproduce the bug\r\n\r\n```python\r\n>>> dataset = datasets.load_dataset('oscar', 'unshuffled_original_ko')\r\nNonMatchingSplitsSizesError: [{'expected': SplitInfo(name='train', num_bytes=25292102197, num_examples=7345075, dataset_name='oscar'), 'recorded': SplitInfo(name='train', num_bytes=25284578514, num_examples=7344907, dataset_name='oscar')}]\r\n```\r\n\r\n## Expected results\r\n\r\nLoading is successful.\r\n\r\n## Actual results\r\n\r\nLoading throws above error.\r\n\r\n## Environment info\r\n\r\n- `datasets` version: 1.12.1\r\n- Platform: Linux-5.4.0-81-generic-x86_64-with-glibc2.29\r\n- Python version: 3.8.10\r\n- PyArrow version: 5.0.0\r\n\nI tried `unshuffled_original_da` and it is also not 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{"html_url":"https:\/\/github.com\/huggingface\/datasets\/issues\/2937","title":"load_dataset using default cache on Windows causes PermissionError: [WinError 5] Access is denied","comments":"Hi @daqieq, thanks for reporting.\r\n\r\nUnfortunately, I was not able to reproduce this bug:\r\n```ipython\r\nIn [1]: from datasets import load_dataset\r\n ...: ds = load_dataset('wiki_bio')\r\nDownloading: 7.58kB [00:00, 26.3kB\/s]\r\nDownloading: 2.71kB [00:00, ?B\/s]\r\nUsing custom data configuration default\r\nDownloading and preparing dataset wiki_bio\/default (download: 318.53 MiB, generated: 736.94 MiB, post-processed: Unknown size, total: 1.03 GiB) to C:\\Users\\username\\.cache\\huggingface\\datasets\\wiki_bio\\default\\\r\n1.1.0\\5293ce565954ba965dada626f1e79684e98172d950371d266bf3caaf87e911c9...\r\nDownloading: 334MB [01:17, 4.32MB\/s]\r\nDataset wiki_bio downloaded and prepared to C:\\Users\\username\\.cache\\huggingface\\datasets\\wiki_bio\\default\\1.1.0\\5293ce565954ba965dada626f1e79684e98172d950371d266bf3caaf87e911c9. Subsequent calls will reuse thi\r\ns data.\r\n```\r\n\r\nThis kind of error messages usually happen because:\r\n- Your running Python script hasn't write access to that directory\r\n- You have another program (the File Explorer?) already browsing inside that directory","body":"## Describe the bug\r\nStandard process to download and load the wiki_bio dataset causes PermissionError in Windows 10 and 11.\r\n\r\n## Steps to reproduce the bug\r\n```python\r\nfrom datasets import load_dataset\r\nds = load_dataset('wiki_bio')\r\n```\r\n\r\n## Expected results\r\nIt is expected that the dataset downloads without any errors.\r\n\r\n## Actual results\r\nPermissionError see trace below:\r\n```\r\nUsing custom data configuration default\r\nDownloading and preparing dataset wiki_bio\/default (download: 318.53 MiB, generated: 736.94 MiB, post-processed: Unknown size, total: 1.03 GiB) to C:\\Users\\username\\.cache\\huggingface\\datasets\\wiki_bio\\default\\1.1.0\\5293ce565954ba965dada626f1e79684e98172d950371d266bf3caaf87e911c9...\r\nTraceback (most recent call last):\r\n File \"\", line 1, in \r\n File \"C:\\Users\\username\\.conda\\envs\\hf\\lib\\site-packages\\datasets\\load.py\", line 1112, in load_dataset\r\n builder_instance.download_and_prepare(\r\n File \"C:\\Users\\username\\.conda\\envs\\hf\\lib\\site-packages\\datasets\\builder.py\", line 644, in download_and_prepare\r\n self._save_info()\r\n File \"C:\\Users\\username\\.conda\\envs\\hf\\lib\\contextlib.py\", line 120, in __exit__\r\n next(self.gen)\r\n File \"C:\\Users\\username\\.conda\\envs\\hf\\lib\\site-packages\\datasets\\builder.py\", line 598, in incomplete_dir\r\n os.rename(tmp_dir, dirname)\r\nPermissionError: [WinError 5] Access is denied: 'C:\\\\Users\\\\username\\\\.cache\\\\huggingface\\\\datasets\\\\wiki_bio\\\\default\\\\1.1.0\\\\5293ce565954ba965dada626f1e79684e98172d950371d266bf3caaf87e911c9.incomplete' -> 'C:\\\\Users\\\\username\\\\.cache\\\\huggingface\\\\datasets\\\\wiki_bio\\\\default\\\\1.1.0\\\\5293ce565954ba965dada626f1e79684e98172d950371d266bf3caaf87e911c9'\r\n```\r\nBy commenting out the os.rename() [L604](https:\/\/github.com\/huggingface\/datasets\/blob\/master\/src\/datasets\/builder.py#L604) and the shutil.rmtree() [L607](https:\/\/github.com\/huggingface\/datasets\/blob\/master\/src\/datasets\/builder.py#L607) lines, in my virtual environment, I was able to get the load process to complete, rename the directory manually and then rerun the `load_dataset('wiki_bio')` to get what I needed.\r\n\r\nIt seems that os.rename() in the `incomplete_dir` content manager is the culprit. Here's another project [Conan](https:\/\/github.com\/conan-io\/conan\/issues\/6560) with similar issue with os.rename() if it helps debug this issue.\r\n\r\n## Environment info\r\n- `datasets` version: 1.12.1\r\n- Platform: Windows-10-10.0.22449-SP0\r\n- Python version: 3.8.12\r\n- PyArrow version: 5.0.0\r\n","comment_length":109,"text":"load_dataset using default cache on Windows causes PermissionError: [WinError 5] Access is denied\n## Describe the bug\r\nStandard process to download and load the wiki_bio dataset causes PermissionError in Windows 10 and 11.\r\n\r\n## Steps to reproduce the bug\r\n```python\r\nfrom datasets import load_dataset\r\nds = load_dataset('wiki_bio')\r\n```\r\n\r\n## Expected results\r\nIt is expected that the dataset downloads without any errors.\r\n\r\n## Actual results\r\nPermissionError see trace below:\r\n```\r\nUsing custom data configuration default\r\nDownloading and preparing dataset wiki_bio\/default (download: 318.53 MiB, generated: 736.94 MiB, post-processed: Unknown size, total: 1.03 GiB) to C:\\Users\\username\\.cache\\huggingface\\datasets\\wiki_bio\\default\\1.1.0\\5293ce565954ba965dada626f1e79684e98172d950371d266bf3caaf87e911c9...\r\nTraceback (most recent call last):\r\n File \"\", line 1, in \r\n File \"C:\\Users\\username\\.conda\\envs\\hf\\lib\\site-packages\\datasets\\load.py\", line 1112, in load_dataset\r\n builder_instance.download_and_prepare(\r\n File \"C:\\Users\\username\\.conda\\envs\\hf\\lib\\site-packages\\datasets\\builder.py\", line 644, in download_and_prepare\r\n self._save_info()\r\n File \"C:\\Users\\username\\.conda\\envs\\hf\\lib\\contextlib.py\", line 120, in __exit__\r\n next(self.gen)\r\n File \"C:\\Users\\username\\.conda\\envs\\hf\\lib\\site-packages\\datasets\\builder.py\", line 598, in incomplete_dir\r\n os.rename(tmp_dir, dirname)\r\nPermissionError: [WinError 5] Access is denied: 'C:\\\\Users\\\\username\\\\.cache\\\\huggingface\\\\datasets\\\\wiki_bio\\\\default\\\\1.1.0\\\\5293ce565954ba965dada626f1e79684e98172d950371d266bf3caaf87e911c9.incomplete' -> 'C:\\\\Users\\\\username\\\\.cache\\\\huggingface\\\\datasets\\\\wiki_bio\\\\default\\\\1.1.0\\\\5293ce565954ba965dada626f1e79684e98172d950371d266bf3caaf87e911c9'\r\n```\r\nBy commenting out the os.rename() [L604](https:\/\/github.com\/huggingface\/datasets\/blob\/master\/src\/datasets\/builder.py#L604) and the shutil.rmtree() [L607](https:\/\/github.com\/huggingface\/datasets\/blob\/master\/src\/datasets\/builder.py#L607) lines, in my virtual environment, I was able to get the load process to complete, rename the directory manually and then rerun the `load_dataset('wiki_bio')` to get what I needed.\r\n\r\nIt seems that os.rename() in the `incomplete_dir` content manager is the culprit. Here's another project [Conan](https:\/\/github.com\/conan-io\/conan\/issues\/6560) with similar issue with os.rename() if it helps debug this issue.\r\n\r\n## Environment info\r\n- `datasets` version: 1.12.1\r\n- Platform: Windows-10-10.0.22449-SP0\r\n- Python version: 3.8.12\r\n- PyArrow version: 5.0.0\r\n\nHi @daqieq, thanks for reporting.\r\n\r\nUnfortunately, I was not able to reproduce this bug:\r\n```ipython\r\nIn [1]: from datasets import load_dataset\r\n ...: ds = load_dataset('wiki_bio')\r\nDownloading: 7.58kB [00:00, 26.3kB\/s]\r\nDownloading: 2.71kB [00:00, ?B\/s]\r\nUsing custom data configuration default\r\nDownloading and preparing dataset wiki_bio\/default (download: 318.53 MiB, generated: 736.94 MiB, post-processed: Unknown size, total: 1.03 GiB) to C:\\Users\\username\\.cache\\huggingface\\datasets\\wiki_bio\\default\\\r\n1.1.0\\5293ce565954ba965dada626f1e79684e98172d950371d266bf3caaf87e911c9...\r\nDownloading: 334MB [01:17, 4.32MB\/s]\r\nDataset wiki_bio downloaded and prepared to C:\\Users\\username\\.cache\\huggingface\\datasets\\wiki_bio\\default\\1.1.0\\5293ce565954ba965dada626f1e79684e98172d950371d266bf3caaf87e911c9. Subsequent calls will reuse thi\r\ns data.\r\n```\r\n\r\nThis kind of error messages usually happen because:\r\n- Your running Python script hasn't write access to that directory\r\n- You have another program (the File Explorer?) already browsing inside that 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{"html_url":"https:\/\/github.com\/huggingface\/datasets\/issues\/2937","title":"load_dataset using default cache on Windows causes PermissionError: [WinError 5] Access is denied","comments":"Thanks @albertvillanova for looking at it! I tried on my personal Windows machine and it downloaded just fine.\r\n\r\nRunning on my work machine and on a colleague's machine it is consistently hitting this error. It's not a write access issue because the `.incomplete` directory is written just fine. It just won't rename and then it deletes the directory in the `finally` step. Also the zip file is written and extracted fine in the downloads directory.\r\n\r\nThat leaves another program that might be interfering, and there are plenty of those in my work machine ... (full antivirus, data loss prevention, etc.). So the question remains, why not extend the `try` block to allow catching the error and circle back to the rename after the unknown program is finished doing its 'stuff'. This is the approach that I read about in the linked repo (see my comments above).\r\n\r\nIf it's not high priority, that's fine. However, if someone were to write an PR that solved this issue in our environment in an `except` clause, would it be reviewed for inclusion in a future release? Just wondering whether I should spend any more time on this issue.","body":"## Describe the bug\r\nStandard process to download and load the wiki_bio dataset causes PermissionError in Windows 10 and 11.\r\n\r\n## Steps to reproduce the bug\r\n```python\r\nfrom datasets import load_dataset\r\nds = load_dataset('wiki_bio')\r\n```\r\n\r\n## Expected results\r\nIt is expected that the dataset downloads without any errors.\r\n\r\n## Actual results\r\nPermissionError see trace below:\r\n```\r\nUsing custom data configuration default\r\nDownloading and preparing dataset wiki_bio\/default (download: 318.53 MiB, generated: 736.94 MiB, post-processed: Unknown size, total: 1.03 GiB) to C:\\Users\\username\\.cache\\huggingface\\datasets\\wiki_bio\\default\\1.1.0\\5293ce565954ba965dada626f1e79684e98172d950371d266bf3caaf87e911c9...\r\nTraceback (most recent call last):\r\n File \"\", line 1, in \r\n File \"C:\\Users\\username\\.conda\\envs\\hf\\lib\\site-packages\\datasets\\load.py\", line 1112, in load_dataset\r\n builder_instance.download_and_prepare(\r\n File \"C:\\Users\\username\\.conda\\envs\\hf\\lib\\site-packages\\datasets\\builder.py\", line 644, in download_and_prepare\r\n self._save_info()\r\n File \"C:\\Users\\username\\.conda\\envs\\hf\\lib\\contextlib.py\", line 120, in __exit__\r\n next(self.gen)\r\n File \"C:\\Users\\username\\.conda\\envs\\hf\\lib\\site-packages\\datasets\\builder.py\", line 598, in incomplete_dir\r\n os.rename(tmp_dir, dirname)\r\nPermissionError: [WinError 5] Access is denied: 'C:\\\\Users\\\\username\\\\.cache\\\\huggingface\\\\datasets\\\\wiki_bio\\\\default\\\\1.1.0\\\\5293ce565954ba965dada626f1e79684e98172d950371d266bf3caaf87e911c9.incomplete' -> 'C:\\\\Users\\\\username\\\\.cache\\\\huggingface\\\\datasets\\\\wiki_bio\\\\default\\\\1.1.0\\\\5293ce565954ba965dada626f1e79684e98172d950371d266bf3caaf87e911c9'\r\n```\r\nBy commenting out the os.rename() [L604](https:\/\/github.com\/huggingface\/datasets\/blob\/master\/src\/datasets\/builder.py#L604) and the shutil.rmtree() [L607](https:\/\/github.com\/huggingface\/datasets\/blob\/master\/src\/datasets\/builder.py#L607) lines, in my virtual environment, I was able to get the load process to complete, rename the directory manually and then rerun the `load_dataset('wiki_bio')` to get what I needed.\r\n\r\nIt seems that os.rename() in the `incomplete_dir` content manager is the culprit. Here's another project [Conan](https:\/\/github.com\/conan-io\/conan\/issues\/6560) with similar issue with os.rename() if it helps debug this issue.\r\n\r\n## Environment info\r\n- `datasets` version: 1.12.1\r\n- Platform: Windows-10-10.0.22449-SP0\r\n- Python version: 3.8.12\r\n- PyArrow version: 5.0.0\r\n","comment_length":194,"text":"load_dataset using default cache on Windows causes PermissionError: [WinError 5] Access is denied\n## Describe the bug\r\nStandard process to download and load the wiki_bio dataset causes PermissionError in Windows 10 and 11.\r\n\r\n## Steps to reproduce the bug\r\n```python\r\nfrom datasets import load_dataset\r\nds = load_dataset('wiki_bio')\r\n```\r\n\r\n## Expected results\r\nIt is expected that the dataset downloads without any errors.\r\n\r\n## Actual results\r\nPermissionError see trace below:\r\n```\r\nUsing custom data configuration default\r\nDownloading and preparing dataset wiki_bio\/default (download: 318.53 MiB, generated: 736.94 MiB, post-processed: Unknown size, total: 1.03 GiB) to C:\\Users\\username\\.cache\\huggingface\\datasets\\wiki_bio\\default\\1.1.0\\5293ce565954ba965dada626f1e79684e98172d950371d266bf3caaf87e911c9...\r\nTraceback (most recent call last):\r\n File \"\", line 1, in \r\n File \"C:\\Users\\username\\.conda\\envs\\hf\\lib\\site-packages\\datasets\\load.py\", line 1112, in load_dataset\r\n builder_instance.download_and_prepare(\r\n File \"C:\\Users\\username\\.conda\\envs\\hf\\lib\\site-packages\\datasets\\builder.py\", line 644, in download_and_prepare\r\n self._save_info()\r\n File \"C:\\Users\\username\\.conda\\envs\\hf\\lib\\contextlib.py\", line 120, in __exit__\r\n next(self.gen)\r\n File \"C:\\Users\\username\\.conda\\envs\\hf\\lib\\site-packages\\datasets\\builder.py\", line 598, in incomplete_dir\r\n os.rename(tmp_dir, dirname)\r\nPermissionError: [WinError 5] Access is denied: 'C:\\\\Users\\\\username\\\\.cache\\\\huggingface\\\\datasets\\\\wiki_bio\\\\default\\\\1.1.0\\\\5293ce565954ba965dada626f1e79684e98172d950371d266bf3caaf87e911c9.incomplete' -> 'C:\\\\Users\\\\username\\\\.cache\\\\huggingface\\\\datasets\\\\wiki_bio\\\\default\\\\1.1.0\\\\5293ce565954ba965dada626f1e79684e98172d950371d266bf3caaf87e911c9'\r\n```\r\nBy commenting out the os.rename() [L604](https:\/\/github.com\/huggingface\/datasets\/blob\/master\/src\/datasets\/builder.py#L604) and the shutil.rmtree() [L607](https:\/\/github.com\/huggingface\/datasets\/blob\/master\/src\/datasets\/builder.py#L607) lines, in my virtual environment, I was able to get the load process to complete, rename the directory manually and then rerun the `load_dataset('wiki_bio')` to get what I needed.\r\n\r\nIt seems that os.rename() in the `incomplete_dir` content manager is the culprit. Here's another project [Conan](https:\/\/github.com\/conan-io\/conan\/issues\/6560) with similar issue with os.rename() if it helps debug this issue.\r\n\r\n## Environment info\r\n- `datasets` version: 1.12.1\r\n- Platform: Windows-10-10.0.22449-SP0\r\n- Python version: 3.8.12\r\n- PyArrow version: 5.0.0\r\n\nThanks @albertvillanova for looking at it! I tried on my personal Windows machine and it downloaded just fine.\r\n\r\nRunning on my work machine and on a colleague's machine it is consistently hitting this error. It's not a write access issue because the `.incomplete` directory is written just fine. It just won't rename and then it deletes the directory in the `finally` step. Also the zip file is written and extracted fine in the downloads directory.\r\n\r\nThat leaves another program that might be interfering, and there are plenty of those in my work machine ... (full antivirus, data loss prevention, etc.). So the question remains, why not extend the `try` block to allow catching the error and circle back to the rename after the unknown program is finished doing its 'stuff'. This is the approach that I read about in the linked repo (see my comments above).\r\n\r\nIf it's not high priority, that's fine. However, if someone were to write an PR that solved this issue in our environment in an `except` clause, would it be reviewed for inclusion in a future release? Just wondering whether I should spend any more time on this 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{"html_url":"https:\/\/github.com\/huggingface\/datasets\/issues\/2934","title":"to_tf_dataset keeps a reference to the open data somewhere, causing issues on windows","comments":"I did some investigation and, as it seems, the bug stems from [this line](https:\/\/github.com\/huggingface\/datasets\/blob\/8004d7c3e1d74b29c3e5b0d1660331cd26758363\/src\/datasets\/arrow_dataset.py#L325). The lifecycle of the dataset from the linked line is bound to one of the returned `tf.data.Dataset`. So my (hacky) solution involves wrapping the linked dataset with `weakref.proxy` and adding a custom `__del__` to `tf.python.data.ops.dataset_ops.TensorSliceDataset` (this is the type of a dataset that is returned by `tf.data.Dataset.from_tensor_slices`; this works for TF 2.x, but I'm not sure `tf.python.data.ops.dataset_ops` is a valid path for TF 1.x) that deletes the linked dataset, which is assigned to the dataset object as a property. Will open a draft PR soon!","body":"To reproduce:\r\n```python\r\nimport datasets as ds\r\nimport weakref\r\nimport gc\r\n\r\nd = ds.load_dataset(\"mnist\", split=\"train\")\r\nref = weakref.ref(d._data.table)\r\ntfd = d.to_tf_dataset(\"image\", batch_size=1, shuffle=False, label_cols=\"label\")\r\ndel tfd, d\r\ngc.collect()\r\nassert ref() is None, \"Error: there is at least one reference left\"\r\n```\r\n\r\nThis causes issues because the table holds a reference to an open arrow file that should be closed. So on windows it's not possible to delete or move the arrow file afterwards.\r\n\r\nMoreover the CI test of the `to_tf_dataset` method isn't able to clean up the temporary arrow files because of this.\r\n\r\ncc @Rocketknight1 ","comment_length":99,"text":"to_tf_dataset keeps a reference to the open data somewhere, causing issues on windows\nTo reproduce:\r\n```python\r\nimport datasets as ds\r\nimport weakref\r\nimport gc\r\n\r\nd = ds.load_dataset(\"mnist\", split=\"train\")\r\nref = weakref.ref(d._data.table)\r\ntfd = d.to_tf_dataset(\"image\", batch_size=1, shuffle=False, label_cols=\"label\")\r\ndel tfd, d\r\ngc.collect()\r\nassert ref() is None, \"Error: there is at least one reference left\"\r\n```\r\n\r\nThis causes issues because the table holds a reference to an open arrow file that should be closed. So on windows it's not possible to delete or move the arrow file afterwards.\r\n\r\nMoreover the CI test of the `to_tf_dataset` method isn't able to clean up the temporary arrow files because of this.\r\n\r\ncc @Rocketknight1 \nI did some investigation and, as it seems, the bug stems from [this line](https:\/\/github.com\/huggingface\/datasets\/blob\/8004d7c3e1d74b29c3e5b0d1660331cd26758363\/src\/datasets\/arrow_dataset.py#L325). The lifecycle of the dataset from the linked line is bound to one of the returned `tf.data.Dataset`. So my (hacky) solution involves wrapping the linked dataset with `weakref.proxy` and adding a custom `__del__` to `tf.python.data.ops.dataset_ops.TensorSliceDataset` (this is the type of a dataset that is returned by `tf.data.Dataset.from_tensor_slices`; this works for TF 2.x, but I'm not sure `tf.python.data.ops.dataset_ops` is a valid path for TF 1.x) that deletes the linked dataset, which is assigned to the dataset object as a property. Will open a draft PR soon!","embeddings":[0.0456905663,0.334508419,0.1148890257,0.080451034,0.2304305583,0.118887417,0.3988847136,0.2548755109,-0.1099170819,0.2674386799,-0.3353850842,0.4040720165,-0.1644811481,-0.1074629501,-0.0342661999,-0.1066431478,0.0511111543,0.1451645941,-0.1666638404,-0.1226122081,-0.2103365809,0.0351907536,-0.1274755448,0.0721159056,-0.1387630254,-0.2811448276,0.0105889933,0.1006439403,0.2441080213,-0.0183550231,0.0682790056,0.0211760364,-0.177796185,0.4797623456,-0.0001166652,0.4142073989,-0.0061280271,0.1301537901,-0.235580802,0.0565112084,-0.0690313205,0.1760492623,0.1245584637,-0.0599729307,0.1349684894,-0.2177767009,-0.0255724229,0.0832738504,0.2824953496,0.4290578365,0.1738771498,0.6678111553,0.049049627,-0.0070738695,0.3827846944,0.0638971701,-0.2742590308,0.1161701158,-0.2370098084,-0.1935149431,0.0655605793,0.5781883597,0.0173822027,-0.0747985765,-0.1346866488,0.0793781951,-0.2474848479,-0.2132097334,0.099155657,0.3864619732,0.2942140996,-0.3177287579,-0.0704426542,-0.1286842972,-0.0647842139,-0.1454346925,0.1913480461,-0.046207156,0.017386917,0.1348499954,0.0582152456,-0.1622668952,-0.2615066469,-0.0061726281,-0.2810887992,-0.1072200239,0.0857438222,0.0801384374,0.414106369,-0.0271513741,-0.068637602,0.0416288003,0.0201638471,0.1307915747,-0.2023018003,-0.1820205152,-0.039258711,-0.1770301908,-0.0281791855,-0.0708624199,0.2392520756,-0.1570445448,-0.2443445474,0.0677398071,0.1644194871,-0.0705889463,-0.6655332446,0.1451997906,0.2680508792,-0.1933338344,-0.1119194329,-0.0286712628,0.0027096812,-0.4981434345,0.2861165106,-0.2107922286,0.4638061523,0.0174133722,-0.5834477544,0.1703355759,-0.1943601817,0.4077934325,-0.0392662771,0.3421227634,-0.0097245919,0.2793687582,0.3236465752,-0.0036117642,-0.4970700741,-0.0162012,-0.0546676517,-0.0396631137,-0.2995604873,-0.3319226205,0.1675927192,0.0439531542,-0.0478496067,0.1728075743,0.0610827021,0.1025774032,0.2539456487,-0.2808155119,0.5246316791,0.4812721908,-0.0750983432,0.2450949699,0.094958134,-0.2507022023,-0.1292222887,0.3534173071,-0.271070987,-0.1585650742,-0.2603646219,0.1245645955,-0.1202328429,-0.1552468389,-0.2238021493,0.0450044461,0.0343124531,-0.0595466755,0.0862919539,-0.1370920688,-0.5523484945,-0.2646558583,0.0109041464,0.3650912344,-0.1991525888,0.1795514375,-0.0254384503,-0.3931910396,-0.1594114155,0.2656008899,0.0646049082,0.315382421,-0.4097916186,-0.1304864734,0.4838194549,-0.2320678681,-0.4512842894,0.2350057364,-0.2089946121,0.2142525762,-0.3518173099,0.1303172708,0.2125170231,-0.2066658884,0.0123629132,-0.0070450343,-0.0623103678,-0.009127249,-0.3236092329,-0.0108151864,-0.0680190474,-0.1952706128,-0.0157284178,-0.0124156829,0.1318649352,-0.0493954197,0.3902634382,0.2196914554,0.1933261156,0.1417482793,0.3756607473,-0.1729133576,-0.0790883675,0.0432887785,-0.3775175512,0.1395243853,0.4056796134,0.0777031407,-0.1249045953,-0.1479362994,-0.0149693638,0.1985371262,-0.1919496357,0.0200802218,0.0784440786,-0.1194245666,-0.3427836597,0.2097568363,-0.0087737013,-0.1780350357,-0.2768910527,0.0820086896,0.1028026193,0.4687242806,0.2687642872,-0.0055732708,-0.2448134571,0.1943388134,-0.1494650543,-0.0300622191,-0.2719019651,0.4269395471,0.1747722477,0.3178130984,-0.1541973948,0.5814441442,0.1066848487,-0.5981359482,-0.2948667407,0.345680505,0.1319477111,-0.028623648,-0.0179552659,0.1456983984,-0.2007223219,0.171325475,0.0537690409,0.2392641008,0.0408273004,0.0214642044,-0.0363970362,-0.3410162628,-0.048381418,0.3614164293,-0.3146298528,0.2986148596,-0.6084029675,0.1965304911,0.3866648376,0.0311069563,-0.1689249873,0.2358304709,-0.1694948971,0.0617295206,0.0715380758,0.3181353509,0.476054281,0.0223621521,0.1727905273,0.1367983967,-0.193739742,-0.0902521908,0.2373642176,0.0887134001,0.3124321103,0.2039208263,0.25704512,0.193103686,-0.3024033606,0.0330183432,0.130203411,0.1728958189,-0.4525787234,0.1747216135,-0.1807385981,-0.0388464183,-0.307489574,0.0117461048,0.0377215482,-0.134800002,0.3154597282,0.3967652917,-0.4561233222,0.153922379,-0.3289419115,0.1290333718,0.0227688793,-0.2959315479,0.1803024411,-0.169179216,-0.2714836895,0.0344429053,0.3643865883,-0.24427782,0.4180335999,-0.0013580687,-0.0747494251,-0.1528537571,-0.2631629407,0.0897795483,-0.0949533954,0.1932279766,0.2937165499,0.3368107378,-0.1232672036,-0.1301765293,0.1462340504,-0.4851278663,-0.1786133051,0.1372669786,-0.1227175817,0.209010601,0.0203978065,-0.1520884037,0.0741765723,-0.3432299793,0.0670822263,-0.191379711,0.127728045,0.6203324199,0.1520412415,-0.0800016448,0.3789710402,0.10767968,-0.1736004055,-0.1127346307,0.200914368,-0.2037230134,-0.4296337366,0.2909044921,0.0338889062,0.1325789392,0.295558393,-0.6434158087,-0.1689423323,-0.0437356085,0.1776636243,-0.1088872477,-0.0470069051,0.1665738225,0.1164368093,-0.1197147071,-0.3432469964,0.0827863663,0.3821257651,-0.227080822,-0.0485802852,0.0422705784,0.0882449597,0.313570559,0.6243577003,0.0508327,-0.3429434299,0.0208036136,-0.2982521057,0.5073607564,-0.159090668,-0.2795748413,0.0158007275,0.0420313291,-0.13273637,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{"html_url":"https:\/\/github.com\/huggingface\/datasets\/issues\/2934","title":"to_tf_dataset keeps a reference to the open data somewhere, causing issues on windows","comments":"Thanks a lot for investigating !","body":"To reproduce:\r\n```python\r\nimport datasets as ds\r\nimport weakref\r\nimport gc\r\n\r\nd = ds.load_dataset(\"mnist\", split=\"train\")\r\nref = weakref.ref(d._data.table)\r\ntfd = d.to_tf_dataset(\"image\", batch_size=1, shuffle=False, label_cols=\"label\")\r\ndel tfd, d\r\ngc.collect()\r\nassert ref() is None, \"Error: there is at least one reference left\"\r\n```\r\n\r\nThis causes issues because the table holds a reference to an open arrow file that should be closed. So on windows it's not possible to delete or move the arrow file afterwards.\r\n\r\nMoreover the CI test of the `to_tf_dataset` method isn't able to clean up the temporary arrow files because of this.\r\n\r\ncc @Rocketknight1 ","comment_length":6,"text":"to_tf_dataset keeps a reference to the open data somewhere, causing issues on windows\nTo reproduce:\r\n```python\r\nimport datasets as ds\r\nimport weakref\r\nimport gc\r\n\r\nd = ds.load_dataset(\"mnist\", split=\"train\")\r\nref = weakref.ref(d._data.table)\r\ntfd = d.to_tf_dataset(\"image\", batch_size=1, shuffle=False, label_cols=\"label\")\r\ndel tfd, d\r\ngc.collect()\r\nassert ref() is None, \"Error: there is at least one reference left\"\r\n```\r\n\r\nThis causes issues because the table holds a reference to an open arrow file that should be closed. So on windows it's not possible to delete or move the arrow file afterwards.\r\n\r\nMoreover the CI test of the `to_tf_dataset` method isn't able to clean up the temporary arrow files because of this.\r\n\r\ncc @Rocketknight1 \nThanks a lot for investigating 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{"html_url":"https:\/\/github.com\/huggingface\/datasets\/issues\/2932","title":"Conda build fails","comments":"Why 1.9 ?\r\n\r\nhttps:\/\/anaconda.org\/HuggingFace\/datasets currently says 1.11","body":"## Describe the bug\r\nCurrent `datasets` version in conda is 1.9 instead of 1.12.\r\n\r\nThe build of the conda package fails.\r\n","comment_length":7,"text":"Conda build fails\n## Describe the bug\r\nCurrent `datasets` version in conda is 1.9 instead of 1.12.\r\n\r\nThe build of the conda package fails.\r\n\nWhy 1.9 ?\r\n\r\nhttps:\/\/anaconda.org\/HuggingFace\/datasets currently says 1.11","embeddings":[-0.4641540647,0.0640663132,-0.1675162613,0.0911422074,0.0242198519,-0.1167648509,0.0046573328,0.4584467709,-0.1488352567,0.0218314379,-0.0215561539,0.1672185659,0.1742765456,0.4530319273,-0.0687930807,-0.0687004402,0.2214742899,0.1521472484,-0.3632488847,-0.0315286219,-0.2346586734,0.3556938469,-0.1418616623,0.042954348,-0.1667399108,-0.1111968309,-0.0725726113,-0.0722472295,-0.3525906503,-0.309075892,0.5409511924,-0.1080390662,0.0791708231,0.5754619241,-0.0001009286,-0.0032789898,0.5302111506,0.0530045889,-0.2712946236,-0.1803977042,-0.3502198458,-0.0796987191,-0.0399142914,0.1341502666,-0.025258461,-0.3136871755,-0.0039637419,-0.0812839568,0.2737195194,0.1549416184,0.3308033943,0.0851677209,0.4155243039,-0.4425582886,-0.4505780041,0.2404605895,-0.3100655675,0.1933912933,-0.0950218216,-0.0654576346,0.3125593662,0.2748890221,-0.0108567653,-0.2175896168,0.0571583509,-0.1458919346,-0.1065396816,-0.1608880013,0.2085220516,0.082794033,0.5673777461,-0.3365124464,-0.4459105134,0.116349265,0.0547229312,-0.1276331395,0.2863733172,-0.0994853154,-0.1852693409,0.2289404869,-0.3072545826,-0.1659559906,-0.0998794213,0.0542916507,0.0351006612,0.0843344927,-0.2238170356,0.0395075008,0.0850799978,-0.0795847178,0.0889546126,-0.017170459,-0.0044747209,-0.0164357647,-0.2207264602,-0.1931111813,-0.1908893287,0.0040363134,0.290076822,0.0416318104,-0.2814685106,-0.2031846493,0.0022204889,0.0698052868,-0.057144139,0.0626951158,0.4606170654,0.077450268,0.145462364,0.2514044344,0.1704059988,0.0070430655,0.0021299806,-0.3041796386,-0.2461816221,-0.0392220579,0.2495234162,-0.2043797076,-0.1059830338,0.1044391021,0.0609567054,-0.0279124603,0.0041456302,0.0737251118,-0.098237142,-0.1265262663,0.0580965318,0.1755417287,0.0175039303,0.0253259484,-0.2480798662,-0.19085069,-0.0307969321,0.012003894,0.2311127931,-0.3263165355,0.4701302052,-0.1246728003,0.1668923795,0.0062446366,-0.1614912301,0.078064017,-0.3607374132,0.4940434396,-0.2841105163,0.1163817868,-0.0540333688,-0.0690908656,-0.0722390711,0.009847221,-0.1026801541,-0.0875664055,-0.4648688734,0.2391197681,0.1965070069,0.1023772582,-0.1715472639,-0.0221420992,0.0536693893,-0.0455940999,-0.0935841873,0.0454386994,-0.0532059669,-0.1457587034,0.2406151742,0.2125408351,-0.1583735943,0.1107756644,0.1150244549,-0.2624725103,-0.2327995002,0.0774188638,-0.0671860948,-0.1484007537,-0.0090418523,-0.2777799666,0.1654792577,-0.4407516718,-0.5214886069,0.0708692968,0.1568902731,-0.1486879587,-0.0275132805,-0.1143989637,0.1878380924,-0.1143638939,0.1809624135,0.0786408931,0.005723747,-0.2770836055,-0.1697548479,-0.275437206,-0.241848886,0.0517572574,-0.0461218953,0.1780284196,0.042331446,-0.2044620216,0.1104750931,0.143998459,-0.2036080956,0.2029421628,0.4378283918,-0.1347674131,0.0343065746,-0.1481755525,-0.5067104101,0.0793106779,0.1355768591,-0.0216997135,-0.0685060993,-0.1599982828,-0.3334034681,0.2762608528,0.0679381713,-0.217081219,0.2146058977,-0.1150966808,0.1284432113,0.0793034956,-0.1678137481,0.7080689073,-0.137064442,0.2539079785,-0.1866996437,0.1552656889,-0.2871536613,-0.1260931939,0.2259406298,0.1750397533,0.2101011425,-0.1859097183,-0.1064059809,0.3100512624,-0.1132724211,-0.1732333302,0.1977748871,-0.2093673199,0.0850393698,-0.0069135549,0.1005597562,-0.0600860864,-0.1986969262,0.2078127116,0.0507737435,-0.0467366427,0.1806784272,-0.0241424423,-0.0142447082,0.2336301059,0.4198890924,0.0989498571,0.0189749841,-0.1595772505,0.1046580002,0.1180455238,0.2821315825,0.0236277115,-0.2964583933,0.1159171835,0.3984741867,-0.1097792536,-0.0726787671,0.2535006702,0.0919811726,0.1495138854,0.3302159607,0.2453039587,0.1037769392,0.0531402938,-0.095606029,0.2007383853,-0.3480142057,-0.0298374817,0.1060575619,0.0322836116,0.2812074125,-0.0845356435,-0.1015645266,0.0695140362,-0.0760767683,-0.0694011524,-0.1414503753,0.0929866508,0.0086003188,0.0421742573,-0.1739253849,-0.0793259069,-0.255325973,-0.2271770239,-0.2650867403,-0.020353796,-0.0007564036,0.1378406584,0.210903734,0.4877940714,0.0403436422,0.0395924002,-0.0504935272,0.0288728029,-0.1351604462,0.1316828281,-0.1524738818,0.1581934094,0.0931431428,-0.0417627729,0.1490940601,-0.4683484435,0.1915226579,-0.0488868766,-0.5337531567,0.162495628,-0.2750543058,0.4730536938,0.2408772856,0.0991982818,0.0325651355,-0.2097351402,0.1801730543,-0.1365850121,-0.1675789505,-0.219999522,-0.0309822541,-0.3386186361,-0.0958381668,-0.1521292478,-0.135665834,-0.1753454208,0.3863707185,-0.0221327562,0.0562425889,0.3409312069,0.4604199231,0.0470611565,-0.1918748617,0.0891415849,-0.1435628682,-0.4498470724,0.056229163,-0.2765717506,-0.2497362643,-0.0989951342,0.2478436381,0.4750163257,0.0358756483,-0.5661168694,-0.1986954808,-0.0168789178,0.1650635004,0.0965101942,-0.013050952,0.2417072803,0.0429633595,-0.1247905418,-0.2604943216,-0.3049151897,0.1147871763,-0.2061828226,0.3588501811,-0.0101826554,0.4184118509,-0.1961857677,0.2067244649,0.570433557,-0.193848446,0.1289104074,0.1958947629,0.646984756,-0.2325790077,-0.2919476926,0.1788204014,0.24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{"html_url":"https:\/\/github.com\/huggingface\/datasets\/issues\/2932","title":"Conda build fails","comments":"Alright I added 1.12.0 and 1.12.1 and fixed the conda build #2952 ","body":"## Describe the bug\r\nCurrent `datasets` version in conda is 1.9 instead of 1.12.\r\n\r\nThe build of the conda package fails.\r\n","comment_length":12,"text":"Conda build fails\n## Describe the bug\r\nCurrent `datasets` version in conda is 1.9 instead of 1.12.\r\n\r\nThe build of the conda package fails.\r\n\nAlright I added 1.12.0 and 1.12.1 and fixed the conda build #2952 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{"html_url":"https:\/\/github.com\/huggingface\/datasets\/issues\/2930","title":"Mutable columns argument breaks set_format","comments":"Pushed a fix to my branch #2731 ","body":"## Describe the bug\r\nIf you pass a mutable list to the `columns` argument of `set_format` and then change the list afterwards, the returned columns also change.\r\n\r\n## Steps to reproduce the bug\r\n```python\r\nfrom datasets import load_dataset\r\ndataset = load_dataset(\"glue\", \"cola\")\r\n\r\ncolumn_list = [\"idx\", \"label\"]\r\ndataset.set_format(\"python\", columns=column_list)\r\ncolumn_list[1] = \"foo\" # Change the list after we call `set_format`\r\ndataset['train'][:4].keys()\r\n```\r\n\r\n## Expected results\r\n```python\r\ndict_keys(['idx', 'label'])\r\n```\r\n\r\n## Actual results\r\n```python\r\ndict_keys(['idx'])\r\n```","comment_length":7,"text":"Mutable columns argument breaks set_format\n## Describe the bug\r\nIf you pass a mutable list to the `columns` argument of `set_format` and then change the list afterwards, the returned columns also change.\r\n\r\n## Steps to reproduce the bug\r\n```python\r\nfrom datasets import load_dataset\r\ndataset = load_dataset(\"glue\", \"cola\")\r\n\r\ncolumn_list = [\"idx\", \"label\"]\r\ndataset.set_format(\"python\", columns=column_list)\r\ncolumn_list[1] = \"foo\" # Change the list after we call `set_format`\r\ndataset['train'][:4].keys()\r\n```\r\n\r\n## Expected results\r\n```python\r\ndict_keys(['idx', 'label'])\r\n```\r\n\r\n## Actual results\r\n```python\r\ndict_keys(['idx'])\r\n```\nPushed a fix to my branch #2731 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{"html_url":"https:\/\/github.com\/huggingface\/datasets\/issues\/2927","title":"Datasets 1.12 dataset.filter TypeError: get_indices_from_mask_function() got an unexpected keyword argument","comments":"Thanks for reporting, I'm looking into it :)","body":"## Describe the bug\r\nUpgrading to 1.12 caused `dataset.filter` call to fail with \r\n\r\n> get_indices_from_mask_function() got an unexpected keyword argument valid_rel_labels\r\n\r\n\r\n## Steps to reproduce the bug\r\n```pythondef \r\n\r\nfilter_good_rows(\r\n ex: Dict,\r\n valid_rel_labels: Set[str],\r\n valid_ner_labels: Set[str],\r\n tokenizer: PreTrainedTokenizerFast,\r\n) -> bool:\r\n \"\"\"Get the good rows\"\"\"\r\n encoding = get_encoding_for_text(text=ex[\"text\"], tokenizer=tokenizer)\r\n ex[\"encoding\"] = encoding\r\n for relation in ex[\"relations\"]:\r\n if not is_valid_relation(relation, valid_rel_labels):\r\n return False\r\n for span in ex[\"spans\"]:\r\n if not is_valid_span(span, valid_ner_labels, encoding):\r\n return False\r\n return True\r\n \r\ndef get_dataset(): \r\n loader_path = str(Path(__file__).parent \/ \"prodigy_dataset_builder.py\")\r\n ds = load_dataset(\r\n loader_path,\r\n name=\"prodigy-dataset\",\r\n data_files=sorted(file_paths),\r\n cache_dir=cache_dir,\r\n )[\"train\"]\r\n\r\n valid_ner_labels = set(vocab.ner_category)\r\n valid_relations = set(vocab.relation_types.keys())\r\n ds = ds.filter(\r\n filter_good_rows,\r\n fn_kwargs=dict(\r\n valid_rel_labels=valid_relations,\r\n valid_ner_labels=valid_ner_labels,\r\n tokenizer=vocab.tokenizer,\r\n ),\r\n keep_in_memory=True,\r\n num_proc=num_proc,\r\n )\r\n\r\n```\r\n\r\n`ds` is a `DatasetDict` produced by a jsonl dataset.\r\nThis runs fine on 1.11 but fails on 1.12\r\n\r\n**Stack Trace**\r\n\r\n\r\n\r\n## Expected results\r\n\r\nI expect 1.12 datasets filter to filter the dataset without raising as it does on 1.11\r\n\r\n## Actual results\r\n```\r\ntf_ner_rel_lib\/dataset.py:695: in load_prodigy_arrow_datasets_from_jsonl\r\n ds = ds.filter(\r\n..\/..\/..\/..\/.pyenv\/versions\/tf_ner_rel_lib\/lib\/python3.8\/site-packages\/datasets\/arrow_dataset.py:185: in wrapper\r\n out: Union[\"Dataset\", \"DatasetDict\"] = func(self, *args, **kwargs)\r\n..\/..\/..\/..\/.pyenv\/versions\/tf_ner_rel_lib\/lib\/python3.8\/site-packages\/datasets\/fingerprint.py:398: in wrapper\r\n out = func(self, *args, **kwargs)\r\n..\/..\/..\/..\/.pyenv\/versions\/tf_ner_rel_lib\/lib\/python3.8\/site-packages\/datasets\/arrow_dataset.py:2169: in filter\r\n indices = self.map(\r\n..\/..\/..\/..\/.pyenv\/versions\/tf_ner_rel_lib\/lib\/python3.8\/site-packages\/datasets\/arrow_dataset.py:1686: in map\r\n return self._map_single(\r\n..\/..\/..\/..\/.pyenv\/versions\/tf_ner_rel_lib\/lib\/python3.8\/site-packages\/datasets\/arrow_dataset.py:185: in wrapper\r\n out: Union[\"Dataset\", \"DatasetDict\"] = func(self, *args, **kwargs)\r\n..\/..\/..\/..\/.pyenv\/versions\/tf_ner_rel_lib\/lib\/python3.8\/site-packages\/datasets\/fingerprint.py:398: in wrapper\r\n out = func(self, *args, **kwargs)\r\n..\/..\/..\/..\/.pyenv\/versions\/tf_ner_rel_lib\/lib\/python3.8\/site-packages\/datasets\/arrow_dataset.py:2048: in _map_single\r\n batch = apply_function_on_filtered_inputs(\r\n_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ \r\n\r\ninputs = {'_input_hash': [2108817714, 1477695082, -1021597032, 2130671338, -1260483858, -1203431639, ...], '_task_hash': [18070...ons', 'relations', 'relations', ...], 'answer': ['accept', 'accept', 'accept', 'accept', 'accept', 'accept', ...], ...}\r\nindices = [0, 1, 2, 3, 4, 5, ...], check_same_num_examples = False, offset = 0\r\n\r\n def apply_function_on_filtered_inputs(inputs, indices, check_same_num_examples=False, offset=0):\r\n \"\"\"Utility to apply the function on a selection of columns.\"\"\"\r\n nonlocal update_data\r\n fn_args = [inputs] if input_columns is None else [inputs[col] for col in input_columns]\r\n if offset == 0:\r\n effective_indices = indices\r\n else:\r\n effective_indices = [i + offset for i in indices] if isinstance(indices, list) else indices + offset\r\n processed_inputs = (\r\n> function(*fn_args, effective_indices, **fn_kwargs) if with_indices else function(*fn_args, **fn_kwargs)\r\n )\r\nE TypeError: get_indices_from_mask_function() got an unexpected keyword argument 'valid_rel_labels'\r\n\r\n..\/..\/..\/..\/.pyenv\/versions\/tf_ner_rel_lib\/lib\/python3.8\/site-packages\/datasets\/arrow_dataset.py:1939: TypeError\r\n```\r\n\r\n## Environment info\r\n\r\n- `datasets` version: 1.12.1\r\n- Platform: Mac\r\n- Python version: 3.8.9\r\n- PyArrow version: pyarrow==5.0.0\r\n\r\n","comment_length":8,"text":"Datasets 1.12 dataset.filter TypeError: get_indices_from_mask_function() got an unexpected keyword argument\n## Describe the bug\r\nUpgrading to 1.12 caused `dataset.filter` call to fail with \r\n\r\n> get_indices_from_mask_function() got an unexpected keyword argument valid_rel_labels\r\n\r\n\r\n## Steps to reproduce the bug\r\n```pythondef \r\n\r\nfilter_good_rows(\r\n ex: Dict,\r\n valid_rel_labels: Set[str],\r\n valid_ner_labels: Set[str],\r\n tokenizer: PreTrainedTokenizerFast,\r\n) -> bool:\r\n \"\"\"Get the good rows\"\"\"\r\n encoding = get_encoding_for_text(text=ex[\"text\"], tokenizer=tokenizer)\r\n ex[\"encoding\"] = encoding\r\n for relation in ex[\"relations\"]:\r\n if not is_valid_relation(relation, valid_rel_labels):\r\n return False\r\n for span in ex[\"spans\"]:\r\n if not is_valid_span(span, valid_ner_labels, encoding):\r\n return False\r\n return True\r\n \r\ndef get_dataset(): \r\n loader_path = str(Path(__file__).parent \/ \"prodigy_dataset_builder.py\")\r\n ds = load_dataset(\r\n loader_path,\r\n name=\"prodigy-dataset\",\r\n data_files=sorted(file_paths),\r\n cache_dir=cache_dir,\r\n )[\"train\"]\r\n\r\n valid_ner_labels = set(vocab.ner_category)\r\n valid_relations = set(vocab.relation_types.keys())\r\n ds = ds.filter(\r\n filter_good_rows,\r\n fn_kwargs=dict(\r\n valid_rel_labels=valid_relations,\r\n valid_ner_labels=valid_ner_labels,\r\n tokenizer=vocab.tokenizer,\r\n ),\r\n keep_in_memory=True,\r\n num_proc=num_proc,\r\n )\r\n\r\n```\r\n\r\n`ds` is a `DatasetDict` produced by a jsonl dataset.\r\nThis runs fine on 1.11 but fails on 1.12\r\n\r\n**Stack Trace**\r\n\r\n\r\n\r\n## Expected results\r\n\r\nI expect 1.12 datasets filter to filter the dataset without raising as it does on 1.11\r\n\r\n## Actual results\r\n```\r\ntf_ner_rel_lib\/dataset.py:695: in load_prodigy_arrow_datasets_from_jsonl\r\n ds = ds.filter(\r\n..\/..\/..\/..\/.pyenv\/versions\/tf_ner_rel_lib\/lib\/python3.8\/site-packages\/datasets\/arrow_dataset.py:185: in wrapper\r\n out: Union[\"Dataset\", \"DatasetDict\"] = func(self, *args, **kwargs)\r\n..\/..\/..\/..\/.pyenv\/versions\/tf_ner_rel_lib\/lib\/python3.8\/site-packages\/datasets\/fingerprint.py:398: in wrapper\r\n out = func(self, *args, **kwargs)\r\n..\/..\/..\/..\/.pyenv\/versions\/tf_ner_rel_lib\/lib\/python3.8\/site-packages\/datasets\/arrow_dataset.py:2169: in filter\r\n indices = self.map(\r\n..\/..\/..\/..\/.pyenv\/versions\/tf_ner_rel_lib\/lib\/python3.8\/site-packages\/datasets\/arrow_dataset.py:1686: in map\r\n return self._map_single(\r\n..\/..\/..\/..\/.pyenv\/versions\/tf_ner_rel_lib\/lib\/python3.8\/site-packages\/datasets\/arrow_dataset.py:185: in wrapper\r\n out: Union[\"Dataset\", \"DatasetDict\"] = func(self, *args, **kwargs)\r\n..\/..\/..\/..\/.pyenv\/versions\/tf_ner_rel_lib\/lib\/python3.8\/site-packages\/datasets\/fingerprint.py:398: in wrapper\r\n out = func(self, *args, **kwargs)\r\n..\/..\/..\/..\/.pyenv\/versions\/tf_ner_rel_lib\/lib\/python3.8\/site-packages\/datasets\/arrow_dataset.py:2048: in _map_single\r\n batch = apply_function_on_filtered_inputs(\r\n_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ \r\n\r\ninputs = {'_input_hash': [2108817714, 1477695082, -1021597032, 2130671338, -1260483858, -1203431639, ...], '_task_hash': [18070...ons', 'relations', 'relations', ...], 'answer': ['accept', 'accept', 'accept', 'accept', 'accept', 'accept', ...], ...}\r\nindices = [0, 1, 2, 3, 4, 5, ...], check_same_num_examples = False, offset = 0\r\n\r\n def apply_function_on_filtered_inputs(inputs, indices, check_same_num_examples=False, offset=0):\r\n \"\"\"Utility to apply the function on a selection of columns.\"\"\"\r\n nonlocal update_data\r\n fn_args = [inputs] if input_columns is None else [inputs[col] for col in input_columns]\r\n if offset == 0:\r\n effective_indices = indices\r\n else:\r\n effective_indices = [i + offset for i in indices] if isinstance(indices, list) else indices + offset\r\n processed_inputs = (\r\n> function(*fn_args, effective_indices, **fn_kwargs) if with_indices else function(*fn_args, **fn_kwargs)\r\n )\r\nE TypeError: get_indices_from_mask_function() got an unexpected keyword argument 'valid_rel_labels'\r\n\r\n..\/..\/..\/..\/.pyenv\/versions\/tf_ner_rel_lib\/lib\/python3.8\/site-packages\/datasets\/arrow_dataset.py:1939: TypeError\r\n```\r\n\r\n## Environment info\r\n\r\n- `datasets` version: 1.12.1\r\n- Platform: Mac\r\n- Python version: 3.8.9\r\n- PyArrow version: pyarrow==5.0.0\r\n\r\n\nThanks for reporting, I'm looking into it 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{"html_url":"https:\/\/github.com\/huggingface\/datasets\/issues\/2927","title":"Datasets 1.12 dataset.filter TypeError: get_indices_from_mask_function() got an unexpected keyword argument","comments":"Fixed by #2950.","body":"## Describe the bug\r\nUpgrading to 1.12 caused `dataset.filter` call to fail with \r\n\r\n> get_indices_from_mask_function() got an unexpected keyword argument valid_rel_labels\r\n\r\n\r\n## Steps to reproduce the bug\r\n```pythondef \r\n\r\nfilter_good_rows(\r\n ex: Dict,\r\n valid_rel_labels: Set[str],\r\n valid_ner_labels: Set[str],\r\n tokenizer: PreTrainedTokenizerFast,\r\n) -> bool:\r\n \"\"\"Get the good rows\"\"\"\r\n encoding = get_encoding_for_text(text=ex[\"text\"], tokenizer=tokenizer)\r\n ex[\"encoding\"] = encoding\r\n for relation in ex[\"relations\"]:\r\n if not is_valid_relation(relation, valid_rel_labels):\r\n return False\r\n for span in ex[\"spans\"]:\r\n if not is_valid_span(span, valid_ner_labels, encoding):\r\n return False\r\n return True\r\n \r\ndef get_dataset(): \r\n loader_path = str(Path(__file__).parent \/ \"prodigy_dataset_builder.py\")\r\n ds = load_dataset(\r\n loader_path,\r\n name=\"prodigy-dataset\",\r\n data_files=sorted(file_paths),\r\n cache_dir=cache_dir,\r\n )[\"train\"]\r\n\r\n valid_ner_labels = set(vocab.ner_category)\r\n valid_relations = set(vocab.relation_types.keys())\r\n ds = ds.filter(\r\n filter_good_rows,\r\n fn_kwargs=dict(\r\n valid_rel_labels=valid_relations,\r\n valid_ner_labels=valid_ner_labels,\r\n tokenizer=vocab.tokenizer,\r\n ),\r\n keep_in_memory=True,\r\n num_proc=num_proc,\r\n )\r\n\r\n```\r\n\r\n`ds` is a `DatasetDict` produced by a jsonl dataset.\r\nThis runs fine on 1.11 but fails on 1.12\r\n\r\n**Stack Trace**\r\n\r\n\r\n\r\n## Expected results\r\n\r\nI expect 1.12 datasets filter to filter the dataset without raising as it does on 1.11\r\n\r\n## Actual results\r\n```\r\ntf_ner_rel_lib\/dataset.py:695: in load_prodigy_arrow_datasets_from_jsonl\r\n ds = ds.filter(\r\n..\/..\/..\/..\/.pyenv\/versions\/tf_ner_rel_lib\/lib\/python3.8\/site-packages\/datasets\/arrow_dataset.py:185: in wrapper\r\n out: Union[\"Dataset\", \"DatasetDict\"] = func(self, *args, **kwargs)\r\n..\/..\/..\/..\/.pyenv\/versions\/tf_ner_rel_lib\/lib\/python3.8\/site-packages\/datasets\/fingerprint.py:398: in wrapper\r\n out = func(self, *args, **kwargs)\r\n..\/..\/..\/..\/.pyenv\/versions\/tf_ner_rel_lib\/lib\/python3.8\/site-packages\/datasets\/arrow_dataset.py:2169: in filter\r\n indices = self.map(\r\n..\/..\/..\/..\/.pyenv\/versions\/tf_ner_rel_lib\/lib\/python3.8\/site-packages\/datasets\/arrow_dataset.py:1686: in map\r\n return self._map_single(\r\n..\/..\/..\/..\/.pyenv\/versions\/tf_ner_rel_lib\/lib\/python3.8\/site-packages\/datasets\/arrow_dataset.py:185: in wrapper\r\n out: Union[\"Dataset\", \"DatasetDict\"] = func(self, *args, **kwargs)\r\n..\/..\/..\/..\/.pyenv\/versions\/tf_ner_rel_lib\/lib\/python3.8\/site-packages\/datasets\/fingerprint.py:398: in wrapper\r\n out = func(self, *args, **kwargs)\r\n..\/..\/..\/..\/.pyenv\/versions\/tf_ner_rel_lib\/lib\/python3.8\/site-packages\/datasets\/arrow_dataset.py:2048: in _map_single\r\n batch = apply_function_on_filtered_inputs(\r\n_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ \r\n\r\ninputs = {'_input_hash': [2108817714, 1477695082, -1021597032, 2130671338, -1260483858, -1203431639, ...], '_task_hash': [18070...ons', 'relations', 'relations', ...], 'answer': ['accept', 'accept', 'accept', 'accept', 'accept', 'accept', ...], ...}\r\nindices = [0, 1, 2, 3, 4, 5, ...], check_same_num_examples = False, offset = 0\r\n\r\n def apply_function_on_filtered_inputs(inputs, indices, check_same_num_examples=False, offset=0):\r\n \"\"\"Utility to apply the function on a selection of columns.\"\"\"\r\n nonlocal update_data\r\n fn_args = [inputs] if input_columns is None else [inputs[col] for col in input_columns]\r\n if offset == 0:\r\n effective_indices = indices\r\n else:\r\n effective_indices = [i + offset for i in indices] if isinstance(indices, list) else indices + offset\r\n processed_inputs = (\r\n> function(*fn_args, effective_indices, **fn_kwargs) if with_indices else function(*fn_args, **fn_kwargs)\r\n )\r\nE TypeError: get_indices_from_mask_function() got an unexpected keyword argument 'valid_rel_labels'\r\n\r\n..\/..\/..\/..\/.pyenv\/versions\/tf_ner_rel_lib\/lib\/python3.8\/site-packages\/datasets\/arrow_dataset.py:1939: TypeError\r\n```\r\n\r\n## Environment info\r\n\r\n- `datasets` version: 1.12.1\r\n- Platform: Mac\r\n- Python version: 3.8.9\r\n- PyArrow version: pyarrow==5.0.0\r\n\r\n","comment_length":3,"text":"Datasets 1.12 dataset.filter TypeError: get_indices_from_mask_function() got an unexpected keyword argument\n## Describe the bug\r\nUpgrading to 1.12 caused `dataset.filter` call to fail with \r\n\r\n> get_indices_from_mask_function() got an unexpected keyword argument valid_rel_labels\r\n\r\n\r\n## Steps to reproduce the bug\r\n```pythondef \r\n\r\nfilter_good_rows(\r\n ex: Dict,\r\n valid_rel_labels: Set[str],\r\n valid_ner_labels: Set[str],\r\n tokenizer: PreTrainedTokenizerFast,\r\n) -> bool:\r\n \"\"\"Get the good rows\"\"\"\r\n encoding = get_encoding_for_text(text=ex[\"text\"], tokenizer=tokenizer)\r\n ex[\"encoding\"] = encoding\r\n for relation in ex[\"relations\"]:\r\n if not is_valid_relation(relation, valid_rel_labels):\r\n return False\r\n for span in ex[\"spans\"]:\r\n if not is_valid_span(span, valid_ner_labels, encoding):\r\n return False\r\n return True\r\n \r\ndef get_dataset(): \r\n loader_path = str(Path(__file__).parent \/ \"prodigy_dataset_builder.py\")\r\n ds = load_dataset(\r\n loader_path,\r\n name=\"prodigy-dataset\",\r\n data_files=sorted(file_paths),\r\n cache_dir=cache_dir,\r\n )[\"train\"]\r\n\r\n valid_ner_labels = set(vocab.ner_category)\r\n valid_relations = set(vocab.relation_types.keys())\r\n ds = ds.filter(\r\n filter_good_rows,\r\n fn_kwargs=dict(\r\n valid_rel_labels=valid_relations,\r\n valid_ner_labels=valid_ner_labels,\r\n tokenizer=vocab.tokenizer,\r\n ),\r\n keep_in_memory=True,\r\n num_proc=num_proc,\r\n )\r\n\r\n```\r\n\r\n`ds` is a `DatasetDict` produced by a jsonl dataset.\r\nThis runs fine on 1.11 but fails on 1.12\r\n\r\n**Stack Trace**\r\n\r\n\r\n\r\n## Expected results\r\n\r\nI expect 1.12 datasets filter to filter the dataset without raising as it does on 1.11\r\n\r\n## Actual results\r\n```\r\ntf_ner_rel_lib\/dataset.py:695: in load_prodigy_arrow_datasets_from_jsonl\r\n ds = ds.filter(\r\n..\/..\/..\/..\/.pyenv\/versions\/tf_ner_rel_lib\/lib\/python3.8\/site-packages\/datasets\/arrow_dataset.py:185: in wrapper\r\n out: Union[\"Dataset\", \"DatasetDict\"] = func(self, *args, **kwargs)\r\n..\/..\/..\/..\/.pyenv\/versions\/tf_ner_rel_lib\/lib\/python3.8\/site-packages\/datasets\/fingerprint.py:398: in wrapper\r\n out = func(self, *args, **kwargs)\r\n..\/..\/..\/..\/.pyenv\/versions\/tf_ner_rel_lib\/lib\/python3.8\/site-packages\/datasets\/arrow_dataset.py:2169: in filter\r\n indices = self.map(\r\n..\/..\/..\/..\/.pyenv\/versions\/tf_ner_rel_lib\/lib\/python3.8\/site-packages\/datasets\/arrow_dataset.py:1686: in map\r\n return self._map_single(\r\n..\/..\/..\/..\/.pyenv\/versions\/tf_ner_rel_lib\/lib\/python3.8\/site-packages\/datasets\/arrow_dataset.py:185: in wrapper\r\n out: Union[\"Dataset\", \"DatasetDict\"] = func(self, *args, **kwargs)\r\n..\/..\/..\/..\/.pyenv\/versions\/tf_ner_rel_lib\/lib\/python3.8\/site-packages\/datasets\/fingerprint.py:398: in wrapper\r\n out = func(self, *args, **kwargs)\r\n..\/..\/..\/..\/.pyenv\/versions\/tf_ner_rel_lib\/lib\/python3.8\/site-packages\/datasets\/arrow_dataset.py:2048: in _map_single\r\n batch = apply_function_on_filtered_inputs(\r\n_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ \r\n\r\ninputs = {'_input_hash': [2108817714, 1477695082, -1021597032, 2130671338, -1260483858, -1203431639, ...], '_task_hash': [18070...ons', 'relations', 'relations', ...], 'answer': ['accept', 'accept', 'accept', 'accept', 'accept', 'accept', ...], ...}\r\nindices = [0, 1, 2, 3, 4, 5, ...], check_same_num_examples = False, offset = 0\r\n\r\n def apply_function_on_filtered_inputs(inputs, indices, check_same_num_examples=False, offset=0):\r\n \"\"\"Utility to apply the function on a selection of columns.\"\"\"\r\n nonlocal update_data\r\n fn_args = [inputs] if input_columns is None else [inputs[col] for col in input_columns]\r\n if offset == 0:\r\n effective_indices = indices\r\n else:\r\n effective_indices = [i + offset for i in indices] if isinstance(indices, list) else indices + offset\r\n processed_inputs = (\r\n> function(*fn_args, effective_indices, **fn_kwargs) if with_indices else function(*fn_args, **fn_kwargs)\r\n )\r\nE TypeError: get_indices_from_mask_function() got an unexpected keyword argument 'valid_rel_labels'\r\n\r\n..\/..\/..\/..\/.pyenv\/versions\/tf_ner_rel_lib\/lib\/python3.8\/site-packages\/datasets\/arrow_dataset.py:1939: TypeError\r\n```\r\n\r\n## Environment info\r\n\r\n- `datasets` version: 1.12.1\r\n- Platform: Mac\r\n- Python version: 3.8.9\r\n- PyArrow version: pyarrow==5.0.0\r\n\r\n\nFixed by #2950.","embeddings":[-0.2829699218,0.2777142525,-0.0049711736,0.1934766024,0.0521399193,-0.0259464737,0.2624210715,0.4762106538,-0.1806556284,0.0836368799,-0.1311300099,0.3645940423,-0.1310276091,0.1083575189,-0.1563561261,-0.2259478569,0.0624334253,0.0486550108,-0.0484171957,0.0325096361,-0.3871050477,0.4132161736,-0.4211299717,0.1235269159,0.008685492,-0.1654407978,0.1102141142,-0.0451107584,0.0099631641,-0.4235654175,0.4564154148,0.0699703991,0.0381553695,0.2566260993,-0.0001171321,0.2396819293,0.366684258,-0.0842551515,-0.2250449806,-0.405336529,-0.1809279323,-0.0339020006,0.1180416197,-0.1935501844,-0.0818986967,0.0174917243,-0.249624908,-0.2356579155,0.2611079514,0.4325256944,0.1972838491,0.0717553645,-0.0651654452,0.0339874998,0.0209346432,0.2666781545,0.0185224004,-0.0007021982,0.0587287322,-0.1879914552,0.2919843793,0.5048494339,-0.2368306518,-0.2743239999,0.2988181114,-0.2294282466,0.223627314,-0.3586401939,0.2479036301,-0.0648386478,0.0247386377,-0.0833451524,-0.3223271072,-0.1697276235,-0.2576664686,-0.1120616794,0.3498052657,-0.1584596336,-0.244265154,-0.0208345316,-0.07258939,-0.0044453437,-0.0193625409,0.0009967602,-0.0110017173,0.5335709453,0.051298894,0.0513096936,0.0644063577,-0.1729889512,0.3066953421,-0.1076792255,-0.1007701308,0.0617668256,-0.170351848,0.0725231543,0.2387878448,-0.100716427,0.0953230187,-0.0929184034,-0.0909817293,-0.1606885344,0.0432230234,-0.0053427084,-0.0694495961,0.0904969722,0.3082866073,0.6318762302,0.0672923923,0.058587458,-0.0616019517,0.0373482928,-0.0557346605,0.0807316527,0.0906984136,0.2471883744,0.3795975745,-0.3135053217,-0.6206545234,0.1616326869,-0.5770969987,-0.1607390344,0.237689808,0.0602699555,0.2683047056,0.0857219398,-0.0559860393,0.0184919778,-0.0884351358,0.0967272371,-0.1329723448,0.0300575532,-0.0661212951,-0.288934499,0.1902687103,-0.5589625239,0.0788640752,-0.1231013834,-0.090868406,-0.0088757947,-0.0592832975,0.0524173155,0.1024440303,0.3941213489,-0.4122775495,0.1066873968,0.2004962116,-0.5756821632,-0.1944885254,0.2767592072,-0.4454355538,-0.0839394405,0.0214587208,0.2041888237,0.1366723627,-0.1789838076,-0.3426181376,0.5803837776,0.0123843485,-0.2307372093,-0.0704242811,-0.2239028215,-0.3683023453,-0.0239756647,0.1340631545,0.1689580381,-0.8365051746,-0.2499466091,0.2335013896,-0.0030520104,-0.0626956224,-0.2084405422,-0.1818583906,-0.0219790861,-0.1172454953,-0.0013353637,0.3576940596,-0.485848546,-0.6574547887,0.1390666068,0.1225496158,0.1945742369,0.1314316094,-0.1025109664,0.3647884429,-0.0999429971,0.1756555587,0.2742819488,0.0124631217,-0.0347300246,-0.2012303621,0.0749125555,0.2941287458,-0.0680078194,0.1666537374,0.27637887,-0.1620120853,-0.0194072127,0.2861098051,0.1138249561,-0.0791773424,0.0638790876,0.2018899024,-0.1175088063,0.232898742,-0.4075967968,-0.1830836833,-0.0563953593,0.0861253291,0.130115062,-0.3378736973,-0.214805305,-0.3373113871,0.0745304599,-0.2046329081,-0.1652506888,0.1793425977,0.1503546089,-0.0387527496,-0.0507269315,-0.0374752656,0.4382365942,0.2428901494,0.1432452947,-0.2216559798,0.2244707495,-0.0795594603,-0.080613561,0.0983143821,-0.0522183664,0.36705634,-0.0016741207,-0.3518117964,0.2039128393,0.0591471232,-0.3466408551,-0.0529597551,0.0107605178,0.0352687202,0.0651153848,0.1794721931,0.3757503927,-0.0078226523,0.1407691687,-0.1973981559,0.4055749774,-0.0408483408,0.4687744379,0.0199777223,0.0771241337,0.3994879127,0.2407884896,-0.1524210721,-0.1703110337,0.0525777675,0.3843008578,0.2716696858,-0.1308127642,-0.0575984567,0.2184957862,0.3087431192,-0.0334121324,0.0245152265,0.3014428616,0.015626844,0.0211441629,0.1812755615,0.4510476291,0.150378406,0.1575035751,-0.1162214428,0.0736183971,-0.2908762395,-0.0003076858,0.2586247027,0.1770678163,-0.2355194986,-0.0977184847,0.2307223231,0.0618963651,-0.3269087672,-0.4945808649,-0.1177324802,0.1571106911,-0.2101406008,0.4223466814,-0.2563110888,-0.360643059,0.2112966329,-0.1757659763,-0.1615487486,-0.4771515429,0.0838324726,0.1105318815,0.0211028233,0.4142694175,-0.1944871247,0.2770675421,0.1981321126,-0.2057120055,-0.2324998677,-0.3686217666,-0.3903269172,0.0790402368,0.1200828031,0.0579170287,0.1671684682,-0.0545275025,0.1354142576,0.083329387,-0.7349098921,0.1015762314,-0.3447894454,0.4745761752,0.1016463861,0.038837377,-0.3328078389,-0.2596282661,0.1520894766,0.304394573,-0.2402678132,0.1635410488,0.0431459434,0.2904618084,-0.2289169878,-0.4294990301,-0.2123535424,-0.1180267707,-0.1469037235,-0.1846597344,0.3282931149,0.5408180952,0.0719795302,-0.0907747671,0.1236420944,0.184421882,-0.3507416248,-0.4034056962,0.144136861,-0.0788861513,-0.1249593124,-0.1968769431,-0.2286561579,0.0848402604,0.2183059156,-0.5605329275,-0.4192049503,0.0349870473,0.0410459861,0.1570181549,-0.1139656156,0.1058698297,0.2774389684,0.030448081,-0.2724855542,-0.3693442345,0.0357308015,-0.2114411592,0.0473164991,0.0135325789,0.9601014853,0.0164168794,0.0682780072,0.1694822013,0.2156193256,0.3308912516,0.0938933268,0.4396736324,-0.2999125719,-0.2751029134,-0.1022948325,0.0323724411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{"html_url":"https:\/\/github.com\/huggingface\/datasets\/issues\/2924","title":"\"File name too long\" error for file locks","comments":"Hi, the filename here is less than 255\r\n```python\r\n>>> len(\"_home_garrett_.cache_huggingface_datasets_csv_test-7c856aea083a7043_0.0.0_9144e0a4e8435090117cea53e6c7537173ef2304525df4a077c435d8ee7828ff.incomplete.lock\")\r\n154\r\n```\r\nso not sure why it's considered too long for your filesystem.\r\n(also note that the lock files we use always have smaller filenames than 255)\r\n\r\nhttps:\/\/github.com\/huggingface\/datasets\/blob\/5d1a9f1e3c6c495dc0610b459e39d2eb8893f152\/src\/datasets\/utils\/filelock.py#L135-L135","body":"## Describe the bug\r\n\r\nGetting the following error when calling `load_dataset(\"gar1t\/test\")`:\r\n\r\n```\r\nOSError: [Errno 36] File name too long: '\/.cache\/huggingface\/datasets\/_home_garrett_.cache_huggingface_datasets_csv_test-7c856aea083a7043_0.0.0_9144e0a4e8435090117cea53e6c7537173ef2304525df4a077c435d8ee7828ff.incomplete.lock'\r\n```\r\n\r\n## Steps to reproduce the bug\r\n\r\nWhere the user cache dir (e.g. `~\/.cache`) is on a file system that limits filenames to 255 chars (e.g. ext4):\r\n\r\n```python\r\nfrom datasets import load_dataset\r\nload_dataset(\"gar1t\/test\")\r\n```\r\n\r\n## Expected results\r\n\r\nExpect the function to return without an error.\r\n\r\n## Actual results\r\n\r\n```\r\nTraceback (most recent call last):\r\n File \"\", line 1, in \r\n File \"\/lib\/python3.9\/site-packages\/datasets\/load.py\", line 1112, in load_dataset\r\n builder_instance.download_and_prepare(\r\n File \"\/lib\/python3.9\/site-packages\/datasets\/builder.py\", line 644, in download_and_prepare\r\n self._save_info()\r\n File \"\/lib\/python3.9\/site-packages\/datasets\/builder.py\", line 765, in _save_info\r\n with FileLock(lock_path):\r\n File \"\/lib\/python3.9\/site-packages\/datasets\/utils\/filelock.py\", line 323, in __enter__\r\n self.acquire()\r\n File \"\/lib\/python3.9\/site-packages\/datasets\/utils\/filelock.py\", line 272, in acquire\r\n self._acquire()\r\n File \"\/lib\/python3.9\/site-packages\/datasets\/utils\/filelock.py\", line 403, in _acquire\r\n fd = os.open(self._lock_file, open_mode)\r\nOSError: [Errno 36] File name too long: '\/.cache\/huggingface\/datasets\/_home_garrett_.cache_huggingface_datasets_csv_test-7c856aea083a7043_0.0.0_9144e0a4e8435090117cea53e6c7537173ef2304525df4a077c435d8ee7828ff.incomplete.lock'\r\n```\r\n\r\n## Environment info\r\n\r\n- `datasets` version: 1.12.1\r\n- Platform: Linux-5.11.0-27-generic-x86_64-with-glibc2.31\r\n- Python version: 3.9.7\r\n- PyArrow version: 5.0.0\r\n","comment_length":39,"text":"\"File name too long\" error for file locks\n## Describe the bug\r\n\r\nGetting the following error when calling `load_dataset(\"gar1t\/test\")`:\r\n\r\n```\r\nOSError: [Errno 36] File name too long: '\/.cache\/huggingface\/datasets\/_home_garrett_.cache_huggingface_datasets_csv_test-7c856aea083a7043_0.0.0_9144e0a4e8435090117cea53e6c7537173ef2304525df4a077c435d8ee7828ff.incomplete.lock'\r\n```\r\n\r\n## Steps to reproduce the bug\r\n\r\nWhere the user cache dir (e.g. `~\/.cache`) is on a file system that limits filenames to 255 chars (e.g. ext4):\r\n\r\n```python\r\nfrom datasets import load_dataset\r\nload_dataset(\"gar1t\/test\")\r\n```\r\n\r\n## Expected results\r\n\r\nExpect the function to return without an error.\r\n\r\n## Actual results\r\n\r\n```\r\nTraceback (most recent call last):\r\n File \"\", line 1, in \r\n File \"\/lib\/python3.9\/site-packages\/datasets\/load.py\", line 1112, in load_dataset\r\n builder_instance.download_and_prepare(\r\n File \"\/lib\/python3.9\/site-packages\/datasets\/builder.py\", line 644, in download_and_prepare\r\n self._save_info()\r\n File \"\/lib\/python3.9\/site-packages\/datasets\/builder.py\", line 765, in _save_info\r\n with FileLock(lock_path):\r\n File \"\/lib\/python3.9\/site-packages\/datasets\/utils\/filelock.py\", line 323, in __enter__\r\n self.acquire()\r\n File \"\/lib\/python3.9\/site-packages\/datasets\/utils\/filelock.py\", line 272, in acquire\r\n self._acquire()\r\n File \"\/lib\/python3.9\/site-packages\/datasets\/utils\/filelock.py\", line 403, in _acquire\r\n fd = os.open(self._lock_file, open_mode)\r\nOSError: [Errno 36] File name too long: '\/.cache\/huggingface\/datasets\/_home_garrett_.cache_huggingface_datasets_csv_test-7c856aea083a7043_0.0.0_9144e0a4e8435090117cea53e6c7537173ef2304525df4a077c435d8ee7828ff.incomplete.lock'\r\n```\r\n\r\n## Environment info\r\n\r\n- `datasets` version: 1.12.1\r\n- Platform: Linux-5.11.0-27-generic-x86_64-with-glibc2.31\r\n- Python version: 3.9.7\r\n- PyArrow version: 5.0.0\r\n\nHi, the filename here is less than 255\r\n```python\r\n>>> len(\"_home_garrett_.cache_huggingface_datasets_csv_test-7c856aea083a7043_0.0.0_9144e0a4e8435090117cea53e6c7537173ef2304525df4a077c435d8ee7828ff.incomplete.lock\")\r\n154\r\n```\r\nso not sure why it's considered too long for your filesystem.\r\n(also note that the lock files we use always have smaller filenames than 255)\r\n\r\nhttps:\/\/github.com\/huggingface\/datasets\/blob\/5d1a9f1e3c6c495dc0610b459e39d2eb8893f152\/src\/datasets\/utils\/filelock.py#L135-L135","embeddings":[0.0493393503,0.0920015797,-0.0714939609,0.401848346,0.4151671231,0.2358950824,0.636662364,0.2435030937,0.2364584506,0.2313297093,0.0185690634,0.0077435691,-0.1442685276,-0.3100371361,-0.2015356272,-0.2443143427,-0.1581087261,-0.0416213386,-0.177078411,0.2177188098,-0.1549794227,0.3787363768,0.0246380791,0.1570838541,-0.536006391,0.0900789052,-0.1295958906,0.3886749148,-0.0215065964,-0.3729913831,0.051118616,-0.0467165932,0.068814449,0.811357677,-0.0001220686,-0.3334413767,0.3542338908,-0.0562992319,-0.3271892667,-0.2243083715,-0.1685816944,-0.5117114782,-0.0345845483,-0.4988024831,0.1872130185,-0.0951189175,-0.0331902057,-0.8021566272,0.0410523824,0.3733062148,0.1268745214,-0.0783471763,0.0910248384,-0.2579201758,0.3323119283,-0.2779891491,0.0040849657,0.3564026058,0.3556975722,-0.0988676101,-0.1546510309,0.2032182515,-0.0368982404,0.027324792,0.2471582741,0.079537563,-0.0874831453,-0.3261650205,0.2958056033,0.4273941517,0.4714379907,-0.0930620804,-0.2793129385,-0.4676968753,0.1424980164,-0.1251056492,0.4885306358,-0.0762403011,-0.222074613,0.1514885277,-0.1704189926,-0.0196209587,-0.1235974729,-0.1102384105,-0.101975739,0.0650619194,0.0405084826,0.0362790599,0.3111130893,-0.3483147621,0.2395959646,0.0048368936,0.0742740706,0.2694604397,-0.6542128921,0.1469123662,0.0842684656,0.3854928017,0.2534896433,0.0576435812,-0.2659727335,-0.1483078748,0.1947487742,-0.0104660569,-0.0649549589,0.374206692,0.1944440156,0.1612814218,0.3012429774,0.0779469088,-0.3367469013,-0.1069349945,-0.0764871612,-0.5534629822,0.445138216,0.133591041,0.0741629824,-0.3437117934,0.1504577994,0.5705774426,0.1057047993,-0.0104902675,0.2287023664,0.2733797133,0.005434711,0.1107665151,0.0830746666,-0.0899362862,-0.0889612734,0.1008345857,-0.1473124325,-0.1107303202,-0.2270203382,0.1124099791,0.0892055854,-0.3083957136,0.2792796195,-0.227465108,0.3152402341,-0.1249576584,-0.0829687566,-0.2377426773,-0.0667323172,0.1559667885,-0.0591069087,0.0739138052,0.2452137023,-0.2607334852,-0.1920612901,-0.0771116391,-0.3397490382,-0.2851879001,-0.0617215782,0.0947027877,-0.165494591,0.2159024328,0.2415551245,-0.2671947181,0.6555529237,-0.0798197463,0.0127971917,-0.2747654617,-0.1418095827,0.0071923966,0.0408245735,0.5534662008,0.1325238049,-0.0175623372,-0.0687896535,0.1282823086,0.0086658485,0.3722593188,0.1806448698,-0.0535966232,-0.4219526947,0.3124936521,0.2268715054,-0.2402810752,-0.486985594,0.3215382397,-0.394307375,0.0994933695,0.3229117393,0.0228835605,0.0517107099,-0.024582373,0.2853384614,0.1822132915,-0.0260959864,0.0234954469,-0.202524215,-0.1073302031,-0.0406307913,0.1691952497,0.0109546073,0.0670013204,0.0796193257,-0.1751918048,0.2715687454,0.0314637274,-0.2040443867,0.3736798167,0.1382424235,0.3267191648,0.1860822737,-0.106327638,-0.5937504768,0.2637207508,-0.1218474954,-0.1787318438,-0.2175928354,-0.1868047863,-0.1749890149,-0.0072898441,0.0034599197,0.1351371109,-0.0204084311,0.2832494974,0.1417792886,-0.1317639649,0.0934399366,0.5883799791,-0.1702439636,0.0175973084,-0.4781179428,-0.17112647,-0.1178534552,-0.1125399023,0.0315071233,0.0016395918,0.3693964779,-0.1089679822,-0.3443510532,0.4480753541,0.3332768381,0.0975331739,-0.1789251119,-0.0044306694,-0.0568642691,0.220775336,0.0726701096,0.0398121551,0.0077854944,0.0142915584,-0.2135758698,0.3000163734,-0.14081572,0.27127707,0.0286138058,0.1682408899,0.2461784184,-0.1318140626,0.0643453151,-0.1761228889,0.6458842158,-0.2163528502,0.3048405647,0.0547799319,0.0452316478,-0.1448624283,0.6458194852,0.045392707,-0.0001772655,0.1977425367,0.1562013328,-0.0408157147,-0.0193730351,0.3064890206,0.5111442208,0.1920584738,-0.0301231258,-0.2065057307,0.3941576481,-0.2238344699,0.256319046,0.0159084182,-0.0027928618,0.3657026589,0.0496179983,-0.1290875971,0.0514840819,-0.8095089197,-0.050442148,0.223697722,-0.3198090196,0.1480822265,-0.1393256634,-0.0432045013,-0.0237995591,0.0004903108,-0.0803984031,-0.4162820876,-0.1888936311,0.2785063386,-0.1418365985,0.2145332694,-0.1188357845,-0.0032407197,0.2712830305,-0.0145387761,-0.2950273752,-0.0016123495,0.0803438574,-0.0837603286,0.2720724344,-0.5375033021,0.0838043541,-0.1256639212,-0.0631072521,-0.5083525181,-0.2498805523,0.1116677895,-0.1684809178,0.2935642302,0.3413662016,0.2078575194,0.1666625887,0.0588969849,0.0049610781,-0.2622792721,0.0236695651,0.2326634228,0.1779004186,-0.0228179917,-0.1917994022,-0.023455549,-0.3463737965,-0.4160333872,0.3207841218,0.0303027015,0.2279285491,0.3411892653,-0.0708731115,0.2978130579,-0.0179723371,0.2472920567,-0.0879067779,-0.446370095,0.4170611799,-0.1525222659,-0.2990590334,-0.4318080544,0.2980134785,-0.0355094597,0.1055189967,-0.4223643541,-0.1790713519,-0.401260376,0.3389849961,-0.3357198238,0.0054093283,0.1970204413,-0.0852006599,-0.0563488565,-0.1069383249,0.0849020928,0.2325119227,0.0110361269,-0.009520852,0.0929816738,0.2951667607,-0.11607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{"html_url":"https:\/\/github.com\/huggingface\/datasets\/issues\/2924","title":"\"File name too long\" error for file locks","comments":"Yes, you're right! I need to get you more info here. Either there's something going with the name itself that the file system doesn't like (an encoding that blows up the name length??) or perhaps there's something with the path that's causing the entire string to be used as a name. I haven't seen this on any system before and the Internet's not forthcoming with any info.","body":"## Describe the bug\r\n\r\nGetting the following error when calling `load_dataset(\"gar1t\/test\")`:\r\n\r\n```\r\nOSError: [Errno 36] File name too long: '\/.cache\/huggingface\/datasets\/_home_garrett_.cache_huggingface_datasets_csv_test-7c856aea083a7043_0.0.0_9144e0a4e8435090117cea53e6c7537173ef2304525df4a077c435d8ee7828ff.incomplete.lock'\r\n```\r\n\r\n## Steps to reproduce the bug\r\n\r\nWhere the user cache dir (e.g. `~\/.cache`) is on a file system that limits filenames to 255 chars (e.g. ext4):\r\n\r\n```python\r\nfrom datasets import load_dataset\r\nload_dataset(\"gar1t\/test\")\r\n```\r\n\r\n## Expected results\r\n\r\nExpect the function to return without an error.\r\n\r\n## Actual results\r\n\r\n```\r\nTraceback (most recent call last):\r\n File \"\", line 1, in \r\n File \"\/lib\/python3.9\/site-packages\/datasets\/load.py\", line 1112, in load_dataset\r\n builder_instance.download_and_prepare(\r\n File \"\/lib\/python3.9\/site-packages\/datasets\/builder.py\", line 644, in download_and_prepare\r\n self._save_info()\r\n File \"\/lib\/python3.9\/site-packages\/datasets\/builder.py\", line 765, in _save_info\r\n with FileLock(lock_path):\r\n File \"\/lib\/python3.9\/site-packages\/datasets\/utils\/filelock.py\", line 323, in __enter__\r\n self.acquire()\r\n File \"\/lib\/python3.9\/site-packages\/datasets\/utils\/filelock.py\", line 272, in acquire\r\n self._acquire()\r\n File \"\/lib\/python3.9\/site-packages\/datasets\/utils\/filelock.py\", line 403, in _acquire\r\n fd = os.open(self._lock_file, open_mode)\r\nOSError: [Errno 36] File name too long: '\/.cache\/huggingface\/datasets\/_home_garrett_.cache_huggingface_datasets_csv_test-7c856aea083a7043_0.0.0_9144e0a4e8435090117cea53e6c7537173ef2304525df4a077c435d8ee7828ff.incomplete.lock'\r\n```\r\n\r\n## Environment info\r\n\r\n- `datasets` version: 1.12.1\r\n- Platform: Linux-5.11.0-27-generic-x86_64-with-glibc2.31\r\n- Python version: 3.9.7\r\n- PyArrow version: 5.0.0\r\n","comment_length":67,"text":"\"File name too long\" error for file locks\n## Describe the bug\r\n\r\nGetting the following error when calling `load_dataset(\"gar1t\/test\")`:\r\n\r\n```\r\nOSError: [Errno 36] File name too long: '\/.cache\/huggingface\/datasets\/_home_garrett_.cache_huggingface_datasets_csv_test-7c856aea083a7043_0.0.0_9144e0a4e8435090117cea53e6c7537173ef2304525df4a077c435d8ee7828ff.incomplete.lock'\r\n```\r\n\r\n## Steps to reproduce the bug\r\n\r\nWhere the user cache dir (e.g. `~\/.cache`) is on a file system that limits filenames to 255 chars (e.g. ext4):\r\n\r\n```python\r\nfrom datasets import load_dataset\r\nload_dataset(\"gar1t\/test\")\r\n```\r\n\r\n## Expected results\r\n\r\nExpect the function to return without an error.\r\n\r\n## Actual results\r\n\r\n```\r\nTraceback (most recent call last):\r\n File \"\", line 1, in \r\n File \"\/lib\/python3.9\/site-packages\/datasets\/load.py\", line 1112, in load_dataset\r\n builder_instance.download_and_prepare(\r\n File \"\/lib\/python3.9\/site-packages\/datasets\/builder.py\", line 644, in download_and_prepare\r\n self._save_info()\r\n File \"\/lib\/python3.9\/site-packages\/datasets\/builder.py\", line 765, in _save_info\r\n with FileLock(lock_path):\r\n File \"\/lib\/python3.9\/site-packages\/datasets\/utils\/filelock.py\", line 323, in __enter__\r\n self.acquire()\r\n File \"\/lib\/python3.9\/site-packages\/datasets\/utils\/filelock.py\", line 272, in acquire\r\n self._acquire()\r\n File \"\/lib\/python3.9\/site-packages\/datasets\/utils\/filelock.py\", line 403, in _acquire\r\n fd = os.open(self._lock_file, open_mode)\r\nOSError: [Errno 36] File name too long: '\/.cache\/huggingface\/datasets\/_home_garrett_.cache_huggingface_datasets_csv_test-7c856aea083a7043_0.0.0_9144e0a4e8435090117cea53e6c7537173ef2304525df4a077c435d8ee7828ff.incomplete.lock'\r\n```\r\n\r\n## Environment info\r\n\r\n- `datasets` version: 1.12.1\r\n- Platform: Linux-5.11.0-27-generic-x86_64-with-glibc2.31\r\n- Python version: 3.9.7\r\n- PyArrow version: 5.0.0\r\n\nYes, you're right! I need to get you more info here. Either there's something going with the name itself that the file system doesn't like (an encoding that blows up the name length??) or perhaps there's something with the path that's causing the entire string to be used as a name. I haven't seen this on any system before and the Internet's not forthcoming with any info.","embeddings":[0.0493393503,0.0920015797,-0.0714939609,0.401848346,0.4151671231,0.2358950824,0.636662364,0.2435030937,0.2364584506,0.2313297093,0.0185690634,0.0077435691,-0.1442685276,-0.3100371361,-0.2015356272,-0.2443143427,-0.1581087261,-0.0416213386,-0.177078411,0.2177188098,-0.1549794227,0.3787363768,0.0246380791,0.1570838541,-0.536006391,0.0900789052,-0.1295958906,0.3886749148,-0.0215065964,-0.3729913831,0.051118616,-0.0467165932,0.068814449,0.811357677,-0.0001220686,-0.3334413767,0.3542338908,-0.0562992319,-0.3271892667,-0.2243083715,-0.1685816944,-0.5117114782,-0.0345845483,-0.4988024831,0.1872130185,-0.0951189175,-0.0331902057,-0.8021566272,0.0410523824,0.3733062148,0.1268745214,-0.0783471763,0.0910248384,-0.2579201758,0.3323119283,-0.2779891491,0.0040849657,0.3564026058,0.3556975722,-0.0988676101,-0.1546510309,0.2032182515,-0.0368982404,0.027324792,0.2471582741,0.079537563,-0.0874831453,-0.3261650205,0.2958056033,0.4273941517,0.4714379907,-0.0930620804,-0.2793129385,-0.4676968753,0.1424980164,-0.1251056492,0.4885306358,-0.0762403011,-0.222074613,0.1514885277,-0.1704189926,-0.0196209587,-0.1235974729,-0.1102384105,-0.101975739,0.0650619194,0.0405084826,0.0362790599,0.3111130893,-0.3483147621,0.2395959646,0.0048368936,0.0742740706,0.2694604397,-0.6542128921,0.1469123662,0.0842684656,0.3854928017,0.2534896433,0.0576435812,-0.2659727335,-0.1483078748,0.1947487742,-0.0104660569,-0.0649549589,0.374206692,0.1944440156,0.1612814218,0.3012429774,0.0779469088,-0.3367469013,-0.1069349945,-0.0764871612,-0.5534629822,0.445138216,0.133591041,0.0741629824,-0.3437117934,0.1504577994,0.5705774426,0.1057047993,-0.0104902675,0.2287023664,0.2733797133,0.005434711,0.1107665151,0.0830746666,-0.0899362862,-0.0889612734,0.1008345857,-0.1473124325,-0.1107303202,-0.2270203382,0.1124099791,0.0892055854,-0.3083957136,0.2792796195,-0.227465108,0.3152402341,-0.1249576584,-0.0829687566,-0.2377426773,-0.0667323172,0.1559667885,-0.0591069087,0.0739138052,0.2452137023,-0.2607334852,-0.1920612901,-0.0771116391,-0.3397490382,-0.2851879001,-0.0617215782,0.0947027877,-0.165494591,0.2159024328,0.2415551245,-0.2671947181,0.6555529237,-0.0798197463,0.0127971917,-0.2747654617,-0.1418095827,0.0071923966,0.0408245735,0.5534662008,0.1325238049,-0.0175623372,-0.0687896535,0.1282823086,0.0086658485,0.3722593188,0.1806448698,-0.0535966232,-0.4219526947,0.3124936521,0.2268715054,-0.2402810752,-0.486985594,0.3215382397,-0.394307375,0.0994933695,0.3229117393,0.0228835605,0.0517107099,-0.024582373,0.2853384614,0.1822132915,-0.0260959864,0.0234954469,-0.202524215,-0.1073302031,-0.0406307913,0.1691952497,0.0109546073,0.0670013204,0.0796193257,-0.1751918048,0.2715687454,0.0314637274,-0.2040443867,0.3736798167,0.1382424235,0.3267191648,0.1860822737,-0.106327638,-0.5937504768,0.2637207508,-0.1218474954,-0.1787318438,-0.2175928354,-0.1868047863,-0.1749890149,-0.0072898441,0.0034599197,0.1351371109,-0.0204084311,0.2832494974,0.1417792886,-0.1317639649,0.0934399366,0.5883799791,-0.1702439636,0.0175973084,-0.4781179428,-0.17112647,-0.1178534552,-0.1125399023,0.0315071233,0.0016395918,0.3693964779,-0.1089679822,-0.3443510532,0.4480753541,0.3332768381,0.0975331739,-0.1789251119,-0.0044306694,-0.0568642691,0.220775336,0.0726701096,0.0398121551,0.0077854944,0.0142915584,-0.2135758698,0.3000163734,-0.14081572,0.27127707,0.0286138058,0.1682408899,0.2461784184,-0.1318140626,0.0643453151,-0.1761228889,0.6458842158,-0.2163528502,0.3048405647,0.0547799319,0.0452316478,-0.1448624283,0.6458194852,0.045392707,-0.0001772655,0.1977425367,0.1562013328,-0.0408157147,-0.0193730351,0.3064890206,0.5111442208,0.1920584738,-0.0301231258,-0.2065057307,0.3941576481,-0.2238344699,0.256319046,0.0159084182,-0.0027928618,0.3657026589,0.0496179983,-0.1290875971,0.0514840819,-0.8095089197,-0.050442148,0.223697722,-0.3198090196,0.1480822265,-0.1393256634,-0.0432045013,-0.0237995591,0.0004903108,-0.0803984031,-0.4162820876,-0.1888936311,0.2785063386,-0.1418365985,0.2145332694,-0.1188357845,-0.0032407197,0.2712830305,-0.0145387761,-0.2950273752,-0.0016123495,0.0803438574,-0.0837603286,0.2720724344,-0.5375033021,0.0838043541,-0.1256639212,-0.0631072521,-0.5083525181,-0.2498805523,0.1116677895,-0.1684809178,0.2935642302,0.3413662016,0.2078575194,0.1666625887,0.0588969849,0.0049610781,-0.2622792721,0.0236695651,0.2326634228,0.1779004186,-0.0228179917,-0.1917994022,-0.023455549,-0.3463737965,-0.4160333872,0.3207841218,0.0303027015,0.2279285491,0.3411892653,-0.0708731115,0.2978130579,-0.0179723371,0.2472920567,-0.0879067779,-0.446370095,0.4170611799,-0.1525222659,-0.2990590334,-0.4318080544,0.2980134785,-0.0355094597,0.1055189967,-0.4223643541,-0.1790713519,-0.401260376,0.3389849961,-0.3357198238,0.0054093283,0.1970204413,-0.0852006599,-0.0563488565,-0.1069383249,0.0849020928,0.2325119227,0.0110361269,-0.009520852,0.0929816738,0.2951667607,-0.1160778701,0.4532136321,0.3616654873,0.0753480569,0.1953798383,-0.1376693696,0.1843574643,-0.2590326071,-0.1269457787,0.0239379536,0.1744713485,-0.2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{"html_url":"https:\/\/github.com\/huggingface\/datasets\/issues\/2919","title":"Unwanted progress bars when accessing examples","comments":"doing a patch release now :)","body":"When accessing examples from a dataset formatted for pytorch, some progress bars appear when accessing examples:\r\n```python\r\nIn [1]: import datasets as ds \r\n\r\nIn [2]: d = ds.Dataset.from_dict({\"a\": [0, 1, 2]}).with_format(\"torch\") \r\n\r\nIn [3]: d[0] \r\n100%|\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588| 1\/1 [00:00<00:00, 3172.70it\/s]\r\nOut[3]: {'a': tensor(0)}\r\n```\r\n\r\nThis is because the pytorch formatter calls `map_nested` that uses progress bars\r\n\r\ncc @sgugger ","comment_length":6,"text":"Unwanted progress bars when accessing examples\nWhen accessing examples from a dataset formatted for pytorch, some progress bars appear when accessing examples:\r\n```python\r\nIn [1]: import datasets as ds \r\n\r\nIn [2]: d = ds.Dataset.from_dict({\"a\": [0, 1, 2]}).with_format(\"torch\") \r\n\r\nIn [3]: d[0] \r\n100%|\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588| 1\/1 [00:00<00:00, 3172.70it\/s]\r\nOut[3]: {'a': tensor(0)}\r\n```\r\n\r\nThis is because the pytorch formatter calls `map_nested` that uses progress bars\r\n\r\ncc @sgugger \ndoing a patch release now :)","embeddings":[-0.0175480675,-0.2450868934,0.0048732767,0.10296496,0.1993564218,0.0186564457,0.5456594825,0.2686175108,-0.3862209618,0.233160913,0.1730663329,0.4301009178,-0.0968903452,0.163059175,0.0540391617,-0.2791727781,-0.0362301841,0.0565355495,0.0279643871,0.077860862,-0.0710608438,-0.1534580141,-0.1631929129,0.2491238117,-0.4254311323,-0.1769718528,0.0204986315,-0.2497630417,0.2259828746,-0.6468563676,0.3624524474,0.017228175,0.0925377831,0.4540927112,-0.0001153858,0.1453293562,0.4285333753,0.2160392404,-0.2830248773,-0.1382309198,0.3537838757,-0.5126107335,0.3308672309,-0.3774378598,-0.0873389021,-0.5281800628,-0.2761972547,-0.4125932157,0.2876147032,0.0940865576,0.219826296,0.4712334275,-0.2902378142,-0.0055018864,0.5578870773,0.1376104206,-0.3271023929,0.1375557929,0.5464651585,0.0474077016,-0.2612787783,0.6541268229,-0.1014311612,0.3388422132,0.1663392037,-0.0622609183,0.0632258281,-0.1512287408,-0.1227654591,0.3897278607,0.1704966575,-0.0895446837,-0.2203477472,-0.810939312,-0.0621742755,-0.0982443467,-0.1076176837,0.1732621491,0.0438141599,0.0303935967,-0.5963996649,0.0290020015,-0.27146402,0.0937305689,0.0468122512,-0.1223285422,-0.0942170694,0.0973090604,-0.0284371544,0.083435297,0.1243030503,-0.1994698942,0.0932707414,0.0010196987,-0.046441935,0.1807018369,0.1487292498,-0.170697704,-0.1335336268,0.1875990182,-0.0521841459,0.1841346174,-0.0532620661,0.1660489142,-0.0838754028,-0.0406403653,0.1388794035,-0.2265987098,0.3347287774,0.0163198523,0.2717235684,0.1755359918,-0.037750639,-0.2225538939,0.1156542897,0.1093147323,0.1090649813,0.0211095344,-0.1003607139,0.2777105868,-0.3311299682,0.0219744816,0.2182542384,0.2214021385,-0.1495082378,-0.2717446387,0.1687204242,0.2794264853,-0.0600529797,0.2132000178,-0.0743473321,-0.1063092723,-0.4007383883,-0.1235286221,0.2540350258,0.1500249505,0.2033672929,0.1358764023,0.1028876156,-0.0182450209,0.3721589446,0.0875214264,0.4961630404,0.1029135436,-0.2043189853,0.2071680725,0.075304538,0.4455814064,-0.0290748477,0.271618098,-0.0666681826,-0.3141361773,-0.5112269521,0.1377267092,-0.4437701702,0.2145982981,-0.0623717569,-0.0707329661,0.3211912811,-0.0310045853,-0.0121499458,-0.3121537268,-0.247789517,0.0666654259,0.2291434705,0.2016990036,-0.1900742948,-0.2416289151,0.2099226117,0.0013152339,0.0640754029,0.1185466424,-0.23551175,0.3596096337,-0.2419328094,-0.026544407,0.1872638762,-0.1814072579,-0.3709934652,-0.1421762258,0.0234307237,0.3529036641,0.0315213762,-0.1182669997,0.3322122693,0.0806723833,-0.0189346746,0.1771131754,0.2529223263,-0.0826835856,-0.2729922533,0.0518658161,0.3036836088,0.2500388026,-0.0991399065,-0.1353184134,-0.2295820117,-0.2512821555,0.1488023102,-0.2186713666,0.1137110293,-0.0783538818,-0.0333985202,-0.1114408895,0.128908366,-0.200934723,-0.1400860697,0.334146589,-0.1916212291,-0.0585839003,-0.1843481064,-0.1486791074,-0.2163711041,-0.0423979238,-0.2948601842,-0.0875450671,0.0705130398,-0.1551547796,-0.0718744695,-0.0183241945,0.0944948941,-0.1325680614,-0.3775212765,-0.0472109951,0.0076890243,0.3263398409,0.1543031484,-0.3600919843,-0.1122498885,0.4872063696,0.0123988334,-0.0924425796,-0.1616445929,0.0323477574,0.1806732118,0.020979777,-0.418775171,0.1870616972,0.3267300427,0.1015354991,-0.0053531798,0.2033281922,-0.110525474,-0.0142406756,-0.2096013576,0.275554955,0.3913537562,0.3871296048,-0.0188226514,0.1576325446,0.2299357653,0.2287838459,-0.19063811,-0.3708530664,0.3794209957,0.1164492592,0.2275259644,-0.1128709093,-0.056149736,0.2054813057,0.2419088632,-0.0129112294,0.0710802451,0.1942463815,-0.3472250402,-0.0823546052,0.2821888328,-0.0113203805,0.0383519419,0.0980123356,0.1815937907,0.1735030264,-0.0646654963,-0.2774474919,-0.002367727,0.252974838,0.1037782133,0.0188018717,0.1014887169,0.0363334157,-0.0996529385,-0.2078756839,-0.2531711757,0.0459414124,-0.4298397005,0.2782195807,-0.3938655257,-0.2349227965,-0.1121626124,-0.1845910549,0.0518490709,-0.2728966475,0.1209051535,0.5843741298,-0.1538914591,0.3002850115,0.3276317716,0.1584009379,0.0250879861,0.1818053126,-0.0510158017,-0.510720849,-0.2618277967,0.0802433044,-0.0849289596,-0.1672920287,0.5056151152,-0.1877110004,0.4327579141,-0.3314349949,-0.1271976531,0.1429183781,-0.0935863927,0.0404994525,-0.0486703366,0.1458623558,-0.0911693275,0.233792603,0.160188511,-0.1380708367,-0.0214611273,0.16621387,-0.2902114093,-0.0336513706,-0.2353742123,0.0583931804,-0.3945191801,-0.1011367291,0.0673801824,-0.1514690369,0.1948434263,-0.0160474218,-0.3517791629,0.4189587235,0.1875047535,-0.1340169311,-0.1357982308,-0.3630971313,0.2848750055,-0.41330266,-0.2727486789,-0.0870164037,-0.2269277126,-0.2414059043,0.2249186188,-0.4970188141,-0.4690299034,-0.2819567025,0.2401717752,0.0068959016,-0.0885310322,0.2275055051,0.2388396263,-0.0490171686,-0.1502595395,-0.1286485642,0.2684911489,-0.2023165226,-0.0526690036,-0.2280971706,-0.1113249883,0.1931582093,0.4944924414,0.1924008131,-0.2912912369,0.1187348738,0.0306330826,0.1123298332,-0.1802462488,-0.1826853007,-0.1176944822,-0.4110670388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{"html_url":"https:\/\/github.com\/huggingface\/datasets\/issues\/2918","title":"`Can not decode content-encoding: gzip` when loading `scitldr` dataset with streaming","comments":"Hi @SBrandeis, thanks for reporting! ^^\r\n\r\nI think this is an issue with `fsspec`: https:\/\/github.com\/intake\/filesystem_spec\/issues\/389\r\n\r\nI will ask them if they are planning to fix it...","body":"## Describe the bug\r\n\r\nTrying to load the `\"FullText\"` config of the `\"scitldr\"` dataset with `streaming=True` raises an error from `aiohttp`:\r\n```python\r\nClientPayloadError: 400, message='Can not decode content-encoding: gzip'\r\n```\r\n\r\ncc @lhoestq \r\n\r\n## Steps to reproduce the bug\r\n```python\r\nfrom datasets import load_dataset\r\n\r\niter_dset = iter(\r\n load_dataset(\"scitldr\", name=\"FullText\", split=\"test\", streaming=True)\r\n)\r\n\r\nnext(iter_dset)\r\n```\r\n\r\n## Expected results\r\nReturns the first sample of the dataset\r\n\r\n## Actual results\r\nCalling `__next__` crashes with the following Traceback:\r\n\r\n```python\r\n----> 1 next(dset_iter)\r\n\r\n~\\miniconda3\\envs\\datasets\\lib\\site-packages\\datasets\\iterable_dataset.py in __iter__(self)\r\n 339\r\n 340 def __iter__(self):\r\n--> 341 for key, example in self._iter():\r\n 342 if self.features:\r\n 343 # we encode the example for ClassLabel feature types for example\r\n\r\n~\\miniconda3\\envs\\datasets\\lib\\site-packages\\datasets\\iterable_dataset.py in _iter(self)\r\n 336 else:\r\n 337 ex_iterable = self._ex_iterable\r\n--> 338 yield from ex_iterable\r\n 339\r\n 340 def __iter__(self):\r\n\r\n~\\miniconda3\\envs\\datasets\\lib\\site-packages\\datasets\\iterable_dataset.py in __iter__(self)\r\n 76\r\n 77 def __iter__(self):\r\n---> 78 for key, example in self.generate_examples_fn(**self.kwargs):\r\n 79 yield key, example\r\n 80\r\n\r\n~\\.cache\\huggingface\\modules\\datasets_modules\\datasets\\scitldr\\72d6e2195786c57e1d343066fb2cc4f93ea39c5e381e53e6ae7c44bbfd1f05ef\\scitldr.py in _generate_examples(self, filepath, split)\r\n 162\r\n 163 with open(filepath, encoding=\"utf-8\") as f:\r\n--> 164 for id_, row in enumerate(f):\r\n 165 data = json.loads(row)\r\n 166 if self.config.name == \"AIC\":\r\n\r\n~\\miniconda3\\envs\\datasets\\lib\\site-packages\\fsspec\\implementations\\http.py in read(self, length)\r\n 496 else:\r\n 497 length = min(self.size - self.loc, length)\r\n--> 498 return super().read(length)\r\n 499\r\n 500 async def async_fetch_all(self):\r\n\r\n~\\miniconda3\\envs\\datasets\\lib\\site-packages\\fsspec\\spec.py in read(self, length)\r\n 1481 # don't even bother calling fetch\r\n 1482 return b\"\"\r\n-> 1483 out = self.cache._fetch(self.loc, self.loc + length)\r\n 1484 self.loc += len(out)\r\n 1485 return out\r\n\r\n~\\miniconda3\\envs\\datasets\\lib\\site-packages\\fsspec\\caching.py in _fetch(self, start, end)\r\n 378 elif start < self.start:\r\n 379 if self.end - end > self.blocksize:\r\n--> 380 self.cache = self.fetcher(start, bend)\r\n 381 self.start = start\r\n 382 else:\r\n\r\n~\\miniconda3\\envs\\datasets\\lib\\site-packages\\fsspec\\asyn.py in wrapper(*args, **kwargs)\r\n 86 def wrapper(*args, **kwargs):\r\n 87 self = obj or args[0]\r\n---> 88 return sync(self.loop, func, *args, **kwargs)\r\n 89\r\n 90 return wrapper\r\n\r\n~\\miniconda3\\envs\\datasets\\lib\\site-packages\\fsspec\\asyn.py in sync(loop, func, timeout, *args, **kwargs)\r\n 67 raise FSTimeoutError\r\n 68 if isinstance(result[0], BaseException):\r\n---> 69 raise result[0]\r\n 70 return result[0]\r\n 71\r\n\r\n~\\miniconda3\\envs\\datasets\\lib\\site-packages\\fsspec\\asyn.py in _runner(event, coro, result, timeout)\r\n 23 coro = asyncio.wait_for(coro, timeout=timeout)\r\n 24 try:\r\n---> 25 result[0] = await coro\r\n 26 except Exception as ex:\r\n 27 result[0] = ex\r\n\r\n~\\miniconda3\\envs\\datasets\\lib\\site-packages\\fsspec\\implementations\\http.py in async_fetch_range(self, start, end)\r\n 538 if r.status == 206:\r\n 539 # partial content, as expected\r\n--> 540 out = await r.read()\r\n 541 elif \"Content-Length\" in r.headers:\r\n 542 cl = int(r.headers[\"Content-Length\"])\r\n\r\n~\\miniconda3\\envs\\datasets\\lib\\site-packages\\aiohttp\\client_reqrep.py in read(self)\r\n 1030 if self._body is None:\r\n 1031 try:\r\n-> 1032 self._body = await self.content.read()\r\n 1033 for trace in self._traces:\r\n 1034 await trace.send_response_chunk_received(\r\n\r\n~\\miniconda3\\envs\\datasets\\lib\\site-packages\\aiohttp\\streams.py in read(self, n)\r\n 342 async def read(self, n: int = -1) -> bytes:\r\n 343 if self._exception is not None:\r\n--> 344 raise self._exception\r\n 345\r\n 346 # migration problem; with DataQueue you have to catch\r\n\r\nClientPayloadError: 400, message='Can not decode content-encoding: gzip'\r\n```\r\n\r\n## Environment info\r\n\r\n- `datasets` version: 1.12.0\r\n- Platform: Windows-10-10.0.19041-SP0\r\n- Python version: 3.8.5\r\n- PyArrow version: 2.0.0\r\n- aiohttp version: 3.7.4.post0\r\n","comment_length":26,"text":"`Can not decode content-encoding: gzip` when loading `scitldr` dataset with streaming\n## Describe the bug\r\n\r\nTrying to load the `\"FullText\"` config of the `\"scitldr\"` dataset with `streaming=True` raises an error from `aiohttp`:\r\n```python\r\nClientPayloadError: 400, message='Can not decode content-encoding: gzip'\r\n```\r\n\r\ncc @lhoestq \r\n\r\n## Steps to reproduce the bug\r\n```python\r\nfrom datasets import load_dataset\r\n\r\niter_dset = iter(\r\n load_dataset(\"scitldr\", name=\"FullText\", split=\"test\", streaming=True)\r\n)\r\n\r\nnext(iter_dset)\r\n```\r\n\r\n## Expected results\r\nReturns the first sample of the dataset\r\n\r\n## Actual results\r\nCalling `__next__` crashes with the following Traceback:\r\n\r\n```python\r\n----> 1 next(dset_iter)\r\n\r\n~\\miniconda3\\envs\\datasets\\lib\\site-packages\\datasets\\iterable_dataset.py in __iter__(self)\r\n 339\r\n 340 def __iter__(self):\r\n--> 341 for key, example in self._iter():\r\n 342 if self.features:\r\n 343 # we encode the example for ClassLabel feature types for example\r\n\r\n~\\miniconda3\\envs\\datasets\\lib\\site-packages\\datasets\\iterable_dataset.py in _iter(self)\r\n 336 else:\r\n 337 ex_iterable = self._ex_iterable\r\n--> 338 yield from ex_iterable\r\n 339\r\n 340 def __iter__(self):\r\n\r\n~\\miniconda3\\envs\\datasets\\lib\\site-packages\\datasets\\iterable_dataset.py in __iter__(self)\r\n 76\r\n 77 def __iter__(self):\r\n---> 78 for key, example in self.generate_examples_fn(**self.kwargs):\r\n 79 yield key, example\r\n 80\r\n\r\n~\\.cache\\huggingface\\modules\\datasets_modules\\datasets\\scitldr\\72d6e2195786c57e1d343066fb2cc4f93ea39c5e381e53e6ae7c44bbfd1f05ef\\scitldr.py in _generate_examples(self, filepath, split)\r\n 162\r\n 163 with open(filepath, encoding=\"utf-8\") as f:\r\n--> 164 for id_, row in enumerate(f):\r\n 165 data = json.loads(row)\r\n 166 if self.config.name == \"AIC\":\r\n\r\n~\\miniconda3\\envs\\datasets\\lib\\site-packages\\fsspec\\implementations\\http.py in read(self, length)\r\n 496 else:\r\n 497 length = min(self.size - self.loc, length)\r\n--> 498 return super().read(length)\r\n 499\r\n 500 async def async_fetch_all(self):\r\n\r\n~\\miniconda3\\envs\\datasets\\lib\\site-packages\\fsspec\\spec.py in read(self, length)\r\n 1481 # don't even bother calling fetch\r\n 1482 return b\"\"\r\n-> 1483 out = self.cache._fetch(self.loc, self.loc + length)\r\n 1484 self.loc += len(out)\r\n 1485 return out\r\n\r\n~\\miniconda3\\envs\\datasets\\lib\\site-packages\\fsspec\\caching.py in _fetch(self, start, end)\r\n 378 elif start < self.start:\r\n 379 if self.end - end > self.blocksize:\r\n--> 380 self.cache = self.fetcher(start, bend)\r\n 381 self.start = start\r\n 382 else:\r\n\r\n~\\miniconda3\\envs\\datasets\\lib\\site-packages\\fsspec\\asyn.py in wrapper(*args, **kwargs)\r\n 86 def wrapper(*args, **kwargs):\r\n 87 self = obj or args[0]\r\n---> 88 return sync(self.loop, func, *args, **kwargs)\r\n 89\r\n 90 return wrapper\r\n\r\n~\\miniconda3\\envs\\datasets\\lib\\site-packages\\fsspec\\asyn.py in sync(loop, func, timeout, *args, **kwargs)\r\n 67 raise FSTimeoutError\r\n 68 if isinstance(result[0], BaseException):\r\n---> 69 raise result[0]\r\n 70 return result[0]\r\n 71\r\n\r\n~\\miniconda3\\envs\\datasets\\lib\\site-packages\\fsspec\\asyn.py in _runner(event, coro, result, timeout)\r\n 23 coro = asyncio.wait_for(coro, timeout=timeout)\r\n 24 try:\r\n---> 25 result[0] = await coro\r\n 26 except Exception as ex:\r\n 27 result[0] = ex\r\n\r\n~\\miniconda3\\envs\\datasets\\lib\\site-packages\\fsspec\\implementations\\http.py in async_fetch_range(self, start, end)\r\n 538 if r.status == 206:\r\n 539 # partial content, as expected\r\n--> 540 out = await r.read()\r\n 541 elif \"Content-Length\" in r.headers:\r\n 542 cl = int(r.headers[\"Content-Length\"])\r\n\r\n~\\miniconda3\\envs\\datasets\\lib\\site-packages\\aiohttp\\client_reqrep.py in read(self)\r\n 1030 if self._body is None:\r\n 1031 try:\r\n-> 1032 self._body = await self.content.read()\r\n 1033 for trace in self._traces:\r\n 1034 await trace.send_response_chunk_received(\r\n\r\n~\\miniconda3\\envs\\datasets\\lib\\site-packages\\aiohttp\\streams.py in read(self, n)\r\n 342 async def read(self, n: int = -1) -> bytes:\r\n 343 if self._exception is not None:\r\n--> 344 raise self._exception\r\n 345\r\n 346 # migration problem; with DataQueue you have to catch\r\n\r\nClientPayloadError: 400, message='Can not decode content-encoding: gzip'\r\n```\r\n\r\n## Environment info\r\n\r\n- `datasets` version: 1.12.0\r\n- Platform: Windows-10-10.0.19041-SP0\r\n- Python version: 3.8.5\r\n- PyArrow version: 2.0.0\r\n- aiohttp version: 3.7.4.post0\r\n\nHi @SBrandeis, thanks for reporting! ^^\r\n\r\nI think this is an issue with `fsspec`: https:\/\/github.com\/intake\/filesystem_spec\/issues\/389\r\n\r\nI will ask them if they are planning to fix it...","embeddings":[-0.3864652812,-0.2204957455,0.0894813165,0.4387415349,0.2157086134,0.1229229122,-0.0369101539,0.3093882799,0.2422775328,0.0955026373,-0.2264311761,0.4261536002,-0.0641756877,0.3035327792,-0.0094654057,-0.2584786713,-0.0045060921,0.2337655127,-0.1049250886,0.1268760264,-0.0005556306,0.1941259056,-0.2469931841,-0.0304233823,0.0522285365,-0.1081046611,0.0124065261,0.2805253863,-0.059583243,-0.4462363422,0.1259049624,-0.0615482517,0.4121141732,0.3375741839,-0.0001145032,0.2734483778,0.4721427262,-0.1227841005,-0.4853440225,-0.2121149004,-0.218665719,0.0961271524,0.0428345352,-0.1566341817,-0.0091345869,0.0711133331,-0.0400046185,-0.7192984819,0.3178500533,0.3456359804,0.1778500229,0.0426006429,-0.0489487238,0.0268223863,0.0688386783,-0.2986646891,-0.0920066237,0.1218173951,0.2367273569,0.2287404835,-0.359573245,0.3845903277,-0.0658884048,0.0601840951,0.0944148228,0.0967238322,-0.1749501675,-0.4655051231,-0.0073944977,0.1884141415,0.6776787043,-0.2555166781,-0.4161978662,-0.2319881767,-0.0021713183,-0.6144573689,0.1465310603,0.1489273459,-0.4673651159,0.1280597448,0.2459630817,-0.0087530883,-0.327994585,-0.0458728224,-0.246799171,0.3882223368,-0.0767857283,-0.0237270929,0.0272943433,-0.0866289064,0.4627741277,-0.2110346556,-0.28459391,-0.0639790371,-0.4397552311,-0.0420655161,-0.0765123144,-0.094109349,0.1681466401,0.215389967,0.2953203917,0.0882523879,0.1543645263,0.0926442817,0.3555265665,0.3608669043,0.0598316565,0.1853774786,0.1311810017,0.0994313955,0.1366237104,-0.1818924248,-0.146488592,-0.0372809209,0.0281632524,-0.0131140957,0.262858659,-0.1912374645,-0.4138476253,0.0297920927,-0.3057210743,-0.0794672072,0.0055905147,0.2817168832,0.0958337784,0.4187803268,0.0194585584,0.1178673506,-0.2396837324,-0.4573938251,-0.0878112167,-0.1247182265,-0.0010082311,0.1233467832,0.0153353186,-0.1886832416,0.1515737325,-0.0465371571,0.1621010602,-0.0012185768,-0.0208561029,-0.1100598425,0.0216292907,0.2049841285,0.2180556655,0.1640353203,0.1795151085,-0.1997850239,-0.130682379,0.0886226073,-0.1416330338,-0.1671275645,-0.1788374335,0.2166469097,-0.0094024939,-0.3506264389,-0.0937892571,0.2814688385,0.1409590542,-0.2037903368,-0.1410701424,-0.0391465798,-0.1241319329,-0.0865156949,0.1792775393,0.5796180964,-0.1855915189,-0.1380216628,-0.1521381587,0.0008939481,0.5833057165,0.3344025612,-0.0607914478,0.0897968188,-0.170722425,0.1476544142,0.5017140508,-0.1436584145,-0.5796036124,0.5436661243,-0.0396082066,0.5637281537,0.2707032561,0.0225366186,0.1742344499,0.0431196988,0.313529104,0.3299024701,-0.1484592706,0.0074822325,-0.3334669471,-0.0399836674,0.4416950643,0.1912285686,0.0674290732,0.2502861321,-0.0288742278,-0.0626677349,0.3689779639,-0.0752563924,0.0763451308,0.0410080664,0.1468882412,0.0120370993,0.1379382461,-0.2090770155,-0.310682714,0.2972311378,0.0749307722,0.0127100674,-0.3174338341,0.2031723857,-0.1906455308,0.0017469627,-0.2628925443,-0.2590405643,0.1243173257,0.3170801699,0.0756207108,0.0505895875,-0.3084766865,0.7747215033,-0.078255251,-0.0131349247,-0.4549141228,0.2024929523,-0.0707768798,-0.2014573216,0.0164402872,-0.1265656799,0.0672556311,-0.0161919687,-0.2579914331,0.3313200772,-0.2366138101,0.2640713155,-0.1954375803,-0.1731976271,0.2897448838,-0.3644788265,-0.0866843313,0.2706028223,0.1075036898,0.125209704,-0.0176983867,0.1480839401,0.2070915401,0.2361335903,0.0434485786,0.1143487841,0.1961675137,-0.0816748515,-0.0686837137,-0.0850090533,0.3240485787,-0.3548789024,0.0467500761,-0.1989230514,-0.2722995877,0.0823631957,0.3586791456,-0.1403829306,-0.0492737889,0.3303245604,-0.2885322273,-0.1639078707,0.3145605624,0.1940099597,0.4723360538,0.0815219656,0.2573353648,-0.0455882996,-0.1886483878,-0.0735060051,0.3051697612,0.1374839693,0.0314638466,0.2464114279,0.0300063528,0.1635747999,-0.3229160309,-0.3497166634,-0.0566318817,0.1123416647,-0.3201874793,0.306302011,-0.38826859,-0.4651744664,0.0463509336,0.1560085416,0.0689049438,-0.3235587776,-0.1817550063,0.2325055152,-0.0038823313,0.1636838913,-0.2682063282,0.0895485729,0.2671770155,-0.0134687945,-0.3418698907,-0.1808474958,-0.2729629874,0.0591977872,0.1749951094,-0.4228037596,0.1769195795,0.0074260007,-0.1160532683,-0.1401735991,-0.076423429,0.3197818398,-0.1554995328,0.1568958163,-0.2774865627,0.1493911892,0.2213633955,-0.2503491044,0.1219482496,0.0908335969,-0.1187844425,0.2801248431,0.0940897167,-0.0241610892,-0.0305832736,-0.3010807931,-0.3024666011,-0.6177494526,0.0549683981,0.0454257689,0.2135515809,0.1643050015,0.0170347784,0.0968246013,0.3292069137,0.0991601422,-0.114212282,-0.4199434817,0.3747448325,-0.1670146883,-0.2868409157,-0.3248367012,-0.0681597292,0.1972901672,0.2589270473,-0.5295243263,0.2950162292,-0.4081035852,0.0848925635,-0.3834516108,-0.0974560827,-0.0261515174,0.0762885734,-0.0349893123,-0.1878576428,0.0707459524,-0.1169513538,0.0990076959,0.2066107839,0.1086307243,0.3698712885,0.1008493528,0.3327504098,0.4812112153,-0.0195496976,0.3407551944,0.082891129,0.1538007557,-0.1004334986,-0.0627885908,-0.0955621898,-0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{"html_url":"https:\/\/github.com\/huggingface\/datasets\/issues\/2918","title":"`Can not decode content-encoding: gzip` when loading `scitldr` dataset with streaming","comments":"Code to reproduce the bug: `ClientPayloadError: 400, message='Can not decode content-encoding: gzip'`\r\n```python\r\nIn [1]: import fsspec\r\n\r\nIn [2]: import json\r\n\r\nIn [3]: with fsspec.open('https:\/\/raw.githubusercontent.com\/allenai\/scitldr\/master\/SciTLDR-Data\/SciTLDR-FullText\/test.jsonl', encoding=\"utf-8\") as f:\r\n ...: for row in f:\r\n ...: data = json.loads(row)\r\n ...:\r\n---------------------------------------------------------------------------\r\nClientPayloadError Traceback (most recent call last)\r\n```","body":"## Describe the bug\r\n\r\nTrying to load the `\"FullText\"` config of the `\"scitldr\"` dataset with `streaming=True` raises an error from `aiohttp`:\r\n```python\r\nClientPayloadError: 400, message='Can not decode content-encoding: gzip'\r\n```\r\n\r\ncc @lhoestq \r\n\r\n## Steps to reproduce the bug\r\n```python\r\nfrom datasets import load_dataset\r\n\r\niter_dset = iter(\r\n load_dataset(\"scitldr\", name=\"FullText\", split=\"test\", streaming=True)\r\n)\r\n\r\nnext(iter_dset)\r\n```\r\n\r\n## Expected results\r\nReturns the first sample of the dataset\r\n\r\n## Actual results\r\nCalling `__next__` crashes with the following Traceback:\r\n\r\n```python\r\n----> 1 next(dset_iter)\r\n\r\n~\\miniconda3\\envs\\datasets\\lib\\site-packages\\datasets\\iterable_dataset.py in __iter__(self)\r\n 339\r\n 340 def __iter__(self):\r\n--> 341 for key, example in self._iter():\r\n 342 if self.features:\r\n 343 # we encode the example for ClassLabel feature types for example\r\n\r\n~\\miniconda3\\envs\\datasets\\lib\\site-packages\\datasets\\iterable_dataset.py in _iter(self)\r\n 336 else:\r\n 337 ex_iterable = self._ex_iterable\r\n--> 338 yield from ex_iterable\r\n 339\r\n 340 def __iter__(self):\r\n\r\n~\\miniconda3\\envs\\datasets\\lib\\site-packages\\datasets\\iterable_dataset.py in __iter__(self)\r\n 76\r\n 77 def __iter__(self):\r\n---> 78 for key, example in self.generate_examples_fn(**self.kwargs):\r\n 79 yield key, example\r\n 80\r\n\r\n~\\.cache\\huggingface\\modules\\datasets_modules\\datasets\\scitldr\\72d6e2195786c57e1d343066fb2cc4f93ea39c5e381e53e6ae7c44bbfd1f05ef\\scitldr.py in _generate_examples(self, filepath, split)\r\n 162\r\n 163 with open(filepath, encoding=\"utf-8\") as f:\r\n--> 164 for id_, row in enumerate(f):\r\n 165 data = json.loads(row)\r\n 166 if self.config.name == \"AIC\":\r\n\r\n~\\miniconda3\\envs\\datasets\\lib\\site-packages\\fsspec\\implementations\\http.py in read(self, length)\r\n 496 else:\r\n 497 length = min(self.size - self.loc, length)\r\n--> 498 return super().read(length)\r\n 499\r\n 500 async def async_fetch_all(self):\r\n\r\n~\\miniconda3\\envs\\datasets\\lib\\site-packages\\fsspec\\spec.py in read(self, length)\r\n 1481 # don't even bother calling fetch\r\n 1482 return b\"\"\r\n-> 1483 out = self.cache._fetch(self.loc, self.loc + length)\r\n 1484 self.loc += len(out)\r\n 1485 return out\r\n\r\n~\\miniconda3\\envs\\datasets\\lib\\site-packages\\fsspec\\caching.py in _fetch(self, start, end)\r\n 378 elif start < self.start:\r\n 379 if self.end - end > self.blocksize:\r\n--> 380 self.cache = self.fetcher(start, bend)\r\n 381 self.start = start\r\n 382 else:\r\n\r\n~\\miniconda3\\envs\\datasets\\lib\\site-packages\\fsspec\\asyn.py in wrapper(*args, **kwargs)\r\n 86 def wrapper(*args, **kwargs):\r\n 87 self = obj or args[0]\r\n---> 88 return sync(self.loop, func, *args, **kwargs)\r\n 89\r\n 90 return wrapper\r\n\r\n~\\miniconda3\\envs\\datasets\\lib\\site-packages\\fsspec\\asyn.py in sync(loop, func, timeout, *args, **kwargs)\r\n 67 raise FSTimeoutError\r\n 68 if isinstance(result[0], BaseException):\r\n---> 69 raise result[0]\r\n 70 return result[0]\r\n 71\r\n\r\n~\\miniconda3\\envs\\datasets\\lib\\site-packages\\fsspec\\asyn.py in _runner(event, coro, result, timeout)\r\n 23 coro = asyncio.wait_for(coro, timeout=timeout)\r\n 24 try:\r\n---> 25 result[0] = await coro\r\n 26 except Exception as ex:\r\n 27 result[0] = ex\r\n\r\n~\\miniconda3\\envs\\datasets\\lib\\site-packages\\fsspec\\implementations\\http.py in async_fetch_range(self, start, end)\r\n 538 if r.status == 206:\r\n 539 # partial content, as expected\r\n--> 540 out = await r.read()\r\n 541 elif \"Content-Length\" in r.headers:\r\n 542 cl = int(r.headers[\"Content-Length\"])\r\n\r\n~\\miniconda3\\envs\\datasets\\lib\\site-packages\\aiohttp\\client_reqrep.py in read(self)\r\n 1030 if self._body is None:\r\n 1031 try:\r\n-> 1032 self._body = await self.content.read()\r\n 1033 for trace in self._traces:\r\n 1034 await trace.send_response_chunk_received(\r\n\r\n~\\miniconda3\\envs\\datasets\\lib\\site-packages\\aiohttp\\streams.py in read(self, n)\r\n 342 async def read(self, n: int = -1) -> bytes:\r\n 343 if self._exception is not None:\r\n--> 344 raise self._exception\r\n 345\r\n 346 # migration problem; with DataQueue you have to catch\r\n\r\nClientPayloadError: 400, message='Can not decode content-encoding: gzip'\r\n```\r\n\r\n## Environment info\r\n\r\n- `datasets` version: 1.12.0\r\n- Platform: Windows-10-10.0.19041-SP0\r\n- Python version: 3.8.5\r\n- PyArrow version: 2.0.0\r\n- aiohttp version: 3.7.4.post0\r\n","comment_length":46,"text":"`Can not decode content-encoding: gzip` when loading `scitldr` dataset with streaming\n## Describe the bug\r\n\r\nTrying to load the `\"FullText\"` config of the `\"scitldr\"` dataset with `streaming=True` raises an error from `aiohttp`:\r\n```python\r\nClientPayloadError: 400, message='Can not decode content-encoding: gzip'\r\n```\r\n\r\ncc @lhoestq \r\n\r\n## Steps to reproduce the bug\r\n```python\r\nfrom datasets import load_dataset\r\n\r\niter_dset = iter(\r\n load_dataset(\"scitldr\", name=\"FullText\", split=\"test\", streaming=True)\r\n)\r\n\r\nnext(iter_dset)\r\n```\r\n\r\n## Expected results\r\nReturns the first sample of the dataset\r\n\r\n## Actual results\r\nCalling `__next__` crashes with the following Traceback:\r\n\r\n```python\r\n----> 1 next(dset_iter)\r\n\r\n~\\miniconda3\\envs\\datasets\\lib\\site-packages\\datasets\\iterable_dataset.py in __iter__(self)\r\n 339\r\n 340 def __iter__(self):\r\n--> 341 for key, example in self._iter():\r\n 342 if self.features:\r\n 343 # we encode the example for ClassLabel feature types for example\r\n\r\n~\\miniconda3\\envs\\datasets\\lib\\site-packages\\datasets\\iterable_dataset.py in _iter(self)\r\n 336 else:\r\n 337 ex_iterable = self._ex_iterable\r\n--> 338 yield from ex_iterable\r\n 339\r\n 340 def __iter__(self):\r\n\r\n~\\miniconda3\\envs\\datasets\\lib\\site-packages\\datasets\\iterable_dataset.py in __iter__(self)\r\n 76\r\n 77 def __iter__(self):\r\n---> 78 for key, example in self.generate_examples_fn(**self.kwargs):\r\n 79 yield key, example\r\n 80\r\n\r\n~\\.cache\\huggingface\\modules\\datasets_modules\\datasets\\scitldr\\72d6e2195786c57e1d343066fb2cc4f93ea39c5e381e53e6ae7c44bbfd1f05ef\\scitldr.py in _generate_examples(self, filepath, split)\r\n 162\r\n 163 with open(filepath, encoding=\"utf-8\") as f:\r\n--> 164 for id_, row in enumerate(f):\r\n 165 data = json.loads(row)\r\n 166 if self.config.name == \"AIC\":\r\n\r\n~\\miniconda3\\envs\\datasets\\lib\\site-packages\\fsspec\\implementations\\http.py in read(self, length)\r\n 496 else:\r\n 497 length = min(self.size - self.loc, length)\r\n--> 498 return super().read(length)\r\n 499\r\n 500 async def async_fetch_all(self):\r\n\r\n~\\miniconda3\\envs\\datasets\\lib\\site-packages\\fsspec\\spec.py in read(self, length)\r\n 1481 # don't even bother calling fetch\r\n 1482 return b\"\"\r\n-> 1483 out = self.cache._fetch(self.loc, self.loc + length)\r\n 1484 self.loc += len(out)\r\n 1485 return out\r\n\r\n~\\miniconda3\\envs\\datasets\\lib\\site-packages\\fsspec\\caching.py in _fetch(self, start, end)\r\n 378 elif start < self.start:\r\n 379 if self.end - end > self.blocksize:\r\n--> 380 self.cache = self.fetcher(start, bend)\r\n 381 self.start = start\r\n 382 else:\r\n\r\n~\\miniconda3\\envs\\datasets\\lib\\site-packages\\fsspec\\asyn.py in wrapper(*args, **kwargs)\r\n 86 def wrapper(*args, **kwargs):\r\n 87 self = obj or args[0]\r\n---> 88 return sync(self.loop, func, *args, **kwargs)\r\n 89\r\n 90 return wrapper\r\n\r\n~\\miniconda3\\envs\\datasets\\lib\\site-packages\\fsspec\\asyn.py in sync(loop, func, timeout, *args, **kwargs)\r\n 67 raise FSTimeoutError\r\n 68 if isinstance(result[0], BaseException):\r\n---> 69 raise result[0]\r\n 70 return result[0]\r\n 71\r\n\r\n~\\miniconda3\\envs\\datasets\\lib\\site-packages\\fsspec\\asyn.py in _runner(event, coro, result, timeout)\r\n 23 coro = asyncio.wait_for(coro, timeout=timeout)\r\n 24 try:\r\n---> 25 result[0] = await coro\r\n 26 except Exception as ex:\r\n 27 result[0] = ex\r\n\r\n~\\miniconda3\\envs\\datasets\\lib\\site-packages\\fsspec\\implementations\\http.py in async_fetch_range(self, start, end)\r\n 538 if r.status == 206:\r\n 539 # partial content, as expected\r\n--> 540 out = await r.read()\r\n 541 elif \"Content-Length\" in r.headers:\r\n 542 cl = int(r.headers[\"Content-Length\"])\r\n\r\n~\\miniconda3\\envs\\datasets\\lib\\site-packages\\aiohttp\\client_reqrep.py in read(self)\r\n 1030 if self._body is None:\r\n 1031 try:\r\n-> 1032 self._body = await self.content.read()\r\n 1033 for trace in self._traces:\r\n 1034 await trace.send_response_chunk_received(\r\n\r\n~\\miniconda3\\envs\\datasets\\lib\\site-packages\\aiohttp\\streams.py in read(self, n)\r\n 342 async def read(self, n: int = -1) -> bytes:\r\n 343 if self._exception is not None:\r\n--> 344 raise self._exception\r\n 345\r\n 346 # migration problem; with DataQueue you have to catch\r\n\r\nClientPayloadError: 400, message='Can not decode content-encoding: gzip'\r\n```\r\n\r\n## Environment info\r\n\r\n- `datasets` version: 1.12.0\r\n- Platform: Windows-10-10.0.19041-SP0\r\n- Python version: 3.8.5\r\n- PyArrow version: 2.0.0\r\n- aiohttp version: 3.7.4.post0\r\n\nCode to reproduce the bug: `ClientPayloadError: 400, message='Can not decode content-encoding: gzip'`\r\n```python\r\nIn [1]: import fsspec\r\n\r\nIn [2]: import json\r\n\r\nIn [3]: with fsspec.open('https:\/\/raw.githubusercontent.com\/allenai\/scitldr\/master\/SciTLDR-Data\/SciTLDR-FullText\/test.jsonl', encoding=\"utf-8\") as f:\r\n ...: for row in f:\r\n ...: data = json.loads(row)\r\n ...:\r\n---------------------------------------------------------------------------\r\nClientPayloadError Traceback (most recent call last)\r\n```","embeddings":[-0.3864652812,-0.2204957455,0.0894813165,0.4387415349,0.2157086134,0.1229229122,-0.0369101539,0.3093882799,0.2422775328,0.0955026373,-0.2264311761,0.4261536002,-0.0641756877,0.3035327792,-0.0094654057,-0.2584786713,-0.0045060921,0.2337655127,-0.1049250886,0.1268760264,-0.0005556306,0.1941259056,-0.2469931841,-0.0304233823,0.0522285365,-0.1081046611,0.0124065261,0.2805253863,-0.059583243,-0.4462363422,0.1259049624,-0.0615482517,0.4121141732,0.3375741839,-0.0001145032,0.2734483778,0.4721427262,-0.1227841005,-0.4853440225,-0.2121149004,-0.218665719,0.0961271524,0.0428345352,-0.1566341817,-0.0091345869,0.0711133331,-0.0400046185,-0.7192984819,0.3178500533,0.3456359804,0.1778500229,0.0426006429,-0.0489487238,0.0268223863,0.0688386783,-0.2986646891,-0.0920066237,0.1218173951,0.2367273569,0.2287404835,-0.359573245,0.3845903277,-0.0658884048,0.0601840951,0.0944148228,0.0967238322,-0.1749501675,-0.4655051231,-0.0073944977,0.1884141415,0.6776787043,-0.2555166781,-0.4161978662,-0.2319881767,-0.0021713183,-0.6144573689,0.1465310603,0.1489273459,-0.4673651159,0.1280597448,0.2459630817,-0.0087530883,-0.327994585,-0.0458728224,-0.246799171,0.3882223368,-0.0767857283,-0.0237270929,0.0272943433,-0.0866289064,0.4627741277,-0.2110346556,-0.28459391,-0.0639790371,-0.4397552311,-0.0420655161,-0.0765123144,-0.094109349,0.1681466401,0.215389967,0.2953203917,0.0882523879,0.1543645263,0.0926442817,0.3555265665,0.3608669043,0.0598316565,0.1853774786,0.1311810017,0.0994313955,0.1366237104,-0.1818924248,-0.146488592,-0.0372809209,0.0281632524,-0.0131140957,0.262858659,-0.1912374645,-0.4138476253,0.0297920927,-0.3057210743,-0.0794672072,0.0055905147,0.2817168832,0.0958337784,0.4187803268,0.0194585584,0.1178673506,-0.2396837324,-0.4573938251,-0.0878112167,-0.1247182265,-0.0010082311,0.1233467832,0.0153353186,-0.1886832416,0.1515737325,-0.0465371571,0.1621010602,-0.0012185768,-0.0208561029,-0.1100598425,0.0216292907,0.2049841285,0.2180556655,0.1640353203,0.1795151085,-0.1997850239,-0.130682379,0.0886226073,-0.1416330338,-0.1671275645,-0.1788374335,0.2166469097,-0.0094024939,-0.3506264389,-0.0937892571,0.2814688385,0.1409590542,-0.2037903368,-0.1410701424,-0.0391465798,-0.1241319329,-0.0865156949,0.1792775393,0.5796180964,-0.1855915189,-0.1380216628,-0.1521381587,0.0008939481,0.5833057165,0.3344025612,-0.0607914478,0.0897968188,-0.170722425,0.1476544142,0.5017140508,-0.1436584145,-0.5796036124,0.5436661243,-0.0396082066,0.5637281537,0.2707032561,0.0225366186,0.1742344499,0.0431196988,0.313529104,0.3299024701,-0.1484592706,0.0074822325,-0.3334669471,-0.0399836674,0.4416950643,0.1912285686,0.0674290732,0.2502861321,-0.0288742278,-0.0626677349,0.3689779639,-0.0752563924,0.0763451308,0.0410080664,0.1468882412,0.0120370993,0.1379382461,-0.2090770155,-0.310682714,0.2972311378,0.0749307722,0.0127100674,-0.3174338341,0.2031723857,-0.1906455308,0.0017469627,-0.2628925443,-0.2590405643,0.1243173257,0.3170801699,0.0756207108,0.0505895875,-0.3084766865,0.7747215033,-0.078255251,-0.0131349247,-0.4549141228,0.2024929523,-0.0707768798,-0.2014573216,0.0164402872,-0.1265656799,0.0672556311,-0.0161919687,-0.2579914331,0.3313200772,-0.2366138101,0.2640713155,-0.1954375803,-0.1731976271,0.2897448838,-0.3644788265,-0.0866843313,0.2706028223,0.1075036898,0.125209704,-0.0176983867,0.1480839401,0.2070915401,0.2361335903,0.0434485786,0.1143487841,0.1961675137,-0.0816748515,-0.0686837137,-0.0850090533,0.3240485787,-0.3548789024,0.0467500761,-0.1989230514,-0.2722995877,0.0823631957,0.3586791456,-0.1403829306,-0.0492737889,0.3303245604,-0.2885322273,-0.1639078707,0.3145605624,0.1940099597,0.4723360538,0.0815219656,0.2573353648,-0.0455882996,-0.1886483878,-0.0735060051,0.3051697612,0.1374839693,0.0314638466,0.2464114279,0.0300063528,0.1635747999,-0.3229160309,-0.3497166634,-0.0566318817,0.1123416647,-0.3201874793,0.306302011,-0.38826859,-0.4651744664,0.0463509336,0.1560085416,0.0689049438,-0.3235587776,-0.1817550063,0.2325055152,-0.0038823313,0.1636838913,-0.2682063282,0.0895485729,0.2671770155,-0.0134687945,-0.3418698907,-0.1808474958,-0.2729629874,0.0591977872,0.1749951094,-0.4228037596,0.1769195795,0.0074260007,-0.1160532683,-0.1401735991,-0.076423429,0.3197818398,-0.1554995328,0.1568958163,-0.2774865627,0.1493911892,0.2213633955,-0.2503491044,0.1219482496,0.0908335969,-0.1187844425,0.2801248431,0.0940897167,-0.0241610892,-0.0305832736,-0.3010807931,-0.3024666011,-0.6177494526,0.0549683981,0.0454257689,0.2135515809,0.1643050015,0.0170347784,0.0968246013,0.3292069137,0.0991601422,-0.114212282,-0.4199434817,0.3747448325,-0.1670146883,-0.2868409157,-0.3248367012,-0.0681597292,0.1972901672,0.2589270473,-0.5295243263,0.2950162292,-0.4081035852,0.0848925635,-0.3834516108,-0.0974560827,-0.0261515174,0.0762885734,-0.0349893123,-0.1878576428,0.0707459524,-0.1169513538,0.0990076959,0.2066107839,0.1086307243,0.3698712885,0.1008493528,0.3327504098,0.4812112153,-0.0195496976,0.3407551944,0.082891129,0.1538007557,-0.1004334986,-0.0627885908,-0.095562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{"html_url":"https:\/\/github.com\/huggingface\/datasets\/issues\/2918","title":"`Can not decode content-encoding: gzip` when loading `scitldr` dataset with streaming","comments":"Thanks for investigating @albertvillanova ! \ud83e\udd17 ","body":"## Describe the bug\r\n\r\nTrying to load the `\"FullText\"` config of the `\"scitldr\"` dataset with `streaming=True` raises an error from `aiohttp`:\r\n```python\r\nClientPayloadError: 400, message='Can not decode content-encoding: gzip'\r\n```\r\n\r\ncc @lhoestq \r\n\r\n## Steps to reproduce the bug\r\n```python\r\nfrom datasets import load_dataset\r\n\r\niter_dset = iter(\r\n load_dataset(\"scitldr\", name=\"FullText\", split=\"test\", streaming=True)\r\n)\r\n\r\nnext(iter_dset)\r\n```\r\n\r\n## Expected results\r\nReturns the first sample of the dataset\r\n\r\n## Actual results\r\nCalling `__next__` crashes with the following Traceback:\r\n\r\n```python\r\n----> 1 next(dset_iter)\r\n\r\n~\\miniconda3\\envs\\datasets\\lib\\site-packages\\datasets\\iterable_dataset.py in __iter__(self)\r\n 339\r\n 340 def __iter__(self):\r\n--> 341 for key, example in self._iter():\r\n 342 if self.features:\r\n 343 # we encode the example for ClassLabel feature types for example\r\n\r\n~\\miniconda3\\envs\\datasets\\lib\\site-packages\\datasets\\iterable_dataset.py in _iter(self)\r\n 336 else:\r\n 337 ex_iterable = self._ex_iterable\r\n--> 338 yield from ex_iterable\r\n 339\r\n 340 def __iter__(self):\r\n\r\n~\\miniconda3\\envs\\datasets\\lib\\site-packages\\datasets\\iterable_dataset.py in __iter__(self)\r\n 76\r\n 77 def __iter__(self):\r\n---> 78 for key, example in self.generate_examples_fn(**self.kwargs):\r\n 79 yield key, example\r\n 80\r\n\r\n~\\.cache\\huggingface\\modules\\datasets_modules\\datasets\\scitldr\\72d6e2195786c57e1d343066fb2cc4f93ea39c5e381e53e6ae7c44bbfd1f05ef\\scitldr.py in _generate_examples(self, filepath, split)\r\n 162\r\n 163 with open(filepath, encoding=\"utf-8\") as f:\r\n--> 164 for id_, row in enumerate(f):\r\n 165 data = json.loads(row)\r\n 166 if self.config.name == \"AIC\":\r\n\r\n~\\miniconda3\\envs\\datasets\\lib\\site-packages\\fsspec\\implementations\\http.py in read(self, length)\r\n 496 else:\r\n 497 length = min(self.size - self.loc, length)\r\n--> 498 return super().read(length)\r\n 499\r\n 500 async def async_fetch_all(self):\r\n\r\n~\\miniconda3\\envs\\datasets\\lib\\site-packages\\fsspec\\spec.py in read(self, length)\r\n 1481 # don't even bother calling fetch\r\n 1482 return b\"\"\r\n-> 1483 out = self.cache._fetch(self.loc, self.loc + length)\r\n 1484 self.loc += len(out)\r\n 1485 return out\r\n\r\n~\\miniconda3\\envs\\datasets\\lib\\site-packages\\fsspec\\caching.py in _fetch(self, start, end)\r\n 378 elif start < self.start:\r\n 379 if self.end - end > self.blocksize:\r\n--> 380 self.cache = self.fetcher(start, bend)\r\n 381 self.start = start\r\n 382 else:\r\n\r\n~\\miniconda3\\envs\\datasets\\lib\\site-packages\\fsspec\\asyn.py in wrapper(*args, **kwargs)\r\n 86 def wrapper(*args, **kwargs):\r\n 87 self = obj or args[0]\r\n---> 88 return sync(self.loop, func, *args, **kwargs)\r\n 89\r\n 90 return wrapper\r\n\r\n~\\miniconda3\\envs\\datasets\\lib\\site-packages\\fsspec\\asyn.py in sync(loop, func, timeout, *args, **kwargs)\r\n 67 raise FSTimeoutError\r\n 68 if isinstance(result[0], BaseException):\r\n---> 69 raise result[0]\r\n 70 return result[0]\r\n 71\r\n\r\n~\\miniconda3\\envs\\datasets\\lib\\site-packages\\fsspec\\asyn.py in _runner(event, coro, result, timeout)\r\n 23 coro = asyncio.wait_for(coro, timeout=timeout)\r\n 24 try:\r\n---> 25 result[0] = await coro\r\n 26 except Exception as ex:\r\n 27 result[0] = ex\r\n\r\n~\\miniconda3\\envs\\datasets\\lib\\site-packages\\fsspec\\implementations\\http.py in async_fetch_range(self, start, end)\r\n 538 if r.status == 206:\r\n 539 # partial content, as expected\r\n--> 540 out = await r.read()\r\n 541 elif \"Content-Length\" in r.headers:\r\n 542 cl = int(r.headers[\"Content-Length\"])\r\n\r\n~\\miniconda3\\envs\\datasets\\lib\\site-packages\\aiohttp\\client_reqrep.py in read(self)\r\n 1030 if self._body is None:\r\n 1031 try:\r\n-> 1032 self._body = await self.content.read()\r\n 1033 for trace in self._traces:\r\n 1034 await trace.send_response_chunk_received(\r\n\r\n~\\miniconda3\\envs\\datasets\\lib\\site-packages\\aiohttp\\streams.py in read(self, n)\r\n 342 async def read(self, n: int = -1) -> bytes:\r\n 343 if self._exception is not None:\r\n--> 344 raise self._exception\r\n 345\r\n 346 # migration problem; with DataQueue you have to catch\r\n\r\nClientPayloadError: 400, message='Can not decode content-encoding: gzip'\r\n```\r\n\r\n## Environment info\r\n\r\n- `datasets` version: 1.12.0\r\n- Platform: Windows-10-10.0.19041-SP0\r\n- Python version: 3.8.5\r\n- PyArrow version: 2.0.0\r\n- aiohttp version: 3.7.4.post0\r\n","comment_length":6,"text":"`Can not decode content-encoding: gzip` when loading `scitldr` dataset with streaming\n## Describe the bug\r\n\r\nTrying to load the `\"FullText\"` config of the `\"scitldr\"` dataset with `streaming=True` raises an error from `aiohttp`:\r\n```python\r\nClientPayloadError: 400, message='Can not decode content-encoding: gzip'\r\n```\r\n\r\ncc @lhoestq \r\n\r\n## Steps to reproduce the bug\r\n```python\r\nfrom datasets import load_dataset\r\n\r\niter_dset = iter(\r\n load_dataset(\"scitldr\", name=\"FullText\", split=\"test\", streaming=True)\r\n)\r\n\r\nnext(iter_dset)\r\n```\r\n\r\n## Expected results\r\nReturns the first sample of the dataset\r\n\r\n## Actual results\r\nCalling `__next__` crashes with the following Traceback:\r\n\r\n```python\r\n----> 1 next(dset_iter)\r\n\r\n~\\miniconda3\\envs\\datasets\\lib\\site-packages\\datasets\\iterable_dataset.py in __iter__(self)\r\n 339\r\n 340 def __iter__(self):\r\n--> 341 for key, example in self._iter():\r\n 342 if self.features:\r\n 343 # we encode the example for ClassLabel feature types for example\r\n\r\n~\\miniconda3\\envs\\datasets\\lib\\site-packages\\datasets\\iterable_dataset.py in _iter(self)\r\n 336 else:\r\n 337 ex_iterable = self._ex_iterable\r\n--> 338 yield from ex_iterable\r\n 339\r\n 340 def __iter__(self):\r\n\r\n~\\miniconda3\\envs\\datasets\\lib\\site-packages\\datasets\\iterable_dataset.py in __iter__(self)\r\n 76\r\n 77 def __iter__(self):\r\n---> 78 for key, example in self.generate_examples_fn(**self.kwargs):\r\n 79 yield key, example\r\n 80\r\n\r\n~\\.cache\\huggingface\\modules\\datasets_modules\\datasets\\scitldr\\72d6e2195786c57e1d343066fb2cc4f93ea39c5e381e53e6ae7c44bbfd1f05ef\\scitldr.py in _generate_examples(self, filepath, split)\r\n 162\r\n 163 with open(filepath, encoding=\"utf-8\") as f:\r\n--> 164 for id_, row in enumerate(f):\r\n 165 data = json.loads(row)\r\n 166 if self.config.name == \"AIC\":\r\n\r\n~\\miniconda3\\envs\\datasets\\lib\\site-packages\\fsspec\\implementations\\http.py in read(self, length)\r\n 496 else:\r\n 497 length = min(self.size - self.loc, length)\r\n--> 498 return super().read(length)\r\n 499\r\n 500 async def async_fetch_all(self):\r\n\r\n~\\miniconda3\\envs\\datasets\\lib\\site-packages\\fsspec\\spec.py in read(self, length)\r\n 1481 # don't even bother calling fetch\r\n 1482 return b\"\"\r\n-> 1483 out = self.cache._fetch(self.loc, self.loc + length)\r\n 1484 self.loc += len(out)\r\n 1485 return out\r\n\r\n~\\miniconda3\\envs\\datasets\\lib\\site-packages\\fsspec\\caching.py in _fetch(self, start, end)\r\n 378 elif start < self.start:\r\n 379 if self.end - end > self.blocksize:\r\n--> 380 self.cache = self.fetcher(start, bend)\r\n 381 self.start = start\r\n 382 else:\r\n\r\n~\\miniconda3\\envs\\datasets\\lib\\site-packages\\fsspec\\asyn.py in wrapper(*args, **kwargs)\r\n 86 def wrapper(*args, **kwargs):\r\n 87 self = obj or args[0]\r\n---> 88 return sync(self.loop, func, *args, **kwargs)\r\n 89\r\n 90 return wrapper\r\n\r\n~\\miniconda3\\envs\\datasets\\lib\\site-packages\\fsspec\\asyn.py in sync(loop, func, timeout, *args, **kwargs)\r\n 67 raise FSTimeoutError\r\n 68 if isinstance(result[0], BaseException):\r\n---> 69 raise result[0]\r\n 70 return result[0]\r\n 71\r\n\r\n~\\miniconda3\\envs\\datasets\\lib\\site-packages\\fsspec\\asyn.py in _runner(event, coro, result, timeout)\r\n 23 coro = asyncio.wait_for(coro, timeout=timeout)\r\n 24 try:\r\n---> 25 result[0] = await coro\r\n 26 except Exception as ex:\r\n 27 result[0] = ex\r\n\r\n~\\miniconda3\\envs\\datasets\\lib\\site-packages\\fsspec\\implementations\\http.py in async_fetch_range(self, start, end)\r\n 538 if r.status == 206:\r\n 539 # partial content, as expected\r\n--> 540 out = await r.read()\r\n 541 elif \"Content-Length\" in r.headers:\r\n 542 cl = int(r.headers[\"Content-Length\"])\r\n\r\n~\\miniconda3\\envs\\datasets\\lib\\site-packages\\aiohttp\\client_reqrep.py in read(self)\r\n 1030 if self._body is None:\r\n 1031 try:\r\n-> 1032 self._body = await self.content.read()\r\n 1033 for trace in self._traces:\r\n 1034 await trace.send_response_chunk_received(\r\n\r\n~\\miniconda3\\envs\\datasets\\lib\\site-packages\\aiohttp\\streams.py in read(self, n)\r\n 342 async def read(self, n: int = -1) -> bytes:\r\n 343 if self._exception is not None:\r\n--> 344 raise self._exception\r\n 345\r\n 346 # migration problem; with DataQueue you have to catch\r\n\r\nClientPayloadError: 400, message='Can not decode content-encoding: gzip'\r\n```\r\n\r\n## Environment info\r\n\r\n- `datasets` version: 1.12.0\r\n- Platform: Windows-10-10.0.19041-SP0\r\n- Python version: 3.8.5\r\n- PyArrow version: 2.0.0\r\n- aiohttp version: 3.7.4.post0\r\n\nThanks for investigating @albertvillanova ! \ud83e\udd17 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{"html_url":"https:\/\/github.com\/huggingface\/datasets\/issues\/2917","title":"windows download abnormal","comments":"Hi ! Is there some kind of proxy that is configured in your browser that gives you access to internet ? If it's the case it could explain why it doesn't work in the code, since the proxy wouldn't be used","body":"## Describe the bug\r\nThe script clearly exists (accessible from the browser), but the script download fails on windows. Then I tried it again and it can be downloaded normally on linux. why??\r\n## Steps to reproduce the bug\r\n```python3.7 + windows\r\n![image](https:\/\/user-images.githubusercontent.com\/52347799\/133436174-4303f847-55d5-434f-a749-08da3bb9b654.png)\r\n\r\n\r\n# Sample code to reproduce the bug\r\n```\r\n\r\n## Expected results\r\nIt can be downloaded normally.\r\n\r\n## Actual results\r\nit cann't\r\n\r\n## Environment info\r\n\r\n- `datasets` version:1.11.0\r\n- Platform:windows\r\n- Python version:3.7\r\n- PyArrow version:\r\n","comment_length":41,"text":"windows download abnormal\n## Describe the bug\r\nThe script clearly exists (accessible from the browser), but the script download fails on windows. Then I tried it again and it can be downloaded normally on linux. why??\r\n## Steps to reproduce the bug\r\n```python3.7 + windows\r\n![image](https:\/\/user-images.githubusercontent.com\/52347799\/133436174-4303f847-55d5-434f-a749-08da3bb9b654.png)\r\n\r\n\r\n# Sample code to reproduce the bug\r\n```\r\n\r\n## Expected results\r\nIt can be downloaded normally.\r\n\r\n## Actual results\r\nit cann't\r\n\r\n## Environment info\r\n\r\n- `datasets` version:1.11.0\r\n- Platform:windows\r\n- Python version:3.7\r\n- PyArrow version:\r\n\nHi ! Is there some kind of proxy that is configured in your browser that gives you access to internet ? If it's the case it could explain why it doesn't work in the code, since the proxy wouldn't be 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{"html_url":"https:\/\/github.com\/huggingface\/datasets\/issues\/2917","title":"windows download abnormal","comments":"It is indeed an agency problem, thank you very, very much","body":"## Describe the bug\r\nThe script clearly exists (accessible from the browser), but the script download fails on windows. Then I tried it again and it can be downloaded normally on linux. why??\r\n## Steps to reproduce the bug\r\n```python3.7 + windows\r\n![image](https:\/\/user-images.githubusercontent.com\/52347799\/133436174-4303f847-55d5-434f-a749-08da3bb9b654.png)\r\n\r\n\r\n# Sample code to reproduce the bug\r\n```\r\n\r\n## Expected results\r\nIt can be downloaded normally.\r\n\r\n## Actual results\r\nit cann't\r\n\r\n## Environment info\r\n\r\n- `datasets` version:1.11.0\r\n- Platform:windows\r\n- Python version:3.7\r\n- PyArrow version:\r\n","comment_length":11,"text":"windows download abnormal\n## Describe the bug\r\nThe script clearly exists (accessible from the browser), but the script download fails on windows. Then I tried it again and it can be downloaded normally on linux. why??\r\n## Steps to reproduce the bug\r\n```python3.7 + windows\r\n![image](https:\/\/user-images.githubusercontent.com\/52347799\/133436174-4303f847-55d5-434f-a749-08da3bb9b654.png)\r\n\r\n\r\n# Sample code to reproduce the bug\r\n```\r\n\r\n## Expected results\r\nIt can be downloaded normally.\r\n\r\n## Actual results\r\nit cann't\r\n\r\n## Environment info\r\n\r\n- `datasets` version:1.11.0\r\n- Platform:windows\r\n- Python version:3.7\r\n- PyArrow version:\r\n\nIt is indeed an agency problem, thank you very, very much","embeddings":[-0.1819480658,-0.0709162951,-0.0238682684,0.1505493969,0.2515722811,-0.0938239619,0.0032474413,0.11024113,0.362229526,0.0661070794,0.1379210949,0.2134204656,0.0683521777,0.2074184567,-0.0183983669,-0.2120418251,0.0408425257,0.169603616,-0.109351553,0.0084896954,-0.3778957427,0.1222561225,-0.244990021,-0.0322376676,0.118513003,-0.1276566386,-0.3018625379,0.1873607785,-0.2236432433,-0.0762688667,0.1565723866,-0.1330198646,0.1389445513,0.3082361221,-0.0001110555,0.0270548705,0.4110395014,0.0012154208,0.0296383202,-0.0051854542,-0.1887717694,-0.1067669615,-0.2910613418,-0.3018800914,0.163738668,0.3125549555,0.0271366872,-0.1251074672,0.2193036079,0.4728519619,0.3004208505,0.4755435586,-0.1050411314,-0.052202031,0.2329810262,0.0768294707,-0.1763174385,0.1497375667,0.254214406,-0.2113004178,0.2170041651,-0.0690852255,-0.0610753894,-0.0063095409,-0.0902022049,0.0914523378,0.0073744375,-0.5927760601,0.1896012127,0.1313620359,0.401514262,-0.1367759109,-0.1460928917,0.1193385571,0.0144195985,-0.0813066885,0.2506349087,0.3949985504,-0.1587389559,0.0788909793,-0.2028708309,0.2833296955,-0.2204892486,0.1841510683,-0.1222558171,0.1910018027,-0.0857133567,0.2090443075,-0.0596853197,0.2083201408,0.0718123764,-0.2013075501,-0.0888202116,-0.0001829596,-0.0580069907,0.0415586531,-0.0736014992,0.6245207787,0.131835565,0.1753447503,0.0869020745,-0.1592784822,0.183907643,0.0403068922,0.224851951,0.1441524029,-0.2167216688,0.1089039966,0.2697381079,0.2915846109,-0.0308599882,0.1096391231,-0.107437022,-0.41546157,0.2698237002,0.0131871272,0.4077474773,-0.1028263122,-0.62843436,0.0436515138,0.1092530116,0.1674703658,-0.1276319921,0.1536726505,-0.2733654976,0.013872534,0.0132438513,0.32657969,-0.1094577163,0.206694752,0.0036723863,0.0926430076,-0.2967072725,-0.2458346933,0.3576645553,-0.1119512916,0.1324940771,0.2963950634,-0.1462853998,-0.01702445,0.0105879866,-0.1995972842,-0.1029506847,0.439473182,0.2255644649,0.3950291276,-0.1827936321,0.0152565576,-0.0298757032,0.1933602542,-0.1561779529,-0.0408560708,-0.0541052632,0.2324374616,-0.3855369985,-0.1968738139,-0.039049834,-0.2038401216,-0.0327578783,0.1615654677,0.2465070188,-0.2326217592,-0.2305738032,-0.3819427192,-0.0289984588,0.5887625813,-0.2635556459,0.2373927385,-0.0847060829,-0.328499347,0.4486765862,0.1127441376,0.1913455874,0.1516014636,-0.4607596099,0.0760051906,0.0519300029,-0.4398525655,-0.5598500967,0.4142220914,-0.0977686942,0.1617045999,0.1845384538,0.1501137912,0.3036822379,-0.0325751416,-0.0825505555,0.4031662345,-0.0779337958,0.1080535129,-0.1352510601,-0.134622559,0.201709345,0.168760702,0.1124669537,0.0058825724,0.1826209873,-0.1374899149,0.298524797,0.2201125473,0.0792058855,0.2166628391,0.2818375528,0.1327269375,0.1090106666,-0.1992979348,-0.0353883244,0.2307479382,-0.068367891,-0.1274635792,-0.1719899923,-0.2560539246,-0.4767713547,0.0034119347,-0.2548181415,-0.193141818,0.1301574707,0.0973193944,0.1700637639,0.1048899293,0.1259745061,0.1654153466,0.0545962192,0.0097603537,0.1804260761,0.302990526,-0.2577700615,-0.0457421467,0.0831682235,-0.0594857298,0.2411195934,0.0036581284,-0.0234827735,0.1238250807,0.0947378799,-0.0369792469,0.0112583535,-0.0961492285,-0.0187985655,-0.0046684532,0.208516553,0.5677586794,0.1324958503,-0.0747223124,0.0045230803,0.1056594327,-0.1307276189,-0.0183902904,-0.0695005655,0.2344380766,0.2568475604,-0.3220861256,-0.0040925732,-0.0434314571,0.2829574049,0.0912019163,-0.0603718944,-0.0726193562,-0.1558505297,0.1877027154,0.700084269,-0.1830890179,0.3046873808,0.1612100303,-0.1223507747,0.1930639148,-0.137361154,0.2834716737,0.7158129215,0.0091603678,0.0177161265,0.2950727344,0.002406968,-0.1490684301,0.2080704123,0.0997634754,-0.2241471559,0.3055584729,-0.0021933594,0.2467545569,-0.3240906596,-0.2490481883,0.0142765054,0.1652551144,-0.2981254756,0.098265931,-0.1337950528,-0.2373199612,-0.4030925632,0.0705316141,-0.0321190171,-0.0354837701,0.0339757949,0.1372851282,-0.0919834822,-0.1605060548,-0.3157018423,0.1221830696,0.1355545223,-0.1588221341,-0.0273725614,0.2578795254,-0.2540774345,0.1120541915,0.2056393325,-0.0287933052,0.5603420734,-0.496091783,0.2043798715,-0.4564227164,-0.2349586785,0.0054968395,0.0737605169,0.429425329,0.1740064919,0.074083142,0.0736172348,0.0474765077,0.1184441224,-0.2257839143,-0.2208357453,0.1726488918,-0.0539861992,-0.4455712736,-0.3471133113,-0.2787243128,-0.2446427345,-0.3425118625,0.228518784,0.1010006294,0.1010874212,-0.1100757569,0.0160217341,-0.098175019,-0.0168962702,0.1484609544,-0.0863984153,0.1665725112,0.4026210904,-0.0914862081,-0.6598010659,0.226908803,-0.0615841225,-0.2246567756,0.2210018188,-0.4095596969,-0.1262203753,-0.342110157,0.129096806,0.0070878975,0.2367419153,0.2802664936,-0.2545440495,0.0095087094,-0.2566065192,-0.113554813,-0.3147451878,-0.0000580463,0.1881667525,0.0922107473,0.2084324509,0.0313312039,0.1634161323,0.3067245483,-0.0213810727,0.3113523424,-0.4172437489,0.4244993925,0.0466330908,-0.2214517891,0.0983962864,-0.2498928308,0.1606142521,0.3106133938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{"html_url":"https:\/\/github.com\/huggingface\/datasets\/issues\/2917","title":"windows download abnormal","comments":"Let me know if you have other questions :)\r\n\r\nClosing this issue now","body":"## Describe the bug\r\nThe script clearly exists (accessible from the browser), but the script download fails on windows. Then I tried it again and it can be downloaded normally on linux. why??\r\n## Steps to reproduce the bug\r\n```python3.7 + windows\r\n![image](https:\/\/user-images.githubusercontent.com\/52347799\/133436174-4303f847-55d5-434f-a749-08da3bb9b654.png)\r\n\r\n\r\n# Sample code to reproduce the bug\r\n```\r\n\r\n## Expected results\r\nIt can be downloaded normally.\r\n\r\n## Actual results\r\nit cann't\r\n\r\n## Environment info\r\n\r\n- `datasets` version:1.11.0\r\n- Platform:windows\r\n- Python version:3.7\r\n- PyArrow version:\r\n","comment_length":13,"text":"windows download abnormal\n## Describe the bug\r\nThe script clearly exists (accessible from the browser), but the script download fails on windows. Then I tried it again and it can be downloaded normally on linux. why??\r\n## Steps to reproduce the bug\r\n```python3.7 + windows\r\n![image](https:\/\/user-images.githubusercontent.com\/52347799\/133436174-4303f847-55d5-434f-a749-08da3bb9b654.png)\r\n\r\n\r\n# Sample code to reproduce the bug\r\n```\r\n\r\n## Expected results\r\nIt can be downloaded normally.\r\n\r\n## Actual results\r\nit cann't\r\n\r\n## Environment info\r\n\r\n- `datasets` version:1.11.0\r\n- Platform:windows\r\n- Python version:3.7\r\n- PyArrow version:\r\n\nLet me know if you have other questions :)\r\n\r\nClosing this issue now","embeddings":[-0.2207755297,-0.1332957745,-0.0691334903,0.1743752807,0.2265743613,-0.0526336357,-0.0071485089,0.1346075088,0.2471247762,0.0947366357,0.1111562997,0.2011167854,0.0616793036,0.1772477627,0.0522657,-0.2514466345,0.0436070114,0.1884659082,-0.1824369282,-0.0201365389,-0.3421658576,0.1442255229,-0.2688935101,-0.0980346352,0.1254004985,0.0039493642,-0.3086517155,0.2426276207,-0.2599076033,-0.1714349836,0.0742157772,-0.0879949704,0.163236618,0.2892299891,-0.0001064452,0.0440166481,0.3960578144,0.0089306133,0.0559709035,-0.0490174629,-0.2207491994,-0.1396053284,-0.2216101736,-0.293787092,0.1231971383,0.110559687,0.0049971924,-0.0325602069,0.0923154652,0.3783936501,0.3404253125,0.4842950702,-0.0722764954,-0.0313038714,0.4031114578,0.1105747074,-0.1911314577,0.1367606372,0.2970518172,-0.2148809433,0.1355500519,-0.0241315197,-0.0474884994,0.0302238148,-0.0182570498,0.0709910616,-0.0401855186,-0.4947353899,0.1354618669,0.1454054117,0.3116519451,-0.1623973399,-0.1045670062,0.1049401239,-0.0483258218,-0.2304380387,0.1805960536,0.3206201792,-0.1367184669,0.1314445287,-0.2873217762,0.2481869012,-0.2228381038,0.1704220772,-0.0978163704,0.2842999995,-0.0906764865,0.1684194952,-0.0802360848,0.2447992563,0.0083303163,-0.2286545932,-0.100851655,0.0021302979,-0.0380191021,0.0297235865,0.0100703146,0.5421230197,0.129743889,0.2278441787,-0.0172666032,-0.1089707017,0.2379163802,0.0550119728,0.1292164624,0.1321160793,-0.1616736948,0.0282277036,0.3516741991,0.2831096351,0.0294332672,0.1096406654,-0.0448804945,-0.409791708,0.3063461781,0.067747876,0.3790406585,-0.1142017618,-0.6207893491,0.0279846601,0.070389539,0.1223948523,-0.0729451403,0.2053755671,-0.2115510255,-0.038713377,0.0287676323,0.3363829255,-0.1473443359,0.1672639549,-0.0085347267,0.1934087873,-0.2336297184,-0.2789251506,0.4062646925,-0.1174081489,0.1876767725,0.2447303534,-0.1050559208,0.0019086151,-0.0402509943,-0.1226891279,-0.0408820026,0.4309408367,0.2251872271,0.4080506861,-0.1081939563,0.1009383127,-0.0526482053,0.2426031977,-0.1097498462,-0.0552099682,-0.0306426436,0.2899795175,-0.3140995502,-0.2293543965,-0.0028886429,-0.2081403881,-0.0288612023,0.0473718569,0.2172876298,-0.2606675923,-0.2903537452,-0.4342307448,0.0709750429,0.4895909131,-0.4144755304,0.227134794,0.0270461105,-0.3316147923,0.4150057137,0.1907803267,0.1251585186,0.099305965,-0.4734765291,0.1356181353,0.0067809615,-0.3681674302,-0.4561111033,0.3584577441,-0.1014415994,0.1938920319,0.1865333468,0.0966966599,0.288595438,-0.0928407386,-0.0392433889,0.2651661932,-0.0274843816,0.0421565957,-0.1506972611,-0.1795333475,0.1650287658,0.1755206734,0.0803700909,0.0484602489,0.2923360765,-0.1043607593,0.3381632268,0.1145961881,0.0599303469,0.2569989264,0.3408920169,0.1021471545,0.1144763753,-0.243753314,-0.0720155612,0.2930027843,-0.0744742453,-0.1432997733,-0.2104561776,-0.2281522602,-0.4664123952,0.0491136797,-0.2203699201,-0.2093744129,0.1927045584,0.105987832,0.2294083834,0.0463595949,0.0988513902,0.1179435104,-0.0049782838,-0.0852696747,0.1478122175,0.2976120412,-0.358138293,-0.0983111262,0.0428683013,-0.0618663542,0.2359926403,-0.0797711536,0.0072130114,0.1748583913,0.0909606516,0.0120516652,-0.0225844402,-0.0551137365,-0.0018050423,-0.0328586437,0.2095954716,0.4951538742,0.1691873521,-0.1198024824,-0.0891863182,0.0866177082,-0.056391336,-0.0603130832,-0.0616283976,0.135127157,0.2473331243,-0.2254518121,-0.0318140052,-0.0709816664,0.4197178185,0.1235188469,-0.1139750704,-0.1175652519,-0.1888785362,0.154271245,0.6313266158,-0.2423981279,0.2632025778,0.0781768337,-0.0857005417,0.1904331446,-0.1035305858,0.2065784037,0.6523746848,0.0705253407,0.0944053531,0.3197440505,-0.0311157349,-0.1489898115,0.2229828387,0.0814782307,-0.1967646033,0.2652468383,-0.0275604334,0.1729865074,-0.3210701644,-0.1806147695,0.0191281699,0.1348359287,-0.3165897727,0.1074841619,-0.1814183295,-0.0536290482,-0.3422429264,-0.0320985392,-0.0447294153,-0.053749226,0.0935111567,0.2653478086,-0.1439779252,-0.1548583955,-0.2668774128,0.0174200926,0.2345918864,-0.2694985569,-0.021260269,0.218075648,-0.2366104424,0.1633965224,0.1720452756,-0.0932388231,0.5667923689,-0.4775111079,0.1574508995,-0.4538096488,-0.1942285001,-0.0239324756,-0.0515272506,0.3197554946,0.2340763509,0.1066003665,0.0461247899,0.0562638193,0.1573544741,-0.2489885539,-0.2195565104,0.2129605412,0.0073923734,-0.4503124058,-0.3328312635,-0.2961428463,-0.202512458,-0.3447829485,0.1225349307,0.1772155315,0.1006463096,-0.0672200993,0.0358825438,-0.0186522044,0.047959242,0.0959775299,-0.1114475951,0.1012131572,0.4370254278,-0.108312875,-0.668559134,0.2404889315,0.0451720208,-0.2690183222,0.241299063,-0.5037426949,-0.088098526,-0.3273606896,0.2437198013,0.0548889041,0.2400947064,0.2727158964,-0.2193379253,-0.0504980423,-0.2639805079,-0.0492048077,-0.2931634486,-0.0319044366,0.1824257225,0.0572677068,0.2499418408,0.0213780347,0.2403487414,0.2202055752,0.0174942203,0.3821138442,-0.3713877201,0.4587187171,-0.0153283775,-0.1699820757,0.0924867392,-0.2480327785,0.0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{"html_url":"https:\/\/github.com\/huggingface\/datasets\/issues\/2914","title":"Having a dependency defining fsspec entrypoint raises an AttributeError when importing datasets","comments":"Closed by #2915.","body":"## Describe the bug\r\nIn one of my project, I defined a custom fsspec filesystem with an entrypoint.\r\nMy guess is that by doing so, a variable named `spec` is created in the module `fsspec` (created by entering a for loop as there are entrypoints defined, see the loop in question [here](https:\/\/github.com\/intake\/filesystem_spec\/blob\/0589358d8a029ed6b60d031018f52be2eb721291\/fsspec\/__init__.py#L55)).\r\nSo that `fsspec.spec`, that was previously referring to the `spec` submodule, is now referring to that `spec` variable.\r\nThis make the import of datasets failing as it is using that `fsspec.spec`.\r\n\r\n## Steps to reproduce the bug\r\nI could reproduce the bug with a dummy poetry project.\r\n\r\nHere is the pyproject.toml:\r\n```toml\r\n[tool.poetry]\r\nname = \"debug-datasets\"\r\nversion = \"0.1.0\"\r\ndescription = \"\"\r\nauthors = [\"Pierre Godard\"]\r\n\r\n[tool.poetry.dependencies]\r\npython = \"^3.8\"\r\ndatasets = \"^1.11.0\"\r\n\r\n[tool.poetry.dev-dependencies]\r\n\r\n[build-system]\r\nrequires = [\"poetry-core>=1.0.0\"]\r\nbuild-backend = \"poetry.core.masonry.api\"\r\n\r\n[tool.poetry.plugins.\"fsspec.specs\"]\r\n\"file2\" = \"fsspec.implementations.local.LocalFileSystem\"\r\n```\r\n\r\nThe only other file being a `debug_datasets\/__init__.py` empty file.\r\n\r\nThe overall structure of the project is as follows:\r\n```\r\n.\r\n\u251c\u2500\u2500 pyproject.toml\r\n\u2514\u2500\u2500 debug_datasets\r\n \u2514\u2500\u2500 __init__.py\r\n```\r\n\r\nThen, within the project folder run:\r\n\r\n```\r\npoetry install\r\npoetry run python\r\n```\r\n\r\nAnd in the python interpreter, try to import `datasets`:\r\n\r\n```\r\nimport datasets\r\n```\r\n\r\n## Expected results\r\nThe import should run successfully.\r\n\r\n## Actual results\r\n\r\nHere is the trace of the error I get:\r\n\r\n```\r\nTraceback (most recent call last):\r\n File \"\", line 1, in \r\n File \"\/home\/godarpi\/.cache\/pypoetry\/virtualenvs\/debug-datasets-JuFzTKL--py3.8\/lib\/python3.8\/site-packages\/datasets\/__init__.py\", line 33, in \r\n from .arrow_dataset import Dataset, concatenate_datasets\r\n File \"\/home\/godarpi\/.cache\/pypoetry\/virtualenvs\/debug-datasets-JuFzTKL--py3.8\/lib\/python3.8\/site-packages\/datasets\/arrow_dataset.py\", line 48, in \r\n from .filesystems import extract_path_from_uri, is_remote_filesystem\r\n File \"\/home\/godarpi\/.cache\/pypoetry\/virtualenvs\/debug-datasets-JuFzTKL--py3.8\/lib\/python3.8\/site-packages\/datasets\/filesystems\/__init__.py\", line 30, in \r\n def is_remote_filesystem(fs: fsspec.spec.AbstractFileSystem) -> bool:\r\nAttributeError: 'EntryPoint' object has no attribute 'AbstractFileSystem'\r\n```\r\n\r\n## Suggested fix\r\n\r\n`datasets\/filesystems\/__init__.py`, line 30, replace:\r\n```\r\n def is_remote_filesystem(fs: fsspec.spec.AbstractFileSystem) -> bool:\r\n```\r\nby:\r\n```\r\n def is_remote_filesystem(fs: fsspec.AbstractFileSystem) -> bool:\r\n```\r\n\r\nI will come up with a PR soon if this effectively solves the issue.\r\n\r\n## Environment info\r\n\r\n- `datasets` version: 1.11.0\r\n- Platform: WSL2 (Ubuntu 20.04.1 LTS)\r\n- Python version: 3.8.5\r\n- PyArrow version: 5.0.0\r\n- `fsspec` version: 2021.8.1\r\n","comment_length":3,"text":"Having a dependency defining fsspec entrypoint raises an AttributeError when importing datasets\n## Describe the bug\r\nIn one of my project, I defined a custom fsspec filesystem with an entrypoint.\r\nMy guess is that by doing so, a variable named `spec` is created in the module `fsspec` (created by entering a for loop as there are entrypoints defined, see the loop in question [here](https:\/\/github.com\/intake\/filesystem_spec\/blob\/0589358d8a029ed6b60d031018f52be2eb721291\/fsspec\/__init__.py#L55)).\r\nSo that `fsspec.spec`, that was previously referring to the `spec` submodule, is now referring to that `spec` variable.\r\nThis make the import of datasets failing as it is using that `fsspec.spec`.\r\n\r\n## Steps to reproduce the bug\r\nI could reproduce the bug with a dummy poetry project.\r\n\r\nHere is the pyproject.toml:\r\n```toml\r\n[tool.poetry]\r\nname = \"debug-datasets\"\r\nversion = \"0.1.0\"\r\ndescription = \"\"\r\nauthors = [\"Pierre Godard\"]\r\n\r\n[tool.poetry.dependencies]\r\npython = \"^3.8\"\r\ndatasets = \"^1.11.0\"\r\n\r\n[tool.poetry.dev-dependencies]\r\n\r\n[build-system]\r\nrequires = [\"poetry-core>=1.0.0\"]\r\nbuild-backend = \"poetry.core.masonry.api\"\r\n\r\n[tool.poetry.plugins.\"fsspec.specs\"]\r\n\"file2\" = \"fsspec.implementations.local.LocalFileSystem\"\r\n```\r\n\r\nThe only other file being a `debug_datasets\/__init__.py` empty file.\r\n\r\nThe overall structure of the project is as follows:\r\n```\r\n.\r\n\u251c\u2500\u2500 pyproject.toml\r\n\u2514\u2500\u2500 debug_datasets\r\n \u2514\u2500\u2500 __init__.py\r\n```\r\n\r\nThen, within the project folder run:\r\n\r\n```\r\npoetry install\r\npoetry run python\r\n```\r\n\r\nAnd in the python interpreter, try to import `datasets`:\r\n\r\n```\r\nimport datasets\r\n```\r\n\r\n## Expected results\r\nThe import should run successfully.\r\n\r\n## Actual results\r\n\r\nHere is the trace of the error I get:\r\n\r\n```\r\nTraceback (most recent call last):\r\n File \"\", line 1, in \r\n File \"\/home\/godarpi\/.cache\/pypoetry\/virtualenvs\/debug-datasets-JuFzTKL--py3.8\/lib\/python3.8\/site-packages\/datasets\/__init__.py\", line 33, in \r\n from .arrow_dataset import Dataset, concatenate_datasets\r\n File \"\/home\/godarpi\/.cache\/pypoetry\/virtualenvs\/debug-datasets-JuFzTKL--py3.8\/lib\/python3.8\/site-packages\/datasets\/arrow_dataset.py\", line 48, in \r\n from .filesystems import extract_path_from_uri, is_remote_filesystem\r\n File \"\/home\/godarpi\/.cache\/pypoetry\/virtualenvs\/debug-datasets-JuFzTKL--py3.8\/lib\/python3.8\/site-packages\/datasets\/filesystems\/__init__.py\", line 30, in \r\n def is_remote_filesystem(fs: fsspec.spec.AbstractFileSystem) -> bool:\r\nAttributeError: 'EntryPoint' object has no attribute 'AbstractFileSystem'\r\n```\r\n\r\n## Suggested fix\r\n\r\n`datasets\/filesystems\/__init__.py`, line 30, replace:\r\n```\r\n def is_remote_filesystem(fs: fsspec.spec.AbstractFileSystem) -> bool:\r\n```\r\nby:\r\n```\r\n def is_remote_filesystem(fs: fsspec.AbstractFileSystem) -> bool:\r\n```\r\n\r\nI will come up with a PR soon if this effectively solves the issue.\r\n\r\n## Environment info\r\n\r\n- `datasets` version: 1.11.0\r\n- Platform: WSL2 (Ubuntu 20.04.1 LTS)\r\n- Python version: 3.8.5\r\n- PyArrow version: 5.0.0\r\n- `fsspec` version: 2021.8.1\r\n\nClosed by #2915.","embeddings":[-0.0430343486,0.1729615629,-0.0092811715,0.3249996006,0.3824198246,-0.027578136,0.3696733415,-0.0281330235,0.0717082471,0.1155877039,0.2619345784,0.4130678475,-0.0622646138,0.0250749756,-0.1028326377,0.2277256399,0.0461320765,0.1964916587,-0.1118648723,0.0814422593,-0.2052113712,0.284779042,-0.20766671,0.1609047055,-0.3610854745,0.1328371316,0.0609395057,0.3386956155,-0.1826843172,-0.6150017977,0.3787585497,-0.1575102955,-0.0890944004,0.1836814284,-0.0001192106,0.0413611904,0.1138052121,-0.081654422,-0.2293013632,0.0555289909,-0.5713313818,-0.1993799657,0.1814039797,-0.2640390098,0.4023723602,-0.2299601138,-0.0492568053,-0.720643878,-0.0350703783,0.4295676649,0.151214689,-0.0737293884,0.1342175305,-0.078581512,0.1368629038,0.0073615429,-0.2163085043,0.0665525422,-0.1590744555,-0.1137120426,-0.0247463994,0.028592892,-0.073725529,0.2388720959,0.7355163693,-0.1537969112,0.4934940934,-0.2031433433,-0.0418940075,0.1256616861,0.4556665123,-0.4343681931,-0.3134965301,-0.1376075,0.0694526061,-0.099931106,0.2053408474,-0.0052408343,0.0365756415,0.1147769988,0.4034270048,-0.0220960025,-0.1103871539,0.0099158697,-0.1579840034,-0.1949465275,-0.2566277087,0.1149913594,-0.0176931862,-0.1435348243,-0.293458432,-0.1421632171,-0.1392901242,0.0056826379,-0.3321452439,0.0178288762,0.2203202993,-0.1728139669,-0.2198076099,0.2929090858,-0.0524715595,0.0291536804,0.1376225799,0.3627481163,0.1954966486,0.1123554483,0.3818732202,0.0941143036,0.2671805918,0.0105761532,-0.1745803803,-0.0067355181,-0.0322308168,-0.4421686828,0.196050778,-0.0149847884,0.7509050965,-0.3278894126,-0.3549762964,0.2934402525,-0.2196975499,0.0935777649,-0.0379396789,0.2039052695,0.2131644487,0.5074154139,0.110865429,0.1131861806,-0.2682110369,0.0728461221,-0.0765823871,0.0951074734,-0.1301978678,0.1238338947,-0.0917826369,-0.0535966307,0.3636643291,-0.0692519695,0.087492682,-0.2760293186,0.0895308852,-0.1705299467,-0.2756180465,0.268853128,-0.4858374298,0.0634655356,0.0485958643,-0.1812145114,-0.1407059431,-0.0891787559,-0.223374173,-0.2241652012,-0.2036291659,0.1713668406,-0.227433145,0.0364288799,-0.2167210281,-0.2242526561,0.2060965598,-0.0521678776,0.1591060758,-0.2531750202,-0.255607456,-0.1615860462,-0.0356961787,0.3934701979,-0.2941275835,-0.1991277188,-0.1428540945,-0.2064636052,-0.1786636561,-0.1581739336,-0.1267938614,0.4374836683,-0.2819532752,0.1414128244,0.2326729596,-0.4089822769,-0.1433600485,0.2878492177,0.0954126939,-0.1979203522,0.1566754282,-0.1284318268,0.1135182604,-0.1574372202,0.1001495048,0.0776153803,0.1633862257,0.007081199,-0.0188464597,0.036375124,-0.1544718444,-0.0118767954,-0.2443443686,0.0282867458,0.1455506384,0.1295337081,-0.1325169504,-0.1437395513,0.102992475,0.446297735,0.5053649545,0.2926414013,0.0018090364,-0.3757137656,-0.1410629153,0.2792656124,0.0050844387,-0.0705011711,-0.0173224658,-0.0369233266,-0.2272954136,0.4188956916,-0.0087850615,-0.3483618796,-0.0021392696,0.1587059945,-0.2076747417,-0.1167936027,-0.2682749331,0.0590893626,-0.1198496372,0.0904613882,-0.2748877108,0.0908167139,-0.0701375529,-0.0042610434,-0.0235086512,0.3459734023,0.1507644355,0.122721754,-0.1112476885,0.3404074907,0.5937909484,0.2090201527,-0.0751314461,0.3819218576,0.1279350668,-0.2491389513,-0.0412581488,0.1454391629,0.2145802826,-0.0491112433,0.4379881918,0.2754839659,0.2406787723,0.3727573156,-0.0556240045,0.1264895946,0.242703557,-0.1039064452,-0.1578007489,-0.2713132203,0.1646561325,-0.0192193184,0.4520537555,0.2100941837,0.0325967483,-0.0543766543,-0.0089729214,0.0672252774,0.0164770391,-0.0364091173,-0.2857769132,0.0901333243,0.2991757691,0.056403894,0.7109383345,0.0651810989,-0.3750181198,0.1294146478,-0.020994015,-0.0924102291,0.232258141,0.1908759773,0.1215177178,0.1294934303,0.2365701497,-0.1311048567,0.0034811324,-0.2910775244,-0.0515734069,0.1591396928,-0.7218433022,0.3073238134,-0.4488064051,0.0365511924,-0.3674746156,-0.1099792644,0.039912153,-0.3449760377,-0.1204983741,0.0795523301,-0.1346967071,0.1161788553,-0.3224466741,0.0531995259,-0.0138493739,-0.4038999677,0.0998631865,-0.1038146392,-0.0749545097,-0.0629806891,0.2017032653,0.4358921051,0.1518811285,0.2062558383,-0.0725508034,-0.2794296741,-0.1373124123,0.0227615349,-0.0418572798,0.7079858184,0.4984821379,-0.0236271117,0.334713608,-0.052583456,-0.022778647,-0.0508446507,-0.1497538984,0.1021313444,-0.0401481725,-0.159014374,-0.1050804704,-0.2614705265,0.0915697217,-0.2867671847,-0.0876169056,-0.0462019704,0.0119425636,0.4248128235,0.1451478451,0.4045563936,0.2662217617,-0.0025793067,-0.0106500452,0.1442047656,0.1359658688,-0.1538913995,-0.2444033176,0.2343038619,-0.2083691955,-0.0294864997,-0.0952343643,-0.4535905719,-0.25282076,-0.1953865737,0.3757030666,-0.0514751524,-0.1112330109,0.4003503621,0.165106073,-0.0839413479,-0.3391038477,-0.2688634694,0.0547733977,0.0875356421,-0.0184010845,0.1420247257,-0.1215976626,-0.1658104211,0.374792397,0.2599062324,-0.1457856596,0.2739960253,-0.1751101613,0.2564560473,0.1007364541,-0.3438697755,-0.0130711058,-0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{"html_url":"https:\/\/github.com\/huggingface\/datasets\/issues\/2913","title":"timit_asr dataset only includes one text phrase","comments":"Hi @margotwagner, \r\nThis bug was fixed in #1995. Upgrading the datasets should work (min v1.8.0 ideally)","body":"## Describe the bug\r\nThe dataset 'timit_asr' only includes one text phrase. It only includes the transcription \"Would such an act of refusal be useful?\" multiple times rather than different phrases.\r\n\r\n## Steps to reproduce the bug\r\nNote: I am following the tutorial https:\/\/huggingface.co\/blog\/fine-tune-wav2vec2-english\r\n\r\n1. Install the dataset and other packages\r\n```python\r\n!pip install datasets>=1.5.0\r\n!pip install transformers==4.4.0\r\n!pip install soundfile\r\n!pip install jiwer\r\n```\r\n2. Load the dataset\r\n```python\r\nfrom datasets import load_dataset, load_metric\r\n\r\ntimit = load_dataset(\"timit_asr\")\r\n```\r\n3. Remove columns that we don't want\r\n```python\r\ntimit = timit.remove_columns([\"phonetic_detail\", \"word_detail\", \"dialect_region\", \"id\", \"sentence_type\", \"speaker_id\"])\r\n```\r\n4. Write a short function to display some random samples of the dataset.\r\n```python\r\nfrom datasets import ClassLabel\r\nimport random\r\nimport pandas as pd\r\nfrom IPython.display import display, HTML\r\n\r\ndef show_random_elements(dataset, num_examples=10):\r\n assert num_examples <= len(dataset), \"Can't pick more elements than there are in the dataset.\"\r\n picks = []\r\n for _ in range(num_examples):\r\n pick = random.randint(0, len(dataset)-1)\r\n while pick in picks:\r\n pick = random.randint(0, len(dataset)-1)\r\n picks.append(pick)\r\n \r\n df = pd.DataFrame(dataset[picks])\r\n display(HTML(df.to_html()))\r\n\r\nshow_random_elements(timit[\"train\"].remove_columns([\"file\"]))\r\n```\r\n\r\n## Expected results\r\n10 random different transcription phrases.\r\n\r\n## Actual results\r\n10 of the same transcription phrase \"Would such an act of refusal be useful?\"\r\n\r\n## Environment info\r\n\r\n- `datasets` version: 1.4.1\r\n- Platform: macOS-10.15.7-x86_64-i386-64bit\r\n- Python version: 3.8.5\r\n- PyArrow version: not listed\r\n","comment_length":16,"text":"timit_asr dataset only includes one text phrase\n## Describe the bug\r\nThe dataset 'timit_asr' only includes one text phrase. It only includes the transcription \"Would such an act of refusal be useful?\" multiple times rather than different phrases.\r\n\r\n## Steps to reproduce the bug\r\nNote: I am following the tutorial https:\/\/huggingface.co\/blog\/fine-tune-wav2vec2-english\r\n\r\n1. Install the dataset and other packages\r\n```python\r\n!pip install datasets>=1.5.0\r\n!pip install transformers==4.4.0\r\n!pip install soundfile\r\n!pip install jiwer\r\n```\r\n2. Load the dataset\r\n```python\r\nfrom datasets import load_dataset, load_metric\r\n\r\ntimit = load_dataset(\"timit_asr\")\r\n```\r\n3. Remove columns that we don't want\r\n```python\r\ntimit = timit.remove_columns([\"phonetic_detail\", \"word_detail\", \"dialect_region\", \"id\", \"sentence_type\", \"speaker_id\"])\r\n```\r\n4. Write a short function to display some random samples of the dataset.\r\n```python\r\nfrom datasets import ClassLabel\r\nimport random\r\nimport pandas as pd\r\nfrom IPython.display import display, HTML\r\n\r\ndef show_random_elements(dataset, num_examples=10):\r\n assert num_examples <= len(dataset), \"Can't pick more elements than there are in the dataset.\"\r\n picks = []\r\n for _ in range(num_examples):\r\n pick = random.randint(0, len(dataset)-1)\r\n while pick in picks:\r\n pick = random.randint(0, len(dataset)-1)\r\n picks.append(pick)\r\n \r\n df = pd.DataFrame(dataset[picks])\r\n display(HTML(df.to_html()))\r\n\r\nshow_random_elements(timit[\"train\"].remove_columns([\"file\"]))\r\n```\r\n\r\n## Expected results\r\n10 random different transcription phrases.\r\n\r\n## Actual results\r\n10 of the same transcription phrase \"Would such an act of refusal be useful?\"\r\n\r\n## Environment info\r\n\r\n- `datasets` version: 1.4.1\r\n- Platform: macOS-10.15.7-x86_64-i386-64bit\r\n- Python version: 3.8.5\r\n- PyArrow version: not listed\r\n\nHi @margotwagner, \r\nThis bug was fixed in #1995. Upgrading the datasets should work (min v1.8.0 ideally)","embeddings":[0.161290288,-0.138053894,-0.0205535926,0.2273859978,0.1176241562,-0.1575979143,0.2670057416,0.2965668142,-0.5154975653,0.2804078162,0.1454417259,0.4508609474,-0.0935282409,-0.1810210198,0.0642997622,-0.0575755686,0.1221427992,0.2484459728,-0.0715344921,-0.2342915833,0.1333340406,0.3007956445,-0.257455945,-0.064661555,-0.2737528682,0.0847610086,-0.0766698644,-0.2693378031,-0.0532864109,-0.4953400791,0.1539394706,0.011122671,0.1049450785,0.3338848054,-0.0001155144,-0.0851756856,0.0369373672,0.0938122272,-0.2382418513,-0.157761991,-0.0969283953,0.0886226818,-0.0698391646,-0.0270568896,-0.0834104121,0.1937476546,0.0529345647,-0.0981618613,0.5147064328,0.2915839553,0.1400677115,-0.0102435909,-0.252120316,-0.0659528822,0.3198694587,0.0313076191,0.0850752518,-0.1780210286,0.2137241364,0.0314790495,-0.0516679287,0.6180685163,-0.2813329399,0.3980180621,-0.2359145284,0.1242306605,-0.3711774647,-0.5308332443,0.266836673,0.2161651254,0.6465566754,-0.2296243757,-0.2044666409,-0.2378615439,0.3199083507,-0.1560648084,-0.0421698391,-0.0161785055,-0.2728021145,0.2350625694,-0.0094950655,0.047647696,-0.2204018831,0.1288805455,0.055811543,-0.1074196771,-0.0731363669,0.1385569125,-0.177100122,0.0994098634,-0.085863933,0.2703583241,-0.1442609429,0.0341251902,-0.149762243,-0.0674901083,0.1925161034,-0.3049418628,0.2917183042,-0.0602199472,0.3855580986,0.0374377258,-0.1856715977,-0.0881002173,0.3239292204,0.019808244,-0.0233677495,0.1496766508,0.2298973352,-0.2340689301,-0.2531856596,0.0737158284,-0.0144180572,0.2162737995,0.331879437,-0.265029937,0.2829249203,-0.367644608,-0.5658828616,0.0642497912,-0.4400376976,-0.1030385345,-0.2528013289,0.1816738844,0.1693309546,0.3009272516,0.1449253261,0.2226763219,0.0693393499,-0.5628325343,-0.1818621904,0.0267980024,0.2098604441,0.0308408234,0.211329326,-0.3237065375,0.3398516774,0.3417337835,0.195452705,-0.4315991104,-0.2874112725,-0.1301766485,0.0255610328,0.0091858534,-0.0264903046,0.3916681111,0.0738202855,-0.0182900447,0.0072356486,0.0610072762,-0.106556423,0.0068646008,0.2134016603,0.1364102364,-0.0805880576,-0.167114675,0.1232486665,0.3605877757,0.0779450312,-0.3008698225,0.0030109661,-0.1827422976,-0.3919641674,-0.0386591218,0.0773314983,0.3024609089,-0.3355484903,0.0954273939,0.178534463,0.4213981032,0.3944750428,0.2484540939,-0.0465098992,0.3734385073,-0.1168166548,0.3001444042,0.2041984349,-0.4621309042,-0.3920545578,0.2112352401,-0.049603641,0.2464142591,0.1548911184,-0.1627833694,0.3027338982,0.1427075267,0.3759355247,0.153478384,0.1457359195,0.0334793814,-0.2446887195,-0.057892438,0.0868306831,-0.0131406998,0.0021537091,0.0955836996,0.0370380655,0.232970655,0.5271517038,0.0745838359,0.0924516246,0.1029673368,0.1659648567,-0.1012314931,0.2239779383,-0.2631005943,0.2564861178,-0.0488976687,0.2068272382,0.2837985456,0.1540315896,0.0793671757,-0.3015450537,-0.2188883722,-0.2991884053,-0.3250726461,0.1145813912,0.3257373571,-0.0144317094,0.0420466512,-0.1898579001,0.3409897685,-0.2203264982,-0.0741857439,0.0233059432,0.0991652608,0.0638567135,-0.2447074801,0.5248855948,0.2528992593,0.18514961,0.2016844004,-0.1669406295,0.22355178,-0.0147676235,0.0219675135,-0.2637168467,-0.3288924992,0.1099477187,-0.5017575622,-0.1113948897,0.2344709635,0.3181717396,-0.0044999369,-0.3036757708,-0.1834709942,0.1390077174,0.2569387853,-0.0554442666,-0.0651190057,0.3011586368,0.0662527978,-0.3537996113,-0.3246975541,0.2526897192,-0.234980613,0.2662216723,0.0202848148,-0.3417770565,0.1585986316,0.3928949833,0.2245113999,0.085208714,0.2943886817,-0.2468326539,-0.027017273,-0.1227289811,0.0949118286,0.2907651365,0.2881050408,0.0543022901,-0.3764547706,-0.1287488788,-0.1132321432,0.1393357515,-0.1307561845,-0.3556297719,0.4351170063,0.3585450351,0.0863876268,-0.4493839145,-0.1341284662,-0.1424040794,0.4703677297,-0.3349600732,-0.2316320539,-0.4356057346,-0.1205524206,-0.109005712,0.0662912875,0.0797044486,0.225550577,0.2190566808,-0.0127121434,0.0054567931,0.1367991865,0.1719908118,0.2340619862,0.0712051392,0.1117983311,0.086305052,0.3191404045,-0.306956917,-0.0242702272,0.0259727612,0.271612525,0.1074295118,-0.1422471404,-0.1639021039,-0.4067965448,0.0590180866,0.2675643563,-0.3301686049,0.5113489628,0.1133633777,0.1602485478,-0.2251659632,-0.3130813539,0.0026724054,-0.0268870983,-0.1686861217,0.3555683196,0.0829433203,-0.0056142341,-0.0482558794,-0.698974669,0.099392809,-0.2500089407,0.2359433323,-0.0328674652,0.2103785425,0.3641358316,0.0606071204,0.0812700838,-0.0587117635,0.2053699046,-0.410209775,-0.2545935214,0.3561107218,0.2606044412,-0.3348694146,-0.3405695558,0.0509579033,0.2652952075,-0.0322290994,-0.4001087248,0.1003541723,-0.2674658895,0.1453480273,-0.2972603142,0.2826198339,0.3854675591,0.0129939336,-0.1495047212,-0.1692215204,-0.1078334898,0.2658356428,0.3553734422,0.0058843573,0.1011472642,0.0699137822,-0.0593266487,0.3506978154,0.6459594965,0.2025162727,0.2600066662,-0.1765539348,0.0942354053,-0.0324934721,-0.3995798528,0.0270797946,-0.1173247173,-0.062567167,0.4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{"html_url":"https:\/\/github.com\/huggingface\/datasets\/issues\/2913","title":"timit_asr dataset only includes one text phrase","comments":"Hi @margotwagner,\r\n\r\nYes, as @bhavitvyamalik has commented, this bug was fixed in `datasets` version 1.5.0. You need to update it, as your current version is 1.4.1:\r\n> Environment info\r\n> - `datasets` version: 1.4.1","body":"## Describe the bug\r\nThe dataset 'timit_asr' only includes one text phrase. It only includes the transcription \"Would such an act of refusal be useful?\" multiple times rather than different phrases.\r\n\r\n## Steps to reproduce the bug\r\nNote: I am following the tutorial https:\/\/huggingface.co\/blog\/fine-tune-wav2vec2-english\r\n\r\n1. Install the dataset and other packages\r\n```python\r\n!pip install datasets>=1.5.0\r\n!pip install transformers==4.4.0\r\n!pip install soundfile\r\n!pip install jiwer\r\n```\r\n2. Load the dataset\r\n```python\r\nfrom datasets import load_dataset, load_metric\r\n\r\ntimit = load_dataset(\"timit_asr\")\r\n```\r\n3. Remove columns that we don't want\r\n```python\r\ntimit = timit.remove_columns([\"phonetic_detail\", \"word_detail\", \"dialect_region\", \"id\", \"sentence_type\", \"speaker_id\"])\r\n```\r\n4. Write a short function to display some random samples of the dataset.\r\n```python\r\nfrom datasets import ClassLabel\r\nimport random\r\nimport pandas as pd\r\nfrom IPython.display import display, HTML\r\n\r\ndef show_random_elements(dataset, num_examples=10):\r\n assert num_examples <= len(dataset), \"Can't pick more elements than there are in the dataset.\"\r\n picks = []\r\n for _ in range(num_examples):\r\n pick = random.randint(0, len(dataset)-1)\r\n while pick in picks:\r\n pick = random.randint(0, len(dataset)-1)\r\n picks.append(pick)\r\n \r\n df = pd.DataFrame(dataset[picks])\r\n display(HTML(df.to_html()))\r\n\r\nshow_random_elements(timit[\"train\"].remove_columns([\"file\"]))\r\n```\r\n\r\n## Expected results\r\n10 random different transcription phrases.\r\n\r\n## Actual results\r\n10 of the same transcription phrase \"Would such an act of refusal be useful?\"\r\n\r\n## Environment info\r\n\r\n- `datasets` version: 1.4.1\r\n- Platform: macOS-10.15.7-x86_64-i386-64bit\r\n- Python version: 3.8.5\r\n- PyArrow version: not listed\r\n","comment_length":34,"text":"timit_asr dataset only includes one text phrase\n## Describe the bug\r\nThe dataset 'timit_asr' only includes one text phrase. It only includes the transcription \"Would such an act of refusal be useful?\" multiple times rather than different phrases.\r\n\r\n## Steps to reproduce the bug\r\nNote: I am following the tutorial https:\/\/huggingface.co\/blog\/fine-tune-wav2vec2-english\r\n\r\n1. Install the dataset and other packages\r\n```python\r\n!pip install datasets>=1.5.0\r\n!pip install transformers==4.4.0\r\n!pip install soundfile\r\n!pip install jiwer\r\n```\r\n2. Load the dataset\r\n```python\r\nfrom datasets import load_dataset, load_metric\r\n\r\ntimit = load_dataset(\"timit_asr\")\r\n```\r\n3. Remove columns that we don't want\r\n```python\r\ntimit = timit.remove_columns([\"phonetic_detail\", \"word_detail\", \"dialect_region\", \"id\", \"sentence_type\", \"speaker_id\"])\r\n```\r\n4. Write a short function to display some random samples of the dataset.\r\n```python\r\nfrom datasets import ClassLabel\r\nimport random\r\nimport pandas as pd\r\nfrom IPython.display import display, HTML\r\n\r\ndef show_random_elements(dataset, num_examples=10):\r\n assert num_examples <= len(dataset), \"Can't pick more elements than there are in the dataset.\"\r\n picks = []\r\n for _ in range(num_examples):\r\n pick = random.randint(0, len(dataset)-1)\r\n while pick in picks:\r\n pick = random.randint(0, len(dataset)-1)\r\n picks.append(pick)\r\n \r\n df = pd.DataFrame(dataset[picks])\r\n display(HTML(df.to_html()))\r\n\r\nshow_random_elements(timit[\"train\"].remove_columns([\"file\"]))\r\n```\r\n\r\n## Expected results\r\n10 random different transcription phrases.\r\n\r\n## Actual results\r\n10 of the same transcription phrase \"Would such an act of refusal be useful?\"\r\n\r\n## Environment info\r\n\r\n- `datasets` version: 1.4.1\r\n- Platform: macOS-10.15.7-x86_64-i386-64bit\r\n- Python version: 3.8.5\r\n- PyArrow version: not listed\r\n\nHi @margotwagner,\r\n\r\nYes, as @bhavitvyamalik has commented, this bug was fixed in `datasets` version 1.5.0. You need to update it, as your current version is 1.4.1:\r\n> Environment info\r\n> - `datasets` version: 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{"html_url":"https:\/\/github.com\/huggingface\/datasets\/issues\/2904","title":"FORCE_REDOWNLOAD does not work","comments":"Hi ! Thanks for reporting. The error seems to happen only if you use compressed files.\r\n\r\nThe second dataset is prepared in another dataset cache directory than the first - which is normal, since the source file is different. However, it doesn't uncompress the new data file because it finds the old uncompressed data in the extraction cache directory.\r\n\r\nIf we fix the extraction cache mechanism to uncompress a local file if it changed then it should fix the issue.\r\nCurrently the extraction cache mechanism only takes into account the path of the compressed file, which is an issue.","body":"## Describe the bug\r\nWith GenerateMode.FORCE_REDOWNLOAD, the documentation says \r\n +------------------------------------+-----------+---------+\r\n | | Downloads | Dataset |\r\n +====================================+===========+=========+\r\n | `REUSE_DATASET_IF_EXISTS` (default)| Reuse | Reuse |\r\n +------------------------------------+-----------+---------+\r\n | `REUSE_CACHE_IF_EXISTS` | Reuse | Fresh |\r\n +------------------------------------+-----------+---------+\r\n | `FORCE_REDOWNLOAD` | Fresh | Fresh |\r\n +------------------------------------+-----------+---------+\r\n\r\nHowever, the old dataset is loaded even when FORCE_REDOWNLOAD is chosen.\r\n\r\n## Steps to reproduce the bug\r\n```python\r\n\r\nimport pandas as pd\r\nfrom datasets import load_dataset, GenerateMode\r\npd.DataFrame(range(5), columns=['numbers']).to_csv('\/tmp\/test.tsv.gz', index=False)\r\nee = load_dataset('csv', data_files=['\/tmp\/test.tsv.gz'], delimiter='\\t', split='train', download_mode=GenerateMode.FORCE_REDOWNLOAD)\r\nprint(ee)\r\npd.DataFrame(range(10), columns=['numerals']).to_csv('\/tmp\/test.tsv.gz', index=False)\r\nee = load_dataset('csv', data_files=['\/tmp\/test.tsv.gz'], delimiter='\\t', split='train', download_mode=GenerateMode.FORCE_REDOWNLOAD)\r\nprint(ee)\r\n\r\n```\r\n\r\n## Expected results\r\nDataset({\r\n features: ['numbers'],\r\n num_rows: 5\r\n})\r\nDataset({\r\n features: ['numerals'],\r\n num_rows: 10\r\n})\r\n\r\n## Actual results\r\nDataset({\r\n features: ['numbers'],\r\n num_rows: 5\r\n})\r\nDataset({\r\n features: ['numbers'],\r\n num_rows: 5\r\n})\r\n\r\n\r\n## Environment info\r\n\r\n- `datasets` version: 1.8.0\r\n- Platform: Linux-4.14.181-108.257.amzn1.x86_64-x86_64-with-glibc2.10\r\n- Python version: 3.7.10\r\n- PyArrow version: 3.0.0\r\n","comment_length":99,"text":"FORCE_REDOWNLOAD does not work\n## Describe the bug\r\nWith GenerateMode.FORCE_REDOWNLOAD, the documentation says \r\n +------------------------------------+-----------+---------+\r\n | | Downloads | Dataset |\r\n +====================================+===========+=========+\r\n | `REUSE_DATASET_IF_EXISTS` (default)| Reuse | Reuse |\r\n +------------------------------------+-----------+---------+\r\n | `REUSE_CACHE_IF_EXISTS` | Reuse | Fresh |\r\n +------------------------------------+-----------+---------+\r\n | `FORCE_REDOWNLOAD` | Fresh | Fresh |\r\n +------------------------------------+-----------+---------+\r\n\r\nHowever, the old dataset is loaded even when FORCE_REDOWNLOAD is chosen.\r\n\r\n## Steps to reproduce the bug\r\n```python\r\n\r\nimport pandas as pd\r\nfrom datasets import load_dataset, GenerateMode\r\npd.DataFrame(range(5), columns=['numbers']).to_csv('\/tmp\/test.tsv.gz', index=False)\r\nee = load_dataset('csv', data_files=['\/tmp\/test.tsv.gz'], delimiter='\\t', split='train', download_mode=GenerateMode.FORCE_REDOWNLOAD)\r\nprint(ee)\r\npd.DataFrame(range(10), columns=['numerals']).to_csv('\/tmp\/test.tsv.gz', index=False)\r\nee = load_dataset('csv', data_files=['\/tmp\/test.tsv.gz'], delimiter='\\t', split='train', download_mode=GenerateMode.FORCE_REDOWNLOAD)\r\nprint(ee)\r\n\r\n```\r\n\r\n## Expected results\r\nDataset({\r\n features: ['numbers'],\r\n num_rows: 5\r\n})\r\nDataset({\r\n features: ['numerals'],\r\n num_rows: 10\r\n})\r\n\r\n## Actual results\r\nDataset({\r\n features: ['numbers'],\r\n num_rows: 5\r\n})\r\nDataset({\r\n features: ['numbers'],\r\n num_rows: 5\r\n})\r\n\r\n\r\n## Environment info\r\n\r\n- `datasets` version: 1.8.0\r\n- Platform: Linux-4.14.181-108.257.amzn1.x86_64-x86_64-with-glibc2.10\r\n- Python version: 3.7.10\r\n- PyArrow version: 3.0.0\r\n\nHi ! Thanks for reporting. The error seems to happen only if you use compressed files.\r\n\r\nThe second dataset is prepared in another dataset cache directory than the first - which is normal, since the source file is different. However, it doesn't uncompress the new data file because it finds the old uncompressed data in the extraction cache directory.\r\n\r\nIf we fix the extraction cache mechanism to uncompress a local file if it changed then it should fix the issue.\r\nCurrently the extraction cache mechanism only takes into account the path of the compressed file, which is an issue.","embeddings":[-0.1253399998,0.0207806267,0.0185128842,0.0257142521,0.1821040809,0.0251934566,0.4788660109,0.2438331693,0.145796001,-0.2338699996,-0.1162827983,0.2687395215,0.0850747079,0.1121127084,-0.0132031068,0.3137487769,0.1101020277,0.2144826502,-0.0722493976,-0.0051923986,-0.2910993099,0.0634416714,-0.1833185554,-0.2195639163,-0.2065767497,0.2937990129,-0.0426936708,0.3026081324,0.0084062209,-0.3731202781,0.1836812794,0.2550069988,0.1820975393,0.6611076593,-0.000109716,0.0617883578,0.1586239785,-0.1793150753,-0.1704429984,-0.1858083606,0.0419964381,-0.0455286205,-0.3461463749,-0.0842089579,-0.0005326021,-0.2207529545,-0.1748757958,-0.346149832,0.5304936767,0.4028010368,0.2320071906,-0.1104161516,0.1202409416,-0.0721178129,0.1787120998,0.1414143443,-0.0699524358,0.3557813466,0.1045252755,-0.2072234303,-0.090631254,0.071447365,-0.1301329136,0.0515931137,-0.100776881,-0.0266386643,0.3280929923,-0.1360899806,0.0856568739,0.1706219614,0.60730654,-0.3377125561,-0.4172843397,0.0072846375,0.0183145087,-0.2442766875,0.1650239527,0.1792488247,-0.0074186656,0.368666321,-0.0126197841,-0.0254521314,0.0721380934,-0.1948064566,-0.0308580399,-0.117819868,-0.1004166082,-0.0362135768,0.0516206622,0.0602480508,0.4213585556,-0.021616118,-0.0566806123,0.0342854224,-0.029152602,-0.1342204958,0.2153061479,-0.3018356264,0.0747370049,-0.0281923842,-0.0764476284,-0.1077359319,-0.0592024475,0.04897983,0.0738615543,0.2261448056,-0.1387252212,-0.090120174,0.3910107613,0.1767412573,-0.251378119,-0.0726185068,0.0294129029,-0.2027270049,0.5261009336,-0.0647600591,0.2614033818,-0.1472218335,-0.4044618905,-0.0181814078,-0.1070784777,0.0032655229,-0.2742642164,0.1767624319,0.0207748469,0.3530659974,0.1341454834,0.1856252104,0.0581398085,-0.206356883,-0.2226337641,-0.1226871684,-0.3091842234,0.0251519568,0.2947835922,-0.1708823442,0.3440055251,0.5470414758,-0.3297725916,-0.5027301908,-0.0621353462,-0.0314323269,0.145252198,0.2964736819,-0.0633033141,-0.0863981768,0.2666994929,-0.0901367441,-0.0168399569,0.3552974463,-0.0914471522,-0.3069287539,0.1436540186,0.1975634098,0.0511063114,0.1958488077,-0.127950877,0.0155278603,0.3042720556,-0.0705190748,-0.0457962081,-0.0500780307,-0.0136534544,-0.2484514862,0.0476663969,0.7337824702,-0.4681162834,0.0081418594,-0.2922013104,-0.0890840143,0.2404547036,0.0785243809,0.0047035967,0.0423925631,-0.4686902165,-0.3603310585,0.1667583287,-0.1669529229,-0.4369600415,0.0058205375,-0.2142421305,0.2575328946,0.1665146351,-0.0449789762,-0.1501356214,-0.0172337554,0.0970478505,0.2490073442,0.0923939124,0.044823505,-0.3246170282,-0.288821131,0.0455681123,-0.1329662651,0.2855156362,0.405680865,0.3521436155,0.166620627,0.3699389398,0.0625928491,0.0155102359,0.0143866315,0.3367364407,-0.0180097409,0.0330760218,0.1765212268,-0.5221686959,0.3075172901,-0.0336038433,-0.2515624166,0.0728959292,-0.0701956674,-0.5719491243,-0.228338778,-0.0720977858,-0.3877546787,0.1725475937,0.4713421166,-0.033435449,0.0184990596,-0.1434893459,0.4725981653,-0.3845011294,-0.0480710976,-0.0413006209,0.2855195999,-0.1932275444,-0.1814612597,-0.2195678353,-0.1187965199,0.2447945774,-0.1625743508,-0.1215589717,0.4299698472,0.186048165,0.2720868587,-0.3619140387,-0.1950368881,0.1652197391,-0.016063001,-0.1465488821,0.1185459942,0.2459682077,-0.0776265264,-0.0173951481,0.2789047956,-0.4031001925,-0.0885871574,0.071374476,-0.0462191142,0.2666130662,-0.2367841005,0.2828178704,-0.340397954,-0.3403313756,0.2947020233,0.0018241567,0.1279272139,0.0989251807,0.1121156812,0.2867794335,0.0066712452,0.047518149,-0.0890123621,-0.1765656024,-0.2945634723,0.0131209595,0.3500014544,0.5929611921,0.1657259762,-0.0166612044,0.1451728493,-0.0583074652,-0.253809005,0.2043861002,-0.2613338828,-0.0064706737,0.3311824501,0.1878525168,0.0763362646,-0.6370830536,0.186015591,0.5140926838,0.2118025422,-0.2195149958,-0.1909853667,-0.1673647612,0.4125154912,0.0630170777,0.1435509324,-0.0300561506,-0.1065669209,-0.138272956,0.5955287814,-0.0589990281,0.073292084,-0.1850811243,-0.0749260634,-0.0461661331,-0.1426966637,-0.1472802907,-0.1764150858,-0.1414824873,0.0910957381,0.2313582748,-0.3419226408,0.1477311403,-0.1828764379,-0.1267566234,-0.3267619908,-0.0833430737,-0.1293941885,0.0340040401,0.2224018276,-0.2250489891,0.1471374333,0.169757694,0.0325683653,-0.004715248,-0.3554546833,-0.3341683745,-0.1029282659,0.1511999369,0.2633759975,-0.2600107789,-0.4554549158,0.1090232432,-0.3711392879,-0.0406573713,0.1015611589,-0.0919760466,0.3833361566,0.2238112539,-0.0310825258,0.4020083547,0.2698833942,-0.4376188517,-0.2790603936,0.0805035159,-0.1014774665,-0.2633699775,0.0147913452,0.0609156676,-0.0121200867,0.2737061381,-0.4307680726,-0.0845259503,-0.2824058533,0.0837785453,-0.0585749894,0.2336959094,0.2081224918,0.1207609028,-0.2136411071,-0.1040799022,-0.0956789032,-0.041495204,0.233491689,0.126982823,0.0212046467,0.2292778045,0.0935394913,0.6404239535,0.0503102541,-0.1254811585,0.5086991787,0.125423491,0.7294853926,-0.074036397,-0.339060694,-0.0056349542,-0.1845843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{"html_url":"https:\/\/github.com\/huggingface\/datasets\/issues\/2902","title":"Add WIT Dataset","comments":"@hassiahk is working on it #2810 ","body":"## Adding a Dataset\r\n- **Name:** *WIT*\r\n- **Description:** *Wikipedia-based Image Text Dataset*\r\n- **Paper:** *[WIT: Wikipedia-based Image Text Dataset for Multimodal Multilingual Machine Learning\r\n](https:\/\/arxiv.org\/abs\/2103.01913)*\r\n- **Data:** *https:\/\/github.com\/google-research-datasets\/wit*\r\n- **Motivation:** (excerpt from their Github README.md)\r\n\r\n> - The largest multimodal dataset (publicly available at the time of this writing) by the number of image-text examples.\r\n> - A massively multilingual dataset (first of its kind) with coverage for over 100+ languages.\r\n> - A collection of diverse set of concepts and real world entities.\r\n> - Brings forth challenging real-world test sets.\r\n\r\nInstructions to add a new dataset can be found [here](https:\/\/github.com\/huggingface\/datasets\/blob\/master\/ADD_NEW_DATASET.md).\r\n","comment_length":6,"text":"Add WIT Dataset\n## Adding a Dataset\r\n- **Name:** *WIT*\r\n- **Description:** *Wikipedia-based Image Text Dataset*\r\n- **Paper:** *[WIT: Wikipedia-based Image Text Dataset for Multimodal Multilingual Machine Learning\r\n](https:\/\/arxiv.org\/abs\/2103.01913)*\r\n- **Data:** *https:\/\/github.com\/google-research-datasets\/wit*\r\n- **Motivation:** (excerpt from their Github README.md)\r\n\r\n> - The largest multimodal dataset (publicly available at the time of this writing) by the number of image-text examples.\r\n> - A massively multilingual dataset (first of its kind) with coverage for over 100+ languages.\r\n> - A collection of diverse set of concepts and real world entities.\r\n> - Brings forth challenging real-world test sets.\r\n\r\nInstructions to add a new dataset can be found [here](https:\/\/github.com\/huggingface\/datasets\/blob\/master\/ADD_NEW_DATASET.md).\r\n\n@hassiahk is working on it #2810 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{"html_url":"https:\/\/github.com\/huggingface\/datasets\/issues\/2902","title":"Add WIT Dataset","comments":"WikiMedia is now hosting the pixel values directly which should make it a lot easier!\r\nThe files can be found here:\r\nhttps:\/\/techblog.wikimedia.org\/2021\/09\/09\/the-wikipedia-image-caption-matching-challenge-and-a-huge-release-of-image-data-for-research\/\r\nhttps:\/\/analytics.wikimedia.org\/published\/datasets\/one-off\/caption_competition\/training\/image_pixels\/","body":"## Adding a Dataset\r\n- **Name:** *WIT*\r\n- **Description:** *Wikipedia-based Image Text Dataset*\r\n- **Paper:** *[WIT: Wikipedia-based Image Text Dataset for Multimodal Multilingual Machine Learning\r\n](https:\/\/arxiv.org\/abs\/2103.01913)*\r\n- **Data:** *https:\/\/github.com\/google-research-datasets\/wit*\r\n- **Motivation:** (excerpt from their Github README.md)\r\n\r\n> - The largest multimodal dataset (publicly available at the time of this writing) by the number of image-text examples.\r\n> - A massively multilingual dataset (first of its kind) with coverage for over 100+ languages.\r\n> - A collection of diverse set of concepts and real world entities.\r\n> - Brings forth challenging real-world test sets.\r\n\r\nInstructions to add a new dataset can be found [here](https:\/\/github.com\/huggingface\/datasets\/blob\/master\/ADD_NEW_DATASET.md).\r\n","comment_length":23,"text":"Add WIT Dataset\n## Adding a Dataset\r\n- **Name:** *WIT*\r\n- **Description:** *Wikipedia-based Image Text Dataset*\r\n- **Paper:** *[WIT: Wikipedia-based Image Text Dataset for Multimodal Multilingual Machine Learning\r\n](https:\/\/arxiv.org\/abs\/2103.01913)*\r\n- **Data:** *https:\/\/github.com\/google-research-datasets\/wit*\r\n- **Motivation:** (excerpt from their Github README.md)\r\n\r\n> - The largest multimodal dataset (publicly available at the time of this writing) by the number of image-text examples.\r\n> - A massively multilingual dataset (first of its kind) with coverage for over 100+ languages.\r\n> - A collection of diverse set of concepts and real world entities.\r\n> - Brings forth challenging real-world test sets.\r\n\r\nInstructions to add a new dataset can be found [here](https:\/\/github.com\/huggingface\/datasets\/blob\/master\/ADD_NEW_DATASET.md).\r\n\nWikiMedia is now hosting the pixel values directly which should make it a lot easier!\r\nThe files can be found here:\r\nhttps:\/\/techblog.wikimedia.org\/2021\/09\/09\/the-wikipedia-image-caption-matching-challenge-and-a-huge-release-of-image-data-for-research\/\r\nhttps:\/\/analytics.wikimedia.org\/published\/datasets\/one-off\/caption_competition\/training\/image_pixels\/","embeddings":[-0.0497860387,-0.0547444522,-0.1280666143,0.0003004618,-0.039354112,-0.0071313963,0.3546046019,0.1659145951,0.1936855763,0.2055810541,0.0639799759,0.1460067928,-0.0242924541,0.1997424215,-0.01487609,-0.2061530054,-0.0474179126,0.007556899,-0.095439963,-0.0953547359,-0.1237470731,0.1472279876,-0.1375917941,-0.2112537324,-0.4580724537,-0.0875768587,-0.1730553955,-0.0253455658,-0.1721293628,-0.0776184052,-0.1209003329,0.1993556619,-0.1435845345,0.3342963159,-0.0000973471,-0.1190001816,0.2309069782,-0.1802841574,-0.0871033221,0.0576613285,0.1233595088,-0.0183559302,-0.3197696805,-0.4497412145,-0.1917415559,-0.0919262245,0.250259757,0.0872264057,0.220723629,-0.0171945672,0.3280394673,-0.1210444793,0.10394793,0.0242980495,0.0408663712,0.1121114418,-0.1330121011,-0.0007004917,0.0150091052,-0.3246178627,-0.1604316086,0.6847470999,0.0171727017,0.0272535756,0.0614412166,0.0563659444,-0.1721417308,-0.3297246695,0.1790350825,0.4349500835,0.5310005546,0.0022286586,-0.1660662293,-0.0531980433,-0.0875593573,-0.0650093853,0.1432961375,0.2999428809,-0.1368227452,0.0239426252,-0.2666973472,0.0028924709,-0.2545390427,0.1593592912,-0.1959471852,0.4131114483,0.021566527,0.0326524712,-0.000182682,-0.2101522833,-0.4369372725,-0.0127320085,0.2125476599,0.0099950507,0.0381090157,-0.2509273589,-0.0503862202,-0.1546090245,0.3692227602,-0.197378248,-0.0403584838,0.0733242184,-0.1825137436,0.3001660109,0.1151571646,-0.273580879,-0.1728782058,0.0215191487,0.1827842295,0.0874059945,-0.2466638684,0.1344642043,0.2253025919,-0.1029728055,-0.1772990078,-0.087800771,0.1070196927,-0.0466616042,-0.1075610965,-0.008583298,-0.146704793,-0.0710129738,-0.4360583127,0.2165165991,-0.0335759073,0.1098506823,0.1278970838,0.1473859996,-0.2188078016,-0.1533710063,-0.1057867333,0.1009146869,0.0557398014,0.2790454626,0.1211024597,0.3311381638,0.256734401,-0.0824029595,0.1746457219,0.0947210044,0.0416478626,-0.0953107029,0.2555246055,-0.008912893,0.0164618678,-0.1227277219,-0.051241383,-0.2793621719,-0.19902426,0.0270652734,-0.1084727868,0.2036321461,-0.3212899566,0.2668367922,0.1446974277,-0.0583522059,-0.0045630517,0.6982100606,0.0959759504,0.0308421906,0.1365310401,0.1445246041,-0.3958842158,-0.0636466146,0.2453516275,0.4490688741,-0.0886282399,0.0255282167,0.3061005771,0.3564617038,0.0193696469,0.1932205856,0.0245506912,-0.0181516893,0.0488758124,0.2630175054,-0.1056210548,-0.2358684838,-0.1195387468,-0.1759907305,-0.0965119898,0.1020767167,0.0326437689,0.2860510647,0.270050317,-0.102775909,0.093534559,0.6013006568,-0.1892949641,0.2137656063,-0.2130067647,-0.3863318861,-0.0069002835,0.3273807466,0.1285638511,-0.4685770869,0.2250231653,0.1909397244,0.0319985636,-0.3845872879,0.0987228006,0.0075334026,-0.0310356412,0.0995845795,0.091828078,-0.2398949265,-0.0079521695,0.0546789393,0.2049163729,0.4832212925,-0.1773444712,-0.2481343299,-0.038995903,-0.1513748169,-0.1065134704,-0.3698706031,0.3537430465,0.1205859855,-0.0045493012,0.2210574299,0.0784497336,-0.0594155379,-0.3163442016,-0.0456017256,0.1199062467,0.1867598891,-0.2623721361,-0.0185750853,0.1218658164,0.0860536918,-0.0835613608,0.1807654053,0.0911751837,0.1913248599,0.0461769477,0.3455418348,0.2677955925,0.0530583225,0.3559015393,-0.6900479198,-0.0690085068,0.19123438,0.0030610359,0.0122600449,-0.2659728527,0.1819274873,0.2717046738,0.0452171303,-0.0804450437,-0.0083080204,0.1473466903,-0.0244393963,0.0583699085,-0.3646619022,-0.0810375884,0.2129996717,-0.2515609264,-0.2326057106,-0.1890244186,0.1808756441,0.5132712126,0.2433172613,0.3687907755,0.1684178561,-0.1199678779,-0.1401048452,-0.0068703438,-0.0960154831,-0.1688125134,0.3742109239,-0.053772375,-0.1260635257,-0.2724704742,-0.0922951698,0.1780956537,-0.0158920027,0.2231312841,0.0486644618,0.2492239773,-0.0249909423,-0.4577436149,-0.1271143258,-0.1574495137,-0.1973033249,0.056282267,0.0784645602,-0.071331881,-0.1991609186,-0.066964902,0.2833610475,-0.3087125421,-0.1143085882,0.3522223532,0.0152183259,-0.1103117839,0.183313325,0.0873240456,0.1733572483,0.1862344891,0.0245582648,-0.1337936968,-0.5455780029,-0.0611162968,0.2686133087,0.1743589044,-0.0161512867,0.5377413034,-0.1781012118,0.0367738679,-0.2884244919,-0.705337286,0.1020467132,-0.1840780228,0.1969555616,0.0155186988,0.2760653794,-0.2234164178,-0.1541128606,0.1546294391,-0.1017461941,-0.1935486495,-0.2209588885,0.0480773337,0.0420998633,-0.2291868478,-0.2148961276,-0.1442073584,-0.2447485179,0.1066020951,0.3489588201,0.1721870601,0.0647365227,0.3838174343,0.0280661602,-0.0238699075,0.2764138281,-0.3571163714,-0.032202661,0.3682462573,-0.3039095998,-0.403814584,-0.1645279825,-0.182767272,0.1699132323,0.0591959544,-0.3852038085,-0.3932056427,0.0160926636,0.1964574009,0.2606021762,0.0776553899,0.1688962281,0.1696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{"html_url":"https:\/\/github.com\/huggingface\/datasets\/issues\/2902","title":"Add WIT Dataset","comments":"> @hassiahk is working on it #2810\r\n\r\nThank you @bhavitvyamalik! Added this issue so we could track progress \ud83d\ude04 . Just linked the PR as well for visibility. ","body":"## Adding a Dataset\r\n- **Name:** *WIT*\r\n- **Description:** *Wikipedia-based Image Text Dataset*\r\n- **Paper:** *[WIT: Wikipedia-based Image Text Dataset for Multimodal Multilingual Machine Learning\r\n](https:\/\/arxiv.org\/abs\/2103.01913)*\r\n- **Data:** *https:\/\/github.com\/google-research-datasets\/wit*\r\n- **Motivation:** (excerpt from their Github README.md)\r\n\r\n> - The largest multimodal dataset (publicly available at the time of this writing) by the number of image-text examples.\r\n> - A massively multilingual dataset (first of its kind) with coverage for over 100+ languages.\r\n> - A collection of diverse set of concepts and real world entities.\r\n> - Brings forth challenging real-world test sets.\r\n\r\nInstructions to add a new dataset can be found [here](https:\/\/github.com\/huggingface\/datasets\/blob\/master\/ADD_NEW_DATASET.md).\r\n","comment_length":28,"text":"Add WIT Dataset\n## Adding a Dataset\r\n- **Name:** *WIT*\r\n- **Description:** *Wikipedia-based Image Text Dataset*\r\n- **Paper:** *[WIT: Wikipedia-based Image Text Dataset for Multimodal Multilingual Machine Learning\r\n](https:\/\/arxiv.org\/abs\/2103.01913)*\r\n- **Data:** *https:\/\/github.com\/google-research-datasets\/wit*\r\n- **Motivation:** (excerpt from their Github README.md)\r\n\r\n> - The largest multimodal dataset (publicly available at the time of this writing) by the number of image-text examples.\r\n> - A massively multilingual dataset (first of its kind) with coverage for over 100+ languages.\r\n> - A collection of diverse set of concepts and real world entities.\r\n> - Brings forth challenging real-world test sets.\r\n\r\nInstructions to add a new dataset can be found [here](https:\/\/github.com\/huggingface\/datasets\/blob\/master\/ADD_NEW_DATASET.md).\r\n\n> @hassiahk is working on it #2810\r\n\r\nThank you @bhavitvyamalik! Added this issue so we could track progress \ud83d\ude04 . Just linked the PR as well for visibility. 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{"html_url":"https:\/\/github.com\/huggingface\/datasets\/issues\/2901","title":"Incompatibility with pytest","comments":"Sorry, my bad... When implementing `xpathopen`, I just considered the use case in the COUNTER dataset... I'm fixing it!","body":"## Describe the bug\r\n\r\npytest complains about xpathopen \/ path.open(\"w\")\r\n\r\n## Steps to reproduce the bug\r\n\r\nCreate a test file, `test.py`:\r\n\r\n```python\r\nimport datasets as ds\r\ndef load_dataset():\r\n ds.load_dataset(\"counter\", split=\"train\", streaming=True)\r\n```\r\n\r\nAnd launch it with pytest:\r\n\r\n```bash\r\npython -m pytest test.py\r\n```\r\n\r\n## Expected results\r\n\r\nIt should give something like:\r\n\r\n```\r\ncollected 1 item\r\n\r\ntest.py . [100%]\r\n\r\n======= 1 passed in 3.15s =======\r\n```\r\n\r\n## Actual results\r\n\r\n```\r\n============================================================================================================================= test session starts ==============================================================================================================================\r\nplatform linux -- Python 3.8.11, pytest-6.2.5, py-1.10.0, pluggy-1.0.0\r\nrootdir: \/home\/slesage\/hf\/datasets-preview-backend, configfile: pyproject.toml\r\nplugins: anyio-3.3.1\r\ncollected 1 item\r\n\r\ntests\/queries\/test_rows.py . [100%]Traceback (most recent call last):\r\n File \"\/home\/slesage\/.pyenv\/versions\/3.8.11\/lib\/python3.8\/runpy.py\", line 194, in _run_module_as_main\r\n return _run_code(code, main_globals, None,\r\n File \"\/home\/slesage\/.pyenv\/versions\/3.8.11\/lib\/python3.8\/runpy.py\", line 87, in _run_code\r\n exec(code, run_globals)\r\n File \"\/home\/slesage\/hf\/datasets-preview-backend\/.venv\/lib\/python3.8\/site-packages\/pytest\/__main__.py\", line 5, in \r\n raise SystemExit(pytest.console_main())\r\n File \"\/home\/slesage\/hf\/datasets-preview-backend\/.venv\/lib\/python3.8\/site-packages\/_pytest\/config\/__init__.py\", line 185, in console_main\r\n code = main()\r\n File \"\/home\/slesage\/hf\/datasets-preview-backend\/.venv\/lib\/python3.8\/site-packages\/_pytest\/config\/__init__.py\", line 162, in main\r\n ret: Union[ExitCode, int] = config.hook.pytest_cmdline_main(\r\n File \"\/home\/slesage\/hf\/datasets-preview-backend\/.venv\/lib\/python3.8\/site-packages\/pluggy\/_hooks.py\", line 265, in __call__\r\n return self._hookexec(self.name, self.get_hookimpls(), kwargs, firstresult)\r\n File \"\/home\/slesage\/hf\/datasets-preview-backend\/.venv\/lib\/python3.8\/site-packages\/pluggy\/_manager.py\", line 80, in _hookexec\r\n return self._inner_hookexec(hook_name, methods, kwargs, firstresult)\r\n File \"\/home\/slesage\/hf\/datasets-preview-backend\/.venv\/lib\/python3.8\/site-packages\/pluggy\/_callers.py\", line 60, in _multicall\r\n return outcome.get_result()\r\n File \"\/home\/slesage\/hf\/datasets-preview-backend\/.venv\/lib\/python3.8\/site-packages\/pluggy\/_result.py\", line 60, in get_result\r\n raise ex[1].with_traceback(ex[2])\r\n File \"\/home\/slesage\/hf\/datasets-preview-backend\/.venv\/lib\/python3.8\/site-packages\/pluggy\/_callers.py\", line 39, in _multicall\r\n res = hook_impl.function(*args)\r\n File \"\/home\/slesage\/hf\/datasets-preview-backend\/.venv\/lib\/python3.8\/site-packages\/_pytest\/main.py\", line 316, in pytest_cmdline_main\r\n return wrap_session(config, _main)\r\n File \"\/home\/slesage\/hf\/datasets-preview-backend\/.venv\/lib\/python3.8\/site-packages\/_pytest\/main.py\", line 304, in wrap_session\r\n config.hook.pytest_sessionfinish(\r\n File \"\/home\/slesage\/hf\/datasets-preview-backend\/.venv\/lib\/python3.8\/site-packages\/pluggy\/_hooks.py\", line 265, in __call__\r\n return self._hookexec(self.name, self.get_hookimpls(), kwargs, firstresult)\r\n File \"\/home\/slesage\/hf\/datasets-preview-backend\/.venv\/lib\/python3.8\/site-packages\/pluggy\/_manager.py\", line 80, in _hookexec\r\n return self._inner_hookexec(hook_name, methods, kwargs, firstresult)\r\n File \"\/home\/slesage\/hf\/datasets-preview-backend\/.venv\/lib\/python3.8\/site-packages\/pluggy\/_callers.py\", line 55, in _multicall\r\n gen.send(outcome)\r\n File \"\/home\/slesage\/hf\/datasets-preview-backend\/.venv\/lib\/python3.8\/site-packages\/_pytest\/terminal.py\", line 803, in pytest_sessionfinish\r\n outcome.get_result()\r\n File \"\/home\/slesage\/hf\/datasets-preview-backend\/.venv\/lib\/python3.8\/site-packages\/pluggy\/_result.py\", line 60, in get_result\r\n raise ex[1].with_traceback(ex[2])\r\n File \"\/home\/slesage\/hf\/datasets-preview-backend\/.venv\/lib\/python3.8\/site-packages\/pluggy\/_callers.py\", line 39, in _multicall\r\n res = hook_impl.function(*args)\r\n File \"\/home\/slesage\/hf\/datasets-preview-backend\/.venv\/lib\/python3.8\/site-packages\/_pytest\/cacheprovider.py\", line 428, in pytest_sessionfinish\r\n config.cache.set(\"cache\/nodeids\", sorted(self.cached_nodeids))\r\n File \"\/home\/slesage\/hf\/datasets-preview-backend\/.venv\/lib\/python3.8\/site-packages\/_pytest\/cacheprovider.py\", line 188, in set\r\n f = path.open(\"w\")\r\nTypeError: xpathopen() takes 1 positional argument but 2 were given\r\n```\r\n\r\n## Environment info\r\n\r\n- `datasets` version: 1.12.0\r\n- Platform: Linux-5.11.0-1017-aws-x86_64-with-glibc2.29\r\n- Python version: 3.8.11\r\n- PyArrow version: 4.0.1\r\n","comment_length":19,"text":"Incompatibility with pytest\n## Describe the bug\r\n\r\npytest complains about xpathopen \/ path.open(\"w\")\r\n\r\n## Steps to reproduce the bug\r\n\r\nCreate a test file, `test.py`:\r\n\r\n```python\r\nimport datasets as ds\r\ndef load_dataset():\r\n ds.load_dataset(\"counter\", split=\"train\", streaming=True)\r\n```\r\n\r\nAnd launch it with pytest:\r\n\r\n```bash\r\npython -m pytest test.py\r\n```\r\n\r\n## Expected results\r\n\r\nIt should give something like:\r\n\r\n```\r\ncollected 1 item\r\n\r\ntest.py . [100%]\r\n\r\n======= 1 passed in 3.15s =======\r\n```\r\n\r\n## Actual results\r\n\r\n```\r\n============================================================================================================================= test session starts ==============================================================================================================================\r\nplatform linux -- Python 3.8.11, pytest-6.2.5, py-1.10.0, pluggy-1.0.0\r\nrootdir: \/home\/slesage\/hf\/datasets-preview-backend, configfile: pyproject.toml\r\nplugins: anyio-3.3.1\r\ncollected 1 item\r\n\r\ntests\/queries\/test_rows.py . [100%]Traceback (most recent call last):\r\n File \"\/home\/slesage\/.pyenv\/versions\/3.8.11\/lib\/python3.8\/runpy.py\", line 194, in _run_module_as_main\r\n return _run_code(code, main_globals, None,\r\n File \"\/home\/slesage\/.pyenv\/versions\/3.8.11\/lib\/python3.8\/runpy.py\", line 87, in _run_code\r\n exec(code, run_globals)\r\n File \"\/home\/slesage\/hf\/datasets-preview-backend\/.venv\/lib\/python3.8\/site-packages\/pytest\/__main__.py\", line 5, in \r\n raise SystemExit(pytest.console_main())\r\n File \"\/home\/slesage\/hf\/datasets-preview-backend\/.venv\/lib\/python3.8\/site-packages\/_pytest\/config\/__init__.py\", line 185, in console_main\r\n code = main()\r\n File \"\/home\/slesage\/hf\/datasets-preview-backend\/.venv\/lib\/python3.8\/site-packages\/_pytest\/config\/__init__.py\", line 162, in main\r\n ret: Union[ExitCode, int] = config.hook.pytest_cmdline_main(\r\n File \"\/home\/slesage\/hf\/datasets-preview-backend\/.venv\/lib\/python3.8\/site-packages\/pluggy\/_hooks.py\", line 265, in __call__\r\n return self._hookexec(self.name, self.get_hookimpls(), kwargs, firstresult)\r\n File \"\/home\/slesage\/hf\/datasets-preview-backend\/.venv\/lib\/python3.8\/site-packages\/pluggy\/_manager.py\", line 80, in _hookexec\r\n return self._inner_hookexec(hook_name, methods, kwargs, firstresult)\r\n File \"\/home\/slesage\/hf\/datasets-preview-backend\/.venv\/lib\/python3.8\/site-packages\/pluggy\/_callers.py\", line 60, in _multicall\r\n return outcome.get_result()\r\n File \"\/home\/slesage\/hf\/datasets-preview-backend\/.venv\/lib\/python3.8\/site-packages\/pluggy\/_result.py\", line 60, in get_result\r\n raise ex[1].with_traceback(ex[2])\r\n File \"\/home\/slesage\/hf\/datasets-preview-backend\/.venv\/lib\/python3.8\/site-packages\/pluggy\/_callers.py\", line 39, in _multicall\r\n res = hook_impl.function(*args)\r\n File \"\/home\/slesage\/hf\/datasets-preview-backend\/.venv\/lib\/python3.8\/site-packages\/_pytest\/main.py\", line 316, in pytest_cmdline_main\r\n return wrap_session(config, _main)\r\n File \"\/home\/slesage\/hf\/datasets-preview-backend\/.venv\/lib\/python3.8\/site-packages\/_pytest\/main.py\", line 304, in wrap_session\r\n config.hook.pytest_sessionfinish(\r\n File \"\/home\/slesage\/hf\/datasets-preview-backend\/.venv\/lib\/python3.8\/site-packages\/pluggy\/_hooks.py\", line 265, in __call__\r\n return self._hookexec(self.name, self.get_hookimpls(), kwargs, firstresult)\r\n File \"\/home\/slesage\/hf\/datasets-preview-backend\/.venv\/lib\/python3.8\/site-packages\/pluggy\/_manager.py\", line 80, in _hookexec\r\n return self._inner_hookexec(hook_name, methods, kwargs, firstresult)\r\n File \"\/home\/slesage\/hf\/datasets-preview-backend\/.venv\/lib\/python3.8\/site-packages\/pluggy\/_callers.py\", line 55, in _multicall\r\n gen.send(outcome)\r\n File \"\/home\/slesage\/hf\/datasets-preview-backend\/.venv\/lib\/python3.8\/site-packages\/_pytest\/terminal.py\", line 803, in pytest_sessionfinish\r\n outcome.get_result()\r\n File \"\/home\/slesage\/hf\/datasets-preview-backend\/.venv\/lib\/python3.8\/site-packages\/pluggy\/_result.py\", line 60, in get_result\r\n raise ex[1].with_traceback(ex[2])\r\n File \"\/home\/slesage\/hf\/datasets-preview-backend\/.venv\/lib\/python3.8\/site-packages\/pluggy\/_callers.py\", line 39, in _multicall\r\n res = hook_impl.function(*args)\r\n File \"\/home\/slesage\/hf\/datasets-preview-backend\/.venv\/lib\/python3.8\/site-packages\/_pytest\/cacheprovider.py\", line 428, in pytest_sessionfinish\r\n config.cache.set(\"cache\/nodeids\", sorted(self.cached_nodeids))\r\n File \"\/home\/slesage\/hf\/datasets-preview-backend\/.venv\/lib\/python3.8\/site-packages\/_pytest\/cacheprovider.py\", line 188, in set\r\n f = path.open(\"w\")\r\nTypeError: xpathopen() takes 1 positional argument but 2 were given\r\n```\r\n\r\n## Environment info\r\n\r\n- `datasets` version: 1.12.0\r\n- Platform: Linux-5.11.0-1017-aws-x86_64-with-glibc2.29\r\n- Python version: 3.8.11\r\n- PyArrow version: 4.0.1\r\n\nSorry, my bad... When implementing `xpathopen`, I just considered the use case in the COUNTER dataset... I'm fixing it!","embeddings":[-0.3016421199,-0.1903872788,-0.0241492949,0.0990293771,0.3130396903,-0.1301406622,0.3114988804,0.2646481693,-0.1026777923,0.2515563369,-0.0329412185,0.3579744101,-0.0499672778,0.0878717452,-0.1619878709,0.1268500686,-0.0505254902,0.0685187206,0.0408935249,0.1136002094,-0.5712682605,-0.1570851803,-0.2168759555,0.0572490133,0.0126794586,0.0686679184,0.0916296914,0.2422822416,-0.0774931237,-0.469889611,0.1960368752,-0.0263328627,-0.0422579646,0.4722329378,-0.0001045304,-0.0628838241,0.4584931433,0.2026191652,-0.4859764278,-0.2095862329,-0.1490363926,0.2063343376,0.2531500459,-0.254386425,-0.1224498227,-0.2215161026,0.0800599158,-0.5224384069,0.0328101404,0.2298583686,0.2646005154,0.5858446956,0.1764907539,-0.1220323741,0.3920012712,-0.1865620166,-0.0988033265,0.1512855589,0.2791418135,0.0342658088,-0.1552184969,0.2738155425,-0.251668334,0.2581939995,0.0830629617,0.0403149687,0.1071799397,0.1236744151,0.0054618004,0.1477268934,-0.0244684517,-0.2447356284,-0.187163204,0.0132809207,-0.1560323685,-0.4709234536,0.0660294145,0.2990931869,-0.1927743256,0.0707391798,-0.1533978879,0.285312593,-0.2178352475,0.2086598277,-0.0414643139,0.474942416,0.0106467595,0.0582296215,0.0282520335,0.0535728522,0.3718392253,-0.029089909,0.0258877985,-0.2166382521,-0.0353300683,0.0447735675,0.3098430037,0.1299089044,0.1545516998,0.1684173644,-0.0050124396,-0.0647071525,0.1099802256,0.1257055998,-0.0156705379,-0.0932240486,-0.0586434491,0.1788802147,0.1195266545,0.1648669541,-0.1360280216,0.0084639816,-0.0668128058,-0.3070941567,0.0900237709,0.1294367164,0.5126132965,-0.0637784451,-0.3585409522,-0.1637833863,-0.3168658912,0.1315581501,-0.0887979344,0.1646843106,-0.3344549835,0.0525158457,0.2567434609,0.1665857583,-0.3985979259,0.1734841466,-0.1729390174,-0.0025560693,-0.2071772516,-0.1981868595,0.0244949311,-0.0061054123,0.0369944014,0.0258494634,-0.0169168096,-0.0553664789,0.1729714125,-0.0304495171,0.0978282169,0.0834876001,0.1038077101,0.0230855085,0.0646957457,0.2365556359,0.0004667089,0.4194874167,-0.1334887296,-0.2440327555,0.1164916679,0.35601753,0.0041353889,-0.1565836817,0.1820517033,-0.3767896891,-0.1220365986,-0.0800681487,0.0026528577,-0.2985853255,-0.1478585154,-0.2639125884,0.2254217416,0.2425370067,0.2605780959,0.0505344905,-0.0282764584,0.1627342254,0.3797311783,0.1669939458,-0.1939092427,-0.0896540061,-0.1379573345,0.1596079618,-0.0486291014,-0.4239594936,-0.3278156817,-0.1607752889,-0.1731502265,-0.07503663,0.049631156,0.0323371701,0.3179516196,0.0479337424,-0.0318585075,0.412953496,-0.0740917623,0.0748518556,-0.4666415751,-0.3798174262,0.5641200542,0.151705578,0.0570301823,-0.3497416377,0.2054489851,0.0608398058,0.5148956776,-0.1897210926,-0.1518411934,-0.2993411422,0.752466023,-0.0474503674,0.0110379141,-0.0960698351,0.0665274039,0.0204419531,0.2316295356,-0.2802456915,-0.1499119997,-0.0891542956,-0.1524517834,0.1319255084,-0.3466832638,-0.382157594,0.2474724054,0.2782579362,-0.1456098258,-0.1309039593,-0.0118953492,-0.0933069214,-0.3045151532,0.100494273,0.0386145003,0.1053284183,-0.0396609642,-0.094124943,-0.1193556041,0.298748225,0.162939176,-0.0600543693,-0.1179709211,0.3312662542,0.2449479997,0.1955460757,-0.0468190089,0.0072500408,0.0820179358,-0.3156889975,-0.2024822384,0.3221007586,0.0975408554,-0.002581625,0.0227513239,0.1893991232,-0.2354014963,0.1002926081,0.2986403406,0.0012304113,-0.0390900932,-0.1708397567,0.0159268808,0.3352193236,0.253539145,0.4091952145,0.4384313226,0.0471034944,-0.1185913086,-0.0291307271,0.1720729768,0.0163347404,0.2327632755,0.1700844765,-0.400013119,-0.0777249038,-0.0843988508,0.3033127487,0.3952056468,0.275578618,0.0969723538,0.0887242258,-0.1324126124,-0.2870429754,0.2992976308,0.1152055711,0.0208725408,0.2415834963,0.2297437042,0.1021427438,-0.2554797828,-0.0732267201,-0.1389924884,0.0566167571,-0.1460091919,-0.0478304811,-0.1640127003,0.2011270672,-0.1140680909,-0.5005605817,0.1816411167,-0.2008605003,0.0143492073,0.3810865879,-0.2022752911,0.1200969592,-0.2770578265,0.0942122191,0.0885685533,-0.0884958655,0.1415922791,-0.2295171022,-0.1760160774,0.1794952601,0.0489556827,-0.1769523323,0.3923769593,-0.0935229883,-0.0265750755,-0.1780978739,-0.1293669194,0.0228035133,0.2426382601,0.0753103569,0.111499086,0.05481565,0.108430557,-0.2810976505,0.3347675204,-0.2257572263,-0.0527905673,0.1222594678,0.0974359363,-0.0859619454,-0.1513888091,-0.6303583384,-0.1750367284,-0.3355152905,0.0044383388,-0.107812658,0.1289740354,0.4354717731,0.0387976281,0.1618680805,-0.1536655724,0.121643737,-0.0872593373,-0.0211115107,0.2169368863,-0.1349027008,-0.4010963142,-0.198347047,-0.1213339865,-0.1045445204,0.433926791,-0.2609218061,-0.2592196167,-0.2313274145,0.0872525126,-0.1294534057,-0.0232153423,0.3986389339,0.2484317124,-0.2569616735,-0.2864581048,0.0643375963,-0.0433718897,-0.0686287582,0.0381864421,-0.087544404,0.1517151892,0.1127801165,0.2772788107,-0.0392005183,-0.0387176163,0.1129376143,-0.2495963275,0.1978779882,-0.0433227047,-0.256916225,0.1621052921,-0.2219971567,-0.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{"html_url":"https:\/\/github.com\/huggingface\/datasets\/issues\/2892","title":"Error when encoding a dataset with None objects with a Sequence feature","comments":"This has been fixed by https:\/\/github.com\/huggingface\/datasets\/pull\/2900\r\nWe're doing a new release 1.12 today to make the fix available :)","body":"There is an error when encoding a dataset with None objects with a Sequence feature\r\n\r\nTo reproduce:\r\n```python\r\nfrom datasets import Dataset, Features, Value, Sequence\r\ndata = {\"a\": [[0], None]}\r\nfeatures = Features({\"a\": Sequence(Value(\"int32\"))})\r\ndataset = Dataset.from_dict(data, features=features)\r\n```\r\nraises\r\n\r\n```python\r\n---------------------------------------------------------------------------\r\nTypeError Traceback (most recent call last)\r\n in \r\n 2 data = {\"a\": [[0], None]}\r\n 3 features = Features({\"a\": Sequence(Value(\"int32\"))})\r\n----> 4 dataset = Dataset.from_dict(data, features=features)\r\n[...]\r\n~\/datasets\/features.py in encode_nested_example(schema, obj)\r\n 888 if isinstance(obj, str): # don't interpret a string as a list\r\n 889 raise ValueError(\"Got a string but expected a list instead: '{}'\".format(obj))\r\n--> 890 return [encode_nested_example(schema.feature, o) for o in obj]\r\n 891 # Object with special encoding:\r\n 892 # ClassLabel will convert from string to int, TranslationVariableLanguages does some checks\r\n\r\nTypeError: 'NoneType' object is not iterable\r\n```\r\n\r\nInstead, if should run without error, as if the `features` were not passed","comment_length":19,"text":"Error when encoding a dataset with None objects with a Sequence feature\nThere is an error when encoding a dataset with None objects with a Sequence feature\r\n\r\nTo reproduce:\r\n```python\r\nfrom datasets import Dataset, Features, Value, Sequence\r\ndata = {\"a\": [[0], None]}\r\nfeatures = Features({\"a\": Sequence(Value(\"int32\"))})\r\ndataset = Dataset.from_dict(data, features=features)\r\n```\r\nraises\r\n\r\n```python\r\n---------------------------------------------------------------------------\r\nTypeError Traceback (most recent call last)\r\n in \r\n 2 data = {\"a\": [[0], None]}\r\n 3 features = Features({\"a\": Sequence(Value(\"int32\"))})\r\n----> 4 dataset = Dataset.from_dict(data, features=features)\r\n[...]\r\n~\/datasets\/features.py in encode_nested_example(schema, obj)\r\n 888 if isinstance(obj, str): # don't interpret a string as a list\r\n 889 raise ValueError(\"Got a string but expected a list instead: '{}'\".format(obj))\r\n--> 890 return [encode_nested_example(schema.feature, o) for o in obj]\r\n 891 # Object with special encoding:\r\n 892 # ClassLabel will convert from string to int, TranslationVariableLanguages does some checks\r\n\r\nTypeError: 'NoneType' object is not iterable\r\n```\r\n\r\nInstead, if should run without error, as if the `features` were not passed\nThis has been fixed by https:\/\/github.com\/huggingface\/datasets\/pull\/2900\r\nWe're doing a new release 1.12 today to make the fix available :)","embeddings":[-0.0830345005,-0.2502439916,-0.0386255719,0.3015390933,0.2076890171,0.0788367391,0.5001555681,0.1494158357,0.1255292594,0.1421060264,0.3285635114,0.0508400798,-0.2967574298,0.3420606554,-0.2623355389,-0.2604982257,0.034535449,0.2224871665,0.0154916933,-0.0224474147,-0.3130667508,0.1709361374,-0.364877075,-0.2221626192,-0.1550774723,-0.2108083814,-0.2160241902,-0.2960246801,0.0542513058,-0.5987132192,0.2819482088,-0.0695153251,0.0921094567,0.2383219302,-0.0001144155,0.105492115,0.6381921768,-0.1453159899,-0.2628047466,-0.3394742906,-0.5618312359,-0.4227804542,0.1241655424,-0.3578473926,-0.0965121984,-0.0749715865,0.094718039,-0.3724971116,0.2042126656,0.2839667201,0.2130593061,0.3830007613,-0.0845509171,-0.0744024739,0.206820786,0.4162615538,-0.2607547045,-0.1798537225,0.035758879,0.2699487805,0.0946282744,0.3492396772,-0.111404337,-0.1305068284,0.2094347328,0.0849353001,0.0885619074,-0.4521939754,-0.2671251595,0.2014953941,0.2924095094,0.0013618435,-0.4084641933,-0.2180884778,0.0188714378,-0.6638943553,0.2666681111,0.055101525,-0.0386278071,-0.0269636735,-0.168392688,0.145001635,-0.2138954103,0.1660171747,-0.1209752634,0.0807909966,-0.1394540668,0.0341406651,-0.2402791679,-0.2700186968,0.0587494336,-0.3706248403,-0.0257517211,0.2111456394,-0.2515126467,-0.1936304271,0.167350471,0.0101849167,0.1697200388,0.1379934251,-0.0658706501,-0.1037984863,-0.0693587288,-0.0169596635,0.2529113591,0.2136024684,0.4112964869,-0.0680698305,-0.0581751354,0.1961243749,0.0996622145,-0.017598724,0.11928422,0.0517393723,0.0169261098,0.4072842002,0.6467193961,-0.0525628068,-0.393481791,0.2361950129,-0.3853594661,-0.0304983035,-0.0211958643,0.0061908085,0.0841203332,0.2916485071,-0.1465796083,0.2161677331,-0.2736037076,-0.250882864,-0.2348464429,0.0721954107,0.1018402427,0.0885985568,-0.0547403768,-0.0332995802,0.0658167601,0.1560105532,0.0796802491,0.1009850726,-0.0555230267,-0.365808934,0.0925061703,0.1523720324,-0.0070467223,0.2921590805,0.3762358427,-0.2581280768,-0.1091617644,0.1182429716,-0.2788110375,-0.217513755,-0.005827894,0.1725294441,-0.3795679808,0.0441264361,-0.0734036565,0.2263460904,0.2108575404,-0.0775901377,-0.0965437591,-0.0869289562,-0.2196164131,-0.1535144448,-0.0919654146,0.6465163827,-0.1091333926,-0.012587267,0.1191491932,-0.0898958445,0.3521232605,0.2327210456,0.0481479168,-0.057248231,-0.3404105306,0.1900297105,0.0831347257,-0.1115483418,-0.208607167,0.5657577515,0.0239907876,0.3598697484,0.1134639457,0.0959460512,0.1585700363,-0.3072297871,0.0130883651,0.1975988597,-0.1874651164,-0.2206795961,-0.2418985963,-0.1231627762,0.5206927657,-0.0743467882,-0.0640905127,0.2067834437,-0.3010617197,-0.0231112521,-0.1406468004,-0.3186881542,0.1288141012,0.5053259134,0.21923922,0.0375964157,-0.0967492238,-0.4028446674,-0.362685889,0.1312113106,-0.179603979,0.2839995623,-0.4932679534,-0.1799385846,0.0735202953,0.019041175,-0.3947464824,0.0015028021,0.1547810137,0.1214925349,-0.1535111219,0.1999227107,-0.0773415267,0.2772004306,0.1561118215,-0.1248554364,-0.567322731,0.3699874282,0.0695009977,-0.2262255251,0.0071669328,0.3033728004,0.3317838013,-0.0899984464,0.018318845,0.1505862623,0.0259702709,-0.3309904039,-0.3067805767,-0.1571382731,0.3190588653,-0.4185928106,-0.2214330137,0.3747457862,0.1825433373,-0.1073410809,-0.1209880039,0.6575422287,0.0157377589,0.4647912979,0.0042069959,0.1760141253,0.0537278354,-0.0018615762,-0.327142626,-0.2869090438,0.1755582988,0.2026411891,-0.1650790572,-0.0399117619,-0.3015944362,0.0058686477,0.4108926356,0.088436991,0.0354627408,0.0570802502,-0.0770579055,-0.0368382186,0.1092406586,-0.159593001,0.299883306,0.0505586714,-0.0732196867,0.0735782608,0.0075443899,0.0745387301,0.1758682579,-0.0350369215,0.2430823594,0.0285679307,0.1076480895,0.1176982298,-0.1779721677,-0.5438489318,-0.0398581289,0.1430890262,-0.2491215467,0.2492818087,-0.4795846939,-0.2056158483,0.130416885,-0.2173653096,-0.386295557,-0.4440228939,-0.1639413238,0.0275585894,-0.14083983,0.2236978859,0.0148140779,-0.26245597,0.2985749543,-0.0243482068,-0.0831376389,-0.1392291635,-0.0242327861,0.0463199466,0.1656642258,-0.0481024235,0.2467772514,0.0332959294,-0.3499837816,-0.2126868665,-0.2085345984,0.0565498397,-0.3503640592,0.0777079239,0.3079494238,0.1277272999,-0.04940686,-0.1998873949,0.4227285385,0.4243725836,-0.2120994478,0.3217695653,-0.102062583,-0.0354735814,-0.2110650837,-0.0959021896,-0.1572214514,-0.2278930247,0.1076047793,0.2109689265,0.2101610452,0.4471023381,0.0470193811,0.120879285,0.1355805248,-0.175628379,0.0008855638,-0.0150361629,0.4824748039,-0.1827880889,-0.2712480724,0.1487995982,-0.2987255454,0.0875612497,0.2041173279,-0.3575526774,-0.1763042659,-0.1413789541,0.1611307114,0.0745360553,-0.1046581939,0.3775742948,0.2816057205,-0.1083455756,-0.0367697217,-0.0508110113,0.2569004595,-0.0705505088,0.0287486054,0.229687199,0.5177567601,0.0398100056,0.0972354859,0.4376426637,0.1970631927,0.4261662662,-0.0823322833,0.0689360127,-0.1690145433,0.0005423338,-0.1711448282,0.107939221,-0.0178557411,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{"html_url":"https:\/\/github.com\/huggingface\/datasets\/issues\/2888","title":"v1.11.1 release date","comments":"Hi ! Probably 1.12 on monday :)\r\n","body":"Hello, i need to use latest features in one of my packages but there have been no new datasets release since 2 months ago.\r\n\r\nWhen do you plan to publush v1.11.1 release?","comment_length":7,"text":"v1.11.1 release date\nHello, i need to use latest features in one of my packages but there have been no new datasets release since 2 months ago.\r\n\r\nWhen do you plan to publush v1.11.1 release?\nHi ! Probably 1.12 on monday :)\r\n","embeddings":[-0.3028725386,-0.1802771538,-0.2127547413,-0.0963228866,0.0652879179,-0.1868623346,0.0738429576,0.3128882051,-0.2114172131,0.4218455255,0.2351345271,0.0822070688,-0.0895844847,0.4837630689,-0.1932857186,-0.2654596269,0.0769366696,0.0144002829,-0.1551253647,0.0024792501,-0.1483270228,0.119360745,-0.3253111243,0.0831760392,0.0989715233,-0.064572975,-0.3526091278,-0.1743175536,-0.5363405347,-0.3933886588,0.461257726,0.1939074099,0.2547554672,0.2986641824,-0.000103304,-0.4014018774,0.5556403995,0.2845861912,-0.2422691882,-0.2193116248,-0.3339813054,-0.5906572938,-0.1154627725,0.0800851658,-0.2921097875,-0.1461395621,0.082330212,-0.0849997699,-0.13677302,-0.1171071827,0.2824233174,0.0947450623,0.1915313601,-0.3989170194,0.284394294,-0.0203845855,-0.2789727747,-0.3155975044,0.5697530508,0.1857773513,0.1898058355,0.326002121,0.2075845897,-0.1623990536,0.1387228072,0.1931113005,0.1122244373,-0.32729882,-0.0393120684,0.1010244489,0.9985591769,-0.0018671668,-0.2434326708,0.2223907411,-0.0925616995,-0.3847976029,0.1220049784,-0.0718759969,0.1220379025,0.1305280179,0.0270749014,-0.3837861121,-0.1746051461,0.2439880967,0.0338811204,0.3361899853,-0.1041846797,-0.0660860538,0.1154744327,-0.2463802546,0.0967559218,-0.0215033758,-0.1613555253,0.2227307111,0.0464089848,-0.4094306231,0.1858401746,-0.0288592987,0.1305842102,0.3231448531,-0.1524199545,-0.1115325615,-0.1302562058,-0.0906753838,0.5744841099,0.0935288742,0.377846837,0.2240085304,0.3023318052,0.1646759659,0.210433796,-0.0148354508,0.131557256,0.2080590278,-0.1345603913,0.092921719,0.2413697988,-0.5841081142,0.3566501141,0.0036136017,0.0791958421,-0.3253816366,-0.3280638158,-0.1975245178,-0.0646178052,0.1883967668,-0.1564619541,-0.1827139109,0.1210180596,-0.2046548128,-0.3577520847,0.0363553949,-0.2325457633,-0.0549016744,0.1167488173,-0.2526122332,-0.1254964024,0.0305748545,-0.04656725,0.0622468628,0.0610297844,0.1928608567,-0.2869565487,0.2402708977,-0.1486318558,0.2325121164,-0.3784801364,0.3326986134,-0.1420154572,0.2017378062,-0.0192655772,-0.2063460052,-0.235487327,0.3137744367,-0.1372713745,-0.3029890358,0.19962731,0.3279361129,-0.3900982738,-0.1182468757,-0.1860611141,0.2779344022,-0.1112236157,-0.2089126557,0.0612550527,-0.0017352636,-0.2293154597,0.0255627967,-0.3211435378,-0.2947054803,0.1805807501,-0.0630620569,0.0061964095,-0.2280418724,0.1311280131,-0.185826987,0.4691230059,-0.3665311038,-0.5793189406,0.4294295013,-0.1866938472,-0.4467723966,0.4338716865,0.2241641581,0.3097352386,-0.1575234383,-0.2617045343,0.2637113929,-0.1526574194,-0.320348084,-0.2554035187,-0.3677665293,-0.0720299929,0.1507190168,0.1602411419,0.2366000116,0.2579503655,0.2858259678,0.1898807436,0.1925684959,0.1466663629,0.0294717029,0.5004534125,-0.10350997,0.2177486867,-0.249899298,-0.2581174374,-0.0229802001,-0.0547287352,0.2486825585,0.5317671299,-0.2098047286,-0.168516919,-0.1740678996,0.2359928489,-0.0324108824,0.1631854326,0.1089351401,-0.0164421871,0.1266786158,-0.3025694489,0.0803501308,0.1472560316,0.0244728141,-0.075657174,0.1248692945,-0.2761505842,0.0051354663,0.2405391037,-0.0050578136,0.0540098287,-0.2141433656,0.1412835717,0.0534515046,-0.0641530901,0.3371422291,0.6113381386,0.1324200183,0.3750956953,0.1302689165,0.219974041,0.0903351158,-0.0883826166,0.2412555814,-0.269962877,0.312500447,0.238511622,-0.2063528448,-0.1451906115,0.118214719,0.1887120157,-0.0891980454,-0.0327405259,-0.3398402631,0.2254811525,-0.1658332348,-0.2750526965,-0.0365721621,-0.2551523447,0.3512178659,0.3392684758,-0.1608422101,-0.1638178527,0.0186253563,0.0652871504,-0.0031810594,0.2478724569,0.0328481123,-0.1552314162,0.3118087649,0.1120094359,-0.0968455672,0.0139505379,0.0497668646,0.0382369012,0.0392541103,-0.3878073692,-0.221236378,0.0975770727,0.1155316979,0.0994631052,0.1521302015,0.1214670911,0.2327489555,0.2580001354,-0.344601959,-0.3168298304,-0.2266455889,0.1146346107,-0.1705584228,0.1445552707,-0.1704138368,0.0943085477,0.0777231157,0.2056838423,0.1746924818,-0.0169805512,0.7022944689,-0.2754337788,0.4286308587,-0.22941567,-0.0400346629,-0.287625581,0.3418615162,-0.1031061858,-0.2282226235,0.195717901,0.0163822714,0.3134722412,-0.4504329264,-0.4006796479,0.0965960771,-0.190621376,-0.0871734247,0.1440135986,-0.3507573307,-0.0002354266,0.2681168616,0.020206226,-0.1884005666,-0.1797890365,-0.1004347429,-0.1091894209,0.0140760578,-0.2644398212,-0.5476667881,-0.23607862,-0.032877408,0.0195357129,0.1396642774,0.0098538799,-0.2097719312,-0.0314573459,-0.0043667466,-0.025507886,-0.3325499892,-0.13492091,-0.1378961504,0.1646966338,-0.1210952327,-0.3690144718,0.0233245771,0.2133821547,-0.1906823069,0.0343830734,-0.608182013,-0.2255241424,-0.07357122,0.0902312696,0.271065414,0.0482119732,0.3331973553,0.0043859896,-0.2407487333,-0.0966798887,-0.289496839,-0.0617358088,0.2790717185,0.3425323367,0.1480440795,0.2153837532,0.0382699557,0.4080422521,0.0405399092,-0.0831079558,0.1420852691,0.0724038258,0.1340251565,-0.0351542011,-0.1449554712,0.1592445076,0.0636743233,0.211825773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{"html_url":"https:\/\/github.com\/huggingface\/datasets\/issues\/2888","title":"v1.11.1 release date","comments":"@albertvillanova i think this issue is still valid and should not be closed till `>1.11.0` is published :)","body":"Hello, i need to use latest features in one of my packages but there have been no new datasets release since 2 months ago.\r\n\r\nWhen do you plan to publush v1.11.1 release?","comment_length":18,"text":"v1.11.1 release date\nHello, i need to use latest features in one of my packages but there have been no new datasets release since 2 months ago.\r\n\r\nWhen do you plan to publush v1.11.1 release?\n@albertvillanova i think this issue is still valid and should not be closed till `>1.11.0` is published :)","embeddings":[-0.2534325421,-0.1435918212,-0.1170087606,0.1250979602,-0.0803622156,-0.2069433331,0.3182261586,0.2595030665,-0.2662116587,0.2432924211,0.4498234391,-0.0775993839,-0.0092685595,0.3775973022,-0.3972175717,-0.0890159234,0.0787209198,0.1089016348,-0.0526827313,0.0449188799,-0.2720680833,0.068657726,-0.3964475691,0.1608792841,0.0663995072,-0.0716037601,-0.1215934381,-0.254347086,-0.6671610475,-0.5313281417,0.3133550286,0.2034503967,0.0760271847,0.271275878,-0.0001110468,-0.423866421,0.5205672979,0.1486699134,-0.2614662647,-0.1513033807,-0.416443944,-0.5353802443,-0.1008592322,-0.0137669845,-0.3072520196,-0.1077144369,0.0117483158,-0.1943197548,-0.0628163069,-0.0156020885,0.2480115443,0.1673386395,0.4019794762,-0.3542853892,0.1863051802,-0.1159243658,-0.3703640699,-0.2499655932,0.5717850924,0.2568038106,0.2268878967,0.384983182,0.1912631989,-0.1922257096,0.1507626772,-0.0097723147,0.2396764457,-0.266032964,0.0415400378,0.0777787417,0.9955607653,0.0203635413,-0.3793464899,0.2034740597,-0.0927332118,-0.2712348402,0.2412474453,0.027283011,-0.0292447396,0.149242878,0.0873354375,-0.4584193528,-0.1516828388,0.1602145284,-0.0272049885,0.459104538,-0.0887170136,0.1067651212,-0.0215255283,-0.3015371263,0.2346984148,0.0200975835,0.0850308686,0.156445384,0.0514521152,-0.4402125776,0.1060789451,-0.0172465947,0.1498136967,0.1924144477,-0.2606184483,-0.249166131,-0.0464439057,0.0133378366,0.6594331861,0.0745627508,0.3405662179,0.188009277,0.4329450428,0.2551412284,0.0644607022,0.0997430384,0.0618424639,-0.0808662996,-0.1455136389,0.2052437365,0.4584421515,-0.5718432069,0.3661362231,0.1454990655,0.0115315011,-0.073997356,-0.3209313154,-0.1508467942,-0.0373871252,0.3257052004,0.0893606171,-0.1983124912,0.1348611414,-0.1805576384,-0.1951919943,-0.1088341549,-0.200981006,-0.0183628146,-0.031561859,-0.3116750121,-0.2775349915,0.1134495363,-0.1254057586,-0.0434127748,0.0564601496,0.1302085966,-0.2246665657,0.4118489623,-0.2466320097,0.082844384,-0.3096274436,0.2402789593,-0.0819456652,0.3500746489,-0.0424564332,-0.1780534983,-0.3391320705,0.2919041216,-0.1666521877,-0.0776967406,0.2701286674,0.1953231692,-0.2030190676,-0.2382987589,-0.0494303294,0.1722414643,-0.2394903451,-0.256931901,-0.0695379525,0.0432331599,-0.1193417981,0.1540283114,-0.2632963657,-0.2000599653,0.1197276041,0.0051401095,-0.1842305511,-0.3186813593,0.1262588203,-0.2455536276,0.3016958833,-0.3433002532,-0.639559865,0.3815340102,-0.284142822,-0.3894364536,0.4114672244,0.1741337925,0.2954373658,-0.3212958872,-0.1409738809,-0.0816188827,-0.1616334766,-0.3559683263,-0.4418994188,-0.4019602537,0.0429010168,0.0181122757,0.2204887122,0.1503366083,0.0952749476,0.4235877395,0.0749824941,0.2797828615,0.2078803927,0.0765660107,0.482803762,-0.1286068708,0.0529898442,-0.1006552279,-0.2767008245,0.0938937142,-0.1435723305,0.1591186672,0.4384330511,-0.3189628124,-0.2085864544,-0.0561050922,0.3260715008,-0.0709523261,0.0817816257,0.0915738642,-0.2478206754,0.1673493236,-0.4060981572,0.1368636489,-0.0098665319,-0.07066378,-0.088441737,0.0452465005,-0.2908729911,0.0994255021,0.1788440347,0.0838302895,0.1694515795,-0.2390569299,0.1745637655,-0.0815123394,-0.0723995641,0.3089856207,0.3561557233,0.1862898916,0.2350969911,0.2040317804,0.0746104494,0.1710554659,-0.0586112514,0.2303554267,-0.2253153324,0.3460877836,0.3549923301,-0.2632007301,-0.0564746819,0.144726038,0.1307961047,-0.2599101663,-0.0981654152,-0.2556478679,0.0817662179,-0.221117571,-0.2914198935,0.2252827138,-0.2763938606,0.2965317667,0.5968988538,0.0184195228,-0.047829736,0.0809677541,0.2573701739,-0.0809906572,0.1679659784,-0.0462063886,0.0884929076,0.2539899945,-0.021018533,0.0049677342,0.1221921593,0.0425285064,0.1027600989,0.0455321558,-0.4042115211,-0.0299377795,0.0530678742,0.0633863062,0.115278326,0.1767398119,0.1918154806,0.2266006619,0.0650810748,-0.3978857994,-0.2835028172,-0.0675668642,0.0291238204,-0.0194902401,0.0619625486,0.0534454733,0.1382157803,0.1468727291,0.0316265896,0.1826277375,-0.1548491269,0.7969358563,-0.3889087141,0.4114349484,-0.156639576,-0.1199053377,-0.3110970855,0.2345053405,-0.0124809062,-0.2776047289,0.2567169964,0.0537328571,0.2090214789,-0.6258595586,-0.4577414989,0.0528508835,-0.144126296,0.0452978648,0.0343037061,-0.2054453045,-0.107023187,0.2207032591,0.0384894237,-0.0587673448,-0.2087223232,-0.1666585952,-0.0068196445,0.0721502379,-0.2570260763,-0.6848427057,-0.1318627,-0.3363646269,-0.0185509361,-0.1078772321,0.1569403857,-0.1232450008,-0.051425416,-0.1049095914,0.0217202213,-0.3342834115,-0.0948758051,-0.048731003,0.0945054814,-0.0082828654,-0.4569739103,0.1993883997,0.1774878055,-0.2025116682,0.1531127393,-0.5314667225,-0.2725237608,0.206520468,-0.043862544,0.1573558748,-0.1776329577,0.5536837578,-0.1086382791,-0.2538419664,-0.1345501691,-0.2866043448,0.0720614642,0.1712462157,0.3246344626,0.409180969,0.2624562085,0.0242744964,0.3892090321,0.1903090924,0.0267370865,0.2341599315,0.1795129925,0.1924058646,-0.0937660858,-0.2098539323,0.0676550344,0.1260128468,0.161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{"html_url":"https:\/\/github.com\/huggingface\/datasets\/issues\/2885","title":"Adding an Elastic Search index to a Dataset","comments":"Hi, is this bug deterministic in your poetry env ? I mean, does it always stop at 90% or is it random ?\r\n\r\nAlso, can you try using another version of Elasticsearch ? Maybe there's an issue with the one of you poetry env","body":"## Describe the bug\r\nWhen trying to index documents from the squad dataset, the connection to ElasticSearch seems to break:\r\n\r\nReusing dataset squad (\/Users\/andreasmotz\/.cache\/huggingface\/datasets\/squad\/plain_text\/1.0.0\/d6ec3ceb99ca480ce37cdd35555d6cb2511d223b9150cce08a837ef62ffea453)\r\n 90%|\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2589 | 9501\/10570 [00:01<00:00, 6335.61docs\/s]\r\n\r\nNo error is thrown, but the indexing breaks ~90%.\r\n\r\n## Steps to reproduce the bug\r\n```python\r\n# Sample code to reproduce the bug\r\nfrom datasets import load_dataset\r\nfrom elasticsearch import Elasticsearch\r\nes = Elasticsearch()\r\nsquad = load_dataset('squad', split='validation')\r\nindex_name = \"corpus\"\r\nes_config = {\r\n \"settings\": {\r\n \"number_of_shards\": 1,\r\n \"analysis\": {\"analyzer\": {\"stop_standard\": {\"type\": \"standard\", \" stopwords\": \"_english_\"}}},\r\n },\r\n \"mappings\": {\r\n \"properties\": {\r\n \"idx\" : {\"type\" : \"keyword\"},\r\n \"title\" : {\"type\" : \"keyword\"},\r\n \"text\": {\r\n \"type\": \"text\",\r\n \"analyzer\": \"standard\",\r\n \"similarity\": \"BM25\"\r\n },\r\n }\r\n },\r\n}\r\nclass IndexBuilder:\r\n \"\"\"\r\n Elastic search indexing of a corpus\r\n \"\"\"\r\n def __init__(\r\n self,\r\n *args,\r\n #corpus : None,\r\n dataset : squad,\r\n index_name = str,\r\n query = str,\r\n config = dict,\r\n **kwargs,\r\n ):\r\n #instantiate HuggingFace dataset\r\n self.dataset = dataset\r\n #instantiate ElasticSearch config\r\n self.config = config\r\n self.es = Elasticsearch()\r\n self.index_name = index_name\r\n self.query = query\r\n def elastic_index(self):\r\n print(self.es.info)\r\n self.es.indices.delete(index=self.index_name, ignore=[400, 404])\r\n search_index = self.dataset.add_elasticsearch_index(column='context', host='localhost', port='9200', es_index_name=self.index_name, es_index_config=self.config)\r\n return search_index\r\n def exact_match_method(self, index):\r\n scores, retrieved_examples = index.get_nearest_examples('context', query=self.query, k=1)\r\n return scores, retrieved_examples\r\nif __name__ == \"__main__\":\r\n print(type(squad))\r\n Index = IndexBuilder(dataset=squad, index_name='corpus_index', query='Where was Chopin born?', config=es_config)\r\n search_index = Index.elastic_index()\r\n scores, examples = Index.exact_match_method(search_index)\r\n print(scores, examples)\r\n for name in squad.column_names:\r\n print(type(squad[name]))\r\n```\r\n\r\n## Environment info\r\nWe run the code in Poetry. This might be the issue, since the script runs successfully in our local environment.\r\n\r\nPoetry:\r\n- Python version: 3.8\r\n- PyArrow: 4.0.1\r\n- Elasticsearch: 7.13.4\r\n- datasets: 1.10.2\r\n\r\nLocal:\r\n- Python version: 3.8\r\n- PyArrow: 3.0.0\r\n- Elasticsearch: 7.7.1\r\n- datasets: 1.7.0\r\n","comment_length":44,"text":"Adding an Elastic Search index to a Dataset\n## Describe the bug\r\nWhen trying to index documents from the squad dataset, the connection to ElasticSearch seems to break:\r\n\r\nReusing dataset squad (\/Users\/andreasmotz\/.cache\/huggingface\/datasets\/squad\/plain_text\/1.0.0\/d6ec3ceb99ca480ce37cdd35555d6cb2511d223b9150cce08a837ef62ffea453)\r\n 90%|\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2589 | 9501\/10570 [00:01<00:00, 6335.61docs\/s]\r\n\r\nNo error is thrown, but the indexing breaks ~90%.\r\n\r\n## Steps to reproduce the bug\r\n```python\r\n# Sample code to reproduce the bug\r\nfrom datasets import load_dataset\r\nfrom elasticsearch import Elasticsearch\r\nes = Elasticsearch()\r\nsquad = load_dataset('squad', split='validation')\r\nindex_name = \"corpus\"\r\nes_config = {\r\n \"settings\": {\r\n \"number_of_shards\": 1,\r\n \"analysis\": {\"analyzer\": {\"stop_standard\": {\"type\": \"standard\", \" stopwords\": \"_english_\"}}},\r\n },\r\n \"mappings\": {\r\n \"properties\": {\r\n \"idx\" : {\"type\" : \"keyword\"},\r\n \"title\" : {\"type\" : \"keyword\"},\r\n \"text\": {\r\n \"type\": \"text\",\r\n \"analyzer\": \"standard\",\r\n \"similarity\": \"BM25\"\r\n },\r\n }\r\n },\r\n}\r\nclass IndexBuilder:\r\n \"\"\"\r\n Elastic search indexing of a corpus\r\n \"\"\"\r\n def __init__(\r\n self,\r\n *args,\r\n #corpus : None,\r\n dataset : squad,\r\n index_name = str,\r\n query = str,\r\n config = dict,\r\n **kwargs,\r\n ):\r\n #instantiate HuggingFace dataset\r\n self.dataset = dataset\r\n #instantiate ElasticSearch config\r\n self.config = config\r\n self.es = Elasticsearch()\r\n self.index_name = index_name\r\n self.query = query\r\n def elastic_index(self):\r\n print(self.es.info)\r\n self.es.indices.delete(index=self.index_name, ignore=[400, 404])\r\n search_index = self.dataset.add_elasticsearch_index(column='context', host='localhost', port='9200', es_index_name=self.index_name, es_index_config=self.config)\r\n return search_index\r\n def exact_match_method(self, index):\r\n scores, retrieved_examples = index.get_nearest_examples('context', query=self.query, k=1)\r\n return scores, retrieved_examples\r\nif __name__ == \"__main__\":\r\n print(type(squad))\r\n Index = IndexBuilder(dataset=squad, index_name='corpus_index', query='Where was Chopin born?', config=es_config)\r\n search_index = Index.elastic_index()\r\n scores, examples = Index.exact_match_method(search_index)\r\n print(scores, examples)\r\n for name in squad.column_names:\r\n print(type(squad[name]))\r\n```\r\n\r\n## Environment info\r\nWe run the code in Poetry. This might be the issue, since the script runs successfully in our local environment.\r\n\r\nPoetry:\r\n- Python version: 3.8\r\n- PyArrow: 4.0.1\r\n- Elasticsearch: 7.13.4\r\n- datasets: 1.10.2\r\n\r\nLocal:\r\n- Python version: 3.8\r\n- PyArrow: 3.0.0\r\n- Elasticsearch: 7.7.1\r\n- datasets: 1.7.0\r\n\nHi, is this bug deterministic in your poetry env ? I mean, does it always stop at 90% or is it random ?\r\n\r\nAlso, can you try using another version of Elasticsearch ? Maybe there's an issue with the one of you poetry 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{"html_url":"https:\/\/github.com\/huggingface\/datasets\/issues\/2882","title":"`load_dataset('docred')` results in a `NonMatchingChecksumError` ","comments":"Hi @tmpr, thanks for reporting.\r\n\r\nTwo weeks ago (23th Aug), the host of the source `docred` dataset updated one of the files (`dev.json`): you can see it [here](https:\/\/drive.google.com\/drive\/folders\/1c5-0YwnoJx8NS6CV2f-NoTHR__BdkNqw).\r\n\r\nTherefore, the checksum needs to be updated.\r\n\r\nNormally, in the meantime, you could avoid the error by passing `ignore_verifications=True` to `load_dataset`. However, as the old link points to a non-existing file, the link must be updated too.\r\n\r\nI'm fixing all this.\r\n\r\n","body":"## Describe the bug\r\nI get consistent `NonMatchingChecksumError: Checksums didn't match for dataset source files` errors when trying to execute `datasets.load_dataset('docred')`.\r\n\r\n## Steps to reproduce the bug\r\nIt is quasi only this code:\r\n```python\r\nimport datasets\r\ndata = datasets.load_dataset('docred')\r\n```\r\n\r\n## Expected results\r\nThe DocRED dataset should be loaded without any problems.\r\n\r\n## Actual results\r\n```\r\nNonMatchingChecksumError Traceback (most recent call last)\r\n in \r\n----> 1 d = datasets.load_dataset('docred')\r\n\r\n~\/anaconda3\/lib\/python3.8\/site-packages\/datasets\/load.py in load_dataset(path, name, data_dir, data_files, split, cache_dir, features, download_config, download_mode, ignore_verifications, keep_in_memory, save_infos, script_version, use_auth_token, task, streaming, **config_kwargs)\r\n 845 \r\n 846 # Download and prepare data\r\n--> 847 builder_instance.download_and_prepare(\r\n 848 download_config=download_config,\r\n 849 download_mode=download_mode,\r\n\r\n~\/anaconda3\/lib\/python3.8\/site-packages\/datasets\/builder.py in download_and_prepare(self, download_config, download_mode, ignore_verifications, try_from_hf_gcs, dl_manager, base_path, use_auth_token, **download_and_prepare_kwargs)\r\n 613 logger.warning(\"HF google storage unreachable. Downloading and preparing it from source\")\r\n 614 if not downloaded_from_gcs:\r\n--> 615 self._download_and_prepare(\r\n 616 dl_manager=dl_manager, verify_infos=verify_infos, **download_and_prepare_kwargs\r\n 617 )\r\n\r\n~\/anaconda3\/lib\/python3.8\/site-packages\/datasets\/builder.py in _download_and_prepare(self, dl_manager, verify_infos, **prepare_split_kwargs)\r\n 673 # Checksums verification\r\n 674 if verify_infos:\r\n--> 675 verify_checksums(\r\n 676 self.info.download_checksums, dl_manager.get_recorded_sizes_checksums(), \"dataset source files\"\r\n 677 )\r\n\r\n~\/anaconda3\/lib\/python3.8\/site-packages\/datasets\/utils\/info_utils.py in verify_checksums(expected_checksums, recorded_checksums, verification_name)\r\n 38 if len(bad_urls) > 0:\r\n 39 error_msg = \"Checksums didn't match\" + for_verification_name + \":\\n\"\r\n---> 40 raise NonMatchingChecksumError(error_msg + str(bad_urls))\r\n 41 logger.info(\"All the checksums matched successfully\" + for_verification_name)\r\n 42 \r\n\r\nNonMatchingChecksumError: Checksums didn't match for dataset source files:\r\n['https:\/\/drive.google.com\/uc?export=download&id=1fDmfUUo5G7gfaoqWWvK81u08m71TK2g7']\r\n```\r\n\r\n## Environment info\r\n- `datasets` version: 1.11.0\r\n- Platform: Linux-5.11.0-7633-generic-x86_64-with-glibc2.10\r\n- Python version: 3.8.5\r\n- PyArrow version: 5.0.0\r\n\r\nThis error also happened on my Windows-partition, after freshly installing python 3.9 and `datasets`.\r\n\r\n## Remarks\r\n\r\n- I have already called `rm -rf \/home\/\/.cache\/huggingface`, i.e., I have tried clearing the cache.\r\n- The problem does not exist for other datasets, i.e., it seems to be DocRED-specific.","comment_length":69,"text":"`load_dataset('docred')` results in a `NonMatchingChecksumError` \n## Describe the bug\r\nI get consistent `NonMatchingChecksumError: Checksums didn't match for dataset source files` errors when trying to execute `datasets.load_dataset('docred')`.\r\n\r\n## Steps to reproduce the bug\r\nIt is quasi only this code:\r\n```python\r\nimport datasets\r\ndata = datasets.load_dataset('docred')\r\n```\r\n\r\n## Expected results\r\nThe DocRED dataset should be loaded without any problems.\r\n\r\n## Actual results\r\n```\r\nNonMatchingChecksumError Traceback (most recent call last)\r\n in \r\n----> 1 d = datasets.load_dataset('docred')\r\n\r\n~\/anaconda3\/lib\/python3.8\/site-packages\/datasets\/load.py in load_dataset(path, name, data_dir, data_files, split, cache_dir, features, download_config, download_mode, ignore_verifications, keep_in_memory, save_infos, script_version, use_auth_token, task, streaming, **config_kwargs)\r\n 845 \r\n 846 # Download and prepare data\r\n--> 847 builder_instance.download_and_prepare(\r\n 848 download_config=download_config,\r\n 849 download_mode=download_mode,\r\n\r\n~\/anaconda3\/lib\/python3.8\/site-packages\/datasets\/builder.py in download_and_prepare(self, download_config, download_mode, ignore_verifications, try_from_hf_gcs, dl_manager, base_path, use_auth_token, **download_and_prepare_kwargs)\r\n 613 logger.warning(\"HF google storage unreachable. Downloading and preparing it from source\")\r\n 614 if not downloaded_from_gcs:\r\n--> 615 self._download_and_prepare(\r\n 616 dl_manager=dl_manager, verify_infos=verify_infos, **download_and_prepare_kwargs\r\n 617 )\r\n\r\n~\/anaconda3\/lib\/python3.8\/site-packages\/datasets\/builder.py in _download_and_prepare(self, dl_manager, verify_infos, **prepare_split_kwargs)\r\n 673 # Checksums verification\r\n 674 if verify_infos:\r\n--> 675 verify_checksums(\r\n 676 self.info.download_checksums, dl_manager.get_recorded_sizes_checksums(), \"dataset source files\"\r\n 677 )\r\n\r\n~\/anaconda3\/lib\/python3.8\/site-packages\/datasets\/utils\/info_utils.py in verify_checksums(expected_checksums, recorded_checksums, verification_name)\r\n 38 if len(bad_urls) > 0:\r\n 39 error_msg = \"Checksums didn't match\" + for_verification_name + \":\\n\"\r\n---> 40 raise NonMatchingChecksumError(error_msg + str(bad_urls))\r\n 41 logger.info(\"All the checksums matched successfully\" + for_verification_name)\r\n 42 \r\n\r\nNonMatchingChecksumError: Checksums didn't match for dataset source files:\r\n['https:\/\/drive.google.com\/uc?export=download&id=1fDmfUUo5G7gfaoqWWvK81u08m71TK2g7']\r\n```\r\n\r\n## Environment info\r\n- `datasets` version: 1.11.0\r\n- Platform: Linux-5.11.0-7633-generic-x86_64-with-glibc2.10\r\n- Python version: 3.8.5\r\n- PyArrow version: 5.0.0\r\n\r\nThis error also happened on my Windows-partition, after freshly installing python 3.9 and `datasets`.\r\n\r\n## Remarks\r\n\r\n- I have already called `rm -rf \/home\/\/.cache\/huggingface`, i.e., I have tried clearing the cache.\r\n- The problem does not exist for other datasets, i.e., it seems to be DocRED-specific.\nHi @tmpr, thanks for reporting.\r\n\r\nTwo weeks ago (23th Aug), the host of the source `docred` dataset updated one of the files (`dev.json`): you can see it [here](https:\/\/drive.google.com\/drive\/folders\/1c5-0YwnoJx8NS6CV2f-NoTHR__BdkNqw).\r\n\r\nTherefore, the checksum needs to be updated.\r\n\r\nNormally, in the meantime, you could avoid the error by passing `ignore_verifications=True` to `load_dataset`. However, as the old link points to a non-existing file, the link must be updated too.\r\n\r\nI'm fixing all this.\r\n\r\n","embeddings":[-0.2659113109,0.3422692716,0.0355651341,0.3098572195,0.214197889,0.0404327884,0.3646671176,0.4201550186,0.3040324748,0.0715031847,-0.2768665254,0.190118432,0.1306955665,-0.1951108724,-0.1612918228,0.3339123726,0.1246936545,0.1053829789,-0.2242237628,-0.1252798289,-0.3106808662,0.1985796541,-0.2184446454,-0.2843059599,-0.0529530197,0.1835617423,0.1532600224,0.2324340791,-0.120700039,-0.3587095439,0.2788212895,0.2785292566,0.173633635,0.4296127558,-0.0001249282,0.1207987443,0.2817707658,-0.0242785923,-0.501280129,-0.2013867348,-0.563354671,-0.3742280006,-0.0070420792,-0.1583375037,-0.0344875716,0.2363334298,-0.0177811794,-0.2750721872,0.0001792605,0.3736942112,0.1168587953,0.3918669224,0.1610017419,0.0871690363,0.3812574446,0.0504982956,-0.0901036188,0.4119758606,0.219390288,-0.0385965593,-0.2582117915,0.1683268249,-0.4234545529,0.3069981039,0.232055366,0.0618822426,0.0648707673,-0.1373666972,0.253241241,0.2994743288,0.4981358647,-0.4146616459,-0.3278160989,-0.2285789698,-0.298303932,-0.3188637793,0.3791346848,0.1398549974,-0.1967773736,0.0336276442,-0.3680784702,0.1499671191,0.0037718869,0.1940130293,0.2422606051,0.0545322224,0.0316536799,-0.0187701844,0.0221102182,0.0298728757,0.3937070668,-0.5399644375,-0.2046732903,0.2048074454,-0.5590269566,0.0730827823,0.0080572087,0.2777058184,0.352638036,0.4925115705,0.08901117,0.2042775005,-0.0375440791,0.188513726,0.1643131375,0.2119750381,-0.0647413805,0.4100411534,0.2708570957,0.3656848669,-0.1347787827,0.0432133712,-0.0054317582,-0.1764253378,0.6712611914,-0.1259947717,0.3084286749,-0.4890398979,-0.4388926029,0.271324724,0.0082045635,-0.0947970897,0.0869470164,0.322021246,-0.226584658,0.1516590118,0.1470181793,0.1476904005,-0.2125054002,-0.0541327372,-0.187337622,-0.0764364675,-0.0596771426,0.1976072788,0.2942281067,-0.1821978688,0.2553656697,0.1083121598,0.2190237492,-0.1960808337,0.2999760807,-0.1955657154,-0.0533633046,0.3180696368,-0.0322253928,0.0961275995,0.2476602346,-0.1812210828,-0.1439391971,0.1828757226,-0.2557730675,-0.2137421519,-0.1269117147,0.1511545181,-0.3514274061,0.0082077142,-0.2378200293,-0.400021553,0.4063311517,-0.3223438859,-0.0154792899,-0.3542110622,-0.2637994885,-0.353944093,0.2389694899,0.4025940299,0.0419260897,0.0426536016,-0.0905655473,-0.0114815729,0.294190079,0.183512345,0.0093251551,0.1056279317,-0.4124949574,-0.0635623261,0.2606512606,-0.3542023897,-0.6170941591,0.1036195084,0.1277518421,0.6295824647,0.0476492308,0.0750021562,0.1831660569,-0.0502420217,0.2216965407,0.1532353014,-0.038785696,0.1244795322,-0.252807796,-0.0325587615,-0.0434744433,0.1044865996,0.0201719031,0.2498004735,0.2889224589,-0.1661153138,0.2637803555,-0.0953656435,-0.1257363111,0.2321082801,0.2991938293,0.046871759,0.0639734343,-0.0167484935,-0.7693794966,0.3423186839,-0.1505941451,-0.0280080661,-0.0733518675,-0.0444171354,-0.1803081781,-0.1141534746,-0.3073681295,-0.0912047699,-0.0125983637,0.3906361461,0.2074201256,0.0201071296,0.0379508324,0.4137744009,-0.3047952354,0.1248284727,-0.3346244991,0.4118489027,0.0743400902,-0.1029386371,0.1318240315,-0.0030252386,0.1328218132,-0.0562920161,-0.2745426893,0.4387036562,0.3811519444,0.1363846362,-0.1962373555,0.3414201438,-0.0699199662,-0.1395473629,-0.012621915,0.4393106699,0.1203998923,-0.1690554321,-0.1533752978,0.3473964334,-0.0006330401,0.1946744621,0.0062628747,0.0575816073,0.2450569719,-0.1167023629,-0.1461344063,-0.261870563,0.3650653958,0.1296010762,0.2266256809,0.196940884,-0.1572066098,-0.0258856118,0.4515280128,-0.0735068321,-0.0205244031,0.1288514733,0.0474654101,-0.0436953641,0.0753767639,0.2370779216,0.5027197599,0.0668875426,-0.04627854,0.1425257325,-0.1718104333,-0.0678285286,0.0968001559,-0.0407392457,0.1989537925,0.6168555617,0.1380470097,-0.1526708454,-0.2818833888,-0.0774433017,-0.1217622161,0.3029148579,-0.4903008938,0.0769678801,-0.1072904691,0.0858299136,-0.3731725514,-0.1268986762,-0.1855977178,-0.2465110421,-0.2533238232,0.6255096793,0.0291794557,0.1770886779,-0.543838203,0.0261698551,-0.0482735336,-0.3658800423,0.0178647786,0.2034313083,-0.2111107856,-0.154736951,0.5788052082,-0.0910895392,0.3742569685,-0.4110652506,-0.0268509947,-0.5500833392,-0.2233636677,0.0945177004,0.0162294693,0.2236746848,0.171183303,0.0406548828,-0.0192004945,-0.2501042485,0.2409217954,-0.0442294776,-0.2682688534,0.3236261308,-0.1510273069,-0.0955459252,0.1709357947,-0.1985139698,-0.0594486669,-0.3050559163,-0.1297381818,0.2319205552,0.2133797705,0.1936335266,-0.0064717545,0.0152918147,0.1389674693,0.337371707,-0.1682054251,-0.6134564281,0.4448585808,-0.0378274098,-0.220205918,-0.1443342119,-0.1069903895,0.1174569875,0.3974053264,-0.5095450878,0.0167492125,-0.288091749,0.0945630446,0.0950631127,-0.0240606703,0.2160272449,0.145353362,0.0829630345,-0.2499599308,-0.2719066143,0.1908720434,-0.1372274011,0.1996578574,0.0226447172,0.3025070429,-0.2673497498,0.5462415814,0.4274444282,0.0061363615,0.2280853242,0.0467436984,0.526879847,-0.1603343338,-0.3179928064,-0.0785081387,-0.2098642141,-0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{"html_url":"https:\/\/github.com\/huggingface\/datasets\/issues\/2879","title":"In v1.4.1, all TIMIT train transcripts are \"Would such an act of refusal be useful?\"","comments":"Hi @rcgale, thanks for reporting.\r\n\r\nPlease note that this bug was fixed on `datasets` version 1.5.0: https:\/\/github.com\/huggingface\/datasets\/commit\/a23c73e526e1c30263834164f16f1fdf76722c8c#diff-f12a7a42d4673bb6c2ca5a40c92c29eb4fe3475908c84fd4ce4fad5dc2514878\r\n\r\nIf you update `datasets` version, that should work.\r\n\r\nOn the other hand, would it be possible for @patrickvonplaten to update the [blog post](https:\/\/huggingface.co\/blog\/fine-tune-wav2vec2-english) with the correct version of `datasets`?","body":"## Describe the bug\r\nUsing version 1.4.1 of `datasets`, TIMIT transcripts are all the same.\r\n\r\n## Steps to reproduce the bug\r\nI was following this tutorial\r\n- https:\/\/huggingface.co\/blog\/fine-tune-wav2vec2-english\r\n\r\nBut here's a distilled repro:\r\n```python\r\n!pip install datasets==1.4.1\r\nfrom datasets import load_dataset\r\ntimit = load_dataset(\"timit_asr\", cache_dir=\".\/temp\")\r\nunique_transcripts = set(timit[\"train\"][\"text\"])\r\nprint(unique_transcripts)\r\nassert len(unique_transcripts) > 1\r\n```\r\n## Expected results\r\nExpected the correct TIMIT data. Or an error saying that this version of `datasets` can't produce it.\r\n\r\n## Actual results\r\nEvery train transcript was \"Would such an act of refusal be useful?\" Every test transcript was \"The bungalow was pleasantly situated near the shore.\"\r\n\r\n## Environment info\r\n- `datasets` version: 1.4.1\r\n- Platform: Darwin-18.7.0-x86_64-i386-64bit\r\n- Python version: 3.7.9\r\n- PyTorch version (GPU?): 1.9.0 (False)\r\n- Tensorflow version (GPU?): not installed (NA)\r\n- Using GPU in script?: tried both\r\n- Using distributed or parallel set-up in script?: no\r\n- \r\n\r\n","comment_length":46,"text":"In v1.4.1, all TIMIT train transcripts are \"Would such an act of refusal be useful?\"\n## Describe the bug\r\nUsing version 1.4.1 of `datasets`, TIMIT transcripts are all the same.\r\n\r\n## Steps to reproduce the bug\r\nI was following this tutorial\r\n- https:\/\/huggingface.co\/blog\/fine-tune-wav2vec2-english\r\n\r\nBut here's a distilled repro:\r\n```python\r\n!pip install datasets==1.4.1\r\nfrom datasets import load_dataset\r\ntimit = load_dataset(\"timit_asr\", cache_dir=\".\/temp\")\r\nunique_transcripts = set(timit[\"train\"][\"text\"])\r\nprint(unique_transcripts)\r\nassert len(unique_transcripts) > 1\r\n```\r\n## Expected results\r\nExpected the correct TIMIT data. Or an error saying that this version of `datasets` can't produce it.\r\n\r\n## Actual results\r\nEvery train transcript was \"Would such an act of refusal be useful?\" Every test transcript was \"The bungalow was pleasantly situated near the shore.\"\r\n\r\n## Environment info\r\n- `datasets` version: 1.4.1\r\n- Platform: Darwin-18.7.0-x86_64-i386-64bit\r\n- Python version: 3.7.9\r\n- PyTorch version (GPU?): 1.9.0 (False)\r\n- Tensorflow version (GPU?): not installed (NA)\r\n- Using GPU in script?: tried both\r\n- Using distributed or parallel set-up in script?: no\r\n- \r\n\r\n\nHi @rcgale, thanks for reporting.\r\n\r\nPlease note that this bug was fixed on `datasets` version 1.5.0: https:\/\/github.com\/huggingface\/datasets\/commit\/a23c73e526e1c30263834164f16f1fdf76722c8c#diff-f12a7a42d4673bb6c2ca5a40c92c29eb4fe3475908c84fd4ce4fad5dc2514878\r\n\r\nIf you update `datasets` version, that should work.\r\n\r\nOn the other hand, would it be possible for @patrickvonplaten to update the [blog post](https:\/\/huggingface.co\/blog\/fine-tune-wav2vec2-english) with the correct version of `datasets`?","embeddings":[0.124852851,-0.0535938032,0.0293307602,0.0739796385,0.2047281116,-0.0245766733,0.2489377707,0.4213005006,-0.4746908545,0.1751622707,0.1361838877,0.5513864756,-0.3347690701,-0.0850955397,0.0344839208,0.1865644157,-0.0921123177,0.1384560466,-0.2678906322,-0.2130741179,0.0398925841,0.3963992298,-0.1676281989,-0.0685962737,-0.3741768599,0.244877398,0.2273771465,-0.2314912826,0.1054832488,-0.4600336254,0.2924744487,0.1486980319,0.0194595307,0.3702304661,-0.0001191299,0.0063226023,0.2757813334,0.0352541432,-0.2775231004,-0.4479430318,-0.0650265291,0.1085864976,0.0530338921,0.1475536525,-0.4656309485,-0.1793126911,0.2337317616,-0.3409471512,0.0292004794,0.4485591054,0.0685843602,-0.0634173304,-0.2496642619,-0.0824669749,0.3689354062,0.0125433672,-0.0625187159,0.0592703931,-0.0683355555,0.2411289513,0.2196747363,0.3755480051,-0.0313242152,0.0129956314,-0.1954175979,-0.0935640186,-0.2540271878,-0.3487864733,0.0747668445,0.2519178092,0.7106900215,-0.2958623171,-0.3215118349,-0.2683330774,0.3369950652,-0.0542203225,0.2911206484,0.0254176874,-0.2944933772,0.1627251506,-0.5629917383,0.0742394626,-0.3041745424,0.1429014206,-0.0974289998,-0.359634608,-0.143771261,0.1845409572,-0.1975932419,0.1163858026,0.114155367,0.1884384304,-0.0396124236,-0.0923857763,-0.3206233084,-0.0985560194,0.0559260733,-0.0815377459,0.0717144236,0.3172878623,0.2842969596,0.0675945058,-0.4174665213,-0.135505408,0.0322321728,0.1481688768,0.1823079735,-0.1778396666,0.101101391,-0.0720301494,-0.1533745825,0.0139923282,0.0189836212,-0.0720990822,-0.1062536985,-0.0153032569,0.3571920395,-0.1175772175,-0.5631766319,0.1525668353,-0.5025030375,0.2135901749,-0.0520560816,0.1947724819,-0.2154174149,0.1350183487,0.2971945703,0.0870628357,0.02487945,-0.3115249574,-0.0998023972,-0.1971572787,0.1828780323,0.0968628898,0.2440094203,-0.2103739977,0.2123047858,0.257135421,0.2615037858,-0.1538840383,-0.0926653668,-0.0326226279,0.2529811561,0.2668441832,-0.1248196512,0.3939130902,-0.0565123409,0.1157927513,0.106670782,0.2571235001,-0.0651218817,-0.0061710193,0.5623431206,0.060588941,-0.1836533099,-0.3006524444,-0.1071216986,0.3333047032,0.0442795344,-0.0668319687,0.2504209578,-0.2751781344,-0.2271512002,0.0158824958,0.1358926445,0.1566538513,-0.5853290558,0.0243286006,0.1945606619,0.0684714913,0.3222911954,0.3032742143,0.1651955247,0.1811709255,-0.0816664621,0.1170219034,-0.219933033,-0.3883649707,-0.2060429454,0.2092838287,-0.2708128095,0.264259249,-0.0832697079,-0.2192167342,0.0802669078,0.0991331413,0.3640693426,-0.1891600937,0.24668172,-0.1056700721,-0.3831144273,-0.1445571929,0.1893382818,0.1755265892,-0.0177115817,0.0532647818,-0.2982362509,-0.0164561383,0.4027053714,0.0394103304,-0.0291296821,-0.025731707,0.3644500375,-0.1775300503,0.263861388,-0.1554233581,0.2872300744,0.0272912737,0.1604733467,0.2198717296,0.5499910116,-0.1693307608,-0.1779452264,-0.0762331262,-0.0728189349,-0.0683157369,0.0582602769,0.1486589164,-0.1489184052,0.3247836828,-0.0285170637,0.4712553024,-0.4881338477,-0.0153098824,-0.0879812539,-0.1088415384,0.3033908308,-0.0987990201,0.1165293604,0.2411418706,0.0172993075,0.0872968137,-0.110142529,0.3636822999,0.2269500643,0.1219172627,-0.3682011068,-0.4027226269,0.2316687405,-0.5078880787,-0.4552857876,0.5677568913,-0.0276648402,0.2008169293,-0.1534452736,0.060441643,0.0807491466,0.0479999073,0.0348503962,0.0360783227,-0.019409189,-0.1079213247,-0.470849663,-0.1607666463,0.2607947588,-0.2098269463,0.1590214819,-0.1161290258,-0.320378989,0.1368902475,0.315872401,-0.0502400734,0.0395470038,0.2744301558,-0.1326723844,0.209983319,0.0375697017,0.1719760001,0.2373924702,0.2009201497,0.0807271153,-0.0535479188,-0.0781926885,-0.2605938315,0.2923918068,0.1945406348,-0.3651409447,0.3840377629,0.2203695476,0.0489813201,-0.4372256994,-0.0049824319,-0.1904428899,0.2824560404,-0.449403286,-0.1749291867,-0.3977611363,0.1078346595,-0.1361607611,-0.0218015406,0.0710958019,0.0436779261,0.3717918992,-0.0027077466,-0.0859973133,0.2743452489,0.1501078755,0.1915652007,-0.110422954,0.1713414043,-0.0096689994,0.1939062178,-0.4710183442,0.0452458076,-0.1606466472,-0.2644656301,0.1504383832,-0.1925743371,-0.1099141166,-0.4143466055,-0.071546413,0.1478731036,-0.1916618943,0.4232137799,0.2326952517,-0.0426136926,-0.1860425919,-0.194430843,-0.1887598336,-0.0310719088,-0.0671303943,0.0714476034,-0.1346394867,0.0415853858,-0.256131798,-0.8888216615,0.1003023162,-0.2204675525,0.7185165882,0.0599351153,-0.0460496917,0.5412366986,0.2822042704,0.2012919486,0.0792539567,0.1863065064,-0.3713881075,-0.1230798736,0.0792584717,0.0293458365,-0.4573093057,-0.298314631,0.1936385185,0.1548020095,0.2074109167,-0.4953384995,0.3497347236,-0.1089945436,-0.1306295842,0.0102862855,-0.1989106387,0.2906790078,-0.2038048357,-0.1159159765,-0.2554523647,0.1175914109,0.3896949589,0.1017955169,0.112711817,0.3398635685,0.2160488367,0.2003065348,0.3506759107,0.7158643007,0.0828187764,0.039017871,-0.1280344874,0.4244537055,-0.0728549138,-0.052832868,0.1600767821,0.0590854175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{"html_url":"https:\/\/github.com\/huggingface\/datasets\/issues\/2879","title":"In v1.4.1, all TIMIT train transcripts are \"Would such an act of refusal be useful?\"","comments":"I just proposed a change in the blog post.\r\n\r\nI had assumed there was a data format change that broke a previous version of the code, since presumably @patrickvonplaten tested the tutorial with the version they explicitly referenced. But that fix you linked suggests a problem in the code, which surprised me.\r\n\r\nI still wonder, though, is there a way for downloads to be invalidated server-side? If the client can announce its version during a download request, perhaps the server could reject known incompatibilities? It would save much valuable time if `datasets` raised an informative error on a known problem (\"Error: the requested data set requires `datasets>=1.5.0`.\"). This kind of API versioning is a prudent move anyhow, as there will surely come a time when you'll need to make a breaking change to data.","body":"## Describe the bug\r\nUsing version 1.4.1 of `datasets`, TIMIT transcripts are all the same.\r\n\r\n## Steps to reproduce the bug\r\nI was following this tutorial\r\n- https:\/\/huggingface.co\/blog\/fine-tune-wav2vec2-english\r\n\r\nBut here's a distilled repro:\r\n```python\r\n!pip install datasets==1.4.1\r\nfrom datasets import load_dataset\r\ntimit = load_dataset(\"timit_asr\", cache_dir=\".\/temp\")\r\nunique_transcripts = set(timit[\"train\"][\"text\"])\r\nprint(unique_transcripts)\r\nassert len(unique_transcripts) > 1\r\n```\r\n## Expected results\r\nExpected the correct TIMIT data. Or an error saying that this version of `datasets` can't produce it.\r\n\r\n## Actual results\r\nEvery train transcript was \"Would such an act of refusal be useful?\" Every test transcript was \"The bungalow was pleasantly situated near the shore.\"\r\n\r\n## Environment info\r\n- `datasets` version: 1.4.1\r\n- Platform: Darwin-18.7.0-x86_64-i386-64bit\r\n- Python version: 3.7.9\r\n- PyTorch version (GPU?): 1.9.0 (False)\r\n- Tensorflow version (GPU?): not installed (NA)\r\n- Using GPU in script?: tried both\r\n- Using distributed or parallel set-up in script?: no\r\n- \r\n\r\n","comment_length":134,"text":"In v1.4.1, all TIMIT train transcripts are \"Would such an act of refusal be useful?\"\n## Describe the bug\r\nUsing version 1.4.1 of `datasets`, TIMIT transcripts are all the same.\r\n\r\n## Steps to reproduce the bug\r\nI was following this tutorial\r\n- https:\/\/huggingface.co\/blog\/fine-tune-wav2vec2-english\r\n\r\nBut here's a distilled repro:\r\n```python\r\n!pip install datasets==1.4.1\r\nfrom datasets import load_dataset\r\ntimit = load_dataset(\"timit_asr\", cache_dir=\".\/temp\")\r\nunique_transcripts = set(timit[\"train\"][\"text\"])\r\nprint(unique_transcripts)\r\nassert len(unique_transcripts) > 1\r\n```\r\n## Expected results\r\nExpected the correct TIMIT data. Or an error saying that this version of `datasets` can't produce it.\r\n\r\n## Actual results\r\nEvery train transcript was \"Would such an act of refusal be useful?\" Every test transcript was \"The bungalow was pleasantly situated near the shore.\"\r\n\r\n## Environment info\r\n- `datasets` version: 1.4.1\r\n- Platform: Darwin-18.7.0-x86_64-i386-64bit\r\n- Python version: 3.7.9\r\n- PyTorch version (GPU?): 1.9.0 (False)\r\n- Tensorflow version (GPU?): not installed (NA)\r\n- Using GPU in script?: tried both\r\n- Using distributed or parallel set-up in script?: no\r\n- \r\n\r\n\nI just proposed a change in the blog post.\r\n\r\nI had assumed there was a data format change that broke a previous version of the code, since presumably @patrickvonplaten tested the tutorial with the version they explicitly referenced. But that fix you linked suggests a problem in the code, which surprised me.\r\n\r\nI still wonder, though, is there a way for downloads to be invalidated server-side? If the client can announce its version during a download request, perhaps the server could reject known incompatibilities? It would save much valuable time if `datasets` raised an informative error on a known problem (\"Error: the requested data set requires `datasets>=1.5.0`.\"). This kind of API versioning is a prudent move anyhow, as there will surely come a time when you'll need to make a breaking change to 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{"html_url":"https:\/\/github.com\/huggingface\/datasets\/issues\/2879","title":"In v1.4.1, all TIMIT train transcripts are \"Would such an act of refusal be useful?\"","comments":"Also, thank you for a quick and helpful reply!","body":"## Describe the bug\r\nUsing version 1.4.1 of `datasets`, TIMIT transcripts are all the same.\r\n\r\n## Steps to reproduce the bug\r\nI was following this tutorial\r\n- https:\/\/huggingface.co\/blog\/fine-tune-wav2vec2-english\r\n\r\nBut here's a distilled repro:\r\n```python\r\n!pip install datasets==1.4.1\r\nfrom datasets import load_dataset\r\ntimit = load_dataset(\"timit_asr\", cache_dir=\".\/temp\")\r\nunique_transcripts = set(timit[\"train\"][\"text\"])\r\nprint(unique_transcripts)\r\nassert len(unique_transcripts) > 1\r\n```\r\n## Expected results\r\nExpected the correct TIMIT data. Or an error saying that this version of `datasets` can't produce it.\r\n\r\n## Actual results\r\nEvery train transcript was \"Would such an act of refusal be useful?\" Every test transcript was \"The bungalow was pleasantly situated near the shore.\"\r\n\r\n## Environment info\r\n- `datasets` version: 1.4.1\r\n- Platform: Darwin-18.7.0-x86_64-i386-64bit\r\n- Python version: 3.7.9\r\n- PyTorch version (GPU?): 1.9.0 (False)\r\n- Tensorflow version (GPU?): not installed (NA)\r\n- Using GPU in script?: tried both\r\n- Using distributed or parallel set-up in script?: no\r\n- \r\n\r\n","comment_length":9,"text":"In v1.4.1, all TIMIT train transcripts are \"Would such an act of refusal be useful?\"\n## Describe the bug\r\nUsing version 1.4.1 of `datasets`, TIMIT transcripts are all the same.\r\n\r\n## Steps to reproduce the bug\r\nI was following this tutorial\r\n- https:\/\/huggingface.co\/blog\/fine-tune-wav2vec2-english\r\n\r\nBut here's a distilled repro:\r\n```python\r\n!pip install datasets==1.4.1\r\nfrom datasets import load_dataset\r\ntimit = load_dataset(\"timit_asr\", cache_dir=\".\/temp\")\r\nunique_transcripts = set(timit[\"train\"][\"text\"])\r\nprint(unique_transcripts)\r\nassert len(unique_transcripts) > 1\r\n```\r\n## Expected results\r\nExpected the correct TIMIT data. Or an error saying that this version of `datasets` can't produce it.\r\n\r\n## Actual results\r\nEvery train transcript was \"Would such an act of refusal be useful?\" Every test transcript was \"The bungalow was pleasantly situated near the shore.\"\r\n\r\n## Environment info\r\n- `datasets` version: 1.4.1\r\n- Platform: Darwin-18.7.0-x86_64-i386-64bit\r\n- Python version: 3.7.9\r\n- PyTorch version (GPU?): 1.9.0 (False)\r\n- Tensorflow version (GPU?): not installed (NA)\r\n- Using GPU in script?: tried both\r\n- Using distributed or parallel set-up in script?: no\r\n- \r\n\r\n\nAlso, thank you for a quick and helpful reply!","embeddings":[0.1843589097,-0.0476369262,0.0212153625,0.1307302862,0.1825789064,-0.0150703816,0.222696349,0.4102399647,-0.4767715335,0.1482010931,0.202155903,0.528619945,-0.3603378236,-0.1392592937,0.0697514489,0.1514802277,-0.0296482705,0.1163090765,-0.2856072187,-0.2430297881,0.0460388102,0.3836102486,-0.181789279,-0.0110180005,-0.3692496121,0.2394319922,0.2274626493,-0.1810913682,0.1650811285,-0.439029336,0.2945018709,0.0983762294,0.0515014045,0.3712564111,-0.0001205389,-0.0167328734,0.2610318065,0.0087951152,-0.2081606984,-0.3689297736,-0.1382844448,0.1195933074,0.0133538358,0.12241216,-0.4130392373,-0.2065744102,0.229093805,-0.3684004843,0.0639212877,0.4605449438,0.0608402193,-0.139359951,-0.2579361498,-0.0793896466,0.3681408465,-0.0151606631,-0.0316133946,0.0299798381,-0.0864327401,0.2322303355,0.2330624908,0.3767850101,-0.0501611307,0.0072294213,-0.2207338065,-0.0934685469,-0.2900455594,-0.3867571652,0.0662252977,0.191508621,0.7059831023,-0.3136530221,-0.2619046569,-0.2177537978,0.3014097512,-0.0254760459,0.2628924251,0.0440048538,-0.2687057555,0.1821450889,-0.5459147692,0.1098416448,-0.3146458268,0.1415527016,-0.0640842691,-0.3725824058,-0.0981010497,0.2094152719,-0.2525474727,0.1218328848,0.1371880621,0.2098523825,-0.0407567285,-0.088758558,-0.3333061934,-0.0827903822,0.0905025452,-0.055269625,0.0512395315,0.2592660189,0.323356241,0.0425352305,-0.3752723634,-0.1163501963,0.0656884834,0.1570367366,0.0961320028,-0.1948441863,0.118978247,-0.1403936744,-0.1913475394,0.040307723,-0.0055863252,-0.091643773,-0.0389522314,-0.0102081764,0.3480204046,-0.1361118853,-0.5908363461,0.1955148876,-0.5599816442,0.2329349965,-0.0647064596,0.2073190659,-0.187874347,0.1397468895,0.3171749115,0.0926715434,-0.0059219012,-0.3403543532,-0.0963281542,-0.1902716309,0.1971511841,0.0616736077,0.2055280805,-0.1423812211,0.2137727439,0.2923474014,0.2695505917,-0.1473722309,-0.0583484769,-0.0180806592,0.219842881,0.2483473718,-0.1362519711,0.4016667604,-0.0248719845,0.1940863729,0.1011622474,0.2526570261,-0.1005024016,-0.0175146516,0.5515517592,0.0515450686,-0.179110989,-0.2624935806,-0.0348146893,0.3113076985,0.0846941844,-0.1433292776,0.2118196636,-0.2840275764,-0.1747703999,-0.003525354,0.0937831625,0.1310754865,-0.5885374546,0.0558972545,0.228044942,0.1303474754,0.3747636676,0.2952876389,0.1747010648,0.2124052495,-0.0759727508,0.1145521328,-0.1961544603,-0.4299621284,-0.214312613,0.1697297394,-0.2615546882,0.2817715108,-0.0361457802,-0.2367807031,0.0825164616,0.075459443,0.409607172,-0.2213379294,0.1860471368,-0.0808987841,-0.3983337283,-0.1363315433,0.2186726034,0.1355755478,-0.0341216251,0.0313469507,-0.2578755319,-0.0243165027,0.3640209436,0.0280066933,-0.004362273,-0.0479702353,0.3482239544,-0.2454301864,0.3254101574,-0.0976360664,0.3093805313,0.0151669383,0.1946799606,0.1750653833,0.6044173241,-0.1565310061,-0.170942992,-0.0799789056,-0.0831656903,-0.0937429518,0.0424369089,0.1493053585,-0.1577068269,0.3208678961,-0.0529016294,0.496171236,-0.4533744752,-0.0638343394,-0.0522209033,-0.1226176918,0.2798168063,-0.0861343965,0.0933275223,0.2543967366,0.0215519834,0.0870476365,-0.0931508169,0.3177514076,0.2538267672,0.1660675555,-0.3391086459,-0.3895223737,0.2347876579,-0.5418210626,-0.418391794,0.6494663954,0.0136631234,0.2044710368,-0.1246106178,0.0206717309,0.0992714986,0.0414252579,0.0030764376,0.0376889668,-0.0036091113,-0.0781878829,-0.4513742328,-0.0986297056,0.2685841024,-0.2420790792,0.1837171465,-0.1044279113,-0.3128755689,0.1960173547,0.3068779707,-0.0273673404,0.0484327525,0.2494100779,-0.1171611398,0.1943250597,0.0596161298,0.2010252476,0.235914126,0.1829334795,0.0925755948,-0.0770207793,-0.0987522975,-0.221516192,0.2697022557,0.1580257267,-0.3681939244,0.4219260216,0.2311407924,0.0666007549,-0.3795827329,0.0026456879,-0.2107334882,0.3031838238,-0.4800261259,-0.1368284374,-0.3495121598,0.1713373214,-0.1380009055,-0.0323438756,0.1311494857,0.0800623074,0.370326221,-0.0135218948,-0.1168963611,0.2293738723,0.1091258749,0.2434615046,-0.0670005679,0.2318317592,0.0222459622,0.2099670172,-0.4693998694,0.015322485,-0.1299001575,-0.2572244108,0.1167961061,-0.1661652029,-0.0994341001,-0.3659780025,-0.0571099631,0.151061967,-0.1844133586,0.4728702605,0.2280860841,-0.0554880984,-0.1941481084,-0.2376766503,-0.2153888792,0.0371617377,-0.0857695267,0.097694777,-0.1236358881,-0.0152868517,-0.2216340005,-0.9135431647,0.0924015716,-0.2011582255,0.6722809076,0.0548606068,-0.0086461287,0.4621789753,0.2635646164,0.1870574206,0.138909936,0.2119944692,-0.3575545549,-0.106063135,0.0823722705,0.0353392884,-0.4551185369,-0.3046335578,0.1577614248,0.1924328506,0.2105116844,-0.4597027302,0.4100583196,-0.0954082683,-0.1507000029,-0.0038921644,-0.2178034633,0.3120282888,-0.1800539792,-0.0859067366,-0.2840114534,0.1318731755,0.3946647942,0.1762969643,0.1079193801,0.3068368733,0.1843467206,0.1532683223,0.3919531703,0.7317343354,0.1164943278,0.0385542214,-0.1042457595,0.3726763427,-0.0699758604,-0.0704685152,0.1748739481,0.0528061725,-0.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{"html_url":"https:\/\/github.com\/huggingface\/datasets\/issues\/2871","title":"datasets.config.PYARROW_VERSION has no attribute 'major'","comments":"I have changed line 288 to `if int(datasets.config.PYARROW_VERSION.split(\".\")[0]) < 3:` just to get around it.","body":"In the test_dataset_common.py script, line 288-289\r\n\r\n```\r\nif datasets.config.PYARROW_VERSION.major < 3:\r\n packaged_datasets = [pd for pd in packaged_datasets if pd[\"dataset_name\"] != \"parquet\"]\r\n```\r\n\r\nwhich throws the error below. `datasets.config.PYARROW_VERSION` itself return the string '4.0.1'. I have tested this on both datasets.__version_=='1.11.0' and '1.9.0'. I am using Mac OS.\r\n\r\n```\r\nimport datasets\r\ndatasets.config.PYARROW_VERSION.major\r\n---------------------------------------------------------------------------\r\nAttributeError Traceback (most recent call last)\r\n\/var\/folders\/1f\/0wqmlgp90qjd5mpj53fnjq440000gn\/T\/ipykernel_73361\/2547517336.py in \r\n 1 import datasets\r\n----> 2 datasets.config.PYARROW_VERSION.major\r\n\r\nAttributeError: 'str' object has no attribute 'major'\r\n```\r\n\r\n## Environment info\r\n- `datasets` version: 1.11.0\r\n- Platform: Darwin-20.6.0-x86_64-i386-64bit\r\n- Python version: 3.7.11\r\n- PyArrow version: 4.0.1\r\n","comment_length":15,"text":"datasets.config.PYARROW_VERSION has no attribute 'major'\nIn the test_dataset_common.py script, line 288-289\r\n\r\n```\r\nif datasets.config.PYARROW_VERSION.major < 3:\r\n packaged_datasets = [pd for pd in packaged_datasets if pd[\"dataset_name\"] != \"parquet\"]\r\n```\r\n\r\nwhich throws the error below. `datasets.config.PYARROW_VERSION` itself return the string '4.0.1'. I have tested this on both datasets.__version_=='1.11.0' and '1.9.0'. I am using Mac OS.\r\n\r\n```\r\nimport datasets\r\ndatasets.config.PYARROW_VERSION.major\r\n---------------------------------------------------------------------------\r\nAttributeError Traceback (most recent call last)\r\n\/var\/folders\/1f\/0wqmlgp90qjd5mpj53fnjq440000gn\/T\/ipykernel_73361\/2547517336.py in \r\n 1 import datasets\r\n----> 2 datasets.config.PYARROW_VERSION.major\r\n\r\nAttributeError: 'str' object has no attribute 'major'\r\n```\r\n\r\n## Environment info\r\n- `datasets` version: 1.11.0\r\n- Platform: Darwin-20.6.0-x86_64-i386-64bit\r\n- Python version: 3.7.11\r\n- PyArrow version: 4.0.1\r\n\nI have changed line 288 to `if int(datasets.config.PYARROW_VERSION.split(\".\")[0]) < 3:` just to get around it.","embeddings":[-0.4137373269,0.0904145688,0.0879315063,0.069035016,0.284868449,0.1465740502,0.1234253496,0.3872671723,-0.1835217625,0.1793182641,0.3690841198,0.3502765,-0.2362233698,0.1078671366,-0.2026903927,0.0031209812,0.0010433139,0.2549073994,0.1760860234,-0.0239745975,-0.1098649725,0.1690122485,-0.2097337544,0.2731696069,-0.2981304824,0.0923148543,0.0032627089,-0.0122110816,-0.1285185814,-0.5752780437,0.2998543382,-0.1617958844,0.2332223952,0.391263485,-0.000127086,0.0779013038,0.3378002942,-0.0440747142,-0.2238939106,-0.4914156199,-0.085651435,0.0122515438,0.3829289079,-0.0937147513,0.0937459767,-0.6206139922,0.0335906781,0.0384339988,-0.1429862529,0.3645019233,0.127537936,0.0344468243,0.1980867237,-0.0754810423,0.2223289162,0.083245784,-0.0282249879,0.5229272842,0.1158733368,-0.1320393234,0.233240068,-0.102022633,0.0575134605,-0.0043903464,0.0711091682,0.1914277673,0.2576049566,-0.1857423782,0.2388329953,0.0887792706,0.5562196374,-0.6069078445,-0.4294553101,0.0136653185,0.174752906,-0.2191064954,0.2185028791,0.2610872686,-0.1244944483,0.1687700599,-0.0923660845,-0.1329844445,-0.0807051584,-0.0779537112,-0.433144331,0.3678042889,0.1932168156,0.1645069569,-0.0400829166,-0.04866606,-0.0215911791,-0.0890382826,0.1259018928,-0.0294876229,-0.2407489419,-0.0118983239,-0.0643955991,0.1943915635,0.2201173902,0.1590977758,0.0649064705,-0.234773919,0.2536158264,0.0735231191,0.0475472286,0.0977316201,-0.0197430961,0.146631524,0.0936663523,-0.0873535872,0.0256926231,-0.0208997894,-0.0806714743,-0.0734729469,0.3739596009,0.0080980845,0.7578920722,-0.3194511235,-0.2312780768,0.1659606099,-0.3343805671,-0.184103936,-0.2247074097,0.2836925983,0.0676930696,0.3056817055,-0.1982494593,0.3802600801,-0.1360655129,-0.1857322007,-0.0950188115,0.1552126408,0.0275490843,-0.1461474597,0.0764700472,-0.2719792128,-0.0986580551,0.3349583149,0.0094830692,0.1213422492,-0.0362757482,-0.0120166596,-0.1315119714,0.4732261598,-0.2890482843,0.2727305889,0.2008104473,-0.4936734736,-0.3582565486,0.2217560709,-0.1869192868,-0.431489408,-0.2999134958,0.1262726486,-0.1483470798,-0.2103191763,-0.0671084151,-0.0523799025,0.1624187529,-0.1384892911,0.0297997743,-0.651668191,0.1586196274,-0.3059960306,0.2577728629,0.161972031,-0.6419220567,0.0579073988,0.1789008826,-0.1889813095,0.1506783217,-0.1405111104,0.0306624472,-0.1734109372,0.074796848,0.0583436862,0.3201945126,-0.2379271239,-0.3039623797,0.1230635121,0.0718067214,0.0206128471,0.2803038359,-0.3171919584,-0.0282199513,0.1652483642,-0.1928093135,0.1018786877,0.0079200668,0.0062634307,-0.1814045608,-0.0036330107,0.4458725452,0.2917095721,0.1499163508,-0.2299220115,-0.0262244828,-0.1855148673,0.1473240703,-0.0819691345,0.0311352387,-0.0489151254,0.3686681986,-0.2390524894,-0.0064461622,-0.2394002527,-0.2902263105,0.2341018915,0.0907700509,0.0002655792,-0.3193365932,-0.219354555,-0.2210743129,0.0846685618,-0.128561303,-0.011503078,-0.0059905695,0.3605172932,0.0959836021,0.0846642852,-0.3152723312,0.1812611818,0.0422432944,0.1910732836,-0.0209711008,0.4087104499,-0.4075936377,-0.3384674788,0.0850214958,0.0640254617,0.1028376147,0.002861667,0.0299142897,0.3328282237,0.0242639706,0.0389278978,0.0299662594,0.2096638978,-0.0284052063,-0.2111189663,-0.1741658747,0.3066881597,-0.0031621177,0.3704423904,0.0565479696,0.4024959803,0.5626875758,0.3306960762,-0.0120106991,0.0881720185,-0.1884381175,-0.0055437423,0.1211171523,-0.1257602423,0.0624467172,-0.1393158436,0.1693660468,-0.0199615061,-0.5397575498,0.0718814433,0.3745397031,-0.0722794607,0.1365482211,0.1192966476,-0.22845155,0.0563648492,0.0482595488,0.0757280812,0.249298811,0.1221992299,-0.1398308128,0.1419418901,-0.3319807649,0.1508725435,0.1715394557,0.053618446,0.1923853457,0.1373240501,0.2172163874,0.0542263351,-0.0518643856,-0.4222477078,0.1137933508,0.4095291495,-0.4994865358,0.2577461302,-0.2975929677,0.2889801562,-0.0827534124,-0.1969304681,-0.4268323183,-0.2678608894,0.0104828775,0.0935454518,0.1283202618,0.1767116934,-0.3569593728,-0.1166164875,0.0463055074,-0.5190931559,-0.252766639,-0.2070385814,-0.4546803832,0.0009253586,0.2450136989,-0.2954372466,0.0536534749,-0.2570302188,-0.2196664661,-0.2246728688,-0.1574143022,0.0125030046,-0.269426465,0.3128561378,0.2743914127,0.0396343656,-0.0582421124,-0.0491061211,0.3296924531,-0.3633875251,-0.2326972187,0.1670558602,-0.0441559106,-0.018140588,-0.0896702781,-0.5271107554,-0.2856226861,-0.174629271,-0.0225634612,0.0724860951,0.1668928266,-0.0621382482,0.2801332772,-0.0787437782,-0.1362159103,0.0422251076,0.0579393916,0.0014274698,0.2768362463,0.0047421837,-0.2914088666,0.010116118,-0.0066247168,0.1385644227,0.0028628155,-0.2945348024,-0.1095552146,0.0360653736,0.5227450132,-0.2845128775,0.0628193095,0.3160390556,0.2878689766,-0.0280942582,0.0421851873,-0.1316174716,-0.1662545651,0.1143491268,0.1350112706,0.1534068286,0.1728003323,-0.0685666278,0.6282868385,-0.0858015046,-0.2661091983,0.19313097,-0.3469312489,0.3226404786,0.0537074953,-0.1815225929,-0.0795435756,0.02120829,0.000880775,0.2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{"html_url":"https:\/\/github.com\/huggingface\/datasets\/issues\/2871","title":"datasets.config.PYARROW_VERSION has no attribute 'major'","comments":"Hi @bwang482,\r\n\r\nI'm sorry but I'm not able to reproduce your bug.\r\n\r\nPlease note that in our current master branch, we made a commit (d03223d4d64b89e76b48b00602aba5aa2f817f1e) that simultaneously modified:\r\n- test_dataset_common.py: https:\/\/github.com\/huggingface\/datasets\/commit\/d03223d4d64b89e76b48b00602aba5aa2f817f1e#diff-a1bc225bd9a5bade373d1f140e24d09cbbdc97971c2f73bb627daaa803ada002L289 that introduces the usage of `datasets.config.PYARROW_VERSION.major`\r\n- but also changed config.py: https:\/\/github.com\/huggingface\/datasets\/commit\/d03223d4d64b89e76b48b00602aba5aa2f817f1e#diff-e021fcfc41811fb970fab889b8d245e68382bca8208e63eaafc9a396a336f8f2L40, so that `datasets.config.PYARROW_VERSION.major` exists\r\n","body":"In the test_dataset_common.py script, line 288-289\r\n\r\n```\r\nif datasets.config.PYARROW_VERSION.major < 3:\r\n packaged_datasets = [pd for pd in packaged_datasets if pd[\"dataset_name\"] != \"parquet\"]\r\n```\r\n\r\nwhich throws the error below. `datasets.config.PYARROW_VERSION` itself return the string '4.0.1'. I have tested this on both datasets.__version_=='1.11.0' and '1.9.0'. I am using Mac OS.\r\n\r\n```\r\nimport datasets\r\ndatasets.config.PYARROW_VERSION.major\r\n---------------------------------------------------------------------------\r\nAttributeError Traceback (most recent call last)\r\n\/var\/folders\/1f\/0wqmlgp90qjd5mpj53fnjq440000gn\/T\/ipykernel_73361\/2547517336.py in \r\n 1 import datasets\r\n----> 2 datasets.config.PYARROW_VERSION.major\r\n\r\nAttributeError: 'str' object has no attribute 'major'\r\n```\r\n\r\n## Environment info\r\n- `datasets` version: 1.11.0\r\n- Platform: Darwin-20.6.0-x86_64-i386-64bit\r\n- Python version: 3.7.11\r\n- PyArrow version: 4.0.1\r\n","comment_length":47,"text":"datasets.config.PYARROW_VERSION has no attribute 'major'\nIn the test_dataset_common.py script, line 288-289\r\n\r\n```\r\nif datasets.config.PYARROW_VERSION.major < 3:\r\n packaged_datasets = [pd for pd in packaged_datasets if pd[\"dataset_name\"] != \"parquet\"]\r\n```\r\n\r\nwhich throws the error below. `datasets.config.PYARROW_VERSION` itself return the string '4.0.1'. I have tested this on both datasets.__version_=='1.11.0' and '1.9.0'. I am using Mac OS.\r\n\r\n```\r\nimport datasets\r\ndatasets.config.PYARROW_VERSION.major\r\n---------------------------------------------------------------------------\r\nAttributeError Traceback (most recent call last)\r\n\/var\/folders\/1f\/0wqmlgp90qjd5mpj53fnjq440000gn\/T\/ipykernel_73361\/2547517336.py in \r\n 1 import datasets\r\n----> 2 datasets.config.PYARROW_VERSION.major\r\n\r\nAttributeError: 'str' object has no attribute 'major'\r\n```\r\n\r\n## Environment info\r\n- `datasets` version: 1.11.0\r\n- Platform: Darwin-20.6.0-x86_64-i386-64bit\r\n- Python version: 3.7.11\r\n- PyArrow version: 4.0.1\r\n\nHi @bwang482,\r\n\r\nI'm sorry but I'm not able to reproduce your bug.\r\n\r\nPlease note that in our current master branch, we made a commit (d03223d4d64b89e76b48b00602aba5aa2f817f1e) that simultaneously modified:\r\n- test_dataset_common.py: https:\/\/github.com\/huggingface\/datasets\/commit\/d03223d4d64b89e76b48b00602aba5aa2f817f1e#diff-a1bc225bd9a5bade373d1f140e24d09cbbdc97971c2f73bb627daaa803ada002L289 that introduces the usage of `datasets.config.PYARROW_VERSION.major`\r\n- but also changed config.py: https:\/\/github.com\/huggingface\/datasets\/commit\/d03223d4d64b89e76b48b00602aba5aa2f817f1e#diff-e021fcfc41811fb970fab889b8d245e68382bca8208e63eaafc9a396a336f8f2L40, so that `datasets.config.PYARROW_VERSION.major` exists\r\n","embeddings":[-0.3742081225,0.1478579193,0.0884070471,0.0458860211,0.2804539502,0.1281492561,0.1531104892,0.3776514232,-0.2427559048,0.2011216879,0.3052013814,0.4177031815,-0.1865011901,0.1149607599,-0.1479020417,0.0099561084,0.0530921482,0.2520797253,0.1291816086,0.0060467068,-0.1062645391,0.1722441018,-0.1942494661,0.2815794349,-0.2942166626,0.1177279726,-0.0419812873,-0.0479392149,-0.1290827245,-0.5930629969,0.3376209438,-0.1589786112,0.2437154949,0.3399909437,-0.0001267467,0.1114126891,0.4063085616,-0.0081883511,-0.2549678385,-0.5021974444,-0.1217937693,-0.0536484718,0.3664952815,-0.0817655995,0.102711834,-0.7000570297,0.013414585,0.049935773,-0.1659477353,0.329718858,0.1360016465,0.0637335405,0.2616915703,-0.097520113,0.2251864374,0.0621270798,-0.0079679107,0.4649414122,0.0723097548,-0.1101893187,0.2356003225,-0.0848251805,0.0687128976,-0.0513685308,0.0992346108,0.1825542748,0.2629990876,-0.1627095938,0.1989027411,0.0318586826,0.5882601738,-0.626342833,-0.4298146665,-0.0176614802,0.220206961,-0.2607834935,0.2262852788,0.2429365963,-0.10094136,0.1628725231,-0.0570024997,-0.1206212416,-0.063535206,-0.0164811891,-0.4222541451,0.3365793526,0.1881286055,0.140308246,-0.1132394746,-0.009329834,0.0025072652,-0.1097331718,0.10199292,-0.078015849,-0.2746736109,-0.0144315083,-0.0775343105,0.1633089334,0.1757595986,0.208408609,0.0030351472,-0.2792110145,0.2704052031,0.0860595629,0.1035962552,0.1269080788,-0.0204388909,0.1562927812,0.1005842909,-0.1070765704,0.0690039098,-0.0031423518,-0.0581314154,-0.0568243526,0.3815536797,0.0283413101,0.742669642,-0.2355119437,-0.2066710442,0.1739302725,-0.3724194169,-0.179325074,-0.2660050988,0.2187235057,0.0305577051,0.3607175052,-0.1849215776,0.3320734203,-0.0956049785,-0.2356070876,-0.113595061,0.1457725167,0.0022741908,-0.1549615115,0.0268715788,-0.3101926744,-0.0878965631,0.3905238211,0.0014736976,0.1260318905,-0.1415989846,-0.0541735664,-0.1536239386,0.4754760563,-0.2805734873,0.2540762722,0.1305656582,-0.4681425095,-0.3429700136,0.2116095126,-0.2033770382,-0.4039486051,-0.4072811902,0.1281087101,-0.1272283196,-0.1809758395,-0.1497177035,-0.0052619977,0.2306341529,-0.1509694159,-0.0146371135,-0.6668522954,0.1032236964,-0.319904536,0.2467015982,0.1543766707,-0.6335021853,0.0087504741,0.1704790741,-0.1900213808,0.165815562,-0.1197979152,0.005515072,-0.1398759037,0.0290386174,0.0476634167,0.3468930423,-0.1955857724,-0.3685946465,0.0728684291,0.058083415,-0.0002380217,0.2594134212,-0.3139027059,0.0474226773,0.1808189899,-0.1679071486,0.0811104253,0.001909597,0.0160693731,-0.1740115881,-0.0421568789,0.3828534484,0.2783960104,0.1499212235,-0.1601757854,0.0293607879,-0.2005846798,0.1286726147,-0.0868610367,0.0851052552,0.0264915284,0.4046365619,-0.1430395395,0.033946842,-0.2175856382,-0.3197008371,0.1867123246,0.0698746145,0.0265328661,-0.3560784161,-0.1848592609,-0.1795900613,0.1088649258,-0.1084898785,-0.0283278916,0.0058021918,0.3832255006,0.0711459517,0.1115474254,-0.2974824309,0.2475375384,0.0242408346,0.1781353056,-0.0212112162,0.4727578163,-0.4082576334,-0.3153122663,0.0902254656,0.0457065813,0.102861464,0.0391666442,-0.0002053427,0.2879419625,-0.0028909601,0.0584653802,-0.0276767816,0.2250142992,0.0138650322,-0.1725012511,-0.192661047,0.2565892935,0.0011452289,0.4009828568,0.1184246242,0.3715967536,0.5518648624,0.3002988398,-0.0234602094,0.09365049,-0.1508531719,-0.0168528631,0.1085570678,-0.0839205682,0.0393525958,-0.1069295108,0.2100666016,-0.0077000619,-0.5035149455,0.0840201378,0.3833167255,-0.0595607199,0.1452025324,0.1436303854,-0.2609054446,0.0484301373,0.0339332297,0.1345786303,0.2741984427,0.1518546194,-0.1246563643,0.1309271455,-0.3188162744,0.164799884,0.1828395575,0.1089477018,0.1759979427,0.1831200421,0.2829288542,0.086618349,-0.0567072183,-0.3240150213,0.1387683749,0.3523797393,-0.4344475269,0.2530074716,-0.3444136083,0.250282228,-0.1669087112,-0.1862104386,-0.4147616029,-0.2429396659,-0.0304380916,0.0963686109,0.1005030423,0.2397005558,-0.3122081459,-0.1296312362,0.0462817326,-0.3988128304,-0.2186687887,-0.1758570671,-0.4486293495,-0.019090917,0.2013365775,-0.3951259255,0.039651487,-0.2199624628,-0.2137857676,-0.3060598373,-0.1851968169,0.0833885372,-0.2393283546,0.2992102802,0.2597303987,-0.038242083,-0.0473477803,-0.0988906175,0.3235451281,-0.3321583569,-0.2847580612,0.1006459966,-0.0942858085,0.0356610715,-0.0603935421,-0.4970048368,-0.3575113714,-0.1462482959,0.0124236261,0.041099906,0.1555687338,-0.0624720603,0.2309518009,-0.1249248758,-0.1489116102,-0.0105736004,-0.0276867412,0.0039672754,0.2373251915,0.0062804506,-0.2969486415,-0.0511556789,0.0064248098,0.0896267369,0.0367034785,-0.3144406974,-0.1439506412,0.0547291487,0.5389251113,-0.3207369447,0.0192000363,0.2970771492,0.2694778144,-0.0367961302,0.0630886257,-0.1787241399,-0.1892798841,0.1446789801,0.1415685862,0.1744132191,0.2410916984,-0.0849480182,0.5790010095,-0.0703439489,-0.298630327,0.2510004938,-0.29209131,0.3411943913,0.0630541816,-0.1798293442,-0.0886980519,-0.0483298227,0.01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{"html_url":"https:\/\/github.com\/huggingface\/datasets\/issues\/2871","title":"datasets.config.PYARROW_VERSION has no attribute 'major'","comments":"Sorted. Thanks!","body":"In the test_dataset_common.py script, line 288-289\r\n\r\n```\r\nif datasets.config.PYARROW_VERSION.major < 3:\r\n packaged_datasets = [pd for pd in packaged_datasets if pd[\"dataset_name\"] != \"parquet\"]\r\n```\r\n\r\nwhich throws the error below. `datasets.config.PYARROW_VERSION` itself return the string '4.0.1'. I have tested this on both datasets.__version_=='1.11.0' and '1.9.0'. I am using Mac OS.\r\n\r\n```\r\nimport datasets\r\ndatasets.config.PYARROW_VERSION.major\r\n---------------------------------------------------------------------------\r\nAttributeError Traceback (most recent call last)\r\n\/var\/folders\/1f\/0wqmlgp90qjd5mpj53fnjq440000gn\/T\/ipykernel_73361\/2547517336.py in \r\n 1 import datasets\r\n----> 2 datasets.config.PYARROW_VERSION.major\r\n\r\nAttributeError: 'str' object has no attribute 'major'\r\n```\r\n\r\n## Environment info\r\n- `datasets` version: 1.11.0\r\n- Platform: Darwin-20.6.0-x86_64-i386-64bit\r\n- Python version: 3.7.11\r\n- PyArrow version: 4.0.1\r\n","comment_length":2,"text":"datasets.config.PYARROW_VERSION has no attribute 'major'\nIn the test_dataset_common.py script, line 288-289\r\n\r\n```\r\nif datasets.config.PYARROW_VERSION.major < 3:\r\n packaged_datasets = [pd for pd in packaged_datasets if pd[\"dataset_name\"] != \"parquet\"]\r\n```\r\n\r\nwhich throws the error below. `datasets.config.PYARROW_VERSION` itself return the string '4.0.1'. I have tested this on both datasets.__version_=='1.11.0' and '1.9.0'. I am using Mac OS.\r\n\r\n```\r\nimport datasets\r\ndatasets.config.PYARROW_VERSION.major\r\n---------------------------------------------------------------------------\r\nAttributeError Traceback (most recent call last)\r\n\/var\/folders\/1f\/0wqmlgp90qjd5mpj53fnjq440000gn\/T\/ipykernel_73361\/2547517336.py in \r\n 1 import datasets\r\n----> 2 datasets.config.PYARROW_VERSION.major\r\n\r\nAttributeError: 'str' object has no attribute 'major'\r\n```\r\n\r\n## Environment info\r\n- `datasets` version: 1.11.0\r\n- Platform: Darwin-20.6.0-x86_64-i386-64bit\r\n- Python version: 3.7.11\r\n- PyArrow version: 4.0.1\r\n\nSorted. Thanks!","embeddings":[-0.3823499978,0.0788534731,0.0835613832,0.0859657899,0.254578352,0.1588205546,0.1258136332,0.378461957,-0.1942313612,0.1517066956,0.3442723751,0.3460409343,-0.2396473587,0.1160570979,-0.1717098057,-0.0130585758,0.0182114933,0.2685298622,0.1562249959,-0.0352682173,-0.0831688866,0.1723991781,-0.221207574,0.2769206166,-0.290784806,0.1055777818,-0.034875419,-0.0441389605,-0.1249132976,-0.5678355694,0.3373684287,-0.1683481783,0.259375453,0.3960514963,-0.0001278901,0.0832418501,0.355887711,-0.0235939845,-0.2130245715,-0.4989036024,-0.1226171777,-0.0021165852,0.3659736514,-0.0908794627,0.0975263193,-0.645819068,0.0478101447,0.0595673434,-0.1573934406,0.3680754006,0.1251091063,0.040394716,0.2063285261,-0.0739030614,0.2163523734,0.092064105,-0.0217345729,0.534501493,0.1071522757,-0.1533436328,0.2649391592,-0.1108957157,0.0430092327,-0.0005163433,0.0668540001,0.1680730581,0.2481788546,-0.173890233,0.2404453605,0.0941150188,0.6074262857,-0.5957273245,-0.4052781761,0.0457946397,0.190304175,-0.2164417058,0.2071692944,0.2712188065,-0.1031831801,0.1470091492,-0.0970233083,-0.1404293329,-0.0962809622,-0.0484454148,-0.4191841185,0.3295250833,0.1847844124,0.1627088934,-0.0797763839,-0.0509254895,0.0048299716,-0.1046580449,0.1460140944,-0.0359727815,-0.2669477761,-0.0234076288,-0.0792864859,0.217488572,0.217696771,0.1644843668,0.0548282899,-0.2486527711,0.2624913454,0.0772697702,0.0593933463,0.1027095169,0.0099531254,0.1515246481,0.0868951976,-0.0923113823,0.0064102691,0.0057760188,-0.0834435672,-0.0731900558,0.4025044441,-0.0256594121,0.7480243444,-0.2999703586,-0.2222675681,0.1814114749,-0.3430295587,-0.2135788947,-0.2431992441,0.2692058682,0.0672401413,0.310472101,-0.2242441922,0.3650549948,-0.1089137867,-0.199377805,-0.0976025686,0.1639591455,0.0397986546,-0.1415777802,0.0776998177,-0.2863925397,-0.0622021332,0.3482494056,-0.0095234104,0.1233666539,-0.0444290265,-0.0483978391,-0.1564430743,0.4665051401,-0.2961569726,0.2662153244,0.1866068244,-0.4694723487,-0.3469584286,0.228558898,-0.2082118392,-0.4199297726,-0.3010490537,0.1158056408,-0.1309493929,-0.2246068269,-0.1018703878,-0.0314013213,0.1599574685,-0.1390951127,0.0193729699,-0.6764339805,0.1813001037,-0.3093072474,0.2377792299,0.1493155062,-0.64209795,0.0522496849,0.1860803813,-0.1793422401,0.1692923605,-0.1044029519,0.0251485948,-0.1446892172,0.074836567,0.0642969012,0.3613587618,-0.2439746708,-0.3121292293,0.0909591764,0.1042780653,0.0162541959,0.2491032332,-0.3086020947,-0.0181004889,0.1892558783,-0.1845408976,0.0676994994,0.013640088,0.0026977183,-0.1708292961,0.0054476419,0.438311249,0.2977380157,0.1350409687,-0.2180688083,-0.0289862547,-0.2037833929,0.1285439283,-0.0818165392,0.0136220716,-0.0355041698,0.407566458,-0.211303249,-0.0053179162,-0.21974428,-0.2898447216,0.2070418745,0.0864594579,0.017564917,-0.310914129,-0.1828339547,-0.2235690951,0.092757836,-0.1389970034,-0.0187330078,-0.008984318,0.3708506823,0.0991881639,0.0983951762,-0.3148404062,0.1957128495,0.0238641016,0.1941994578,0.0052944184,0.4397611022,-0.4184099436,-0.3409918547,0.0828048363,0.0324428193,0.1147675738,-0.0007691359,0.0294004232,0.3283750117,0.0075974376,0.0259973463,0.0376454331,0.2178497612,-0.0152834198,-0.2490369678,-0.1754437536,0.2634848952,-0.0124529032,0.3771674037,0.0714703649,0.367174536,0.5579252839,0.311139673,-0.0011530713,0.081929028,-0.1928504854,0.0089487033,0.1333372742,-0.0902685896,0.0926108137,-0.1520457119,0.1808014959,-0.0404996648,-0.5255686641,0.0326813981,0.378573209,-0.0664351285,0.1505247355,0.1364178061,-0.2508790195,0.0396844186,0.0591744334,0.0680330917,0.2792570293,0.1244287118,-0.1432555616,0.1382844001,-0.3376169503,0.1645191908,0.1703767031,0.0325545408,0.1882993728,0.1368130594,0.2204149663,0.053874325,-0.0650231689,-0.3855646849,0.1114446223,0.4056575298,-0.4613749981,0.2288228422,-0.2899155617,0.3022912443,-0.0999656469,-0.1894143373,-0.4212338924,-0.2496228665,-0.0001924169,0.090336822,0.1711301655,0.1835670471,-0.3250343204,-0.1124665737,0.0609607361,-0.4758637249,-0.2720769644,-0.1949386746,-0.4537057579,-0.0113761248,0.2226081789,-0.3075243831,0.0475775488,-0.2794205546,-0.222134456,-0.2541949451,-0.156630829,0.0200997163,-0.2725733817,0.3184693456,0.2527645528,0.0669448599,-0.0692736134,-0.0613622926,0.3347893357,-0.3718841076,-0.2399427444,0.1490340978,-0.0621652342,-0.0054474128,-0.0345259421,-0.5232025981,-0.3153493404,-0.1216146052,0.0009835535,0.0737264007,0.1543922126,-0.0844632015,0.2646680176,-0.0991962701,-0.1470929235,0.0729259551,0.0268436,-0.0291636419,0.2536785007,0.0024928947,-0.2990637422,-0.015819408,0.006221428,0.1566198468,0.0125369066,-0.2939115465,-0.1314835399,0.0421486981,0.5094447136,-0.2864129841,0.0493873321,0.3148921132,0.2830879986,-0.0151688168,0.0654160902,-0.1438310146,-0.1625919193,0.1416301429,0.1366788298,0.1592234224,0.1884554476,-0.0858525932,0.6183635592,-0.066043742,-0.27083534,0.2227285802,-0.349478066,0.3271769881,0.0535552427,-0.1810140163,-0.0944271237,-0.0012391821,0.0283627696,0.2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{"html_url":"https:\/\/github.com\/huggingface\/datasets\/issues\/2871","title":"datasets.config.PYARROW_VERSION has no attribute 'major'","comments":"Reopening this. Although the `test_dataset_common.py` script works fine now.\r\n\r\nHas this got something to do with my pull request not passing `ci\/circleci: run_dataset_script_tests_pyarrow` tests?\r\n\r\nhttps:\/\/github.com\/huggingface\/datasets\/pull\/2873","body":"In the test_dataset_common.py script, line 288-289\r\n\r\n```\r\nif datasets.config.PYARROW_VERSION.major < 3:\r\n packaged_datasets = [pd for pd in packaged_datasets if pd[\"dataset_name\"] != \"parquet\"]\r\n```\r\n\r\nwhich throws the error below. `datasets.config.PYARROW_VERSION` itself return the string '4.0.1'. I have tested this on both datasets.__version_=='1.11.0' and '1.9.0'. I am using Mac OS.\r\n\r\n```\r\nimport datasets\r\ndatasets.config.PYARROW_VERSION.major\r\n---------------------------------------------------------------------------\r\nAttributeError Traceback (most recent call last)\r\n\/var\/folders\/1f\/0wqmlgp90qjd5mpj53fnjq440000gn\/T\/ipykernel_73361\/2547517336.py in \r\n 1 import datasets\r\n----> 2 datasets.config.PYARROW_VERSION.major\r\n\r\nAttributeError: 'str' object has no attribute 'major'\r\n```\r\n\r\n## Environment info\r\n- `datasets` version: 1.11.0\r\n- Platform: Darwin-20.6.0-x86_64-i386-64bit\r\n- Python version: 3.7.11\r\n- PyArrow version: 4.0.1\r\n","comment_length":25,"text":"datasets.config.PYARROW_VERSION has no attribute 'major'\nIn the test_dataset_common.py script, line 288-289\r\n\r\n```\r\nif datasets.config.PYARROW_VERSION.major < 3:\r\n packaged_datasets = [pd for pd in packaged_datasets if pd[\"dataset_name\"] != \"parquet\"]\r\n```\r\n\r\nwhich throws the error below. `datasets.config.PYARROW_VERSION` itself return the string '4.0.1'. I have tested this on both datasets.__version_=='1.11.0' and '1.9.0'. I am using Mac OS.\r\n\r\n```\r\nimport datasets\r\ndatasets.config.PYARROW_VERSION.major\r\n---------------------------------------------------------------------------\r\nAttributeError Traceback (most recent call last)\r\n\/var\/folders\/1f\/0wqmlgp90qjd5mpj53fnjq440000gn\/T\/ipykernel_73361\/2547517336.py in \r\n 1 import datasets\r\n----> 2 datasets.config.PYARROW_VERSION.major\r\n\r\nAttributeError: 'str' object has no attribute 'major'\r\n```\r\n\r\n## Environment info\r\n- `datasets` version: 1.11.0\r\n- Platform: Darwin-20.6.0-x86_64-i386-64bit\r\n- Python version: 3.7.11\r\n- PyArrow version: 4.0.1\r\n\nReopening this. Although the `test_dataset_common.py` script works fine now.\r\n\r\nHas this got something to do with my pull request not passing `ci\/circleci: run_dataset_script_tests_pyarrow` tests?\r\n\r\nhttps:\/\/github.com\/huggingface\/datasets\/pull\/2873","embeddings":[-0.3413378894,0.2006237954,0.049372945,0.0571891814,0.1107941419,0.0854446366,0.2353403866,0.2816726863,-0.1760852039,0.1386214346,0.438138634,0.3304321766,-0.1294845939,0.297735095,-0.1947026998,0.0704638883,0.0133854281,0.1427540034,0.3433145285,0.0562438518,-0.1254506111,0.239258036,-0.1946723908,0.1283952147,-0.1329981238,0.0372122005,-0.1708889008,0.0408576243,-0.1387818605,-0.4680930376,0.4385558963,0.0902260914,0.1819554418,0.6217026114,-0.0001299516,0.1380508989,0.4102708101,-0.0885429606,-0.2731285393,-0.5470474362,0.0774918571,-0.0126978597,0.3988084793,-0.1040790603,0.0965566337,-0.3847824633,-0.0448813178,0.1092322022,-0.067902863,0.3774314225,0.0892633647,0.2561304271,0.0892846957,0.0130179506,0.2664367259,0.3202229738,-0.1790551394,0.4346646965,0.163472265,-0.1368527114,0.18223162,-0.0818933249,-0.0010008782,0.0381129086,0.058766406,0.0650137067,-0.0054221773,-0.2756882608,0.2095608413,-0.0046765376,0.4738269448,-0.5655617118,-0.4999672771,0.0325702988,0.1310693324,-0.2759967446,0.1989559233,0.1092876121,-0.2475142032,0.1120673195,-0.1768936068,-0.1210495085,-0.0797204599,-0.0597053356,-0.452073127,0.1955433339,0.1415925175,0.1933697611,0.0005672796,-0.0080453893,0.0825758576,-0.1077340245,0.1832358539,-0.0202006698,-0.2383793592,-0.0761251375,0.0723483413,0.3277744949,0.2713033557,0.4195830226,0.0403551459,-0.1306673586,0.174266085,0.0946188346,0.0676531792,0.2015321553,-0.0325329341,0.1100755706,0.26883623,0.0022651241,0.0251369309,-0.0340531245,-0.0820883214,-0.118339479,0.2201674134,0.0307946801,0.8329048157,-0.3100616932,-0.264413327,0.0647196248,-0.4131411612,-0.1456345022,-0.1949632913,0.2120336592,0.1063020974,0.3991495371,-0.054112941,0.3935264349,-0.0928063095,-0.0335153863,-0.1254985034,0.0960216671,0.0741094276,-0.0745831132,0.2243506163,-0.3418166041,-0.0214521512,0.2785852551,0.2317495495,0.1688358784,0.0847869664,0.1707445085,-0.0158238746,0.5882755518,-0.1170846,0.3030574322,0.1808865517,-0.4662673175,-0.3240087628,0.2942477167,-0.3417190313,-0.3119837642,-0.1544470787,0.0395626612,-0.3309824169,-0.2279408276,-0.2230107188,-0.199565202,0.230595544,-0.09554369,0.0377825759,-0.6431552768,0.1101010367,-0.2451968342,0.2660878897,0.2522025704,-0.4532644153,0.0551288687,-0.0058161258,0.0077627655,0.1686531156,0.108184278,-0.0699362084,-0.1368115842,-0.0259974599,0.0800719559,0.4520925283,-0.4171760976,-0.3087326586,0.2504969537,-0.0502103381,0.0525274239,0.2544584572,-0.4099239409,0.0670625865,0.1091564149,-0.1023410782,-0.0559723116,-0.013213655,0.1008469164,-0.1170869917,-0.0518476814,0.4205104113,0.2592461705,0.0945970491,-0.1817267984,-0.0615676045,-0.3520296216,0.2080374062,-0.058173541,-0.00003425,-0.0472278409,0.4154104888,-0.3017407358,0.0376632102,-0.0054553566,-0.2810015082,0.2353983223,0.1397245526,0.1832517534,-0.4007819295,-0.2460963428,-0.2957423627,0.1120926812,-0.1815855503,-0.0250835977,-0.0734229907,0.3676602542,0.0943571031,0.0908512399,-0.2459866107,0.1231615022,-0.0549701601,0.121638678,-0.0157055464,0.36100775,-0.39767465,-0.3114204407,0.1486184299,0.0152983777,0.1552105695,-0.0953354239,0.0492075197,0.1977923065,0.018292509,-0.0615071394,0.0249138251,0.2371177673,0.0974489674,-0.1439389586,-0.1320542544,0.2738496065,-0.0527514368,0.2288922668,0.0089537343,0.4483506978,0.433740288,0.4135373831,-0.0052913735,0.133137241,-0.0789800286,0.0464649163,-0.1026584506,0.0235418081,0.0623203032,-0.1579686403,0.2385576516,-0.0083107622,-0.4083735943,0.1789080948,0.4755972028,0.0045280191,0.0512079373,0.1263181567,-0.0464047939,0.0135155227,0.1427060068,0.1196604073,0.3690716922,0.1315706223,-0.1269986928,0.1280188709,-0.3089667261,-0.000240603,0.0881366134,0.0855403543,-0.0177239105,0.1066989228,0.2934764922,0.0094751427,-0.1714618802,-0.5069288611,0.1386284977,0.3523923159,-0.5544535518,0.2957420945,-0.2599960566,0.2412185669,-0.0563951358,-0.099517554,-0.3512249887,-0.3400330842,-0.0426338091,0.1322477013,0.1855418086,0.1987326741,-0.3156895339,0.085814856,0.0278856121,-0.5474075079,-0.3988513947,-0.2250475734,-0.3432812393,-0.0082413657,0.335031271,-0.3440192044,0.1043744832,-0.3536006212,-0.0615599714,-0.3151578307,-0.3276818395,0.0971032903,-0.3790067136,0.3400279582,0.264110893,0.1390078962,-0.0936909392,-0.0775850788,0.2825623751,-0.3451506793,-0.3498209715,0.0558034815,-0.0393706858,-0.081658788,-0.2297518551,-0.4395770431,-0.1756117046,-0.1256152838,0.1797543317,0.1345760971,0.0894499645,0.1602129638,0.2617307603,-0.1119602174,0.0015942445,-0.080045335,0.0416262671,-0.1853195578,0.311385572,-0.1229889318,-0.2905154526,0.0768143982,0.0901876315,0.2567841113,0.0905639157,-0.3195661008,-0.2088564485,0.0071465177,0.5721033216,-0.1528172642,0.0589576438,0.3950414658,0.1801723242,0.033580374,0.0303174295,-0.2007779479,-0.0994261503,0.1576837599,0.0561942831,0.1956194937,0.2270442694,-0.0645815507,0.7777855992,-0.0912658498,-0.247752741,0.3013986051,-0.3482937813,0.4125493765,0.092459954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{"html_url":"https:\/\/github.com\/huggingface\/datasets\/issues\/2871","title":"datasets.config.PYARROW_VERSION has no attribute 'major'","comments":"Hi @bwang482,\r\n\r\nIf you click on `Details` (on the right of your non passing CI test names: `ci\/circleci: run_dataset_script_tests_pyarrow`), you can have more information about the non-passing tests.\r\n\r\nFor example, for [\"ci\/circleci: run_dataset_script_tests_pyarrow_1\" details](https:\/\/circleci.com\/gh\/huggingface\/datasets\/46324?utm_campaign=vcs-integration-link&utm_medium=referral&utm_source=github-build-link), you can see that the only non-passing test has to do with the dataset card (missing information in the `README.md` file): `test_changed_dataset_card`\r\n```\r\n=========================== short test summary info ============================\r\nFAILED tests\/test_dataset_cards.py::test_changed_dataset_card[swedish_medical_ner]\r\n= 1 failed, 3214 passed, 2874 skipped, 2 xfailed, 1 xpassed, 15 warnings in 175.59s (0:02:55) =\r\n```\r\n\r\nTherefore, your PR non-passing test has nothing to do with this issue.","body":"In the test_dataset_common.py script, line 288-289\r\n\r\n```\r\nif datasets.config.PYARROW_VERSION.major < 3:\r\n packaged_datasets = [pd for pd in packaged_datasets if pd[\"dataset_name\"] != \"parquet\"]\r\n```\r\n\r\nwhich throws the error below. `datasets.config.PYARROW_VERSION` itself return the string '4.0.1'. I have tested this on both datasets.__version_=='1.11.0' and '1.9.0'. I am using Mac OS.\r\n\r\n```\r\nimport datasets\r\ndatasets.config.PYARROW_VERSION.major\r\n---------------------------------------------------------------------------\r\nAttributeError Traceback (most recent call last)\r\n\/var\/folders\/1f\/0wqmlgp90qjd5mpj53fnjq440000gn\/T\/ipykernel_73361\/2547517336.py in \r\n 1 import datasets\r\n----> 2 datasets.config.PYARROW_VERSION.major\r\n\r\nAttributeError: 'str' object has no attribute 'major'\r\n```\r\n\r\n## Environment info\r\n- `datasets` version: 1.11.0\r\n- Platform: Darwin-20.6.0-x86_64-i386-64bit\r\n- Python version: 3.7.11\r\n- PyArrow version: 4.0.1\r\n","comment_length":95,"text":"datasets.config.PYARROW_VERSION has no attribute 'major'\nIn the test_dataset_common.py script, line 288-289\r\n\r\n```\r\nif datasets.config.PYARROW_VERSION.major < 3:\r\n packaged_datasets = [pd for pd in packaged_datasets if pd[\"dataset_name\"] != \"parquet\"]\r\n```\r\n\r\nwhich throws the error below. `datasets.config.PYARROW_VERSION` itself return the string '4.0.1'. I have tested this on both datasets.__version_=='1.11.0' and '1.9.0'. I am using Mac OS.\r\n\r\n```\r\nimport datasets\r\ndatasets.config.PYARROW_VERSION.major\r\n---------------------------------------------------------------------------\r\nAttributeError Traceback (most recent call last)\r\n\/var\/folders\/1f\/0wqmlgp90qjd5mpj53fnjq440000gn\/T\/ipykernel_73361\/2547517336.py in \r\n 1 import datasets\r\n----> 2 datasets.config.PYARROW_VERSION.major\r\n\r\nAttributeError: 'str' object has no attribute 'major'\r\n```\r\n\r\n## Environment info\r\n- `datasets` version: 1.11.0\r\n- Platform: Darwin-20.6.0-x86_64-i386-64bit\r\n- Python version: 3.7.11\r\n- PyArrow version: 4.0.1\r\n\nHi @bwang482,\r\n\r\nIf you click on `Details` (on the right of your non passing CI test names: `ci\/circleci: run_dataset_script_tests_pyarrow`), you can have more information about the non-passing tests.\r\n\r\nFor example, for [\"ci\/circleci: run_dataset_script_tests_pyarrow_1\" details](https:\/\/circleci.com\/gh\/huggingface\/datasets\/46324?utm_campaign=vcs-integration-link&utm_medium=referral&utm_source=github-build-link), you can see that the only non-passing test has to do with the dataset card (missing information in the `README.md` file): `test_changed_dataset_card`\r\n```\r\n=========================== short test summary info ============================\r\nFAILED tests\/test_dataset_cards.py::test_changed_dataset_card[swedish_medical_ner]\r\n= 1 failed, 3214 passed, 2874 skipped, 2 xfailed, 1 xpassed, 15 warnings in 175.59s (0:02:55) =\r\n```\r\n\r\nTherefore, your PR non-passing test has nothing to do with this issue.","embeddings":[-0.4502635896,0.1362894326,0.0727608278,-0.0077378419,0.210440293,0.1455685943,0.1347785145,0.3891866505,-0.2612731457,0.1966487318,0.2423591018,0.4409922659,-0.174534291,0.1955467612,-0.2465572804,0.0201375429,-0.0359608233,0.1991022974,0.1349838227,0.0418360345,-0.1486246139,0.1625906974,-0.2162213922,0.2647956908,-0.1537034959,-0.0562001877,-0.0889388993,-0.0443796068,-0.1304095089,-0.543780148,0.374103874,-0.0596004017,0.1746742874,0.5324847698,-0.0001234938,0.1340290904,0.3579029739,-0.099743627,-0.3201577663,-0.4985058904,0.0303555876,0.0250716098,0.3309290409,-0.1497163624,0.0457028411,-0.5905317068,-0.0560617782,0.011204035,-0.0264583249,0.4921305478,0.1557275653,0.1648605168,0.0755554661,-0.137879923,0.210254997,0.089855887,-0.0760812536,0.4706763327,0.1216390133,-0.0717137381,0.1600072682,-0.1023208573,-0.0217084177,-0.0166188478,-0.0197366606,0.0890951231,0.2701399326,-0.2540642619,0.2758241594,0.0213556439,0.6139847636,-0.6099302769,-0.402813077,0.0584969297,0.1510484666,-0.1977946013,0.2043727338,0.1740199775,-0.2345986068,0.1370747983,-0.1940619051,-0.0381158032,-0.0790201724,-0.1012421995,-0.3796566129,0.2510077655,0.1984764487,0.1712024063,-0.0156713389,-0.1160794795,0.0763115138,-0.1649231166,0.170402199,0.0014240057,-0.2107874453,-0.129683286,-0.0683172196,0.3605410159,0.2446825057,0.2437425703,0.0395206213,-0.2193709165,0.2899466455,0.1493432522,-0.0002065366,0.060830459,0.0282580554,0.1525740027,0.1156841591,-0.0743039697,0.0116414698,0.0125757102,-0.0356712267,-0.1347546428,0.2806890607,-0.0175586436,0.747718215,-0.386092782,-0.3294264078,0.1127082333,-0.3191033304,-0.1121121868,-0.1496730894,0.3698425591,0.0801952556,0.3064732254,-0.2025549859,0.3027328849,-0.148769632,-0.1783850491,-0.0953058898,0.0878898203,-0.0219249018,-0.1331822574,0.1687428951,-0.2979690433,-0.0465056896,0.3469100296,0.1025219187,0.1879935563,0.0652616769,-0.0026953628,-0.0544139817,0.4611659646,-0.2218509614,0.2516638935,0.1780836433,-0.5087434649,-0.3634715378,0.2743989229,-0.2164251655,-0.3534741104,-0.2432028204,0.151281327,-0.0920187533,-0.2586004138,-0.0489280932,-0.0613507144,0.1802086085,-0.1575635076,-0.0042943428,-0.6692387462,0.1291497648,-0.3172443509,0.2979747355,0.2355631441,-0.5403274298,0.0511988066,0.1171005592,-0.1538690478,0.2208563685,-0.0480547622,0.0285186488,-0.1684476435,0.008382176,0.0004034062,0.3619309366,-0.3157839477,-0.2808323205,0.2037823498,0.1373576522,-0.0054161474,0.258110404,-0.3559542,0.0676776916,0.1332530528,-0.2449017912,0.0707520768,-0.0634788871,0.0394995026,-0.1724347621,0.0111094378,0.4310903251,0.2942523658,0.0776945651,-0.1869189143,0.0541623756,-0.2536583543,0.1724235415,-0.153491497,0.0145558426,-0.095210731,0.4228506982,-0.3071483076,0.0091015995,-0.0820851251,-0.3720258772,0.214207381,0.1426869631,0.0427836291,-0.3331008852,-0.2545279264,-0.3418053687,0.0929807276,-0.1178567037,-0.0309027005,0.0290229879,0.3834606111,0.0836455449,0.0666082948,-0.1785346568,0.1259467155,0.0109810112,0.1843898743,-0.021914199,0.4052859545,-0.3848957419,-0.3287788928,0.1164525375,0.0147691462,0.2110700905,-0.0747004673,-0.0244301055,0.3569452167,0.0668140128,0.0324969031,0.0375453681,0.2812748551,0.0095077008,-0.2744845748,-0.2255578786,0.2845185697,-0.0563144833,0.335890919,-0.0477804728,0.4245945215,0.4690216482,0.2440936267,0.0434890911,0.1207157895,-0.0876624584,0.0307110269,0.0175956078,-0.1027696207,0.1239546984,-0.0975145176,0.2429556996,0.0073814266,-0.4534864426,0.0181120038,0.3326230049,-0.1036967561,0.079722181,0.1088910624,-0.1348168701,0.0713322908,0.1310128421,0.1286019683,0.2671003044,0.1545218527,-0.0664283633,0.1459353566,-0.3957768083,0.0493688993,0.1474568546,0.046951104,0.0843830109,0.1178493723,0.2030754983,0.0692258701,-0.1964526772,-0.4386540353,0.1690077484,0.3975126445,-0.4556099176,0.2525030673,-0.2593023777,0.2879627645,-0.1762989759,-0.1013129577,-0.2812756002,-0.2871729434,0.0298087746,0.1613865793,0.1280528903,0.2844814956,-0.3949874341,0.1308537275,0.0604371689,-0.5389617682,-0.3587738574,-0.1788505465,-0.4358465075,0.0564847142,0.2930062115,-0.2936736047,0.1870015562,-0.3385720551,-0.1041571423,-0.2780030966,-0.2546269596,0.1000234187,-0.2732539177,0.4224722683,0.2331346571,0.0653007999,-0.0509411618,-0.1394465566,0.2985119224,-0.5006918907,-0.2382101864,0.1075783893,-0.0609142967,-0.0281601492,-0.0992578343,-0.6026321054,-0.2700941563,-0.1767314076,-0.0738294199,0.1012948826,0.1171792299,0.0153186703,0.1794993281,-0.0414384827,-0.1064003855,0.040731471,0.0227799695,-0.0935673937,0.2638745904,-0.1412262917,-0.247062102,0.0030329677,0.0593990013,0.3075082004,0.0263136849,-0.3157670796,-0.1785085499,0.0870808735,0.5713326931,-0.213754639,-0.0088340752,0.2017015219,0.3253875971,-0.0461313352,0.0438692309,-0.1561382264,-0.2257906049,0.0831051841,0.1174168065,0.1173686087,0.2478049397,-0.0001471604,0.7000100017,0.0901702568,-0.2647122443,0.2456341684,-0.290442735,0.3646113276,0.0215920843,-0.2104302496,-0.0293033496,-0.0032851116,-0.08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{"html_url":"https:\/\/github.com\/huggingface\/datasets\/issues\/2869","title":"TypeError: 'NoneType' object is not callable","comments":"Hi, @Chenfei-Kang.\r\n\r\nI'm sorry, but I'm not able to reproduce your bug:\r\n```python\r\nfrom datasets import load_dataset\r\n\r\nds = load_dataset(\"glue\", 'cola')\r\nds\r\n```\r\n```\r\nDatasetDict({\r\n train: Dataset({\r\n features: ['sentence', 'label', 'idx'],\r\n num_rows: 8551\r\n })\r\n validation: Dataset({\r\n features: ['sentence', 'label', 'idx'],\r\n num_rows: 1043\r\n })\r\n test: Dataset({\r\n features: ['sentence', 'label', 'idx'],\r\n num_rows: 1063\r\n })\r\n})\r\n```\r\n\r\nCould you please give more details and environment info (platform, PyArrow version)?","body":"## Describe the bug\r\n\r\nTypeError: 'NoneType' object is not callable\r\n## Steps to reproduce the bug\r\n```python\r\nfrom datasets import load_dataset, load_metric\r\ndataset = datasets.load_dataset(\"glue\", 'cola')\r\n```\r\n\r\n## Expected results\r\nA clear and concise description of the expected results.\r\n\r\n## Actual results\r\nSpecify the actual results or traceback.\r\n\r\n## Environment info\r\n\r\n- `datasets` version: 1.11.0\r\n- Platform:\r\n- Python version: 3.7\r\n- PyArrow version:\r\n","comment_length":66,"text":"TypeError: 'NoneType' object is not callable\n## Describe the bug\r\n\r\nTypeError: 'NoneType' object is not callable\r\n## Steps to reproduce the bug\r\n```python\r\nfrom datasets import load_dataset, load_metric\r\ndataset = datasets.load_dataset(\"glue\", 'cola')\r\n```\r\n\r\n## Expected results\r\nA clear and concise description of the expected results.\r\n\r\n## Actual results\r\nSpecify the actual results or traceback.\r\n\r\n## Environment info\r\n\r\n- `datasets` version: 1.11.0\r\n- Platform:\r\n- Python version: 3.7\r\n- PyArrow version:\r\n\nHi, @Chenfei-Kang.\r\n\r\nI'm sorry, but I'm not able to reproduce your bug:\r\n```python\r\nfrom datasets import load_dataset\r\n\r\nds = load_dataset(\"glue\", 'cola')\r\nds\r\n```\r\n```\r\nDatasetDict({\r\n train: Dataset({\r\n features: ['sentence', 'label', 'idx'],\r\n num_rows: 8551\r\n })\r\n validation: Dataset({\r\n features: ['sentence', 'label', 'idx'],\r\n num_rows: 1043\r\n })\r\n test: Dataset({\r\n features: ['sentence', 'label', 'idx'],\r\n num_rows: 1063\r\n })\r\n})\r\n```\r\n\r\nCould you please give more details and environment info (platform, PyArrow version)?","embeddings":[-0.0594903305,-0.1199711189,0.0148641206,0.2438831329,0.4732634127,-0.0013416682,0.4414617419,0.1253976971,-0.0142953452,0.2675631642,-0.1799774915,0.4658890367,-0.1191558838,0.1054266095,0.0933514312,-0.1634460539,-0.1493485421,0.1789052486,-0.0950517431,0.0041558379,-0.2951413095,-0.1227453202,-0.2647020519,0.1601062417,-0.4545831382,-0.2473363578,-0.1237274557,-0.0289071202,-0.2947974801,-0.3072052002,0.3905930817,0.045531936,0.0802016407,0.573389709,-0.0001046333,0.085742943,0.5220624804,0.0795245767,-0.1019385606,-0.4897305667,-0.1364545226,-0.1668332815,0.2139172852,-0.393879205,-0.174881503,-0.0434178077,0.0156303495,0.1047198623,0.3135638833,0.4849516451,0.3089385629,0.4509330392,0.0464522541,-0.2673178315,0.2966395915,0.1515992731,-0.0532518923,0.3601016104,-0.0297630094,-0.1026376411,0.1846834719,0.1567065567,-0.2669480145,-0.0967591628,0.1903866827,0.0687865466,-0.0415391438,-0.290898174,-0.0751495287,0.4143870771,0.3387809098,-0.4593112171,-0.2788108885,0.1180645153,0.2122831494,-0.2495187223,0.2602464557,0.0856177285,-0.0128446911,0.1975728273,-0.0867947638,0.2796069086,-0.2793028951,0.3049078584,-0.164794296,0.1444952488,-0.0630867183,0.0345241651,0.2060508877,-0.1919636279,0.059791442,-0.1310273856,0.0356741138,0.1007023752,-0.243607074,-0.1369945854,0.1091515124,-0.0550249554,0.1305607557,-0.2993285656,0.1565279961,-0.0825746059,-0.0047007087,0.4082542658,0.183615461,0.3742778003,0.2353915423,0.146733135,0.2031041682,0.1250560284,-0.2914271057,0.0851671919,-0.1916304976,-0.0795564353,0.3350311518,0.141778037,0.4543803036,0.004213843,-0.4538616538,-0.0484698229,-0.4231696129,0.0795800835,0.2269208282,0.4578568935,-0.1490905881,0.111972034,0.0448685922,0.1539013535,-0.1707451195,-0.2831352949,-0.2869074643,0.4119275212,-0.3327414989,-0.2005894333,0.1725695282,0.0631759688,-0.0440938734,-0.0142761068,-0.124532178,0.0147927273,0.0312171597,-0.4864667356,-0.1069177911,0.1802367717,0.0366909429,-0.1434192806,0.2177432328,-0.4385626912,0.0038601621,0.2104463279,-0.1390264481,-0.2205248028,-0.0644018948,0.2921482325,-0.3297555149,-0.1617497504,-0.1220805645,0.1581521481,0.158894524,0.0152816763,0.0386043973,-0.0845265612,-0.0218608137,-0.3067058027,0.0268780459,0.3902682364,-0.3743338883,-0.2277735025,-0.1163482741,-0.0573342443,0.3309760094,-0.0552941114,-0.0139303515,0.2842520177,-0.0162361618,0.1270191222,0.6166725755,-0.3811469376,-0.3189837933,-0.0871518478,-0.0525565632,-0.0323508754,-0.0000088416,-0.1415092349,0.0952094793,0.1851929128,0.4305967093,0.1825711876,0.0108494731,-0.0068763741,-0.2708131671,-0.2111279964,0.1911941022,0.0895309374,0.1389753819,-0.0566433519,-0.0780088603,-0.24225308,0.0179314129,-0.1191949025,-0.1323612928,-0.0616726913,0.5806082487,-0.0895553157,-0.1220896021,-0.5331310034,-0.2384607494,0.1451272964,-0.0384060368,0.4400658906,-0.1647625715,-0.2132972479,-0.4087546468,0.0467493199,-0.0534322485,-0.0422442332,0.2887170017,0.0327202827,-0.162372008,0.1476718634,-0.093931675,0.0690900609,-0.1891066879,0.1402491182,0.1150690317,0.3248268962,-0.052656617,-0.4338489175,0.0160555448,0.2450485379,0.4278887212,0.0615985915,-0.0518304221,0.3103211224,0.0966809914,-0.2166442722,-0.332929194,-0.0375642255,0.1061588004,-0.3284879327,-0.1916384846,-0.02861006,0.1814015508,-0.0039089932,0.0155871715,0.5066564679,0.073046267,0.0751549006,-0.0834585428,0.1962543428,-0.0521300845,-0.1202241629,-0.1189233959,-0.0935665593,0.0853727981,-0.0516477935,0.1604620069,0.0839664191,-0.1486921012,-0.0939611942,0.6276284456,0.066133365,0.4066179395,-0.1278576404,-0.253084898,0.1469340771,0.0753541663,0.0935161412,0.3560036421,0.309184581,-0.1741830856,-0.012002632,-0.1699138284,0.0200339202,0.0936975554,-0.0300577246,0.3375006616,-0.0191667695,0.326565057,0.0025228276,-0.3129945993,0.1154984683,0.0383162536,0.1894647479,-0.3931485116,0.1806956828,-0.3155397177,-0.1539050788,0.0592735447,-0.1398235261,-0.0421994813,-0.2707495987,-0.085526906,0.1815255284,0.0589705072,0.264362663,0.1597475857,-0.0327072591,0.0847381577,-0.0655731633,-0.102235347,-0.2201068252,-0.1743910462,0.1428499222,0.1274092495,0.2078600228,0.2995347381,-0.1425538957,0.0480940603,0.0138055841,-0.3602250516,0.0857338384,-0.1215342283,0.4401930273,0.3425597847,0.0728836656,-0.2368693203,0.0173933227,0.3619660437,-0.3473314047,-0.108051084,0.3983776271,-0.1979978234,0.0502504855,-0.2161714584,-0.3248876333,-0.510535121,-0.318955183,0.1311067194,-0.0294710808,0.0872791484,0.3062438071,0.2281634808,0.450894624,-0.052571062,0.0395837203,-0.1739951223,-0.0077805598,0.3531797528,-0.0148417056,-0.3720351756,0.0079913763,-0.1651002616,0.1313911527,-0.0117581366,-0.1790904701,-0.5396494269,-0.1400610059,0.288399756,0.0229437444,-0.0957489237,0.3742546439,0.0539732315,-0.1510512084,-0.1974764913,-0.082263872,0.0317742862,0.3172571063,0.0219645612,-0.069488965,0.2908799648,0.0552209876,0.2811395824,-0.113323316,-0.2513748407,0.4198065102,-0.2915056348,0.2461425811,-0.0716580451,-0.4766027927,-0.0776230395,-0.05550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{"html_url":"https:\/\/github.com\/huggingface\/datasets\/issues\/2869","title":"TypeError: 'NoneType' object is not callable","comments":"> Hi, @Chenfei-Kang.\r\n> \r\n> I'm sorry, but I'm not able to reproduce your bug:\r\n> \r\n> ```python\r\n> from datasets import load_dataset\r\n> \r\n> ds = load_dataset(\"glue\", 'cola')\r\n> ds\r\n> ```\r\n> \r\n> ```\r\n> DatasetDict({\r\n> train: Dataset({\r\n> features: ['sentence', 'label', 'idx'],\r\n> num_rows: 8551\r\n> })\r\n> validation: Dataset({\r\n> features: ['sentence', 'label', 'idx'],\r\n> num_rows: 1043\r\n> })\r\n> test: Dataset({\r\n> features: ['sentence', 'label', 'idx'],\r\n> num_rows: 1063\r\n> })\r\n> })\r\n> ```\r\n> \r\n> Could you please give more details and environment info (platform, PyArrow version)?\r\n\r\nSorry to reply you so late.\r\nplatform: pycharm 2021 + anaconda with python 3.7\r\nPyArrow version: 5.0.0\r\nhuggingface-hub: 0.0.16\r\ndatasets: 1.9.0\r\n","body":"## Describe the bug\r\n\r\nTypeError: 'NoneType' object is not callable\r\n## Steps to reproduce the bug\r\n```python\r\nfrom datasets import load_dataset, load_metric\r\ndataset = datasets.load_dataset(\"glue\", 'cola')\r\n```\r\n\r\n## Expected results\r\nA clear and concise description of the expected results.\r\n\r\n## Actual results\r\nSpecify the actual results or traceback.\r\n\r\n## Environment info\r\n\r\n- `datasets` version: 1.11.0\r\n- Platform:\r\n- Python version: 3.7\r\n- PyArrow version:\r\n","comment_length":116,"text":"TypeError: 'NoneType' object is not callable\n## Describe the bug\r\n\r\nTypeError: 'NoneType' object is not callable\r\n## Steps to reproduce the bug\r\n```python\r\nfrom datasets import load_dataset, load_metric\r\ndataset = datasets.load_dataset(\"glue\", 'cola')\r\n```\r\n\r\n## Expected results\r\nA clear and concise description of the expected results.\r\n\r\n## Actual results\r\nSpecify the actual results or traceback.\r\n\r\n## Environment info\r\n\r\n- `datasets` version: 1.11.0\r\n- Platform:\r\n- Python version: 3.7\r\n- PyArrow version:\r\n\n> Hi, @Chenfei-Kang.\r\n> \r\n> I'm sorry, but I'm not able to reproduce your bug:\r\n> \r\n> ```python\r\n> from datasets import load_dataset\r\n> \r\n> ds = load_dataset(\"glue\", 'cola')\r\n> ds\r\n> ```\r\n> \r\n> ```\r\n> DatasetDict({\r\n> train: Dataset({\r\n> features: ['sentence', 'label', 'idx'],\r\n> num_rows: 8551\r\n> })\r\n> validation: Dataset({\r\n> features: ['sentence', 'label', 'idx'],\r\n> num_rows: 1043\r\n> })\r\n> test: Dataset({\r\n> features: ['sentence', 'label', 'idx'],\r\n> num_rows: 1063\r\n> })\r\n> })\r\n> ```\r\n> \r\n> Could you please give more details and environment info (platform, PyArrow version)?\r\n\r\nSorry to reply you so late.\r\nplatform: pycharm 2021 + anaconda with python 3.7\r\nPyArrow version: 5.0.0\r\nhuggingface-hub: 0.0.16\r\ndatasets: 1.9.0\r\n","embeddings":[-0.0170860495,-0.3240526319,0.062121436,0.2103672326,0.3840116858,-0.0155181745,0.415794313,0.1785911471,0.1879396886,0.3116176724,-0.2562711835,0.2617366314,-0.0900785625,0.1555979401,0.134646982,-0.1783233583,-0.1780708879,0.2362426817,-0.1429384202,-0.0610416606,-0.307266444,0.016021125,-0.2765933871,0.1563270539,-0.3710355163,-0.2232295871,-0.0727495775,-0.0337860063,-0.3516702652,-0.3736093938,0.3502697647,-0.0149404071,0.1077515855,0.3936695755,-0.000109273,0.039748542,0.5531897545,0.0916725695,-0.1215192825,-0.486445725,0.0033005883,-0.153592959,0.2640893161,-0.2971876264,-0.2491431981,-0.1678692102,-0.0046053282,0.1546724737,0.3977606893,0.5068286061,0.2735918462,0.5179857016,0.1557451636,-0.3057215512,0.2476199716,0.1295629144,-0.0830335468,0.3645498157,0.0868025422,0.0324823782,0.2476591766,0.22567074,-0.2615945935,-0.1020329148,0.2481998503,0.0714492425,0.0565817356,-0.3359338641,-0.0874417275,0.3458363712,0.2176503092,-0.4307984114,-0.2740110457,-0.0156725049,0.1589335203,-0.2778740823,0.2152642608,0.1117749512,-0.0614302717,0.2610106766,-0.0477596298,0.2916436493,-0.2724869251,0.2264740765,0.0332289934,0.0998286605,-0.0974352807,0.0864828005,0.3184731901,-0.1978602111,-0.0712477863,-0.0744847059,0.0765407234,0.1997876465,-0.2625720799,-0.1569988281,0.0412150547,-0.0527565517,0.2478982806,-0.0623121895,0.010698121,-0.0784372389,0.0866332129,0.3945228755,0.1457946301,0.3695580363,0.2746926844,0.0596430153,0.147126019,0.2683903873,-0.1179920584,0.0550732911,-0.1812906712,-0.0505757257,0.2737024724,0.1010781154,0.4157111049,-0.0123857539,-0.3835292757,-0.0340619497,-0.249388665,0.2141660452,0.2170223594,0.4983271658,-0.1098527387,-0.006581971,0.1920767128,0.1782709658,-0.1211596727,-0.1304862946,-0.2357181609,0.34223032,-0.3215116858,-0.0617322512,0.2265961319,0.0308971405,0.0039272024,-0.0047287471,0.0682608932,-0.0418036729,-0.0186945554,-0.3479027152,-0.0821475908,0.2535481453,0.032342352,-0.0685248151,0.2264001071,-0.410043478,-0.0981175676,0.1309780478,-0.0746355355,-0.1795981675,-0.2304216176,0.2343917638,-0.2560875714,-0.0213031564,-0.0934348702,0.0212540068,0.1447509378,0.1013658345,0.1272418797,-0.0190788247,0.0178890768,-0.2180711776,0.1380720735,0.3654916883,-0.100190863,-0.3205661774,0.0321522094,-0.0025296691,0.1171867996,-0.0173707716,0.0019335701,0.2421703041,-0.0305478908,0.1668857634,0.4985628724,-0.62414819,-0.3442799449,-0.2747382522,-0.0806370825,-0.0161460973,0.0393288732,-0.2286882102,0.1914647818,0.2106938213,0.4453007579,0.1527808011,0.1522087157,0.0167638846,-0.2598265111,-0.2841210067,-0.1492304802,0.1216640398,0.0572228469,-0.0611823201,-0.1246782616,-0.3315826058,0.0439961776,-0.1464243233,-0.0947787762,-0.0285352468,0.4802513719,-0.0267939679,-0.0334615521,-0.5103312731,-0.3110117316,0.1444107145,-0.0827431381,0.4963111877,-0.2935872972,-0.2401018441,-0.3797984123,0.0216788147,-0.0260307416,-0.1073646471,0.2608529925,-0.0292999409,-0.0793902054,0.2039854974,-0.148156628,0.3061811626,-0.0629678369,0.3361934423,-0.0899120644,0.2520775199,-0.0519085974,-0.4535894096,0.0178125389,0.3152451813,0.407485038,0.0184305087,0.0400744975,0.2059644312,0.120073542,-0.2516606152,-0.3879702389,-0.2059795707,0.0908953026,-0.2052687109,-0.1253317297,-0.1193971634,0.1875582188,0.043237146,0.0816024616,0.4845341146,0.0002889726,0.1447360814,-0.0010887757,0.2192268968,0.0464254729,-0.2223508209,-0.1943704933,-0.1116545573,0.1098374575,0.0302241202,0.2480703294,0.0179758705,-0.1454162598,0.0168016292,0.6618235111,0.0420250148,0.258257091,-0.1182069257,-0.3660911322,0.2513479888,0.1739427298,0.087606214,0.3652841747,0.2524144948,-0.2956422269,0.0845419914,-0.1635932028,0.0505567454,0.0976488069,-0.0679601654,0.3684989512,-0.0912532359,0.3075386584,0.0185329355,-0.3090053499,0.0744575188,-0.0793073252,0.1062876061,-0.3757323921,0.1150494143,-0.2569516301,-0.1478515416,-0.0088819563,-0.133373037,0.0266572647,-0.1974048913,-0.0598685481,0.3584897518,0.0893847048,0.282640487,0.2069196403,0.0192367453,-0.0590613931,-0.1030487418,-0.1551313996,-0.108617492,-0.11700131,0.0823388919,0.0588655174,0.1915499568,0.3331587911,-0.3143273294,0.0909607261,0.1370236278,-0.3026359379,0.0858223662,-0.1740436107,0.2888210118,0.2684489191,0.1589440256,-0.1759042591,-0.0624604672,0.4340486825,-0.3824962378,-0.192428574,0.2827731073,-0.1414361447,-0.0550831221,-0.2623379529,-0.2640984952,-0.5197219253,-0.2946316004,0.2550328076,0.0227000788,0.0717564151,0.3472696841,0.234048292,0.452318579,-0.3834219277,-0.0229388513,-0.1441199481,0.0412894003,0.2728778422,-0.0820257813,-0.4259680212,-0.0077680945,-0.0855097622,0.2539603114,-0.1286979169,-0.0568948649,-0.4479478002,-0.1266304255,0.3238252997,0.0086144321,-0.1531503648,0.6029988527,0.0156400762,-0.1078105569,-0.1583341658,-0.1767390072,0.1045932546,0.1382757425,0.0056557627,-0.0700706616,0.2453286648,-0.0265153348,0.351564467,-0.0049929833,-0.2051898241,0.3471206427,-0.304443121,0.3146957457,-0.1218410507,-0.5708717108,-0.0727783665,-0.02652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{"html_url":"https:\/\/github.com\/huggingface\/datasets\/issues\/2869","title":"TypeError: 'NoneType' object is not callable","comments":"- For the platform, we need to know the operating system of your machine. Could you please run the command `datasets-cli env` and copy-and-paste its output below?\r\n- In relation with the error, you just gave us the error type and message (`TypeError: 'NoneType' object is not callable`). Could you please copy-paste the complete stack trace, so that we know exactly which part of the code threw the error?","body":"## Describe the bug\r\n\r\nTypeError: 'NoneType' object is not callable\r\n## Steps to reproduce the bug\r\n```python\r\nfrom datasets import load_dataset, load_metric\r\ndataset = datasets.load_dataset(\"glue\", 'cola')\r\n```\r\n\r\n## Expected results\r\nA clear and concise description of the expected results.\r\n\r\n## Actual results\r\nSpecify the actual results or traceback.\r\n\r\n## Environment info\r\n\r\n- `datasets` version: 1.11.0\r\n- Platform:\r\n- Python version: 3.7\r\n- PyArrow version:\r\n","comment_length":69,"text":"TypeError: 'NoneType' object is not callable\n## Describe the bug\r\n\r\nTypeError: 'NoneType' object is not callable\r\n## Steps to reproduce the bug\r\n```python\r\nfrom datasets import load_dataset, load_metric\r\ndataset = datasets.load_dataset(\"glue\", 'cola')\r\n```\r\n\r\n## Expected results\r\nA clear and concise description of the expected results.\r\n\r\n## Actual results\r\nSpecify the actual results or traceback.\r\n\r\n## Environment info\r\n\r\n- `datasets` version: 1.11.0\r\n- Platform:\r\n- Python version: 3.7\r\n- PyArrow version:\r\n\n- For the platform, we need to know the operating system of your machine. Could you please run the command `datasets-cli env` and copy-and-paste its output below?\r\n- In relation with the error, you just gave us the error type and message (`TypeError: 'NoneType' object is not callable`). Could you please copy-paste the complete stack trace, so that we know exactly which part of the code threw the error?","embeddings":[-0.2277715802,-0.3193501234,0.0008596373,0.3918522596,0.3677237034,0.0557456017,0.3176339269,0.1545283645,0.1733444631,0.2960280478,-0.1426855326,0.4552958608,-0.0776570067,0.2298437357,0.0493332893,-0.1651720107,-0.2105034888,0.2611335516,-0.1191705689,0.0262278002,-0.5092200041,-0.0741478875,-0.1640852094,0.1349925548,-0.237754494,-0.2719812989,-0.1240942478,0.0408258066,-0.1921427697,-0.2838498354,0.3645710647,-0.0801881999,0.2122620046,0.5988925099,-0.0001008489,-0.1514540613,0.435205698,0.0475999489,-0.1254921108,-0.3523042202,-0.2946639061,-0.3847207725,0.18985717,-0.3827019334,-0.0743031427,-0.0082017854,0.0286093634,-0.2426373065,0.3481251895,0.3484524488,0.3506994545,0.415902108,0.0833576843,-0.2823812366,0.1884763539,0.0775724575,-0.1048533171,0.2966803014,0.0623726882,-0.0164799206,0.3145338893,0.1602044255,-0.2830132842,-0.0542992242,0.2551215291,0.0645872205,-0.0053246845,-0.3559243679,0.0310364459,0.3066703379,0.4195721149,-0.3946249187,-0.1872214228,0.0607764684,0.1299087554,-0.2234973758,0.1539727449,-0.0115599288,-0.0064773369,0.0658862591,-0.0040367073,0.1557925344,-0.236804232,0.2183835655,-0.1664240062,0.0901961327,-0.2121003419,0.107063584,0.0670970306,-0.1992394775,0.0452290624,-0.1839859337,0.0351355337,0.1096044704,-0.221126616,-0.1452792883,0.1299011409,0.1111679673,0.1270107031,-0.2455778271,0.1168725938,-0.0483121127,-0.0236970503,0.4071713388,0.239894405,0.2160654962,0.197072491,0.1496332735,0.2160288244,0.0441346094,-0.1853791922,-0.0519746169,-0.1255763024,-0.1014563665,0.2814617455,0.1962061971,0.5696896911,-0.0633770376,-0.4451116025,-0.0369891338,-0.207089901,0.0923674777,0.2541407347,0.3783914447,-0.1840863526,0.1531827301,0.2149179727,0.0695832223,-0.1426405609,-0.2327828705,-0.2402686328,0.3336758912,-0.2726967335,-0.232448265,0.0732233152,0.0800087824,-0.1016519293,0.0515346751,-0.0466053039,0.0736266375,0.1052564383,-0.382973969,-0.1715993732,0.1613502353,0.1342369616,-0.0109220734,0.3575491905,-0.413230598,-0.1455421895,0.1080859974,-0.3179209232,-0.0927535221,-0.0903914943,0.3212557733,-0.2362898439,-0.079209581,-0.1951911002,-0.0047342735,0.0772167519,-0.2021209151,0.0243879221,-0.1612926275,0.0099704852,-0.4124752581,0.0396714061,0.5434519649,-0.3702253997,-0.131931603,-0.3560011387,-0.0526704639,0.3650825024,-0.1617242545,0.0869184732,0.2049263865,-0.1619556099,-0.0783798695,0.5953560472,-0.3626013696,-0.1942619234,0.0556299053,-0.079251498,-0.0580402203,-0.067448847,-0.0140706375,0.0950176865,0.1139309034,0.4095413983,0.2211544514,-0.0633310452,-0.027870506,-0.1950395554,-0.1733900309,0.1649025828,0.0918372497,0.1436722875,0.1626258194,0.0630830303,-0.2037843466,0.0184938218,-0.0397915058,-0.09291742,-0.0652414113,0.6565243602,-0.0872236714,-0.0788739324,-0.5945641994,-0.3253455162,0.1684785038,-0.0677354112,0.2807234824,-0.1447329372,-0.1707682014,-0.3664011657,0.0677765012,-0.0498286709,0.0480884425,0.34265396,0.1271567047,-0.1998722106,0.0451182313,-0.1443472803,0.1779200137,-0.1498600394,0.1019958928,-0.0057357312,0.3032817245,-0.0105569726,-0.3550938368,0.0437213145,0.2375442684,0.5392077565,0.0301111341,-0.0714498162,0.2961714268,0.0839738399,-0.1378158778,-0.1490661353,-0.0828541517,0.1191909611,-0.2615092099,0.0761332735,0.113510862,0.341347903,-0.0806762725,0.1379154474,0.550291419,0.070542492,0.218250677,-0.0425794087,0.2359925807,-0.0458750986,-0.0404749922,-0.09132009,-0.1391232908,0.1310136467,0.0969333351,0.1783950478,-0.0640903935,-0.1166954562,0.0628789961,0.5675632358,0.0985855833,0.3846487105,-0.0710379705,-0.3002262712,0.1548450738,0.0890177488,0.2822793424,0.4458229542,0.3082370758,-0.1348168105,0.0028449628,-0.105473116,0.0909257606,0.069577001,-0.008273948,0.2112074643,-0.0183985941,0.1782516539,0.0799446777,-0.1778929234,-0.0452206172,-0.2235442847,0.2644327581,-0.3588179946,0.1916420162,-0.2675382495,-0.1693612486,0.0416815057,-0.0662412271,-0.1583892852,-0.343996793,-0.2322412729,0.0256404057,0.1133051515,0.1315740049,0.114424713,0.1559722424,0.0080657853,0.0855939835,-0.1656484157,-0.1026751921,-0.1289932728,0.1639403254,0.1211261749,0.2124070823,0.4851669073,-0.2281131297,0.2450820208,-0.0264441986,-0.227994293,0.0638851374,-0.0960038304,0.4924280643,0.4533854723,0.170710057,-0.0828904957,-0.0167101845,0.3197818995,-0.3656996191,0.0297314506,0.2110722065,-0.2212308943,0.1077203006,-0.1586056501,-0.1741414219,-0.5684420466,-0.4111053944,0.1035611331,0.1680119336,0.1657065153,0.1624201983,0.1247417033,0.5334134102,0.1375749111,0.1933974028,-0.195846498,-0.0487356074,0.2836108506,-0.122860238,-0.2586844563,0.1470841169,-0.1050434485,0.2065625638,0.0155344047,-0.1752419621,-0.5393431783,-0.1731456071,0.1546034515,-0.0829334036,0.0919005796,0.2602021098,0.1155401021,-0.1390545517,-0.2339776456,-0.1428654939,-0.1661490351,0.2557976246,0.0672933385,0.0401755236,0.3397756815,-0.178367734,0.1294477135,0.0065674935,-0.2346490473,0.3460306227,-0.2629454136,0.3489436209,-0.1248321906,-0.5024338961,-0.1444402039,0.0058216783,-0.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{"html_url":"https:\/\/github.com\/huggingface\/datasets\/issues\/2869","title":"TypeError: 'NoneType' object is not callable","comments":"> * For the platform, we need to know the operating system of your machine. Could you please run the command `datasets-cli env` and copy-and-paste its output below?\r\n> * In relation with the error, you just gave us the error type and message (`TypeError: 'NoneType' object is not callable`). Could you please copy-paste the complete stack trace, so that we know exactly which part of the code threw the error?\r\n\r\n1. For the platform, here are the output:\r\n - datasets` version: 1.11.0\r\n - Platform: Windows-10-10.0.19041-SP0\r\n - Python version: 3.7.10\r\n - PyArrow version: 5.0.0\r\n2. For the code and error\uff1a\r\n ```python\r\n from datasets import load_dataset, load_metric\r\n dataset = load_dataset(\"glue\", \"cola\")\r\n ```\r\n ```python\r\n Traceback (most recent call last):\r\n ....\r\n ....\r\n File \"my_file.py\", line 2, in \r\n dataset = load_dataset(\"glue\", \"cola\")\r\n File \"My environments\\lib\\site-packages\\datasets\\load.py\", line 830, in load_dataset\r\n **config_kwargs,\r\n File \"My environments\\lib\\site-packages\\datasets\\load.py\", line 710, in load_dataset_builder\r\n **config_kwargs,\r\n TypeError: 'NoneType' object is not callable\r\n ```\r\n Thank you!","body":"## Describe the bug\r\n\r\nTypeError: 'NoneType' object is not callable\r\n## Steps to reproduce the bug\r\n```python\r\nfrom datasets import load_dataset, load_metric\r\ndataset = datasets.load_dataset(\"glue\", 'cola')\r\n```\r\n\r\n## Expected results\r\nA clear and concise description of the expected results.\r\n\r\n## Actual results\r\nSpecify the actual results or traceback.\r\n\r\n## Environment info\r\n\r\n- `datasets` version: 1.11.0\r\n- Platform:\r\n- Python version: 3.7\r\n- PyArrow version:\r\n","comment_length":154,"text":"TypeError: 'NoneType' object is not callable\n## Describe the bug\r\n\r\nTypeError: 'NoneType' object is not callable\r\n## Steps to reproduce the bug\r\n```python\r\nfrom datasets import load_dataset, load_metric\r\ndataset = datasets.load_dataset(\"glue\", 'cola')\r\n```\r\n\r\n## Expected results\r\nA clear and concise description of the expected results.\r\n\r\n## Actual results\r\nSpecify the actual results or traceback.\r\n\r\n## Environment info\r\n\r\n- `datasets` version: 1.11.0\r\n- Platform:\r\n- Python version: 3.7\r\n- PyArrow version:\r\n\n> * For the platform, we need to know the operating system of your machine. Could you please run the command `datasets-cli env` and copy-and-paste its output below?\r\n> * In relation with the error, you just gave us the error type and message (`TypeError: 'NoneType' object is not callable`). Could you please copy-paste the complete stack trace, so that we know exactly which part of the code threw the error?\r\n\r\n1. For the platform, here are the output:\r\n - datasets` version: 1.11.0\r\n - Platform: Windows-10-10.0.19041-SP0\r\n - Python version: 3.7.10\r\n - PyArrow version: 5.0.0\r\n2. For the code and error\uff1a\r\n ```python\r\n from datasets import load_dataset, load_metric\r\n dataset = load_dataset(\"glue\", \"cola\")\r\n ```\r\n ```python\r\n Traceback (most recent call last):\r\n ....\r\n ....\r\n File \"my_file.py\", line 2, in \r\n dataset = load_dataset(\"glue\", \"cola\")\r\n File \"My environments\\lib\\site-packages\\datasets\\load.py\", line 830, in load_dataset\r\n **config_kwargs,\r\n File \"My environments\\lib\\site-packages\\datasets\\load.py\", line 710, in load_dataset_builder\r\n **config_kwargs,\r\n TypeError: 'NoneType' object is not callable\r\n ```\r\n Thank you!","embeddings":[-0.2333926857,-0.2121910155,0.0151841119,0.3540363908,0.431312561,0.0565467253,0.4275258183,0.1668837517,0.1619586647,0.2543876767,-0.1491201818,0.4838864207,-0.032627441,0.2008449435,0.1351143867,-0.1875769347,-0.1780486703,0.1836340725,-0.2075739056,0.0554907173,-0.530339241,-0.0694576576,-0.131100297,0.1142207906,-0.165304631,-0.2441832423,-0.1580201536,0.0987920165,-0.1795856655,-0.3501777351,0.4064702392,-0.1164285839,0.2196373641,0.562065959,-0.0001015193,-0.0459203348,0.4622377753,0.0741885006,-0.1826767772,-0.3177576065,-0.2952965498,-0.3818572462,0.1700716317,-0.3912402391,-0.0481698103,-0.0371986367,-0.0039750608,-0.2519207001,0.2980602384,0.3795799315,0.328802675,0.4538740516,0.0911629125,-0.2488764822,0.1904570758,0.0910028517,-0.0621268973,0.3023785055,0.0538949445,-0.0567670763,0.2113882601,0.1328090131,-0.3628790081,-0.0556369275,0.2719312906,0.0631388128,0.0336346067,-0.3873294294,0.0748671964,0.3000453711,0.4482415617,-0.3896304667,-0.2469754517,0.0235931426,0.1223488674,-0.1994509548,0.2123644501,0.0509505086,0.0008063291,0.0616286434,0.0196442436,0.1935778111,-0.1930177808,0.2497536093,-0.1554912627,0.1598448157,-0.1672741175,0.1182282194,0.0708542541,-0.2033922374,0.049795296,-0.2369977683,0.007765641,0.1136214361,-0.2622630298,-0.0578037463,0.1595869213,0.1328142434,0.0924718305,-0.2031244338,0.0840387568,-0.0385783762,0.0005321783,0.3734622598,0.2657110691,0.2719011307,0.2630656064,0.1053493395,0.3113079071,0.0968508199,-0.1035405621,0.0171599817,-0.1741403788,-0.1962881684,0.2919623256,0.27147156,0.5614536405,-0.0071394164,-0.4003361762,-0.112911351,-0.1762718856,0.0414780118,0.1914937347,0.3488394022,-0.1659963131,0.1930406094,0.2003636211,0.1248331815,-0.2139800191,-0.2602629662,-0.2342291027,0.277436316,-0.2562621832,-0.1992109269,0.1157746315,-0.0126312999,-0.0741725713,0.0112992479,-0.1771455258,0.1546337306,0.1403729916,-0.3297455609,-0.1945308298,0.1805401146,0.0513517223,0.0468542874,0.3396641314,-0.3767827153,-0.1449093074,0.0857277736,-0.2972788513,-0.1406729817,-0.1555437893,0.3137725294,-0.203810975,-0.0596022606,-0.2211033255,-0.0271769408,0.1290350109,-0.1592079401,0.0224947892,-0.1568120122,-0.0343936272,-0.4068643153,0.0919899419,0.6147862673,-0.4071787894,-0.1555482447,-0.3654867709,-0.1499285698,0.3206854463,-0.2192046493,0.0420476161,0.205342114,-0.1884394288,-0.0670418441,0.5587269068,-0.4109594524,-0.2658071816,0.071330905,-0.1287924349,-0.0868593082,0.0406506956,-0.0299981683,0.0722305849,0.1137080416,0.4100970328,0.275131911,0.0011142003,0.0079012448,-0.1864448488,-0.2357116938,0.1991765499,0.0649753883,0.1384467632,0.1023558527,0.1406214237,-0.1971790642,0.0246631335,-0.0487639643,-0.0327245519,-0.057789661,0.5558087826,-0.0468545221,-0.044843737,-0.5518350005,-0.4227520227,0.1897544414,-0.0799524039,0.3541664779,-0.1335036159,-0.1872806996,-0.3264653981,0.0583028831,-0.1389628798,-0.0200660415,0.3176531494,0.1545748264,-0.1688733399,0.0288684927,-0.0564694032,0.2119377255,-0.0756313056,0.0736123845,-0.14293015,0.3080175817,-0.057636708,-0.3339879513,0.0377683677,0.2538016438,0.5117610097,-0.0004488377,-0.0633197874,0.3244804144,0.0866748691,-0.0496543907,-0.1650345176,-0.0999800116,0.1509415507,-0.2289367616,0.0971660241,0.0872650221,0.3168590069,-0.0662048236,0.0954951942,0.4775231183,0.0889250115,0.2869647741,-0.0390479192,0.2417568564,-0.0411586203,-0.0287830383,-0.0849919841,-0.1743532121,0.0761678517,0.1894836426,0.2709217072,-0.0320920832,-0.0377041772,0.0921959504,0.5580118299,0.0443215631,0.3538882732,-0.0804426596,-0.3818507195,0.0884140804,0.1868627965,0.2703929543,0.4774660766,0.3163056076,-0.1177275926,-0.0135746626,-0.0687375143,0.068971917,0.0899053961,0.0000686368,0.2182746232,0.0326346494,0.2071536779,0.0608280376,-0.2113714963,-0.0529055037,-0.1733764559,0.2231471837,-0.4077748656,0.1184415296,-0.3216187358,-0.1545066088,-0.0203860439,0.0009128465,-0.1665667146,-0.3700627089,-0.2923040688,0.0090758018,0.0586140528,0.1355309337,0.0729432553,0.0848084092,0.0303801391,0.0038109233,-0.1432915479,-0.1506637037,-0.197236672,0.1309271753,0.1724003851,0.2035219669,0.4579099715,-0.1723938286,0.1496831626,-0.0668234155,-0.2592738867,0.0691069737,-0.0358921215,0.5192118287,0.4751577079,0.1759489179,0.0373363346,-0.0723712072,0.3917131424,-0.337565273,-0.0363603607,0.2813171744,-0.2262491435,0.1912381798,-0.1627744734,-0.2709549367,-0.5850613117,-0.4314647317,0.0900498033,0.1581933945,0.1574169397,0.2290733755,0.1376304924,0.4989827573,0.0554460995,0.173627466,-0.1574210674,-0.0517017953,0.2869262695,-0.128268525,-0.3188622594,0.0627037063,-0.1610585004,0.2123719454,0.0095064696,-0.2091803849,-0.5421624184,-0.1937738359,0.248898387,-0.0088543072,0.1406644732,0.2820861042,0.1652371287,-0.171896413,-0.2008978277,-0.1451961696,-0.0958748311,0.2647386193,0.003278581,0.1268489063,0.3415829241,-0.1613709927,0.1591750383,0.0212520342,-0.1993087977,0.414999783,-0.2640288472,0.3553538322,-0.1553532481,-0.501110673,-0.1315999925,-0.034155868,0.0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{"html_url":"https:\/\/github.com\/huggingface\/datasets\/issues\/2869","title":"TypeError: 'NoneType' object is not callable","comments":"For that environment, I am sorry but I can't reproduce the bug: I can load the dataset without any problem.","body":"## Describe the bug\r\n\r\nTypeError: 'NoneType' object is not callable\r\n## Steps to reproduce the bug\r\n```python\r\nfrom datasets import load_dataset, load_metric\r\ndataset = datasets.load_dataset(\"glue\", 'cola')\r\n```\r\n\r\n## Expected results\r\nA clear and concise description of the expected results.\r\n\r\n## Actual results\r\nSpecify the actual results or traceback.\r\n\r\n## Environment info\r\n\r\n- `datasets` version: 1.11.0\r\n- Platform:\r\n- Python version: 3.7\r\n- PyArrow version:\r\n","comment_length":20,"text":"TypeError: 'NoneType' object is not callable\n## Describe the bug\r\n\r\nTypeError: 'NoneType' object is not callable\r\n## Steps to reproduce the bug\r\n```python\r\nfrom datasets import load_dataset, load_metric\r\ndataset = datasets.load_dataset(\"glue\", 'cola')\r\n```\r\n\r\n## Expected results\r\nA clear and concise description of the expected results.\r\n\r\n## Actual results\r\nSpecify the actual results or traceback.\r\n\r\n## Environment info\r\n\r\n- `datasets` version: 1.11.0\r\n- Platform:\r\n- Python version: 3.7\r\n- PyArrow version:\r\n\nFor that environment, I am sorry but I can't reproduce the bug: I can load the dataset without any problem.","embeddings":[-0.2608940005,-0.2007066011,0.0660000592,0.400195688,0.4645994604,0.0115246382,0.3240968585,0.0941318348,0.1896000355,0.3001818657,-0.2849094868,0.4667589068,-0.0366437919,0.1668581367,0.0777649358,-0.1253727525,-0.2606472373,0.2636680901,-0.1777470112,0.0613667145,-0.5233634114,0.0231986102,-0.2373635471,0.0858903304,-0.2768001556,-0.1533993483,-0.0281501114,0.079713583,-0.2462176234,-0.2805505991,0.4611611068,-0.0159628261,0.1640476584,0.5104134083,-0.0001023479,-0.0184572823,0.5836995244,0.0530032516,-0.0761517882,-0.4211900532,-0.2255626768,-0.3977999389,0.2870038748,-0.3548148572,-0.1275422126,0.0055780676,0.0405133776,-0.1819047779,0.262139827,0.3204336464,0.3242153823,0.5296897888,0.0114422301,-0.3599714339,0.1965120882,0.0808303058,-0.0636900738,0.3710546196,0.0265845973,-0.0474929884,0.2439645529,0.0870305225,-0.3353538513,0.0527608916,0.2847363651,0.0382548533,0.0095302081,-0.270170182,0.0964757726,0.3694062233,0.3855828047,-0.382620275,-0.1514433473,0.1750092059,0.2239649594,-0.2032708824,0.211649254,0.0468775555,0.0163049828,0.0907445624,-0.0130718844,0.1712200791,-0.2445577234,0.2749375999,-0.2020063251,0.1065498963,-0.1935495585,0.1447966695,0.0849972516,-0.1188601851,0.0763338953,-0.1345976889,-0.0619969517,0.0890901908,-0.2083802223,0.0349296518,0.1152055487,0.1535798907,0.1636114269,-0.2843680978,0.1018852517,-0.0238335412,0.0397984199,0.4501174688,0.2414609641,0.1897560805,0.2724782526,0.088692598,0.2660125494,0.066787757,-0.2417135537,-0.0120890485,-0.1543584764,-0.0610723533,0.2427943051,0.1411847323,0.5124645233,0.0189152192,-0.4501633942,-0.1742377281,-0.172133401,0.0913005546,0.2716411948,0.4277633727,-0.1526402384,0.0820783153,0.1332417876,0.1743281931,-0.1884514242,-0.2918041646,-0.3183253706,0.3539678454,-0.2528480887,-0.2349549532,0.1911386847,0.0579657778,-0.0449788868,0.0134671535,-0.0958473086,0.1027007028,0.0886686146,-0.2908604145,-0.2310647517,0.3132710755,0.2443055063,-0.0628327206,0.2800607383,-0.4668283463,-0.091596745,0.2292028666,-0.2898909748,-0.1671735048,-0.0732437372,0.2667024732,-0.3390546739,-0.0697349459,-0.2441662848,0.0466724299,0.0489577651,-0.1566343755,-0.1024087444,-0.0962615982,-0.112656869,-0.4234251976,0.0446183719,0.5165780783,-0.3751866817,-0.1992407143,-0.3078093231,-0.1091819853,0.352284044,-0.147301212,0.0145670017,0.0629001632,-0.1706566364,-0.0006857364,0.6008648276,-0.3847208321,-0.2815137208,0.0563009419,-0.0371869579,-0.1165539101,-0.0517222472,-0.0470438264,0.0718874335,0.135642916,0.4128215313,0.2446535677,0.0493588299,-0.0324675739,-0.2109587342,-0.2381093949,0.1280453652,0.151658386,0.1272704005,0.1630408317,0.0712505579,-0.2506299913,-0.0557164401,-0.0061886362,-0.1022448465,-0.0301638599,0.5365030169,-0.1599618644,-0.0399641804,-0.532881856,-0.3950176537,0.2330813259,-0.0779283196,0.2541694939,-0.105489023,-0.1700357646,-0.352982074,0.1019510254,-0.0170240067,0.0607618615,0.2853280008,0.0909614265,-0.2011494935,0.1454881579,-0.0809026584,0.1637727916,-0.0873494446,0.0512318462,0.0335052349,0.291667223,0.0747782141,-0.3356175423,0.0352951288,0.1897852123,0.4574481249,-0.0104598496,-0.0672703311,0.2640901804,0.070892185,-0.0788056329,-0.2070860565,-0.05381817,0.0879925042,-0.313064307,-0.0303661674,0.0379851758,0.3187099099,-0.0943738967,0.0625385866,0.6038931608,0.0668431222,0.1744620055,-0.0730603263,0.2714183331,0.0653238744,-0.0385558642,-0.149077177,-0.1286280155,0.0459849499,0.1176183671,0.2486371994,0.0061038891,-0.0880215764,-0.0231885035,0.5769147873,0.0913832113,0.3607913256,-0.1375178844,-0.4204133153,0.1137951836,0.0370061696,0.2525543571,0.5032669902,0.332315892,-0.1068636402,0.023629915,-0.0571720079,0.0404726267,0.1032921076,0.0494471341,0.2328974009,-0.0000240203,0.3057212234,-0.0209420472,-0.3462260365,0.1613915414,-0.053847719,0.1842062026,-0.3760870993,0.1845387518,-0.2747030258,-0.076757893,0.1425513774,-0.1027247906,-0.125005424,-0.3286290169,-0.2664072216,0.1081375405,0.1210905612,0.1938206255,0.1422476321,0.0763199255,0.0087453714,-0.0255289134,-0.1826611906,-0.1326549202,-0.1483921856,0.0880739987,0.1653355807,0.181941092,0.4119828641,-0.1496375948,0.2284882814,-0.0511268564,-0.245598793,0.100177817,-0.0573291741,0.5299291015,0.4086084068,0.0602920428,-0.1410644501,0.1004512459,0.4188910425,-0.4658019245,0.0264790654,0.363583684,-0.3070645034,0.0837264657,-0.2518776655,-0.1060993373,-0.5478299856,-0.4280380607,0.0683310926,0.0958201066,0.0449768677,0.172417596,0.1191176772,0.444096446,0.112392433,0.0157600436,-0.2330423892,-0.0854077637,0.3311132789,-0.1693256646,-0.3608907759,0.2042488754,-0.1323478222,0.2548744977,0.0285864808,-0.1609783471,-0.5243912339,-0.1556174606,0.2053533643,-0.005188724,-0.0110916914,0.2912253737,0.0402853899,-0.1367866844,-0.2726752162,-0.1413018703,-0.0488153882,0.2352696806,-0.0683882236,0.0471838973,0.2825388908,-0.1223018318,0.1436379254,-0.1094394699,-0.1853011698,0.4059675932,-0.3138792515,0.3742715716,-0.1514657587,-0.4665456712,-0.2108906806,0.0278709959,0.04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{"html_url":"https:\/\/github.com\/huggingface\/datasets\/issues\/2869","title":"TypeError: 'NoneType' object is not callable","comments":"One naive question: do you have internet access from the machine where you execute the code?","body":"## Describe the bug\r\n\r\nTypeError: 'NoneType' object is not callable\r\n## Steps to reproduce the bug\r\n```python\r\nfrom datasets import load_dataset, load_metric\r\ndataset = datasets.load_dataset(\"glue\", 'cola')\r\n```\r\n\r\n## Expected results\r\nA clear and concise description of the expected results.\r\n\r\n## Actual results\r\nSpecify the actual results or traceback.\r\n\r\n## Environment info\r\n\r\n- `datasets` version: 1.11.0\r\n- Platform:\r\n- Python version: 3.7\r\n- PyArrow version:\r\n","comment_length":16,"text":"TypeError: 'NoneType' object is not callable\n## Describe the bug\r\n\r\nTypeError: 'NoneType' object is not callable\r\n## Steps to reproduce the bug\r\n```python\r\nfrom datasets import load_dataset, load_metric\r\ndataset = datasets.load_dataset(\"glue\", 'cola')\r\n```\r\n\r\n## Expected results\r\nA clear and concise description of the expected results.\r\n\r\n## Actual results\r\nSpecify the actual results or traceback.\r\n\r\n## Environment info\r\n\r\n- `datasets` version: 1.11.0\r\n- Platform:\r\n- Python version: 3.7\r\n- PyArrow version:\r\n\nOne naive question: do you have internet access from the machine where you execute the code?","embeddings":[-0.2466751933,-0.2166070193,-0.0038837458,0.3586860299,0.4009475708,0.035536956,0.302520901,0.1521895081,0.1650357544,0.3566101193,-0.1779196858,0.4452134073,-0.0382566825,0.1990447938,0.0816193596,-0.0899617374,-0.2033715993,0.2447062582,-0.1842225194,0.010521763,-0.472876668,-0.0185202286,-0.1821441352,0.0199327096,-0.2480738759,-0.2002862543,-0.1163984835,0.0645010024,-0.2148991227,-0.2530295849,0.3632104993,0.0450640544,0.112367928,0.5467213392,-0.0001019842,-0.0862105861,0.4975403249,0.0762401372,-0.1133384928,-0.2227149755,-0.2704135478,-0.3431518078,0.1823247373,-0.4260804653,-0.0916544795,0.028890904,0.0123812426,-0.2481914461,0.3053150177,0.3072139323,0.339708358,0.4426216781,-0.0190175083,-0.3322893977,0.2162288725,0.0421040244,-0.0323108882,0.362215519,0.0633602291,-0.0537314415,0.1767412126,0.1824111044,-0.2867747545,-0.0029550304,0.2504888773,0.0876018703,-0.0245356578,-0.2595610619,0.1022056341,0.2668823004,0.2856886089,-0.4520843923,-0.1245521009,0.1114801839,0.1043578833,-0.2367105186,0.1430763751,-0.0036112065,-0.0197007451,0.1060361415,-0.0351593755,0.133691445,-0.231664747,0.2396647781,-0.209117204,0.1492936164,-0.1894275993,0.0967105776,0.0752299801,-0.0852814317,0.0532211438,-0.1713415682,0.021075597,0.0105691114,-0.1507948637,-0.0472187474,0.1830180287,0.1402942985,0.1534052491,-0.254579097,0.1078871489,-0.0058753905,0.1186242029,0.4159373343,0.154383108,0.2415641695,0.2150634825,0.0880800635,0.2187133878,0.1302518398,-0.2568061352,-0.0115337186,-0.0923354477,-0.0363530554,0.3084871173,0.1605716944,0.4978731573,0.0148101076,-0.43729496,-0.0482422002,-0.2314789593,0.1063998938,0.2851623595,0.3828612864,-0.2197893709,0.0280763004,0.1954128742,0.1463474184,-0.167907998,-0.2896850407,-0.3228367269,0.3451814651,-0.3253240883,-0.1996293813,0.1255358905,0.0956025869,-0.078586556,0.0480372384,-0.035712216,0.0553010367,0.0763480887,-0.2473653257,-0.1706801951,0.1856051236,0.2096802443,-0.1042110175,0.3234429955,-0.4836687446,-0.1027234495,0.1058211848,-0.2070778757,-0.0896052122,-0.0359199308,0.3180175722,-0.3513986468,-0.1008676514,-0.151536569,0.1008075848,0.0612458289,-0.1549359858,0.0268286895,-0.0838796198,-0.0460206904,-0.443877846,0.0987593383,0.5773766041,-0.3923660815,-0.1583535075,-0.3149743676,-0.0410091579,0.3569568694,-0.2316169292,0.1328972578,0.1612990648,-0.1505951583,0.0307376627,0.5494579673,-0.3594247699,-0.298289597,-0.0194503367,-0.1195071116,-0.0845604688,-0.0080690281,-0.1045471951,0.0758646503,0.1502961367,0.4117963314,0.2450776398,-0.0244919565,-0.0403468348,-0.2356397361,-0.2162138522,0.0301526804,0.107242316,0.1855029613,0.160957098,0.1086669937,-0.2254877985,0.0150828492,-0.0165604558,-0.0678272471,-0.0664632171,0.5562682748,-0.1198019162,-0.1030108109,-0.548601687,-0.3364979625,0.2137777209,-0.0644096881,0.2583818138,-0.1622745097,-0.1771243662,-0.4169964492,0.1173746884,-0.0591691211,0.0415956117,0.3479426801,0.1584892571,-0.1316381693,0.1201645657,-0.038923569,0.1554773301,-0.0975586995,0.0615419187,0.002867274,0.3624258637,0.0288160499,-0.3402500749,0.0125437025,0.2390864491,0.492028594,0.0251288041,-0.0366603509,0.3425714076,0.0153975571,-0.0589240976,-0.0713611618,0.0129818767,0.0355378203,-0.3102554679,0.0903075784,0.0740944669,0.274332881,-0.0501489863,0.0455586836,0.5526733398,0.0639084801,0.1631180197,-0.0774783418,0.2747522891,-0.1043333039,-0.0652063265,-0.1591193974,-0.1482455432,0.1165027544,0.0529977977,0.1941261292,-0.0445721783,-0.102411747,0.0711867437,0.6148303747,0.07447882,0.410061866,-0.1350679696,-0.3626484573,0.1363148987,0.0632819161,0.2627360523,0.4373947978,0.3606951535,-0.0552522913,0.0725060552,-0.0956002772,0.0463584624,0.0818468109,0.0780013651,0.1710066646,-0.0235593766,0.2648818195,0.0000485086,-0.302197367,0.0393880606,-0.2115725577,0.1999685317,-0.3442276716,0.2703234255,-0.2761572301,-0.1083705425,0.0665704906,-0.1341651529,-0.1119984984,-0.3972260058,-0.189691484,0.0495734848,0.0846979097,0.1596637964,0.1331482828,0.1780963689,0.0435850658,-0.0506396033,-0.2169364691,-0.0929441899,-0.1491832733,0.124519974,0.1127889976,0.2195498496,0.4563970566,-0.2264528573,0.2653894424,0.0405263342,-0.2521411479,0.1091040745,-0.1113009229,0.5382031798,0.4217015207,0.0849712491,-0.1375489533,0.0479233935,0.4025655687,-0.3987693489,0.0345607921,0.2760668993,-0.1733168364,0.0993671566,-0.2214981169,-0.1091302112,-0.4937289059,-0.430516243,0.0535355024,0.0940166637,0.0907848328,0.1661356688,0.1647697985,0.5161489248,0.0903599858,0.0853421465,-0.2309448719,-0.0928328782,0.2652721107,-0.1356292665,-0.3170882165,0.1164372712,-0.1327296942,0.1882433891,0.0346632898,-0.2173748314,-0.5696399808,-0.1761069298,0.2179042101,-0.0343912281,0.0933210403,0.26999861,0.0949118435,-0.1569222659,-0.2845886648,-0.1625180691,-0.09149611,0.1541405916,-0.0318299644,-0.0249708015,0.2786458135,-0.1161085069,0.1320557296,-0.0665605962,-0.2075443119,0.3825691044,-0.3480911255,0.3984667063,-0.0996405408,-0.5200996399,-0.1736067683,0.0353287831,-0.0609325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{"html_url":"https:\/\/github.com\/huggingface\/datasets\/issues\/2869","title":"TypeError: 'NoneType' object is not callable","comments":"> For that environment, I am sorry but I can't reproduce the bug: I can load the dataset without any problem.\r\n\r\nBut I can download other task dataset such as `dataset = load_dataset('squad')`. I don't know what went wrong. Thank you so much!","body":"## Describe the bug\r\n\r\nTypeError: 'NoneType' object is not callable\r\n## Steps to reproduce the bug\r\n```python\r\nfrom datasets import load_dataset, load_metric\r\ndataset = datasets.load_dataset(\"glue\", 'cola')\r\n```\r\n\r\n## Expected results\r\nA clear and concise description of the expected results.\r\n\r\n## Actual results\r\nSpecify the actual results or traceback.\r\n\r\n## Environment info\r\n\r\n- `datasets` version: 1.11.0\r\n- Platform:\r\n- Python version: 3.7\r\n- PyArrow version:\r\n","comment_length":43,"text":"TypeError: 'NoneType' object is not callable\n## Describe the bug\r\n\r\nTypeError: 'NoneType' object is not callable\r\n## Steps to reproduce the bug\r\n```python\r\nfrom datasets import load_dataset, load_metric\r\ndataset = datasets.load_dataset(\"glue\", 'cola')\r\n```\r\n\r\n## Expected results\r\nA clear and concise description of the expected results.\r\n\r\n## Actual results\r\nSpecify the actual results or traceback.\r\n\r\n## Environment info\r\n\r\n- `datasets` version: 1.11.0\r\n- Platform:\r\n- Python version: 3.7\r\n- PyArrow version:\r\n\n> For that environment, I am sorry but I can't reproduce the bug: I can load the dataset without any problem.\r\n\r\nBut I can download other task dataset such as `dataset = load_dataset('squad')`. I don't know what went wrong. Thank you so much!","embeddings":[-0.2479815185,-0.3217719793,0.0763500705,0.4701036215,0.4953816831,0.0185086466,0.3540442288,0.0614947863,0.2402586043,0.3128617108,-0.276409179,0.4540319443,-0.0626192093,0.1033334509,0.1556583196,-0.1557852626,-0.2255954146,0.1679677218,-0.0779853985,0.1020552889,-0.4996086359,-0.0293463878,-0.2632772624,0.1951634139,-0.1559653133,-0.1863708049,-0.0737683624,0.0807150304,-0.1478742063,-0.2537325919,0.5681656003,-0.1083382666,0.2122284323,0.5568339229,-0.0001079977,0.0223296881,0.5475671887,0.0222912077,-0.181262657,-0.5533769727,-0.1755931079,-0.4152663052,0.3201848865,-0.3743572831,-0.1145967916,0.07909403,0.1459887475,-0.1117727757,0.3590354919,0.4540637732,0.2855804861,0.7018626928,-0.0197485741,-0.342117399,0.1333463937,0.0541447364,-0.1285513192,0.2871590555,0.1371662915,-0.0082335789,0.4334596992,-0.0373312868,-0.3705081344,0.090445593,0.3338054717,0.0173116531,0.0456819162,-0.4084408581,0.0989959762,0.3526202738,0.429132849,-0.2789865732,-0.1445523649,0.1062167063,0.2393290848,-0.061995618,0.1765795946,0.0757426322,-0.0298103616,0.0413528346,0.0463871136,0.1718294621,-0.2779862285,0.2125990689,-0.1284300834,-0.0353670754,-0.1932722479,0.2176218927,0.038036257,-0.0971409827,0.0310842935,-0.1765830368,0.0018496352,0.1451958716,-0.3074558675,-0.0370389931,0.1003599092,0.0206406731,0.2188021392,-0.2397266477,0.2141465396,-0.0814069584,0.0014896236,0.3541437387,0.346270442,0.2386882752,0.2158100009,0.1408458501,0.1517539918,0.022729395,-0.1789033264,0.0621397234,-0.2835336924,-0.0460709594,0.2485666424,0.2519290149,0.62191993,0.0226177946,-0.5257906914,-0.1946161389,-0.0697027519,0.0744651854,0.1489258707,0.4044239819,-0.2522181571,0.0734108984,0.0253312159,0.2898434997,-0.1949325502,-0.3826381862,-0.250428021,0.1526836008,-0.268599242,-0.270571053,0.24026227,0.0925443023,0.0312479846,0.0430606492,-0.2270833105,0.0859438777,0.1672883034,-0.3710010648,-0.3183163404,0.203129068,0.2981905341,0.013275166,0.2797108889,-0.3238164485,-0.0456261188,0.2975198925,-0.3877721429,-0.1205919012,-0.1263929754,0.1935602129,-0.3629306555,-0.1565003395,-0.5364289284,0.0805210099,0.0401180312,-0.0892851651,-0.0573687814,-0.1425406635,-0.0697560161,-0.3583665192,-0.0551820286,0.4761920869,-0.2147590965,-0.2288669497,-0.3011833727,-0.2443191111,0.3596111238,-0.1455561668,-0.0476030186,0.2456554323,-0.1385620087,0.0195395816,0.642037034,-0.5051142573,-0.2810824811,0.1532612592,-0.1181739345,-0.076440759,-0.0949228108,-0.0446148701,0.2863956094,0.0758701265,0.3716195226,0.3349784911,0.0008509188,-0.0305648036,-0.131107524,-0.2105619609,0.2909449637,0.1949024647,0.2361101955,0.2211598307,0.0545335151,-0.1998473704,-0.0358049236,0.0527693219,-0.0813176557,-0.0201197267,0.4355514646,-0.0436968356,-0.0257663354,-0.5290393233,-0.5686805844,0.1899500042,-0.0841612145,0.1826199889,-0.115729481,-0.0673960596,-0.3040428758,0.042624291,-0.078021042,-0.0217457879,0.182378903,0.0794131309,-0.2668655217,0.0558810793,-0.0770036429,0.1909017116,-0.1526100785,0.095781371,0.1212609336,0.2611952722,0.1016982198,-0.3987404406,-0.0017724648,0.1362133324,0.4781362414,0.0183626059,-0.0826574564,0.1729604006,0.0458101444,-0.1020453051,-0.2171081603,-0.1762046367,0.0575558543,-0.3366027176,-0.0447619446,0.1347156912,0.3460848331,-0.160481751,0.0836437568,0.4571962357,-0.0167928878,0.3421861231,-0.1437793821,0.2406296581,0.0525164865,-0.0606726483,-0.1331075728,0.1804715544,0.1290700138,0.0913747177,0.2827326059,-0.0355854295,-0.2706822753,0.0046682684,0.6162089109,0.0299597997,0.3194147646,-0.0831516907,-0.33788234,0.1462022662,0.0000325413,0.0418264307,0.5353927612,0.2632282078,-0.1103599295,-0.0164734125,0.0007065669,0.0515690558,0.0333776362,0.069090277,0.2856582701,0.0461304933,0.2759691477,0.0386635847,-0.2005325556,0.1444482505,-0.0084361024,0.1951348335,-0.3969622254,0.1878786534,-0.1423569769,-0.1165724248,-0.0304603409,0.1370230019,-0.1042248011,-0.1990451217,-0.3134959638,0.1589778066,0.2137130797,0.1265260726,0.1616361737,0.0088405497,-0.0113126803,-0.0408857763,-0.1634947658,-0.1458583921,-0.1861838251,0.0462163053,0.1946974099,0.0735260546,0.3495370448,-0.1900552809,0.1245678738,-0.1000485346,-0.1240719408,0.0189164113,-0.0774424672,0.5409654975,0.2683835626,0.2594893575,-0.162481755,0.1194115579,0.3791869879,-0.4782688916,-0.0512078144,0.3330359757,-0.334943682,0.0967640132,-0.2121215761,-0.3186377287,-0.5942174196,-0.3938197494,0.2054920793,-0.0129524982,0.069686912,0.3293658495,0.1026407331,0.4716491699,0.2177550197,0.0916105509,-0.1844632626,-0.0342024155,0.2794252634,-0.1346675754,-0.3884647787,0.1575419456,-0.0971030593,0.2852425575,0.0799094364,-0.1245648041,-0.2630615234,-0.0669115633,0.197574988,-0.1246708706,-0.1207072735,0.3231537342,-0.0845761523,-0.0469946526,-0.2115352452,-0.1213957146,-0.0482246727,0.3319038451,-0.0071725766,0.0945167914,0.3415936828,-0.1624031961,0.3309932351,-0.0868292972,-0.0393967107,0.4093084633,-0.3032317758,0.2776997089,-0.0965931192,-0.4811325371,0.0073162122,0.0563486181,0.06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{"html_url":"https:\/\/github.com\/huggingface\/datasets\/issues\/2866","title":"\"counter\" dataset raises an error in normal mode, but not in streaming mode","comments":"Hi @severo, thanks for reporting.\r\n\r\nJust note that currently not all canonical datasets support streaming mode: this is one case!\r\n\r\nAll datasets that use `pathlib` joins (using `\/`) instead of `os.path.join` (as in this dataset) do not support streaming mode yet.","body":"## Describe the bug\r\n\r\n`counter` dataset raises an error on `load_dataset()`, but simply returns an empty iterator in streaming mode.\r\n\r\n## Steps to reproduce the bug\r\n\r\n```python\r\n>>> import datasets as ds\r\n>>> a = ds.load_dataset('counter', split=\"train\", streaming=False)\r\nUsing custom data configuration default\r\nDownloading and preparing dataset counter\/default (download: 1.29 MiB, generated: 2.48 MiB, post-processed: Unknown size, total: 3.77 MiB) to \/home\/slesage\/.cache\/huggingface\/datasets\/counter\/default\/1.0.0\/9f84962fa0f35bec5a34fe0bdff8681838d497008c457f7856c48654476ec0e9...\r\nTraceback (most recent call last):\r\n File \"\/home\/slesage\/hf\/datasets-preview-backend\/.venv\/lib\/python3.8\/site-packages\/datasets\/builder.py\", line 726, in _download_and_prepare\r\n self._prepare_split(split_generator, **prepare_split_kwargs)\r\n File \"\/home\/slesage\/hf\/datasets-preview-backend\/.venv\/lib\/python3.8\/site-packages\/datasets\/builder.py\", line 1124, in _prepare_split\r\n for key, record in utils.tqdm(\r\n File \"\/home\/slesage\/hf\/datasets-preview-backend\/.venv\/lib\/python3.8\/site-packages\/tqdm\/std.py\", line 1185, in __iter__\r\n for obj in iterable:\r\n File \"\/home\/slesage\/.cache\/huggingface\/modules\/datasets_modules\/datasets\/counter\/9f84962fa0f35bec5a34fe0bdff8681838d497008c457f7856c48654476ec0e9\/counter.py\", line 161, in _generate_examples\r\n with derived_file.open(encoding=\"utf-8\") as f:\r\n File \"\/home\/slesage\/.pyenv\/versions\/3.8.11\/lib\/python3.8\/pathlib.py\", line 1222, in open\r\n return io.open(self, mode, buffering, encoding, errors, newline,\r\n File \"\/home\/slesage\/.pyenv\/versions\/3.8.11\/lib\/python3.8\/pathlib.py\", line 1078, in _opener\r\n return self._accessor.open(self, flags, mode)\r\nFileNotFoundError: [Errno 2] No such file or directory: '\/home\/slesage\/.cache\/huggingface\/datasets\/downloads\/extracted\/b57aa6db5601a738e57b95c1fd8cced54ff28fc540efcdaf0f6c4f1bb5dfe211\/COUNTER\/0032p.xml'\r\n\r\nDuring handling of the above exception, another exception occurred:\r\n\r\nTraceback (most recent call last):\r\n File \"\", line 1, in \r\n File \"\/home\/slesage\/hf\/datasets-preview-backend\/.venv\/lib\/python3.8\/site-packages\/datasets\/load.py\", line 1112, in load_dataset\r\n builder_instance.download_and_prepare(\r\n File \"\/home\/slesage\/hf\/datasets-preview-backend\/.venv\/lib\/python3.8\/site-packages\/datasets\/builder.py\", line 636, in download_and_prepare\r\n self._download_and_prepare(\r\n File \"\/home\/slesage\/hf\/datasets-preview-backend\/.venv\/lib\/python3.8\/site-packages\/datasets\/builder.py\", line 728, in _download_and_prepare\r\n raise OSError(\r\nOSError: Cannot find data file.\r\nOriginal error:\r\n[Errno 2] No such file or directory: '\/home\/slesage\/.cache\/huggingface\/datasets\/downloads\/extracted\/b57aa6db5601a738e57b95c1fd8cced54ff28fc540efcdaf0f6c4f1bb5dfe211\/COUNTER\/0032p.xml'\r\n```\r\n\r\n```python\r\n>>> import datasets as ds\r\n>>> b = ds.load_dataset('counter', split=\"train\", streaming=True)\r\nUsing custom data configuration default\r\n>>> list(b)\r\n[]\r\n```\r\n\r\n## Expected results\r\n\r\nAn exception should be raised in streaming mode\r\n\r\n## Actual results\r\n\r\nNo exception is raised in streaming mode: there is no way to tell if something has broken or if the dataset is simply empty.\r\n\r\n## Environment info\r\n\r\n- `datasets` version: 1.11.1.dev0\r\n- Platform: Linux-5.11.0-1016-aws-x86_64-with-glibc2.29\r\n- Python version: 3.8.11\r\n- PyArrow version: 4.0.1\r\n","comment_length":41,"text":"\"counter\" dataset raises an error in normal mode, but not in streaming mode\n## Describe the bug\r\n\r\n`counter` dataset raises an error on `load_dataset()`, but simply returns an empty iterator in streaming mode.\r\n\r\n## Steps to reproduce the bug\r\n\r\n```python\r\n>>> import datasets as ds\r\n>>> a = ds.load_dataset('counter', split=\"train\", streaming=False)\r\nUsing custom data configuration default\r\nDownloading and preparing dataset counter\/default (download: 1.29 MiB, generated: 2.48 MiB, post-processed: Unknown size, total: 3.77 MiB) to \/home\/slesage\/.cache\/huggingface\/datasets\/counter\/default\/1.0.0\/9f84962fa0f35bec5a34fe0bdff8681838d497008c457f7856c48654476ec0e9...\r\nTraceback (most recent call last):\r\n File \"\/home\/slesage\/hf\/datasets-preview-backend\/.venv\/lib\/python3.8\/site-packages\/datasets\/builder.py\", line 726, in _download_and_prepare\r\n self._prepare_split(split_generator, **prepare_split_kwargs)\r\n File \"\/home\/slesage\/hf\/datasets-preview-backend\/.venv\/lib\/python3.8\/site-packages\/datasets\/builder.py\", line 1124, in _prepare_split\r\n for key, record in utils.tqdm(\r\n File \"\/home\/slesage\/hf\/datasets-preview-backend\/.venv\/lib\/python3.8\/site-packages\/tqdm\/std.py\", line 1185, in __iter__\r\n for obj in iterable:\r\n File \"\/home\/slesage\/.cache\/huggingface\/modules\/datasets_modules\/datasets\/counter\/9f84962fa0f35bec5a34fe0bdff8681838d497008c457f7856c48654476ec0e9\/counter.py\", line 161, in _generate_examples\r\n with derived_file.open(encoding=\"utf-8\") as f:\r\n File \"\/home\/slesage\/.pyenv\/versions\/3.8.11\/lib\/python3.8\/pathlib.py\", line 1222, in open\r\n return io.open(self, mode, buffering, encoding, errors, newline,\r\n File \"\/home\/slesage\/.pyenv\/versions\/3.8.11\/lib\/python3.8\/pathlib.py\", line 1078, in _opener\r\n return self._accessor.open(self, flags, mode)\r\nFileNotFoundError: [Errno 2] No such file or directory: '\/home\/slesage\/.cache\/huggingface\/datasets\/downloads\/extracted\/b57aa6db5601a738e57b95c1fd8cced54ff28fc540efcdaf0f6c4f1bb5dfe211\/COUNTER\/0032p.xml'\r\n\r\nDuring handling of the above exception, another exception occurred:\r\n\r\nTraceback (most recent call last):\r\n File \"\", line 1, in \r\n File \"\/home\/slesage\/hf\/datasets-preview-backend\/.venv\/lib\/python3.8\/site-packages\/datasets\/load.py\", line 1112, in load_dataset\r\n builder_instance.download_and_prepare(\r\n File \"\/home\/slesage\/hf\/datasets-preview-backend\/.venv\/lib\/python3.8\/site-packages\/datasets\/builder.py\", line 636, in download_and_prepare\r\n self._download_and_prepare(\r\n File \"\/home\/slesage\/hf\/datasets-preview-backend\/.venv\/lib\/python3.8\/site-packages\/datasets\/builder.py\", line 728, in _download_and_prepare\r\n raise OSError(\r\nOSError: Cannot find data file.\r\nOriginal error:\r\n[Errno 2] No such file or directory: '\/home\/slesage\/.cache\/huggingface\/datasets\/downloads\/extracted\/b57aa6db5601a738e57b95c1fd8cced54ff28fc540efcdaf0f6c4f1bb5dfe211\/COUNTER\/0032p.xml'\r\n```\r\n\r\n```python\r\n>>> import datasets as ds\r\n>>> b = ds.load_dataset('counter', split=\"train\", streaming=True)\r\nUsing custom data configuration default\r\n>>> list(b)\r\n[]\r\n```\r\n\r\n## Expected results\r\n\r\nAn exception should be raised in streaming mode\r\n\r\n## Actual results\r\n\r\nNo exception is raised in streaming mode: there is no way to tell if something has broken or if the dataset is simply empty.\r\n\r\n## Environment info\r\n\r\n- `datasets` version: 1.11.1.dev0\r\n- Platform: Linux-5.11.0-1016-aws-x86_64-with-glibc2.29\r\n- Python version: 3.8.11\r\n- PyArrow version: 4.0.1\r\n\nHi @severo, thanks for reporting.\r\n\r\nJust note that currently not all canonical datasets support streaming mode: this is one case!\r\n\r\nAll datasets that use `pathlib` joins (using `\/`) instead of `os.path.join` (as in this dataset) do not support streaming mode yet.","embeddings":[-0.4171527922,-0.2417904586,0.0004065955,0.1936673522,0.187378943,0.0572046041,0.5056346059,0.1059950739,0.1942841411,0.1353349984,0.0698306859,0.1297213137,-0.2097095996,0.3238820434,0.0893105865,0.0787798092,0.0210811216,0.1588886231,0.0335764661,0.0774051622,-0.2157031149,0.2028147876,-0.3865993321,-0.3339452446,-0.1323988438,0.1463262439,0.1100258455,0.1485300958,0.1753474176,-0.6307143569,0.2604337335,0.0775746107,0.2229160666,0.7022691965,-0.0001109225,0.1807845384,0.4209491313,-0.138756454,-0.5919362307,-0.2953981757,-0.3683612645,-0.3184562027,0.1747525334,-0.1460207105,-0.1155984327,0.0416297689,-0.2645150125,-0.4016982019,0.2196300626,0.44546175,0.2117786556,0.4712955952,-0.1509929299,-0.1280123889,0.1274099499,0.0086588459,-0.1527633816,0.2987458408,0.0759947449,0.2464787662,-0.030185163,0.400646776,-0.0477484316,0.1930941492,0.2172417343,-0.0403118059,-0.0638400242,-0.2730312347,0.077968359,0.2267186195,0.3892460763,-0.3428210616,-0.1372255683,-0.4340849519,-0.0550197884,-0.4057786763,0.2461039424,-0.0120554632,-0.2402932793,0.0848756358,-0.3027442396,0.1529166549,0.0393640399,-0.1008400992,-0.0583141893,0.1315543056,-0.0259356182,0.1768467724,-0.0182867814,-0.0394550189,0.1186181977,-0.2750140131,-0.0770448223,0.1979830861,-0.5796865821,0.0617473088,0.3563539386,-0.483120203,0.2248359621,0.0766406581,0.3723810911,0.0808451474,0.1294206977,-0.0054175756,0.3990353644,0.2017412037,0.1839399487,-0.2446381003,0.1665526032,0.3514225185,-0.0046332767,-0.3092014194,0.0455646105,-0.0928959474,0.2623339891,0.0135595212,0.3132420182,0.0522785522,-0.3759042025,0.1984404474,-0.2598961294,0.0621640086,0.3526681066,0.2470078766,0.31240955,0.331681639,-0.1649695039,0.2566638589,-0.170162797,-0.4501440227,-0.2399167717,-0.3115689754,0.0298984032,0.0585324802,0.2430290878,-0.4115727246,0.2127870023,0.1143840179,0.2314704955,-0.1508325785,0.2350654602,-0.3559949696,0.2714752853,0.4170607924,0.2132242918,0.30559659,0.2130860984,-0.078121908,0.0865608603,0.1292979866,-0.0135739716,-0.3794960678,0.1188295856,0.241816327,-0.1854702979,0.0091848904,-0.1626182795,0.3421088755,0.0492594019,-0.490398556,0.1006153971,-0.1393199116,-0.1788205355,-0.1388723552,0.3767809272,0.4191721678,-0.3163481355,-0.0631755143,-0.2223777026,-0.1933458447,0.3068187237,0.0627034158,-0.1867482066,0.0545980632,-0.198907733,-0.0324022733,0.1652611792,-0.133823514,-0.4505629539,0.3839800358,-0.1003188565,0.4216839075,0.003368129,0.0348957814,0.0462126397,-0.1179660112,0.2082362026,-0.0482203104,-0.3429863751,0.1301704794,-0.2063286006,0.0962786973,0.1745322049,0.0263372958,0.1080550551,0.145677641,0.1164149791,-0.0307607576,0.1964359283,-0.0440106578,0.0053364923,0.2909404337,0.0424242578,-0.0588180684,0.000367176,-0.1810717881,-0.4702015221,0.3554164469,0.2276427895,0.0265536476,0.0678977966,0.0377307907,-0.3046529889,0.141935274,-0.2711749077,-0.4771591425,0.1949529499,0.3907288015,-0.1118490696,0.0888262317,-0.277343154,0.3020512462,-0.1696492136,-0.1002230644,-0.1528523117,0.1263132989,0.1340726018,-0.3565647304,-0.0337362103,0.0897880867,0.2519123256,-0.1344926953,-0.234999463,0.4969182014,0.1069902405,0.0699909925,-0.0781737417,-0.173212111,0.1972211748,-0.1314566433,-0.2321656495,0.4724216461,0.3397763968,-0.2434855402,-0.1393338144,0.186793372,-0.2051788419,0.2564637661,-0.0694206432,0.0912937298,0.328330636,0.3197959363,-0.0928376839,-0.0212596133,0.2077185959,-0.1777403057,0.1031239405,-0.1610922515,-0.2532593608,-0.0428625979,0.1946246028,0.1955534518,0.0406559408,0.0529748946,-0.3236100376,-0.2086987048,0.3706597984,0.0880206153,0.4839422405,0.0791207775,0.2721221745,0.0922638103,-0.0106658787,-0.1263325214,0.2971056998,0.0752388388,0.0984970778,0.3603755832,0.2497458905,-0.1204286814,-0.4016779959,-0.2251661271,0.1358991563,0.133663699,-0.3052351177,0.0297692902,-0.1282226741,-0.2748440802,0.1133073941,-0.4021544755,-0.2225447446,-0.3337677121,-0.2058949023,0.3451238573,-0.1469643116,0.1498992443,-0.180475831,-0.0882678479,0.308257699,-0.0031163315,-0.2322359681,-0.1135618612,-0.2594828606,0.03214081,0.0412808843,-0.3234791756,0.434748143,-0.1234986335,-0.1801847816,-0.1512944698,-0.0745449811,0.1740905792,0.0435857289,0.2001399845,0.2743394077,0.2197588384,0.039221812,0.0045702313,0.4241863489,-0.2919014394,0.0146440258,0.3762074709,0.2012005895,0.2666827142,-0.2297265977,-0.2490313053,-0.0779714212,-0.4310797453,-0.174230665,-0.0227724351,0.0273636784,0.4507275522,0.262483865,0.3891989887,0.3127643466,-0.0566316359,-0.3348741829,-0.3475183249,0.1322237253,-0.1974763721,-0.4097188413,0.1144938767,0.0011452077,0.1664847434,0.2781507671,-0.3012993634,0.0803638548,-0.0408363193,0.1188741624,-0.05930195,-0.1120122075,0.1432749033,-0.06137814,-0.1019272059,-0.3338415623,-0.1850921214,-0.1276152432,-0.1529603153,0.2324113101,0.0381841213,0.5950309038,0.207781747,0.5280631185,0.4836455286,0.0581972674,0.6157116294,-0.2377375066,0.2161288112,-0.3673551977,-0.3503070772,0.2225523591,-0.0085880933,-0.1320838332,0.208188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{"html_url":"https:\/\/github.com\/huggingface\/datasets\/issues\/2866","title":"\"counter\" dataset raises an error in normal mode, but not in streaming mode","comments":"OK. Do you think it's possible to detect this, and raise an exception (maybe `NotImplementedError`, or a specific `StreamingError`)?","body":"## Describe the bug\r\n\r\n`counter` dataset raises an error on `load_dataset()`, but simply returns an empty iterator in streaming mode.\r\n\r\n## Steps to reproduce the bug\r\n\r\n```python\r\n>>> import datasets as ds\r\n>>> a = ds.load_dataset('counter', split=\"train\", streaming=False)\r\nUsing custom data configuration default\r\nDownloading and preparing dataset counter\/default (download: 1.29 MiB, generated: 2.48 MiB, post-processed: Unknown size, total: 3.77 MiB) to \/home\/slesage\/.cache\/huggingface\/datasets\/counter\/default\/1.0.0\/9f84962fa0f35bec5a34fe0bdff8681838d497008c457f7856c48654476ec0e9...\r\nTraceback (most recent call last):\r\n File \"\/home\/slesage\/hf\/datasets-preview-backend\/.venv\/lib\/python3.8\/site-packages\/datasets\/builder.py\", line 726, in _download_and_prepare\r\n self._prepare_split(split_generator, **prepare_split_kwargs)\r\n File \"\/home\/slesage\/hf\/datasets-preview-backend\/.venv\/lib\/python3.8\/site-packages\/datasets\/builder.py\", line 1124, in _prepare_split\r\n for key, record in utils.tqdm(\r\n File \"\/home\/slesage\/hf\/datasets-preview-backend\/.venv\/lib\/python3.8\/site-packages\/tqdm\/std.py\", line 1185, in __iter__\r\n for obj in iterable:\r\n File \"\/home\/slesage\/.cache\/huggingface\/modules\/datasets_modules\/datasets\/counter\/9f84962fa0f35bec5a34fe0bdff8681838d497008c457f7856c48654476ec0e9\/counter.py\", line 161, in _generate_examples\r\n with derived_file.open(encoding=\"utf-8\") as f:\r\n File \"\/home\/slesage\/.pyenv\/versions\/3.8.11\/lib\/python3.8\/pathlib.py\", line 1222, in open\r\n return io.open(self, mode, buffering, encoding, errors, newline,\r\n File \"\/home\/slesage\/.pyenv\/versions\/3.8.11\/lib\/python3.8\/pathlib.py\", line 1078, in _opener\r\n return self._accessor.open(self, flags, mode)\r\nFileNotFoundError: [Errno 2] No such file or directory: '\/home\/slesage\/.cache\/huggingface\/datasets\/downloads\/extracted\/b57aa6db5601a738e57b95c1fd8cced54ff28fc540efcdaf0f6c4f1bb5dfe211\/COUNTER\/0032p.xml'\r\n\r\nDuring handling of the above exception, another exception occurred:\r\n\r\nTraceback (most recent call last):\r\n File \"\", line 1, in \r\n File \"\/home\/slesage\/hf\/datasets-preview-backend\/.venv\/lib\/python3.8\/site-packages\/datasets\/load.py\", line 1112, in load_dataset\r\n builder_instance.download_and_prepare(\r\n File \"\/home\/slesage\/hf\/datasets-preview-backend\/.venv\/lib\/python3.8\/site-packages\/datasets\/builder.py\", line 636, in download_and_prepare\r\n self._download_and_prepare(\r\n File \"\/home\/slesage\/hf\/datasets-preview-backend\/.venv\/lib\/python3.8\/site-packages\/datasets\/builder.py\", line 728, in _download_and_prepare\r\n raise OSError(\r\nOSError: Cannot find data file.\r\nOriginal error:\r\n[Errno 2] No such file or directory: '\/home\/slesage\/.cache\/huggingface\/datasets\/downloads\/extracted\/b57aa6db5601a738e57b95c1fd8cced54ff28fc540efcdaf0f6c4f1bb5dfe211\/COUNTER\/0032p.xml'\r\n```\r\n\r\n```python\r\n>>> import datasets as ds\r\n>>> b = ds.load_dataset('counter', split=\"train\", streaming=True)\r\nUsing custom data configuration default\r\n>>> list(b)\r\n[]\r\n```\r\n\r\n## Expected results\r\n\r\nAn exception should be raised in streaming mode\r\n\r\n## Actual results\r\n\r\nNo exception is raised in streaming mode: there is no way to tell if something has broken or if the dataset is simply empty.\r\n\r\n## Environment info\r\n\r\n- `datasets` version: 1.11.1.dev0\r\n- Platform: Linux-5.11.0-1016-aws-x86_64-with-glibc2.29\r\n- Python version: 3.8.11\r\n- PyArrow version: 4.0.1\r\n","comment_length":19,"text":"\"counter\" dataset raises an error in normal mode, but not in streaming mode\n## Describe the bug\r\n\r\n`counter` dataset raises an error on `load_dataset()`, but simply returns an empty iterator in streaming mode.\r\n\r\n## Steps to reproduce the bug\r\n\r\n```python\r\n>>> import datasets as ds\r\n>>> a = ds.load_dataset('counter', split=\"train\", streaming=False)\r\nUsing custom data configuration default\r\nDownloading and preparing dataset counter\/default (download: 1.29 MiB, generated: 2.48 MiB, post-processed: Unknown size, total: 3.77 MiB) to \/home\/slesage\/.cache\/huggingface\/datasets\/counter\/default\/1.0.0\/9f84962fa0f35bec5a34fe0bdff8681838d497008c457f7856c48654476ec0e9...\r\nTraceback (most recent call last):\r\n File \"\/home\/slesage\/hf\/datasets-preview-backend\/.venv\/lib\/python3.8\/site-packages\/datasets\/builder.py\", line 726, in _download_and_prepare\r\n self._prepare_split(split_generator, **prepare_split_kwargs)\r\n File \"\/home\/slesage\/hf\/datasets-preview-backend\/.venv\/lib\/python3.8\/site-packages\/datasets\/builder.py\", line 1124, in _prepare_split\r\n for key, record in utils.tqdm(\r\n File \"\/home\/slesage\/hf\/datasets-preview-backend\/.venv\/lib\/python3.8\/site-packages\/tqdm\/std.py\", line 1185, in __iter__\r\n for obj in iterable:\r\n File \"\/home\/slesage\/.cache\/huggingface\/modules\/datasets_modules\/datasets\/counter\/9f84962fa0f35bec5a34fe0bdff8681838d497008c457f7856c48654476ec0e9\/counter.py\", line 161, in _generate_examples\r\n with derived_file.open(encoding=\"utf-8\") as f:\r\n File \"\/home\/slesage\/.pyenv\/versions\/3.8.11\/lib\/python3.8\/pathlib.py\", line 1222, in open\r\n return io.open(self, mode, buffering, encoding, errors, newline,\r\n File \"\/home\/slesage\/.pyenv\/versions\/3.8.11\/lib\/python3.8\/pathlib.py\", line 1078, in _opener\r\n return self._accessor.open(self, flags, mode)\r\nFileNotFoundError: [Errno 2] No such file or directory: '\/home\/slesage\/.cache\/huggingface\/datasets\/downloads\/extracted\/b57aa6db5601a738e57b95c1fd8cced54ff28fc540efcdaf0f6c4f1bb5dfe211\/COUNTER\/0032p.xml'\r\n\r\nDuring handling of the above exception, another exception occurred:\r\n\r\nTraceback (most recent call last):\r\n File \"\", line 1, in \r\n File \"\/home\/slesage\/hf\/datasets-preview-backend\/.venv\/lib\/python3.8\/site-packages\/datasets\/load.py\", line 1112, in load_dataset\r\n builder_instance.download_and_prepare(\r\n File \"\/home\/slesage\/hf\/datasets-preview-backend\/.venv\/lib\/python3.8\/site-packages\/datasets\/builder.py\", line 636, in download_and_prepare\r\n self._download_and_prepare(\r\n File \"\/home\/slesage\/hf\/datasets-preview-backend\/.venv\/lib\/python3.8\/site-packages\/datasets\/builder.py\", line 728, in _download_and_prepare\r\n raise OSError(\r\nOSError: Cannot find data file.\r\nOriginal error:\r\n[Errno 2] No such file or directory: '\/home\/slesage\/.cache\/huggingface\/datasets\/downloads\/extracted\/b57aa6db5601a738e57b95c1fd8cced54ff28fc540efcdaf0f6c4f1bb5dfe211\/COUNTER\/0032p.xml'\r\n```\r\n\r\n```python\r\n>>> import datasets as ds\r\n>>> b = ds.load_dataset('counter', split=\"train\", streaming=True)\r\nUsing custom data configuration default\r\n>>> list(b)\r\n[]\r\n```\r\n\r\n## Expected results\r\n\r\nAn exception should be raised in streaming mode\r\n\r\n## Actual results\r\n\r\nNo exception is raised in streaming mode: there is no way to tell if something has broken or if the dataset is simply empty.\r\n\r\n## Environment info\r\n\r\n- `datasets` version: 1.11.1.dev0\r\n- Platform: Linux-5.11.0-1016-aws-x86_64-with-glibc2.29\r\n- Python version: 3.8.11\r\n- PyArrow version: 4.0.1\r\n\nOK. Do you think it's possible to detect this, and raise an exception (maybe `NotImplementedError`, or a specific `StreamingError`)?","embeddings":[-0.4171527922,-0.2417904586,0.0004065955,0.1936673522,0.187378943,0.0572046041,0.5056346059,0.1059950739,0.1942841411,0.1353349984,0.0698306859,0.1297213137,-0.2097095996,0.3238820434,0.0893105865,0.0787798092,0.0210811216,0.1588886231,0.0335764661,0.0774051622,-0.2157031149,0.2028147876,-0.3865993321,-0.3339452446,-0.1323988438,0.1463262439,0.1100258455,0.1485300958,0.1753474176,-0.6307143569,0.2604337335,0.0775746107,0.2229160666,0.7022691965,-0.0001109225,0.1807845384,0.4209491313,-0.138756454,-0.5919362307,-0.2953981757,-0.3683612645,-0.3184562027,0.1747525334,-0.1460207105,-0.1155984327,0.0416297689,-0.2645150125,-0.4016982019,0.2196300626,0.44546175,0.2117786556,0.4712955952,-0.1509929299,-0.1280123889,0.1274099499,0.0086588459,-0.1527633816,0.2987458408,0.0759947449,0.2464787662,-0.030185163,0.400646776,-0.0477484316,0.1930941492,0.2172417343,-0.0403118059,-0.0638400242,-0.2730312347,0.077968359,0.2267186195,0.3892460763,-0.3428210616,-0.1372255683,-0.4340849519,-0.0550197884,-0.4057786763,0.2461039424,-0.0120554632,-0.2402932793,0.0848756358,-0.3027442396,0.1529166549,0.0393640399,-0.1008400992,-0.0583141893,0.1315543056,-0.0259356182,0.1768467724,-0.0182867814,-0.0394550189,0.1186181977,-0.2750140131,-0.0770448223,0.1979830861,-0.5796865821,0.0617473088,0.3563539386,-0.483120203,0.2248359621,0.0766406581,0.3723810911,0.0808451474,0.1294206977,-0.0054175756,0.3990353644,0.2017412037,0.1839399487,-0.2446381003,0.1665526032,0.3514225185,-0.0046332767,-0.3092014194,0.0455646105,-0.0928959474,0.2623339891,0.0135595212,0.3132420182,0.0522785522,-0.3759042025,0.1984404474,-0.2598961294,0.0621640086,0.3526681066,0.2470078766,0.31240955,0.331681639,-0.1649695039,0.2566638589,-0.170162797,-0.4501440227,-0.2399167717,-0.3115689754,0.0298984032,0.0585324802,0.2430290878,-0.4115727246,0.2127870023,0.1143840179,0.2314704955,-0.1508325785,0.2350654602,-0.3559949696,0.2714752853,0.4170607924,0.2132242918,0.30559659,0.2130860984,-0.078121908,0.0865608603,0.1292979866,-0.0135739716,-0.3794960678,0.1188295856,0.241816327,-0.1854702979,0.0091848904,-0.1626182795,0.3421088755,0.0492594019,-0.490398556,0.1006153971,-0.1393199116,-0.1788205355,-0.1388723552,0.3767809272,0.4191721678,-0.3163481355,-0.0631755143,-0.2223777026,-0.1933458447,0.3068187237,0.0627034158,-0.1867482066,0.0545980632,-0.198907733,-0.0324022733,0.1652611792,-0.133823514,-0.4505629539,0.3839800358,-0.1003188565,0.4216839075,0.003368129,0.0348957814,0.0462126397,-0.1179660112,0.2082362026,-0.0482203104,-0.3429863751,0.1301704794,-0.2063286006,0.0962786973,0.1745322049,0.0263372958,0.1080550551,0.145677641,0.1164149791,-0.0307607576,0.1964359283,-0.0440106578,0.0053364923,0.2909404337,0.0424242578,-0.0588180684,0.000367176,-0.1810717881,-0.4702015221,0.3554164469,0.2276427895,0.0265536476,0.0678977966,0.0377307907,-0.3046529889,0.141935274,-0.2711749077,-0.4771591425,0.1949529499,0.3907288015,-0.1118490696,0.0888262317,-0.277343154,0.3020512462,-0.1696492136,-0.1002230644,-0.1528523117,0.1263132989,0.1340726018,-0.3565647304,-0.0337362103,0.0897880867,0.2519123256,-0.1344926953,-0.234999463,0.4969182014,0.1069902405,0.0699909925,-0.0781737417,-0.173212111,0.1972211748,-0.1314566433,-0.2321656495,0.4724216461,0.3397763968,-0.2434855402,-0.1393338144,0.186793372,-0.2051788419,0.2564637661,-0.0694206432,0.0912937298,0.328330636,0.3197959363,-0.0928376839,-0.0212596133,0.2077185959,-0.1777403057,0.1031239405,-0.1610922515,-0.2532593608,-0.0428625979,0.1946246028,0.1955534518,0.0406559408,0.0529748946,-0.3236100376,-0.2086987048,0.3706597984,0.0880206153,0.4839422405,0.0791207775,0.2721221745,0.0922638103,-0.0106658787,-0.1263325214,0.2971056998,0.0752388388,0.0984970778,0.3603755832,0.2497458905,-0.1204286814,-0.4016779959,-0.2251661271,0.1358991563,0.133663699,-0.3052351177,0.0297692902,-0.1282226741,-0.2748440802,0.1133073941,-0.4021544755,-0.2225447446,-0.3337677121,-0.2058949023,0.3451238573,-0.1469643116,0.1498992443,-0.180475831,-0.0882678479,0.308257699,-0.0031163315,-0.2322359681,-0.1135618612,-0.2594828606,0.03214081,0.0412808843,-0.3234791756,0.434748143,-0.1234986335,-0.1801847816,-0.1512944698,-0.0745449811,0.1740905792,0.0435857289,0.2001399845,0.2743394077,0.2197588384,0.039221812,0.0045702313,0.4241863489,-0.2919014394,0.0146440258,0.3762074709,0.2012005895,0.2666827142,-0.2297265977,-0.2490313053,-0.0779714212,-0.4310797453,-0.174230665,-0.0227724351,0.0273636784,0.4507275522,0.262483865,0.3891989887,0.3127643466,-0.0566316359,-0.3348741829,-0.3475183249,0.1322237253,-0.1974763721,-0.4097188413,0.1144938767,0.0011452077,0.1664847434,0.2781507671,-0.3012993634,0.0803638548,-0.0408363193,0.1188741624,-0.05930195,-0.1120122075,0.1432749033,-0.06137814,-0.1019272059,-0.3338415623,-0.1850921214,-0.1276152432,-0.1529603153,0.2324113101,0.0381841213,0.5950309038,0.207781747,0.5280631185,0.4836455286,0.0581972674,0.6157116294,-0.2377375066,0.2161288112,-0.3673551977,-0.3503070772,0.2225523591,-0.0085880933,-0.13208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{"html_url":"https:\/\/github.com\/huggingface\/datasets\/issues\/2866","title":"\"counter\" dataset raises an error in normal mode, but not in streaming mode","comments":"We should definitely support datasets using `pathlib` in streaming mode...\r\n\r\nFor non-supported datasets in streaming mode, we have already a request of raising an error\/warning: see #2654.","body":"## Describe the bug\r\n\r\n`counter` dataset raises an error on `load_dataset()`, but simply returns an empty iterator in streaming mode.\r\n\r\n## Steps to reproduce the bug\r\n\r\n```python\r\n>>> import datasets as ds\r\n>>> a = ds.load_dataset('counter', split=\"train\", streaming=False)\r\nUsing custom data configuration default\r\nDownloading and preparing dataset counter\/default (download: 1.29 MiB, generated: 2.48 MiB, post-processed: Unknown size, total: 3.77 MiB) to \/home\/slesage\/.cache\/huggingface\/datasets\/counter\/default\/1.0.0\/9f84962fa0f35bec5a34fe0bdff8681838d497008c457f7856c48654476ec0e9...\r\nTraceback (most recent call last):\r\n File \"\/home\/slesage\/hf\/datasets-preview-backend\/.venv\/lib\/python3.8\/site-packages\/datasets\/builder.py\", line 726, in _download_and_prepare\r\n self._prepare_split(split_generator, **prepare_split_kwargs)\r\n File \"\/home\/slesage\/hf\/datasets-preview-backend\/.venv\/lib\/python3.8\/site-packages\/datasets\/builder.py\", line 1124, in _prepare_split\r\n for key, record in utils.tqdm(\r\n File \"\/home\/slesage\/hf\/datasets-preview-backend\/.venv\/lib\/python3.8\/site-packages\/tqdm\/std.py\", line 1185, in __iter__\r\n for obj in iterable:\r\n File \"\/home\/slesage\/.cache\/huggingface\/modules\/datasets_modules\/datasets\/counter\/9f84962fa0f35bec5a34fe0bdff8681838d497008c457f7856c48654476ec0e9\/counter.py\", line 161, in _generate_examples\r\n with derived_file.open(encoding=\"utf-8\") as f:\r\n File \"\/home\/slesage\/.pyenv\/versions\/3.8.11\/lib\/python3.8\/pathlib.py\", line 1222, in open\r\n return io.open(self, mode, buffering, encoding, errors, newline,\r\n File \"\/home\/slesage\/.pyenv\/versions\/3.8.11\/lib\/python3.8\/pathlib.py\", line 1078, in _opener\r\n return self._accessor.open(self, flags, mode)\r\nFileNotFoundError: [Errno 2] No such file or directory: '\/home\/slesage\/.cache\/huggingface\/datasets\/downloads\/extracted\/b57aa6db5601a738e57b95c1fd8cced54ff28fc540efcdaf0f6c4f1bb5dfe211\/COUNTER\/0032p.xml'\r\n\r\nDuring handling of the above exception, another exception occurred:\r\n\r\nTraceback (most recent call last):\r\n File \"\", line 1, in \r\n File \"\/home\/slesage\/hf\/datasets-preview-backend\/.venv\/lib\/python3.8\/site-packages\/datasets\/load.py\", line 1112, in load_dataset\r\n builder_instance.download_and_prepare(\r\n File \"\/home\/slesage\/hf\/datasets-preview-backend\/.venv\/lib\/python3.8\/site-packages\/datasets\/builder.py\", line 636, in download_and_prepare\r\n self._download_and_prepare(\r\n File \"\/home\/slesage\/hf\/datasets-preview-backend\/.venv\/lib\/python3.8\/site-packages\/datasets\/builder.py\", line 728, in _download_and_prepare\r\n raise OSError(\r\nOSError: Cannot find data file.\r\nOriginal error:\r\n[Errno 2] No such file or directory: '\/home\/slesage\/.cache\/huggingface\/datasets\/downloads\/extracted\/b57aa6db5601a738e57b95c1fd8cced54ff28fc540efcdaf0f6c4f1bb5dfe211\/COUNTER\/0032p.xml'\r\n```\r\n\r\n```python\r\n>>> import datasets as ds\r\n>>> b = ds.load_dataset('counter', split=\"train\", streaming=True)\r\nUsing custom data configuration default\r\n>>> list(b)\r\n[]\r\n```\r\n\r\n## Expected results\r\n\r\nAn exception should be raised in streaming mode\r\n\r\n## Actual results\r\n\r\nNo exception is raised in streaming mode: there is no way to tell if something has broken or if the dataset is simply empty.\r\n\r\n## Environment info\r\n\r\n- `datasets` version: 1.11.1.dev0\r\n- Platform: Linux-5.11.0-1016-aws-x86_64-with-glibc2.29\r\n- Python version: 3.8.11\r\n- PyArrow version: 4.0.1\r\n","comment_length":27,"text":"\"counter\" dataset raises an error in normal mode, but not in streaming mode\n## Describe the bug\r\n\r\n`counter` dataset raises an error on `load_dataset()`, but simply returns an empty iterator in streaming mode.\r\n\r\n## Steps to reproduce the bug\r\n\r\n```python\r\n>>> import datasets as ds\r\n>>> a = ds.load_dataset('counter', split=\"train\", streaming=False)\r\nUsing custom data configuration default\r\nDownloading and preparing dataset counter\/default (download: 1.29 MiB, generated: 2.48 MiB, post-processed: Unknown size, total: 3.77 MiB) to \/home\/slesage\/.cache\/huggingface\/datasets\/counter\/default\/1.0.0\/9f84962fa0f35bec5a34fe0bdff8681838d497008c457f7856c48654476ec0e9...\r\nTraceback (most recent call last):\r\n File \"\/home\/slesage\/hf\/datasets-preview-backend\/.venv\/lib\/python3.8\/site-packages\/datasets\/builder.py\", line 726, in _download_and_prepare\r\n self._prepare_split(split_generator, **prepare_split_kwargs)\r\n File \"\/home\/slesage\/hf\/datasets-preview-backend\/.venv\/lib\/python3.8\/site-packages\/datasets\/builder.py\", line 1124, in _prepare_split\r\n for key, record in utils.tqdm(\r\n File \"\/home\/slesage\/hf\/datasets-preview-backend\/.venv\/lib\/python3.8\/site-packages\/tqdm\/std.py\", line 1185, in __iter__\r\n for obj in iterable:\r\n File \"\/home\/slesage\/.cache\/huggingface\/modules\/datasets_modules\/datasets\/counter\/9f84962fa0f35bec5a34fe0bdff8681838d497008c457f7856c48654476ec0e9\/counter.py\", line 161, in _generate_examples\r\n with derived_file.open(encoding=\"utf-8\") as f:\r\n File \"\/home\/slesage\/.pyenv\/versions\/3.8.11\/lib\/python3.8\/pathlib.py\", line 1222, in open\r\n return io.open(self, mode, buffering, encoding, errors, newline,\r\n File \"\/home\/slesage\/.pyenv\/versions\/3.8.11\/lib\/python3.8\/pathlib.py\", line 1078, in _opener\r\n return self._accessor.open(self, flags, mode)\r\nFileNotFoundError: [Errno 2] No such file or directory: '\/home\/slesage\/.cache\/huggingface\/datasets\/downloads\/extracted\/b57aa6db5601a738e57b95c1fd8cced54ff28fc540efcdaf0f6c4f1bb5dfe211\/COUNTER\/0032p.xml'\r\n\r\nDuring handling of the above exception, another exception occurred:\r\n\r\nTraceback (most recent call last):\r\n File \"\", line 1, in \r\n File \"\/home\/slesage\/hf\/datasets-preview-backend\/.venv\/lib\/python3.8\/site-packages\/datasets\/load.py\", line 1112, in load_dataset\r\n builder_instance.download_and_prepare(\r\n File \"\/home\/slesage\/hf\/datasets-preview-backend\/.venv\/lib\/python3.8\/site-packages\/datasets\/builder.py\", line 636, in download_and_prepare\r\n self._download_and_prepare(\r\n File \"\/home\/slesage\/hf\/datasets-preview-backend\/.venv\/lib\/python3.8\/site-packages\/datasets\/builder.py\", line 728, in _download_and_prepare\r\n raise OSError(\r\nOSError: Cannot find data file.\r\nOriginal error:\r\n[Errno 2] No such file or directory: '\/home\/slesage\/.cache\/huggingface\/datasets\/downloads\/extracted\/b57aa6db5601a738e57b95c1fd8cced54ff28fc540efcdaf0f6c4f1bb5dfe211\/COUNTER\/0032p.xml'\r\n```\r\n\r\n```python\r\n>>> import datasets as ds\r\n>>> b = ds.load_dataset('counter', split=\"train\", streaming=True)\r\nUsing custom data configuration default\r\n>>> list(b)\r\n[]\r\n```\r\n\r\n## Expected results\r\n\r\nAn exception should be raised in streaming mode\r\n\r\n## Actual results\r\n\r\nNo exception is raised in streaming mode: there is no way to tell if something has broken or if the dataset is simply empty.\r\n\r\n## Environment info\r\n\r\n- `datasets` version: 1.11.1.dev0\r\n- Platform: Linux-5.11.0-1016-aws-x86_64-with-glibc2.29\r\n- Python version: 3.8.11\r\n- PyArrow version: 4.0.1\r\n\nWe should definitely support datasets using `pathlib` in streaming mode...\r\n\r\nFor non-supported datasets in streaming mode, we have already a request of raising an error\/warning: see #2654.","embeddings":[-0.4171527922,-0.2417904586,0.0004065955,0.1936673522,0.187378943,0.0572046041,0.5056346059,0.1059950739,0.1942841411,0.1353349984,0.0698306859,0.1297213137,-0.2097095996,0.3238820434,0.0893105865,0.0787798092,0.0210811216,0.1588886231,0.0335764661,0.0774051622,-0.2157031149,0.2028147876,-0.3865993321,-0.3339452446,-0.1323988438,0.1463262439,0.1100258455,0.1485300958,0.1753474176,-0.6307143569,0.2604337335,0.0775746107,0.2229160666,0.7022691965,-0.0001109225,0.1807845384,0.4209491313,-0.138756454,-0.5919362307,-0.2953981757,-0.3683612645,-0.3184562027,0.1747525334,-0.1460207105,-0.1155984327,0.0416297689,-0.2645150125,-0.4016982019,0.2196300626,0.44546175,0.2117786556,0.4712955952,-0.1509929299,-0.1280123889,0.1274099499,0.0086588459,-0.1527633816,0.2987458408,0.0759947449,0.2464787662,-0.030185163,0.400646776,-0.0477484316,0.1930941492,0.2172417343,-0.0403118059,-0.0638400242,-0.2730312347,0.077968359,0.2267186195,0.3892460763,-0.3428210616,-0.1372255683,-0.4340849519,-0.0550197884,-0.4057786763,0.2461039424,-0.0120554632,-0.2402932793,0.0848756358,-0.3027442396,0.1529166549,0.0393640399,-0.1008400992,-0.0583141893,0.1315543056,-0.0259356182,0.1768467724,-0.0182867814,-0.0394550189,0.1186181977,-0.2750140131,-0.0770448223,0.1979830861,-0.5796865821,0.0617473088,0.3563539386,-0.483120203,0.2248359621,0.0766406581,0.3723810911,0.0808451474,0.1294206977,-0.0054175756,0.3990353644,0.2017412037,0.1839399487,-0.2446381003,0.1665526032,0.3514225185,-0.0046332767,-0.3092014194,0.0455646105,-0.0928959474,0.2623339891,0.0135595212,0.3132420182,0.0522785522,-0.3759042025,0.1984404474,-0.2598961294,0.0621640086,0.3526681066,0.2470078766,0.31240955,0.331681639,-0.1649695039,0.2566638589,-0.170162797,-0.4501440227,-0.2399167717,-0.3115689754,0.0298984032,0.0585324802,0.2430290878,-0.4115727246,0.2127870023,0.1143840179,0.2314704955,-0.1508325785,0.2350654602,-0.3559949696,0.2714752853,0.4170607924,0.2132242918,0.30559659,0.2130860984,-0.078121908,0.0865608603,0.1292979866,-0.0135739716,-0.3794960678,0.1188295856,0.241816327,-0.1854702979,0.0091848904,-0.1626182795,0.3421088755,0.0492594019,-0.490398556,0.1006153971,-0.1393199116,-0.1788205355,-0.1388723552,0.3767809272,0.4191721678,-0.3163481355,-0.0631755143,-0.2223777026,-0.1933458447,0.3068187237,0.0627034158,-0.1867482066,0.0545980632,-0.198907733,-0.0324022733,0.1652611792,-0.133823514,-0.4505629539,0.3839800358,-0.1003188565,0.4216839075,0.003368129,0.0348957814,0.0462126397,-0.1179660112,0.2082362026,-0.0482203104,-0.3429863751,0.1301704794,-0.2063286006,0.0962786973,0.1745322049,0.0263372958,0.1080550551,0.145677641,0.1164149791,-0.0307607576,0.1964359283,-0.0440106578,0.0053364923,0.2909404337,0.0424242578,-0.0588180684,0.000367176,-0.1810717881,-0.4702015221,0.3554164469,0.2276427895,0.0265536476,0.0678977966,0.0377307907,-0.3046529889,0.141935274,-0.2711749077,-0.4771591425,0.1949529499,0.3907288015,-0.1118490696,0.0888262317,-0.277343154,0.3020512462,-0.1696492136,-0.1002230644,-0.1528523117,0.1263132989,0.1340726018,-0.3565647304,-0.0337362103,0.0897880867,0.2519123256,-0.1344926953,-0.234999463,0.4969182014,0.1069902405,0.0699909925,-0.0781737417,-0.173212111,0.1972211748,-0.1314566433,-0.2321656495,0.4724216461,0.3397763968,-0.2434855402,-0.1393338144,0.186793372,-0.2051788419,0.2564637661,-0.0694206432,0.0912937298,0.328330636,0.3197959363,-0.0928376839,-0.0212596133,0.2077185959,-0.1777403057,0.1031239405,-0.1610922515,-0.2532593608,-0.0428625979,0.1946246028,0.1955534518,0.0406559408,0.0529748946,-0.3236100376,-0.2086987048,0.3706597984,0.0880206153,0.4839422405,0.0791207775,0.2721221745,0.0922638103,-0.0106658787,-0.1263325214,0.2971056998,0.0752388388,0.0984970778,0.3603755832,0.2497458905,-0.1204286814,-0.4016779959,-0.2251661271,0.1358991563,0.133663699,-0.3052351177,0.0297692902,-0.1282226741,-0.2748440802,0.1133073941,-0.4021544755,-0.2225447446,-0.3337677121,-0.2058949023,0.3451238573,-0.1469643116,0.1498992443,-0.180475831,-0.0882678479,0.308257699,-0.0031163315,-0.2322359681,-0.1135618612,-0.2594828606,0.03214081,0.0412808843,-0.3234791756,0.434748143,-0.1234986335,-0.1801847816,-0.1512944698,-0.0745449811,0.1740905792,0.0435857289,0.2001399845,0.2743394077,0.2197588384,0.039221812,0.0045702313,0.4241863489,-0.2919014394,0.0146440258,0.3762074709,0.2012005895,0.2666827142,-0.2297265977,-0.2490313053,-0.0779714212,-0.4310797453,-0.174230665,-0.0227724351,0.0273636784,0.4507275522,0.262483865,0.3891989887,0.3127643466,-0.0566316359,-0.3348741829,-0.3475183249,0.1322237253,-0.1974763721,-0.4097188413,0.1144938767,0.0011452077,0.1664847434,0.2781507671,-0.3012993634,0.0803638548,-0.0408363193,0.1188741624,-0.05930195,-0.1120122075,0.1432749033,-0.06137814,-0.1019272059,-0.3338415623,-0.1850921214,-0.1276152432,-0.1529603153,0.2324113101,0.0381841213,0.5950309038,0.207781747,0.5280631185,0.4836455286,0.0581972674,0.6157116294,-0.2377375066,0.2161288112,-0.3673551977,-0.3503070772,0.2225523591,-0.0085880933,-0.1320838332,0.2081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{"html_url":"https:\/\/github.com\/huggingface\/datasets\/issues\/2866","title":"\"counter\" dataset raises an error in normal mode, but not in streaming mode","comments":"Hi @severo, please note that \"counter\" dataset will be streamable (at least until it arrives at the missing file, error already in normal mode) once these PRs are merged:\r\n- #2874\r\n- #2876\r\n- #2880\r\n\r\nI have tested it. \ud83d\ude09 ","body":"## Describe the bug\r\n\r\n`counter` dataset raises an error on `load_dataset()`, but simply returns an empty iterator in streaming mode.\r\n\r\n## Steps to reproduce the bug\r\n\r\n```python\r\n>>> import datasets as ds\r\n>>> a = ds.load_dataset('counter', split=\"train\", streaming=False)\r\nUsing custom data configuration default\r\nDownloading and preparing dataset counter\/default (download: 1.29 MiB, generated: 2.48 MiB, post-processed: Unknown size, total: 3.77 MiB) to \/home\/slesage\/.cache\/huggingface\/datasets\/counter\/default\/1.0.0\/9f84962fa0f35bec5a34fe0bdff8681838d497008c457f7856c48654476ec0e9...\r\nTraceback (most recent call last):\r\n File \"\/home\/slesage\/hf\/datasets-preview-backend\/.venv\/lib\/python3.8\/site-packages\/datasets\/builder.py\", line 726, in _download_and_prepare\r\n self._prepare_split(split_generator, **prepare_split_kwargs)\r\n File \"\/home\/slesage\/hf\/datasets-preview-backend\/.venv\/lib\/python3.8\/site-packages\/datasets\/builder.py\", line 1124, in _prepare_split\r\n for key, record in utils.tqdm(\r\n File \"\/home\/slesage\/hf\/datasets-preview-backend\/.venv\/lib\/python3.8\/site-packages\/tqdm\/std.py\", line 1185, in __iter__\r\n for obj in iterable:\r\n File \"\/home\/slesage\/.cache\/huggingface\/modules\/datasets_modules\/datasets\/counter\/9f84962fa0f35bec5a34fe0bdff8681838d497008c457f7856c48654476ec0e9\/counter.py\", line 161, in _generate_examples\r\n with derived_file.open(encoding=\"utf-8\") as f:\r\n File \"\/home\/slesage\/.pyenv\/versions\/3.8.11\/lib\/python3.8\/pathlib.py\", line 1222, in open\r\n return io.open(self, mode, buffering, encoding, errors, newline,\r\n File \"\/home\/slesage\/.pyenv\/versions\/3.8.11\/lib\/python3.8\/pathlib.py\", line 1078, in _opener\r\n return self._accessor.open(self, flags, mode)\r\nFileNotFoundError: [Errno 2] No such file or directory: '\/home\/slesage\/.cache\/huggingface\/datasets\/downloads\/extracted\/b57aa6db5601a738e57b95c1fd8cced54ff28fc540efcdaf0f6c4f1bb5dfe211\/COUNTER\/0032p.xml'\r\n\r\nDuring handling of the above exception, another exception occurred:\r\n\r\nTraceback (most recent call last):\r\n File \"\", line 1, in \r\n File \"\/home\/slesage\/hf\/datasets-preview-backend\/.venv\/lib\/python3.8\/site-packages\/datasets\/load.py\", line 1112, in load_dataset\r\n builder_instance.download_and_prepare(\r\n File \"\/home\/slesage\/hf\/datasets-preview-backend\/.venv\/lib\/python3.8\/site-packages\/datasets\/builder.py\", line 636, in download_and_prepare\r\n self._download_and_prepare(\r\n File \"\/home\/slesage\/hf\/datasets-preview-backend\/.venv\/lib\/python3.8\/site-packages\/datasets\/builder.py\", line 728, in _download_and_prepare\r\n raise OSError(\r\nOSError: Cannot find data file.\r\nOriginal error:\r\n[Errno 2] No such file or directory: '\/home\/slesage\/.cache\/huggingface\/datasets\/downloads\/extracted\/b57aa6db5601a738e57b95c1fd8cced54ff28fc540efcdaf0f6c4f1bb5dfe211\/COUNTER\/0032p.xml'\r\n```\r\n\r\n```python\r\n>>> import datasets as ds\r\n>>> b = ds.load_dataset('counter', split=\"train\", streaming=True)\r\nUsing custom data configuration default\r\n>>> list(b)\r\n[]\r\n```\r\n\r\n## Expected results\r\n\r\nAn exception should be raised in streaming mode\r\n\r\n## Actual results\r\n\r\nNo exception is raised in streaming mode: there is no way to tell if something has broken or if the dataset is simply empty.\r\n\r\n## Environment info\r\n\r\n- `datasets` version: 1.11.1.dev0\r\n- Platform: Linux-5.11.0-1016-aws-x86_64-with-glibc2.29\r\n- Python version: 3.8.11\r\n- PyArrow version: 4.0.1\r\n","comment_length":40,"text":"\"counter\" dataset raises an error in normal mode, but not in streaming mode\n## Describe the bug\r\n\r\n`counter` dataset raises an error on `load_dataset()`, but simply returns an empty iterator in streaming mode.\r\n\r\n## Steps to reproduce the bug\r\n\r\n```python\r\n>>> import datasets as ds\r\n>>> a = ds.load_dataset('counter', split=\"train\", streaming=False)\r\nUsing custom data configuration default\r\nDownloading and preparing dataset counter\/default (download: 1.29 MiB, generated: 2.48 MiB, post-processed: Unknown size, total: 3.77 MiB) to \/home\/slesage\/.cache\/huggingface\/datasets\/counter\/default\/1.0.0\/9f84962fa0f35bec5a34fe0bdff8681838d497008c457f7856c48654476ec0e9...\r\nTraceback (most recent call last):\r\n File \"\/home\/slesage\/hf\/datasets-preview-backend\/.venv\/lib\/python3.8\/site-packages\/datasets\/builder.py\", line 726, in _download_and_prepare\r\n self._prepare_split(split_generator, **prepare_split_kwargs)\r\n File \"\/home\/slesage\/hf\/datasets-preview-backend\/.venv\/lib\/python3.8\/site-packages\/datasets\/builder.py\", line 1124, in _prepare_split\r\n for key, record in utils.tqdm(\r\n File \"\/home\/slesage\/hf\/datasets-preview-backend\/.venv\/lib\/python3.8\/site-packages\/tqdm\/std.py\", line 1185, in __iter__\r\n for obj in iterable:\r\n File \"\/home\/slesage\/.cache\/huggingface\/modules\/datasets_modules\/datasets\/counter\/9f84962fa0f35bec5a34fe0bdff8681838d497008c457f7856c48654476ec0e9\/counter.py\", line 161, in _generate_examples\r\n with derived_file.open(encoding=\"utf-8\") as f:\r\n File \"\/home\/slesage\/.pyenv\/versions\/3.8.11\/lib\/python3.8\/pathlib.py\", line 1222, in open\r\n return io.open(self, mode, buffering, encoding, errors, newline,\r\n File \"\/home\/slesage\/.pyenv\/versions\/3.8.11\/lib\/python3.8\/pathlib.py\", line 1078, in _opener\r\n return self._accessor.open(self, flags, mode)\r\nFileNotFoundError: [Errno 2] No such file or directory: '\/home\/slesage\/.cache\/huggingface\/datasets\/downloads\/extracted\/b57aa6db5601a738e57b95c1fd8cced54ff28fc540efcdaf0f6c4f1bb5dfe211\/COUNTER\/0032p.xml'\r\n\r\nDuring handling of the above exception, another exception occurred:\r\n\r\nTraceback (most recent call last):\r\n File \"\", line 1, in \r\n File \"\/home\/slesage\/hf\/datasets-preview-backend\/.venv\/lib\/python3.8\/site-packages\/datasets\/load.py\", line 1112, in load_dataset\r\n builder_instance.download_and_prepare(\r\n File \"\/home\/slesage\/hf\/datasets-preview-backend\/.venv\/lib\/python3.8\/site-packages\/datasets\/builder.py\", line 636, in download_and_prepare\r\n self._download_and_prepare(\r\n File \"\/home\/slesage\/hf\/datasets-preview-backend\/.venv\/lib\/python3.8\/site-packages\/datasets\/builder.py\", line 728, in _download_and_prepare\r\n raise OSError(\r\nOSError: Cannot find data file.\r\nOriginal error:\r\n[Errno 2] No such file or directory: '\/home\/slesage\/.cache\/huggingface\/datasets\/downloads\/extracted\/b57aa6db5601a738e57b95c1fd8cced54ff28fc540efcdaf0f6c4f1bb5dfe211\/COUNTER\/0032p.xml'\r\n```\r\n\r\n```python\r\n>>> import datasets as ds\r\n>>> b = ds.load_dataset('counter', split=\"train\", streaming=True)\r\nUsing custom data configuration default\r\n>>> list(b)\r\n[]\r\n```\r\n\r\n## Expected results\r\n\r\nAn exception should be raised in streaming mode\r\n\r\n## Actual results\r\n\r\nNo exception is raised in streaming mode: there is no way to tell if something has broken or if the dataset is simply empty.\r\n\r\n## Environment info\r\n\r\n- `datasets` version: 1.11.1.dev0\r\n- Platform: Linux-5.11.0-1016-aws-x86_64-with-glibc2.29\r\n- Python version: 3.8.11\r\n- PyArrow version: 4.0.1\r\n\nHi @severo, please note that \"counter\" dataset will be streamable (at least until it arrives at the missing file, error already in normal mode) once these PRs are merged:\r\n- #2874\r\n- #2876\r\n- #2880\r\n\r\nI have tested it. \ud83d\ude09 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{"html_url":"https:\/\/github.com\/huggingface\/datasets\/issues\/2866","title":"\"counter\" dataset raises an error in normal mode, but not in streaming mode","comments":"Now (on master), we get:\r\n\r\n```\r\nimport datasets as ds\r\nds.load_dataset('counter', split=\"train\", streaming=False)\r\n```\r\n\r\n```\r\nUsing custom data configuration default\r\nDownloading and preparing dataset counter\/default (download: 1.29 MiB, generated: 2.48 MiB, post-processed: Unknown size, total: 3.77 MiB) to \/home\/slesage\/.cache\/huggingface\/datasets\/counter\/default\/1.0.0\/9f84962fa0f35bec5a34fe0bdff8681838d497008c457f7856c48654476ec0e9...\r\nTraceback (most recent call last):\r\n File \"\/home\/slesage\/hf\/datasets\/src\/datasets\/builder.py\", line 726, in _download_and_prepare\r\n self._prepare_split(split_generator, **prepare_split_kwargs)\r\n File \"\/home\/slesage\/hf\/datasets\/src\/datasets\/builder.py\", line 1124, in _prepare_split\r\n for key, record in utils.tqdm(\r\n File \"\/home\/slesage\/hf\/datasets\/.venv\/lib\/python3.8\/site-packages\/tqdm\/std.py\", line 1185, in __iter__\r\n for obj in iterable:\r\n File \"\/home\/slesage\/.cache\/huggingface\/modules\/datasets_modules\/datasets\/counter\/9f84962fa0f35bec5a34fe0bdff8681838d497008c457f7856c48654476ec0e9\/counter.py\", line 161, in _generate_examples\r\n with derived_file.open(encoding=\"utf-8\") as f:\r\n File \"\/home\/slesage\/.pyenv\/versions\/3.8.11\/lib\/python3.8\/pathlib.py\", line 1222, in open\r\n return io.open(self, mode, buffering, encoding, errors, newline,\r\n File \"\/home\/slesage\/.pyenv\/versions\/3.8.11\/lib\/python3.8\/pathlib.py\", line 1078, in _opener\r\n return self._accessor.open(self, flags, mode)\r\nFileNotFoundError: [Errno 2] No such file or directory: '\/home\/slesage\/.cache\/huggingface\/datasets\/downloads\/extracted\/b57aa6db5601a738e57b95c1fd8cced54ff28fc540efcdaf0f6c4f1bb5dfe211\/COUNTER\/0032p.xml'\r\n\r\nDuring handling of the above exception, another exception occurred:\r\n\r\nTraceback (most recent call last):\r\n File \"\", line 1, in \r\n File \"\/home\/slesage\/hf\/datasets\/src\/datasets\/load.py\", line 1112, in load_dataset\r\n builder_instance.download_and_prepare(\r\n File \"\/home\/slesage\/hf\/datasets\/src\/datasets\/builder.py\", line 636, in download_and_prepare\r\n self._download_and_prepare(\r\n File \"\/home\/slesage\/hf\/datasets\/src\/datasets\/builder.py\", line 728, in _download_and_prepare\r\n raise OSError(\r\nOSError: Cannot find data file.\r\nOriginal error:\r\n[Errno 2] No such file or directory: '\/home\/slesage\/.cache\/huggingface\/datasets\/downloads\/extracted\/b57aa6db5601a738e57b95c1fd8cced54ff28fc540efcdaf0f6c4f1bb5dfe211\/COUNTER\/0032p.xml'\r\n```\r\n\r\nThe error is now the same with or without streaming. I close the issue, thanks @albertvillanova and @lhoestq!\r\n","body":"## Describe the bug\r\n\r\n`counter` dataset raises an error on `load_dataset()`, but simply returns an empty iterator in streaming mode.\r\n\r\n## Steps to reproduce the bug\r\n\r\n```python\r\n>>> import datasets as ds\r\n>>> a = ds.load_dataset('counter', split=\"train\", streaming=False)\r\nUsing custom data configuration default\r\nDownloading and preparing dataset counter\/default (download: 1.29 MiB, generated: 2.48 MiB, post-processed: Unknown size, total: 3.77 MiB) to \/home\/slesage\/.cache\/huggingface\/datasets\/counter\/default\/1.0.0\/9f84962fa0f35bec5a34fe0bdff8681838d497008c457f7856c48654476ec0e9...\r\nTraceback (most recent call last):\r\n File \"\/home\/slesage\/hf\/datasets-preview-backend\/.venv\/lib\/python3.8\/site-packages\/datasets\/builder.py\", line 726, in _download_and_prepare\r\n self._prepare_split(split_generator, **prepare_split_kwargs)\r\n File \"\/home\/slesage\/hf\/datasets-preview-backend\/.venv\/lib\/python3.8\/site-packages\/datasets\/builder.py\", line 1124, in _prepare_split\r\n for key, record in utils.tqdm(\r\n File \"\/home\/slesage\/hf\/datasets-preview-backend\/.venv\/lib\/python3.8\/site-packages\/tqdm\/std.py\", line 1185, in __iter__\r\n for obj in iterable:\r\n File \"\/home\/slesage\/.cache\/huggingface\/modules\/datasets_modules\/datasets\/counter\/9f84962fa0f35bec5a34fe0bdff8681838d497008c457f7856c48654476ec0e9\/counter.py\", line 161, in _generate_examples\r\n with derived_file.open(encoding=\"utf-8\") as f:\r\n File \"\/home\/slesage\/.pyenv\/versions\/3.8.11\/lib\/python3.8\/pathlib.py\", line 1222, in open\r\n return io.open(self, mode, buffering, encoding, errors, newline,\r\n File \"\/home\/slesage\/.pyenv\/versions\/3.8.11\/lib\/python3.8\/pathlib.py\", line 1078, in _opener\r\n return self._accessor.open(self, flags, mode)\r\nFileNotFoundError: [Errno 2] No such file or directory: '\/home\/slesage\/.cache\/huggingface\/datasets\/downloads\/extracted\/b57aa6db5601a738e57b95c1fd8cced54ff28fc540efcdaf0f6c4f1bb5dfe211\/COUNTER\/0032p.xml'\r\n\r\nDuring handling of the above exception, another exception occurred:\r\n\r\nTraceback (most recent call last):\r\n File \"\", line 1, in \r\n File \"\/home\/slesage\/hf\/datasets-preview-backend\/.venv\/lib\/python3.8\/site-packages\/datasets\/load.py\", line 1112, in load_dataset\r\n builder_instance.download_and_prepare(\r\n File \"\/home\/slesage\/hf\/datasets-preview-backend\/.venv\/lib\/python3.8\/site-packages\/datasets\/builder.py\", line 636, in download_and_prepare\r\n self._download_and_prepare(\r\n File \"\/home\/slesage\/hf\/datasets-preview-backend\/.venv\/lib\/python3.8\/site-packages\/datasets\/builder.py\", line 728, in _download_and_prepare\r\n raise OSError(\r\nOSError: Cannot find data file.\r\nOriginal error:\r\n[Errno 2] No such file or directory: '\/home\/slesage\/.cache\/huggingface\/datasets\/downloads\/extracted\/b57aa6db5601a738e57b95c1fd8cced54ff28fc540efcdaf0f6c4f1bb5dfe211\/COUNTER\/0032p.xml'\r\n```\r\n\r\n```python\r\n>>> import datasets as ds\r\n>>> b = ds.load_dataset('counter', split=\"train\", streaming=True)\r\nUsing custom data configuration default\r\n>>> list(b)\r\n[]\r\n```\r\n\r\n## Expected results\r\n\r\nAn exception should be raised in streaming mode\r\n\r\n## Actual results\r\n\r\nNo exception is raised in streaming mode: there is no way to tell if something has broken or if the dataset is simply empty.\r\n\r\n## Environment info\r\n\r\n- `datasets` version: 1.11.1.dev0\r\n- Platform: Linux-5.11.0-1016-aws-x86_64-with-glibc2.29\r\n- Python version: 3.8.11\r\n- PyArrow version: 4.0.1\r\n","comment_length":191,"text":"\"counter\" dataset raises an error in normal mode, but not in streaming mode\n## Describe the bug\r\n\r\n`counter` dataset raises an error on `load_dataset()`, but simply returns an empty iterator in streaming mode.\r\n\r\n## Steps to reproduce the bug\r\n\r\n```python\r\n>>> import datasets as ds\r\n>>> a = ds.load_dataset('counter', split=\"train\", streaming=False)\r\nUsing custom data configuration default\r\nDownloading and preparing dataset counter\/default (download: 1.29 MiB, generated: 2.48 MiB, post-processed: Unknown size, total: 3.77 MiB) to \/home\/slesage\/.cache\/huggingface\/datasets\/counter\/default\/1.0.0\/9f84962fa0f35bec5a34fe0bdff8681838d497008c457f7856c48654476ec0e9...\r\nTraceback (most recent call last):\r\n File \"\/home\/slesage\/hf\/datasets-preview-backend\/.venv\/lib\/python3.8\/site-packages\/datasets\/builder.py\", line 726, in _download_and_prepare\r\n self._prepare_split(split_generator, **prepare_split_kwargs)\r\n File \"\/home\/slesage\/hf\/datasets-preview-backend\/.venv\/lib\/python3.8\/site-packages\/datasets\/builder.py\", line 1124, in _prepare_split\r\n for key, record in utils.tqdm(\r\n File \"\/home\/slesage\/hf\/datasets-preview-backend\/.venv\/lib\/python3.8\/site-packages\/tqdm\/std.py\", line 1185, in __iter__\r\n for obj in iterable:\r\n File \"\/home\/slesage\/.cache\/huggingface\/modules\/datasets_modules\/datasets\/counter\/9f84962fa0f35bec5a34fe0bdff8681838d497008c457f7856c48654476ec0e9\/counter.py\", line 161, in _generate_examples\r\n with derived_file.open(encoding=\"utf-8\") as f:\r\n File \"\/home\/slesage\/.pyenv\/versions\/3.8.11\/lib\/python3.8\/pathlib.py\", line 1222, in open\r\n return io.open(self, mode, buffering, encoding, errors, newline,\r\n File \"\/home\/slesage\/.pyenv\/versions\/3.8.11\/lib\/python3.8\/pathlib.py\", line 1078, in _opener\r\n return self._accessor.open(self, flags, mode)\r\nFileNotFoundError: [Errno 2] No such file or directory: '\/home\/slesage\/.cache\/huggingface\/datasets\/downloads\/extracted\/b57aa6db5601a738e57b95c1fd8cced54ff28fc540efcdaf0f6c4f1bb5dfe211\/COUNTER\/0032p.xml'\r\n\r\nDuring handling of the above exception, another exception occurred:\r\n\r\nTraceback (most recent call last):\r\n File \"\", line 1, in \r\n File \"\/home\/slesage\/hf\/datasets-preview-backend\/.venv\/lib\/python3.8\/site-packages\/datasets\/load.py\", line 1112, in load_dataset\r\n builder_instance.download_and_prepare(\r\n File \"\/home\/slesage\/hf\/datasets-preview-backend\/.venv\/lib\/python3.8\/site-packages\/datasets\/builder.py\", line 636, in download_and_prepare\r\n self._download_and_prepare(\r\n File \"\/home\/slesage\/hf\/datasets-preview-backend\/.venv\/lib\/python3.8\/site-packages\/datasets\/builder.py\", line 728, in _download_and_prepare\r\n raise OSError(\r\nOSError: Cannot find data file.\r\nOriginal error:\r\n[Errno 2] No such file or directory: '\/home\/slesage\/.cache\/huggingface\/datasets\/downloads\/extracted\/b57aa6db5601a738e57b95c1fd8cced54ff28fc540efcdaf0f6c4f1bb5dfe211\/COUNTER\/0032p.xml'\r\n```\r\n\r\n```python\r\n>>> import datasets as ds\r\n>>> b = ds.load_dataset('counter', split=\"train\", streaming=True)\r\nUsing custom data configuration default\r\n>>> list(b)\r\n[]\r\n```\r\n\r\n## Expected results\r\n\r\nAn exception should be raised in streaming mode\r\n\r\n## Actual results\r\n\r\nNo exception is raised in streaming mode: there is no way to tell if something has broken or if the dataset is simply empty.\r\n\r\n## Environment info\r\n\r\n- `datasets` version: 1.11.1.dev0\r\n- Platform: Linux-5.11.0-1016-aws-x86_64-with-glibc2.29\r\n- Python version: 3.8.11\r\n- PyArrow version: 4.0.1\r\n\nNow (on master), we get:\r\n\r\n```\r\nimport datasets as ds\r\nds.load_dataset('counter', split=\"train\", streaming=False)\r\n```\r\n\r\n```\r\nUsing custom data configuration default\r\nDownloading and preparing dataset counter\/default (download: 1.29 MiB, generated: 2.48 MiB, post-processed: Unknown size, total: 3.77 MiB) to \/home\/slesage\/.cache\/huggingface\/datasets\/counter\/default\/1.0.0\/9f84962fa0f35bec5a34fe0bdff8681838d497008c457f7856c48654476ec0e9...\r\nTraceback (most recent call last):\r\n File \"\/home\/slesage\/hf\/datasets\/src\/datasets\/builder.py\", line 726, in _download_and_prepare\r\n self._prepare_split(split_generator, **prepare_split_kwargs)\r\n File \"\/home\/slesage\/hf\/datasets\/src\/datasets\/builder.py\", line 1124, in _prepare_split\r\n for key, record in utils.tqdm(\r\n File \"\/home\/slesage\/hf\/datasets\/.venv\/lib\/python3.8\/site-packages\/tqdm\/std.py\", line 1185, in __iter__\r\n for obj in iterable:\r\n File \"\/home\/slesage\/.cache\/huggingface\/modules\/datasets_modules\/datasets\/counter\/9f84962fa0f35bec5a34fe0bdff8681838d497008c457f7856c48654476ec0e9\/counter.py\", line 161, in _generate_examples\r\n with derived_file.open(encoding=\"utf-8\") as f:\r\n File \"\/home\/slesage\/.pyenv\/versions\/3.8.11\/lib\/python3.8\/pathlib.py\", line 1222, in open\r\n return io.open(self, mode, buffering, encoding, errors, newline,\r\n File \"\/home\/slesage\/.pyenv\/versions\/3.8.11\/lib\/python3.8\/pathlib.py\", line 1078, in _opener\r\n return self._accessor.open(self, flags, mode)\r\nFileNotFoundError: [Errno 2] No such file or directory: '\/home\/slesage\/.cache\/huggingface\/datasets\/downloads\/extracted\/b57aa6db5601a738e57b95c1fd8cced54ff28fc540efcdaf0f6c4f1bb5dfe211\/COUNTER\/0032p.xml'\r\n\r\nDuring handling of the above exception, another exception occurred:\r\n\r\nTraceback (most recent call last):\r\n File \"\", line 1, in \r\n File \"\/home\/slesage\/hf\/datasets\/src\/datasets\/load.py\", line 1112, in load_dataset\r\n builder_instance.download_and_prepare(\r\n File \"\/home\/slesage\/hf\/datasets\/src\/datasets\/builder.py\", line 636, in download_and_prepare\r\n self._download_and_prepare(\r\n File \"\/home\/slesage\/hf\/datasets\/src\/datasets\/builder.py\", line 728, in _download_and_prepare\r\n raise OSError(\r\nOSError: Cannot find data file.\r\nOriginal error:\r\n[Errno 2] No such file or directory: '\/home\/slesage\/.cache\/huggingface\/datasets\/downloads\/extracted\/b57aa6db5601a738e57b95c1fd8cced54ff28fc540efcdaf0f6c4f1bb5dfe211\/COUNTER\/0032p.xml'\r\n```\r\n\r\nThe error is now the same with or without streaming. I close the issue, thanks @albertvillanova and @lhoestq!\r\n","embeddings":[-0.4171527922,-0.2417904586,0.0004065955,0.1936673522,0.187378943,0.0572046041,0.5056346059,0.1059950739,0.1942841411,0.1353349984,0.0698306859,0.1297213137,-0.2097095996,0.3238820434,0.0893105865,0.0787798092,0.0210811216,0.1588886231,0.0335764661,0.0774051622,-0.2157031149,0.2028147876,-0.3865993321,-0.3339452446,-0.1323988438,0.1463262439,0.1100258455,0.1485300958,0.1753474176,-0.6307143569,0.2604337335,0.0775746107,0.2229160666,0.7022691965,-0.0001109225,0.1807845384,0.4209491313,-0.138756454,-0.5919362307,-0.2953981757,-0.3683612645,-0.3184562027,0.1747525334,-0.1460207105,-0.1155984327,0.0416297689,-0.2645150125,-0.4016982019,0.2196300626,0.44546175,0.2117786556,0.4712955952,-0.1509929299,-0.1280123889,0.1274099499,0.0086588459,-0.1527633816,0.2987458408,0.0759947449,0.2464787662,-0.030185163,0.400646776,-0.0477484316,0.1930941492,0.2172417343,-0.0403118059,-0.0638400242,-0.2730312347,0.077968359,0.2267186195,0.3892460763,-0.3428210616,-0.1372255683,-0.4340849519,-0.0550197884,-0.4057786763,0.2461039424,-0.0120554632,-0.2402932793,0.0848756358,-0.3027442396,0.1529166549,0.0393640399,-0.1008400992,-0.0583141893,0.1315543056,-0.0259356182,0.1768467724,-0.0182867814,-0.0394550189,0.1186181977,-0.2750140131,-0.0770448223,0.1979830861,-0.5796865821,0.0617473088,0.3563539386,-0.483120203,0.2248359621,0.0766406581,0.3723810911,0.0808451474,0.1294206977,-0.0054175756,0.3990353644,0.2017412037,0.1839399487,-0.2446381003,0.1665526032,0.3514225185,-0.0046332767,-0.3092014194,0.0455646105,-0.0928959474,0.2623339891,0.0135595212,0.3132420182,0.0522785522,-0.3759042025,0.1984404474,-0.2598961294,0.0621640086,0.3526681066,0.2470078766,0.31240955,0.331681639,-0.1649695039,0.2566638589,-0.170162797,-0.4501440227,-0.2399167717,-0.3115689754,0.0298984032,0.0585324802,0.2430290878,-0.4115727246,0.2127870023,0.1143840179,0.2314704955,-0.1508325785,0.2350654602,-0.3559949696,0.2714752853,0.4170607924,0.2132242918,0.30559659,0.2130860984,-0.078121908,0.0865608603,0.1292979866,-0.0135739716,-0.3794960678,0.1188295856,0.241816327,-0.1854702979,0.0091848904,-0.1626182795,0.3421088755,0.0492594019,-0.490398556,0.1006153971,-0.1393199116,-0.1788205355,-0.1388723552,0.3767809272,0.4191721678,-0.3163481355,-0.0631755143,-0.2223777026,-0.1933458447,0.3068187237,0.0627034158,-0.1867482066,0.0545980632,-0.198907733,-0.0324022733,0.1652611792,-0.133823514,-0.4505629539,0.3839800358,-0.1003188565,0.4216839075,0.003368129,0.0348957814,0.0462126397,-0.1179660112,0.2082362026,-0.0482203104,-0.3429863751,0.1301704794,-0.2063286006,0.0962786973,0.1745322049,0.0263372958,0.1080550551,0.145677641,0.1164149791,-0.0307607576,0.1964359283,-0.0440106578,0.0053364923,0.2909404337,0.0424242578,-0.0588180684,0.000367176,-0.1810717881,-0.4702015221,0.3554164469,0.2276427895,0.0265536476,0.0678977966,0.0377307907,-0.3046529889,0.141935274,-0.2711749077,-0.4771591425,0.1949529499,0.3907288015,-0.1118490696,0.0888262317,-0.277343154,0.3020512462,-0.1696492136,-0.1002230644,-0.1528523117,0.1263132989,0.1340726018,-0.3565647304,-0.0337362103,0.0897880867,0.2519123256,-0.1344926953,-0.234999463,0.4969182014,0.1069902405,0.0699909925,-0.0781737417,-0.173212111,0.1972211748,-0.1314566433,-0.2321656495,0.4724216461,0.3397763968,-0.2434855402,-0.1393338144,0.186793372,-0.2051788419,0.2564637661,-0.0694206432,0.0912937298,0.328330636,0.3197959363,-0.0928376839,-0.0212596133,0.2077185959,-0.1777403057,0.1031239405,-0.1610922515,-0.2532593608,-0.0428625979,0.1946246028,0.1955534518,0.0406559408,0.0529748946,-0.3236100376,-0.2086987048,0.3706597984,0.0880206153,0.4839422405,0.0791207775,0.2721221745,0.0922638103,-0.0106658787,-0.1263325214,0.2971056998,0.0752388388,0.0984970778,0.3603755832,0.2497458905,-0.1204286814,-0.4016779959,-0.2251661271,0.1358991563,0.133663699,-0.3052351177,0.0297692902,-0.1282226741,-0.2748440802,0.1133073941,-0.4021544755,-0.2225447446,-0.3337677121,-0.2058949023,0.3451238573,-0.1469643116,0.1498992443,-0.180475831,-0.0882678479,0.308257699,-0.0031163315,-0.2322359681,-0.1135618612,-0.2594828606,0.03214081,0.0412808843,-0.3234791756,0.434748143,-0.1234986335,-0.1801847816,-0.1512944698,-0.0745449811,0.1740905792,0.0435857289,0.2001399845,0.2743394077,0.2197588384,0.039221812,0.0045702313,0.4241863489,-0.2919014394,0.0146440258,0.3762074709,0.2012005895,0.2666827142,-0.2297265977,-0.2490313053,-0.0779714212,-0.4310797453,-0.174230665,-0.0227724351,0.0273636784,0.4507275522,0.262483865,0.3891989887,0.3127643466,-0.0566316359,-0.3348741829,-0.3475183249,0.1322237253,-0.1974763721,-0.4097188413,0.1144938767,0.0011452077,0.1664847434,0.2781507671,-0.3012993634,0.0803638548,-0.0408363193,0.1188741624,-0.05930195,-0.1120122075,0.1432749033,-0.06137814,-0.1019272059,-0.3338415623,-0.1850921214,-0.1276152432,-0.1529603153,0.2324113101,0.0381841213,0.5950309038,0.207781747,0.5280631185,0.4836455286,0.0581972674,0.6157116294,-0.2377375066,0.2161288112,-0.3673551977,-0.3503070772,0.2225523591,-0.0085880933,-0.1320838332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{"html_url":"https:\/\/github.com\/huggingface\/datasets\/issues\/2866","title":"\"counter\" dataset raises an error in normal mode, but not in streaming mode","comments":"Note that we might want to open an issue to fix the \"counter\" dataset by itself, but I let it up to you.","body":"## Describe the bug\r\n\r\n`counter` dataset raises an error on `load_dataset()`, but simply returns an empty iterator in streaming mode.\r\n\r\n## Steps to reproduce the bug\r\n\r\n```python\r\n>>> import datasets as ds\r\n>>> a = ds.load_dataset('counter', split=\"train\", streaming=False)\r\nUsing custom data configuration default\r\nDownloading and preparing dataset counter\/default (download: 1.29 MiB, generated: 2.48 MiB, post-processed: Unknown size, total: 3.77 MiB) to \/home\/slesage\/.cache\/huggingface\/datasets\/counter\/default\/1.0.0\/9f84962fa0f35bec5a34fe0bdff8681838d497008c457f7856c48654476ec0e9...\r\nTraceback (most recent call last):\r\n File \"\/home\/slesage\/hf\/datasets-preview-backend\/.venv\/lib\/python3.8\/site-packages\/datasets\/builder.py\", line 726, in _download_and_prepare\r\n self._prepare_split(split_generator, **prepare_split_kwargs)\r\n File \"\/home\/slesage\/hf\/datasets-preview-backend\/.venv\/lib\/python3.8\/site-packages\/datasets\/builder.py\", line 1124, in _prepare_split\r\n for key, record in utils.tqdm(\r\n File \"\/home\/slesage\/hf\/datasets-preview-backend\/.venv\/lib\/python3.8\/site-packages\/tqdm\/std.py\", line 1185, in __iter__\r\n for obj in iterable:\r\n File \"\/home\/slesage\/.cache\/huggingface\/modules\/datasets_modules\/datasets\/counter\/9f84962fa0f35bec5a34fe0bdff8681838d497008c457f7856c48654476ec0e9\/counter.py\", line 161, in _generate_examples\r\n with derived_file.open(encoding=\"utf-8\") as f:\r\n File \"\/home\/slesage\/.pyenv\/versions\/3.8.11\/lib\/python3.8\/pathlib.py\", line 1222, in open\r\n return io.open(self, mode, buffering, encoding, errors, newline,\r\n File \"\/home\/slesage\/.pyenv\/versions\/3.8.11\/lib\/python3.8\/pathlib.py\", line 1078, in _opener\r\n return self._accessor.open(self, flags, mode)\r\nFileNotFoundError: [Errno 2] No such file or directory: '\/home\/slesage\/.cache\/huggingface\/datasets\/downloads\/extracted\/b57aa6db5601a738e57b95c1fd8cced54ff28fc540efcdaf0f6c4f1bb5dfe211\/COUNTER\/0032p.xml'\r\n\r\nDuring handling of the above exception, another exception occurred:\r\n\r\nTraceback (most recent call last):\r\n File \"\", line 1, in \r\n File \"\/home\/slesage\/hf\/datasets-preview-backend\/.venv\/lib\/python3.8\/site-packages\/datasets\/load.py\", line 1112, in load_dataset\r\n builder_instance.download_and_prepare(\r\n File \"\/home\/slesage\/hf\/datasets-preview-backend\/.venv\/lib\/python3.8\/site-packages\/datasets\/builder.py\", line 636, in download_and_prepare\r\n self._download_and_prepare(\r\n File \"\/home\/slesage\/hf\/datasets-preview-backend\/.venv\/lib\/python3.8\/site-packages\/datasets\/builder.py\", line 728, in _download_and_prepare\r\n raise OSError(\r\nOSError: Cannot find data file.\r\nOriginal error:\r\n[Errno 2] No such file or directory: '\/home\/slesage\/.cache\/huggingface\/datasets\/downloads\/extracted\/b57aa6db5601a738e57b95c1fd8cced54ff28fc540efcdaf0f6c4f1bb5dfe211\/COUNTER\/0032p.xml'\r\n```\r\n\r\n```python\r\n>>> import datasets as ds\r\n>>> b = ds.load_dataset('counter', split=\"train\", streaming=True)\r\nUsing custom data configuration default\r\n>>> list(b)\r\n[]\r\n```\r\n\r\n## Expected results\r\n\r\nAn exception should be raised in streaming mode\r\n\r\n## Actual results\r\n\r\nNo exception is raised in streaming mode: there is no way to tell if something has broken or if the dataset is simply empty.\r\n\r\n## Environment info\r\n\r\n- `datasets` version: 1.11.1.dev0\r\n- Platform: Linux-5.11.0-1016-aws-x86_64-with-glibc2.29\r\n- Python version: 3.8.11\r\n- PyArrow version: 4.0.1\r\n","comment_length":23,"text":"\"counter\" dataset raises an error in normal mode, but not in streaming mode\n## Describe the bug\r\n\r\n`counter` dataset raises an error on `load_dataset()`, but simply returns an empty iterator in streaming mode.\r\n\r\n## Steps to reproduce the bug\r\n\r\n```python\r\n>>> import datasets as ds\r\n>>> a = ds.load_dataset('counter', split=\"train\", streaming=False)\r\nUsing custom data configuration default\r\nDownloading and preparing dataset counter\/default (download: 1.29 MiB, generated: 2.48 MiB, post-processed: Unknown size, total: 3.77 MiB) to \/home\/slesage\/.cache\/huggingface\/datasets\/counter\/default\/1.0.0\/9f84962fa0f35bec5a34fe0bdff8681838d497008c457f7856c48654476ec0e9...\r\nTraceback (most recent call last):\r\n File \"\/home\/slesage\/hf\/datasets-preview-backend\/.venv\/lib\/python3.8\/site-packages\/datasets\/builder.py\", line 726, in _download_and_prepare\r\n self._prepare_split(split_generator, **prepare_split_kwargs)\r\n File \"\/home\/slesage\/hf\/datasets-preview-backend\/.venv\/lib\/python3.8\/site-packages\/datasets\/builder.py\", line 1124, in _prepare_split\r\n for key, record in utils.tqdm(\r\n File \"\/home\/slesage\/hf\/datasets-preview-backend\/.venv\/lib\/python3.8\/site-packages\/tqdm\/std.py\", line 1185, in __iter__\r\n for obj in iterable:\r\n File \"\/home\/slesage\/.cache\/huggingface\/modules\/datasets_modules\/datasets\/counter\/9f84962fa0f35bec5a34fe0bdff8681838d497008c457f7856c48654476ec0e9\/counter.py\", line 161, in _generate_examples\r\n with derived_file.open(encoding=\"utf-8\") as f:\r\n File \"\/home\/slesage\/.pyenv\/versions\/3.8.11\/lib\/python3.8\/pathlib.py\", line 1222, in open\r\n return io.open(self, mode, buffering, encoding, errors, newline,\r\n File \"\/home\/slesage\/.pyenv\/versions\/3.8.11\/lib\/python3.8\/pathlib.py\", line 1078, in _opener\r\n return self._accessor.open(self, flags, mode)\r\nFileNotFoundError: [Errno 2] No such file or directory: '\/home\/slesage\/.cache\/huggingface\/datasets\/downloads\/extracted\/b57aa6db5601a738e57b95c1fd8cced54ff28fc540efcdaf0f6c4f1bb5dfe211\/COUNTER\/0032p.xml'\r\n\r\nDuring handling of the above exception, another exception occurred:\r\n\r\nTraceback (most recent call last):\r\n File \"\", line 1, in \r\n File \"\/home\/slesage\/hf\/datasets-preview-backend\/.venv\/lib\/python3.8\/site-packages\/datasets\/load.py\", line 1112, in load_dataset\r\n builder_instance.download_and_prepare(\r\n File \"\/home\/slesage\/hf\/datasets-preview-backend\/.venv\/lib\/python3.8\/site-packages\/datasets\/builder.py\", line 636, in download_and_prepare\r\n self._download_and_prepare(\r\n File \"\/home\/slesage\/hf\/datasets-preview-backend\/.venv\/lib\/python3.8\/site-packages\/datasets\/builder.py\", line 728, in _download_and_prepare\r\n raise OSError(\r\nOSError: Cannot find data file.\r\nOriginal error:\r\n[Errno 2] No such file or directory: '\/home\/slesage\/.cache\/huggingface\/datasets\/downloads\/extracted\/b57aa6db5601a738e57b95c1fd8cced54ff28fc540efcdaf0f6c4f1bb5dfe211\/COUNTER\/0032p.xml'\r\n```\r\n\r\n```python\r\n>>> import datasets as ds\r\n>>> b = ds.load_dataset('counter', split=\"train\", streaming=True)\r\nUsing custom data configuration default\r\n>>> list(b)\r\n[]\r\n```\r\n\r\n## Expected results\r\n\r\nAn exception should be raised in streaming mode\r\n\r\n## Actual results\r\n\r\nNo exception is raised in streaming mode: there is no way to tell if something has broken or if the dataset is simply empty.\r\n\r\n## Environment info\r\n\r\n- `datasets` version: 1.11.1.dev0\r\n- Platform: Linux-5.11.0-1016-aws-x86_64-with-glibc2.29\r\n- Python version: 3.8.11\r\n- PyArrow version: 4.0.1\r\n\nNote that we might want to open an issue to fix the \"counter\" dataset by itself, but I let it up to you.","embeddings":[-0.4171527922,-0.2417904586,0.0004065955,0.1936673522,0.187378943,0.0572046041,0.5056346059,0.1059950739,0.1942841411,0.1353349984,0.0698306859,0.1297213137,-0.2097095996,0.3238820434,0.0893105865,0.0787798092,0.0210811216,0.1588886231,0.0335764661,0.0774051622,-0.2157031149,0.2028147876,-0.3865993321,-0.3339452446,-0.1323988438,0.1463262439,0.1100258455,0.1485300958,0.1753474176,-0.6307143569,0.2604337335,0.0775746107,0.2229160666,0.7022691965,-0.0001109225,0.1807845384,0.4209491313,-0.138756454,-0.5919362307,-0.2953981757,-0.3683612645,-0.3184562027,0.1747525334,-0.1460207105,-0.1155984327,0.0416297689,-0.2645150125,-0.4016982019,0.2196300626,0.44546175,0.2117786556,0.4712955952,-0.1509929299,-0.1280123889,0.1274099499,0.0086588459,-0.1527633816,0.2987458408,0.0759947449,0.2464787662,-0.030185163,0.400646776,-0.0477484316,0.1930941492,0.2172417343,-0.0403118059,-0.0638400242,-0.2730312347,0.077968359,0.2267186195,0.3892460763,-0.3428210616,-0.1372255683,-0.4340849519,-0.0550197884,-0.4057786763,0.2461039424,-0.0120554632,-0.2402932793,0.0848756358,-0.3027442396,0.1529166549,0.0393640399,-0.1008400992,-0.0583141893,0.1315543056,-0.0259356182,0.1768467724,-0.0182867814,-0.0394550189,0.1186181977,-0.2750140131,-0.0770448223,0.1979830861,-0.5796865821,0.0617473088,0.3563539386,-0.483120203,0.2248359621,0.0766406581,0.3723810911,0.0808451474,0.1294206977,-0.0054175756,0.3990353644,0.2017412037,0.1839399487,-0.2446381003,0.1665526032,0.3514225185,-0.0046332767,-0.3092014194,0.0455646105,-0.0928959474,0.2623339891,0.0135595212,0.3132420182,0.0522785522,-0.3759042025,0.1984404474,-0.2598961294,0.0621640086,0.3526681066,0.2470078766,0.31240955,0.331681639,-0.1649695039,0.2566638589,-0.170162797,-0.4501440227,-0.2399167717,-0.3115689754,0.0298984032,0.0585324802,0.2430290878,-0.4115727246,0.2127870023,0.1143840179,0.2314704955,-0.1508325785,0.2350654602,-0.3559949696,0.2714752853,0.4170607924,0.2132242918,0.30559659,0.2130860984,-0.078121908,0.0865608603,0.1292979866,-0.0135739716,-0.3794960678,0.1188295856,0.241816327,-0.1854702979,0.0091848904,-0.1626182795,0.3421088755,0.0492594019,-0.490398556,0.1006153971,-0.1393199116,-0.1788205355,-0.1388723552,0.3767809272,0.4191721678,-0.3163481355,-0.0631755143,-0.2223777026,-0.1933458447,0.3068187237,0.0627034158,-0.1867482066,0.0545980632,-0.198907733,-0.0324022733,0.1652611792,-0.133823514,-0.4505629539,0.3839800358,-0.1003188565,0.4216839075,0.003368129,0.0348957814,0.0462126397,-0.1179660112,0.2082362026,-0.0482203104,-0.3429863751,0.1301704794,-0.2063286006,0.0962786973,0.1745322049,0.0263372958,0.1080550551,0.145677641,0.1164149791,-0.0307607576,0.1964359283,-0.0440106578,0.0053364923,0.2909404337,0.0424242578,-0.0588180684,0.000367176,-0.1810717881,-0.4702015221,0.3554164469,0.2276427895,0.0265536476,0.0678977966,0.0377307907,-0.3046529889,0.141935274,-0.2711749077,-0.4771591425,0.1949529499,0.3907288015,-0.1118490696,0.0888262317,-0.277343154,0.3020512462,-0.1696492136,-0.1002230644,-0.1528523117,0.1263132989,0.1340726018,-0.3565647304,-0.0337362103,0.0897880867,0.2519123256,-0.1344926953,-0.234999463,0.4969182014,0.1069902405,0.0699909925,-0.0781737417,-0.173212111,0.1972211748,-0.1314566433,-0.2321656495,0.4724216461,0.3397763968,-0.2434855402,-0.1393338144,0.186793372,-0.2051788419,0.2564637661,-0.0694206432,0.0912937298,0.328330636,0.3197959363,-0.0928376839,-0.0212596133,0.2077185959,-0.1777403057,0.1031239405,-0.1610922515,-0.2532593608,-0.0428625979,0.1946246028,0.1955534518,0.0406559408,0.0529748946,-0.3236100376,-0.2086987048,0.3706597984,0.0880206153,0.4839422405,0.0791207775,0.2721221745,0.0922638103,-0.0106658787,-0.1263325214,0.2971056998,0.0752388388,0.0984970778,0.3603755832,0.2497458905,-0.1204286814,-0.4016779959,-0.2251661271,0.1358991563,0.133663699,-0.3052351177,0.0297692902,-0.1282226741,-0.2748440802,0.1133073941,-0.4021544755,-0.2225447446,-0.3337677121,-0.2058949023,0.3451238573,-0.1469643116,0.1498992443,-0.180475831,-0.0882678479,0.308257699,-0.0031163315,-0.2322359681,-0.1135618612,-0.2594828606,0.03214081,0.0412808843,-0.3234791756,0.434748143,-0.1234986335,-0.1801847816,-0.1512944698,-0.0745449811,0.1740905792,0.0435857289,0.2001399845,0.2743394077,0.2197588384,0.039221812,0.0045702313,0.4241863489,-0.2919014394,0.0146440258,0.3762074709,0.2012005895,0.2666827142,-0.2297265977,-0.2490313053,-0.0779714212,-0.4310797453,-0.174230665,-0.0227724351,0.0273636784,0.4507275522,0.262483865,0.3891989887,0.3127643466,-0.0566316359,-0.3348741829,-0.3475183249,0.1322237253,-0.1974763721,-0.4097188413,0.1144938767,0.0011452077,0.1664847434,0.2781507671,-0.3012993634,0.0803638548,-0.0408363193,0.1188741624,-0.05930195,-0.1120122075,0.1432749033,-0.06137814,-0.1019272059,-0.3338415623,-0.1850921214,-0.1276152432,-0.1529603153,0.2324113101,0.0381841213,0.5950309038,0.207781747,0.5280631185,0.4836455286,0.0581972674,0.6157116294,-0.2377375066,0.2161288112,-0.3673551977,-0.3503070772,0.2225523591,-0.0085880933,-0.1320838332,0.208188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{"html_url":"https:\/\/github.com\/huggingface\/datasets\/issues\/2866","title":"\"counter\" dataset raises an error in normal mode, but not in streaming mode","comments":"Fixed here: https:\/\/github.com\/huggingface\/datasets\/pull\/2894. Thanks @albertvillanova ","body":"## Describe the bug\r\n\r\n`counter` dataset raises an error on `load_dataset()`, but simply returns an empty iterator in streaming mode.\r\n\r\n## Steps to reproduce the bug\r\n\r\n```python\r\n>>> import datasets as ds\r\n>>> a = ds.load_dataset('counter', split=\"train\", streaming=False)\r\nUsing custom data configuration default\r\nDownloading and preparing dataset counter\/default (download: 1.29 MiB, generated: 2.48 MiB, post-processed: Unknown size, total: 3.77 MiB) to \/home\/slesage\/.cache\/huggingface\/datasets\/counter\/default\/1.0.0\/9f84962fa0f35bec5a34fe0bdff8681838d497008c457f7856c48654476ec0e9...\r\nTraceback (most recent call last):\r\n File \"\/home\/slesage\/hf\/datasets-preview-backend\/.venv\/lib\/python3.8\/site-packages\/datasets\/builder.py\", line 726, in _download_and_prepare\r\n self._prepare_split(split_generator, **prepare_split_kwargs)\r\n File \"\/home\/slesage\/hf\/datasets-preview-backend\/.venv\/lib\/python3.8\/site-packages\/datasets\/builder.py\", line 1124, in _prepare_split\r\n for key, record in utils.tqdm(\r\n File \"\/home\/slesage\/hf\/datasets-preview-backend\/.venv\/lib\/python3.8\/site-packages\/tqdm\/std.py\", line 1185, in __iter__\r\n for obj in iterable:\r\n File \"\/home\/slesage\/.cache\/huggingface\/modules\/datasets_modules\/datasets\/counter\/9f84962fa0f35bec5a34fe0bdff8681838d497008c457f7856c48654476ec0e9\/counter.py\", line 161, in _generate_examples\r\n with derived_file.open(encoding=\"utf-8\") as f:\r\n File \"\/home\/slesage\/.pyenv\/versions\/3.8.11\/lib\/python3.8\/pathlib.py\", line 1222, in open\r\n return io.open(self, mode, buffering, encoding, errors, newline,\r\n File \"\/home\/slesage\/.pyenv\/versions\/3.8.11\/lib\/python3.8\/pathlib.py\", line 1078, in _opener\r\n return self._accessor.open(self, flags, mode)\r\nFileNotFoundError: [Errno 2] No such file or directory: '\/home\/slesage\/.cache\/huggingface\/datasets\/downloads\/extracted\/b57aa6db5601a738e57b95c1fd8cced54ff28fc540efcdaf0f6c4f1bb5dfe211\/COUNTER\/0032p.xml'\r\n\r\nDuring handling of the above exception, another exception occurred:\r\n\r\nTraceback (most recent call last):\r\n File \"\", line 1, in \r\n File \"\/home\/slesage\/hf\/datasets-preview-backend\/.venv\/lib\/python3.8\/site-packages\/datasets\/load.py\", line 1112, in load_dataset\r\n builder_instance.download_and_prepare(\r\n File \"\/home\/slesage\/hf\/datasets-preview-backend\/.venv\/lib\/python3.8\/site-packages\/datasets\/builder.py\", line 636, in download_and_prepare\r\n self._download_and_prepare(\r\n File \"\/home\/slesage\/hf\/datasets-preview-backend\/.venv\/lib\/python3.8\/site-packages\/datasets\/builder.py\", line 728, in _download_and_prepare\r\n raise OSError(\r\nOSError: Cannot find data file.\r\nOriginal error:\r\n[Errno 2] No such file or directory: '\/home\/slesage\/.cache\/huggingface\/datasets\/downloads\/extracted\/b57aa6db5601a738e57b95c1fd8cced54ff28fc540efcdaf0f6c4f1bb5dfe211\/COUNTER\/0032p.xml'\r\n```\r\n\r\n```python\r\n>>> import datasets as ds\r\n>>> b = ds.load_dataset('counter', split=\"train\", streaming=True)\r\nUsing custom data configuration default\r\n>>> list(b)\r\n[]\r\n```\r\n\r\n## Expected results\r\n\r\nAn exception should be raised in streaming mode\r\n\r\n## Actual results\r\n\r\nNo exception is raised in streaming mode: there is no way to tell if something has broken or if the dataset is simply empty.\r\n\r\n## Environment info\r\n\r\n- `datasets` version: 1.11.1.dev0\r\n- Platform: Linux-5.11.0-1016-aws-x86_64-with-glibc2.29\r\n- Python version: 3.8.11\r\n- PyArrow version: 4.0.1\r\n","comment_length":5,"text":"\"counter\" dataset raises an error in normal mode, but not in streaming mode\n## Describe the bug\r\n\r\n`counter` dataset raises an error on `load_dataset()`, but simply returns an empty iterator in streaming mode.\r\n\r\n## Steps to reproduce the bug\r\n\r\n```python\r\n>>> import datasets as ds\r\n>>> a = ds.load_dataset('counter', split=\"train\", streaming=False)\r\nUsing custom data configuration default\r\nDownloading and preparing dataset counter\/default (download: 1.29 MiB, generated: 2.48 MiB, post-processed: Unknown size, total: 3.77 MiB) to \/home\/slesage\/.cache\/huggingface\/datasets\/counter\/default\/1.0.0\/9f84962fa0f35bec5a34fe0bdff8681838d497008c457f7856c48654476ec0e9...\r\nTraceback (most recent call last):\r\n File \"\/home\/slesage\/hf\/datasets-preview-backend\/.venv\/lib\/python3.8\/site-packages\/datasets\/builder.py\", line 726, in _download_and_prepare\r\n self._prepare_split(split_generator, **prepare_split_kwargs)\r\n File \"\/home\/slesage\/hf\/datasets-preview-backend\/.venv\/lib\/python3.8\/site-packages\/datasets\/builder.py\", line 1124, in _prepare_split\r\n for key, record in utils.tqdm(\r\n File \"\/home\/slesage\/hf\/datasets-preview-backend\/.venv\/lib\/python3.8\/site-packages\/tqdm\/std.py\", line 1185, in __iter__\r\n for obj in iterable:\r\n File \"\/home\/slesage\/.cache\/huggingface\/modules\/datasets_modules\/datasets\/counter\/9f84962fa0f35bec5a34fe0bdff8681838d497008c457f7856c48654476ec0e9\/counter.py\", line 161, in _generate_examples\r\n with derived_file.open(encoding=\"utf-8\") as f:\r\n File \"\/home\/slesage\/.pyenv\/versions\/3.8.11\/lib\/python3.8\/pathlib.py\", line 1222, in open\r\n return io.open(self, mode, buffering, encoding, errors, newline,\r\n File \"\/home\/slesage\/.pyenv\/versions\/3.8.11\/lib\/python3.8\/pathlib.py\", line 1078, in _opener\r\n return self._accessor.open(self, flags, mode)\r\nFileNotFoundError: [Errno 2] No such file or directory: '\/home\/slesage\/.cache\/huggingface\/datasets\/downloads\/extracted\/b57aa6db5601a738e57b95c1fd8cced54ff28fc540efcdaf0f6c4f1bb5dfe211\/COUNTER\/0032p.xml'\r\n\r\nDuring handling of the above exception, another exception occurred:\r\n\r\nTraceback (most recent call last):\r\n File \"\", line 1, in \r\n File \"\/home\/slesage\/hf\/datasets-preview-backend\/.venv\/lib\/python3.8\/site-packages\/datasets\/load.py\", line 1112, in load_dataset\r\n builder_instance.download_and_prepare(\r\n File \"\/home\/slesage\/hf\/datasets-preview-backend\/.venv\/lib\/python3.8\/site-packages\/datasets\/builder.py\", line 636, in download_and_prepare\r\n self._download_and_prepare(\r\n File \"\/home\/slesage\/hf\/datasets-preview-backend\/.venv\/lib\/python3.8\/site-packages\/datasets\/builder.py\", line 728, in _download_and_prepare\r\n raise OSError(\r\nOSError: Cannot find data file.\r\nOriginal error:\r\n[Errno 2] No such file or directory: '\/home\/slesage\/.cache\/huggingface\/datasets\/downloads\/extracted\/b57aa6db5601a738e57b95c1fd8cced54ff28fc540efcdaf0f6c4f1bb5dfe211\/COUNTER\/0032p.xml'\r\n```\r\n\r\n```python\r\n>>> import datasets as ds\r\n>>> b = ds.load_dataset('counter', split=\"train\", streaming=True)\r\nUsing custom data configuration default\r\n>>> list(b)\r\n[]\r\n```\r\n\r\n## Expected results\r\n\r\nAn exception should be raised in streaming mode\r\n\r\n## Actual results\r\n\r\nNo exception is raised in streaming mode: there is no way to tell if something has broken or if the dataset is simply empty.\r\n\r\n## Environment info\r\n\r\n- `datasets` version: 1.11.1.dev0\r\n- Platform: Linux-5.11.0-1016-aws-x86_64-with-glibc2.29\r\n- Python version: 3.8.11\r\n- PyArrow version: 4.0.1\r\n\nFixed here: https:\/\/github.com\/huggingface\/datasets\/pull\/2894. Thanks @albertvillanova 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{"html_url":"https:\/\/github.com\/huggingface\/datasets\/issues\/2860","title":"Cannot download TOTTO dataset","comments":"Hola @mrm8488, thanks for reporting.\r\n\r\nApparently, the data source host changed their URL one week ago: https:\/\/github.com\/google-research-datasets\/ToTTo\/commit\/cebeb430ec2a97747e704d16a9354f7d9073ff8f\r\n\r\nI'm fixing it.","body":"Error: Couldn't find file at https:\/\/storage.googleapis.com\/totto\/totto_data.zip\r\n\r\n`datasets version: 1.11.0`\r\n# How to reproduce:\r\n\r\n```py\r\nfrom datasets import load_dataset\r\ndataset = load_dataset('totto')\r\n```\r\n\r\n\r\n","comment_length":20,"text":"Cannot download TOTTO dataset\nError: Couldn't find file at https:\/\/storage.googleapis.com\/totto\/totto_data.zip\r\n\r\n`datasets version: 1.11.0`\r\n# How to reproduce:\r\n\r\n```py\r\nfrom datasets import load_dataset\r\ndataset = load_dataset('totto')\r\n```\r\n\r\n\r\n\nHola @mrm8488, thanks for reporting.\r\n\r\nApparently, the data source host changed their URL one week ago: https:\/\/github.com\/google-research-datasets\/ToTTo\/commit\/cebeb430ec2a97747e704d16a9354f7d9073ff8f\r\n\r\nI'm fixing it.","embeddings":[-0.294410497,0.3900933862,-0.1317222118,0.0641968921,0.4353683293,0.1393284649,0.1679639369,0.5879850388,-0.0261453092,0.234341681,-0.1947766393,-0.0473091528,0.0594171956,0.318708539,0.0643599778,-0.3084107637,0.0652796775,-0.0130061815,-0.1395519972,0.0308540575,-0.0759798363,0.2782622278,0.0100256903,0.0654641986,0.0181091316,0.0936704874,-0.0290926713,-0.0979447067,-0.2686378956,-0.4347373247,0.415338546,0.0613755472,0.1697377264,0.3808855712,-0.0001073767,0.1472330391,0.3291011751,0.0086168032,-0.2500196695,-0.3531953394,-0.3288857937,-0.1141764298,-0.1965035796,-0.1480810791,-0.0830533355,0.2127362341,0.2038826346,-0.2607125342,0.0417160429,0.5573630333,0.2718278766,0.0907747969,0.3079364896,-0.3647713363,0.2657265365,0.1475394666,-0.0365158468,0.0705632791,0.0785895288,0.0108668972,0.3562817872,0.0460000634,-0.133997038,0.0743308142,-0.1553672999,-0.0840224102,-0.0924824327,-0.3424230218,0.2141403407,0.3073774278,0.5096561313,-0.2044652998,-0.3611649573,0.2267780751,-0.0598624535,-0.279548645,0.2698038518,0.1802222282,0.0900912583,0.1698183566,0.1732570976,-0.4855846167,-0.1890135705,0.0528651476,-0.4141792953,0.107604593,-0.1177637801,0.0048586847,-0.105921559,-0.1203932837,0.3016455472,-0.0547915176,-0.0375608727,0.2590126991,-0.2745456994,-0.1841468364,0.1035571247,0.0311484244,0.1920053959,0.0490685366,0.1362317801,-0.1017651632,-0.5062013865,-0.0666135922,0.2418693751,-0.1095379815,-0.000267334,0.3804118037,0.2949267328,0.2416359335,0.1484333426,0.0473140553,-0.1586337984,-0.358691752,0.0269331522,-0.113104403,0.4928361177,-0.2085420638,-0.3899927437,0.1616978347,-0.090081729,-0.1214746758,-0.0176121797,0.1517945826,-0.0244206693,0.0753581598,0.0918698311,0.1404550672,-0.008870828,-0.1377051771,-0.2661000192,0.2041446567,-0.0125199547,0.0713024735,0.2309996784,-0.3448672891,0.0952998549,-0.0258917771,-0.4624833763,0.0828349143,-0.1361531913,-0.0750162899,-0.2458771169,0.3632289171,0.2265905589,0.1045650393,0.0013822372,0.1134399772,-0.2949780524,0.2792558968,-0.3678761125,-0.0678893328,-0.1227772906,0.2941953242,-0.3227276504,-0.2466399968,-0.5071802735,-0.0339648984,-0.0525038354,-0.3056934178,-0.1234595478,-0.1745043397,-0.1805351675,-0.2515176237,0.1124921739,0.6013523936,-0.2063911855,0.020885611,-0.4196625054,-0.4201267958,0.1899637729,0.269320339,-0.0341406576,0.4477233887,-0.3025847673,0.0885180831,0.4392078221,-0.1254837364,-0.7618820667,-0.0033206183,-0.2596687675,-0.1898869723,0.1790016443,0.0790314004,0.2497510761,-0.1000057384,0.2460572869,0.1370749325,0.012917717,-0.0459471196,-0.2387001067,-0.0105643263,-0.0045891618,0.1839413047,0.1048776805,0.2050982267,0.1501675397,-0.0725682601,0.2357553095,0.1607331038,0.0994152948,0.3408914506,0.3313904405,-0.0265133232,-0.0199446026,-0.1835746318,-0.1992596537,0.1232289374,-0.2162778378,0.1084155291,-0.2086808383,-0.1304647177,-0.4467419088,0.0594626628,-0.0653845966,-0.093042925,0.1499165893,-0.0002756922,0.310736388,0.3321071863,-0.02035648,0.320682168,0.2677029073,0.1030120254,-0.1375443637,0.5665908456,-0.1339783072,0.0568048991,0.4536594152,-0.2412828058,0.236403808,-0.0816816241,-0.0623007454,0.1545941979,0.0604691543,0.3422275782,0.4771338403,0.2396415472,0.1721659005,-0.4354346693,0.2116640955,0.343268007,0.0783024356,0.1411449164,-0.1909071058,0.0213618409,0.1539851874,0.0350163281,-0.0176072661,0.2682003379,0.544712007,0.0763235763,0.0633015558,-0.0642063022,0.2679164112,0.5545850396,0.2224782109,-0.0677626878,-0.2419174314,0.1457102746,0.538705945,-0.0372180901,0.0527861379,0.2355953008,-0.1476681978,-0.0631909966,-0.1953097135,0.2097867578,0.1718096882,0.0596293472,0.1565221548,0.0106348377,-0.0777703449,-0.2667880654,0.0551331863,0.0725212246,0.1452946216,0.1418423057,0.1219759658,0.2061336339,-0.08200939,-0.3087290227,0.0309936032,0.3056665659,-0.1311020702,-0.0795388594,-0.2592677772,0.0049411356,-0.2732787728,-0.3159630299,-0.2113306969,-0.3952906728,0.0605420209,0.2657611072,0.0656001344,-0.0102982745,-0.0560371764,0.0340000875,0.2436152548,-0.3098428249,-0.1341092587,-0.150049597,-0.1041894853,0.170614779,0.2377597988,-0.3077156246,0.3982344866,-0.6368309259,0.1199382693,-0.5480054617,-0.234621942,-0.043117255,-0.1899068207,0.0942285731,0.175614506,0.4022015929,0.3427299261,0.2505168021,0.1937542409,-0.0247938484,-0.0510064363,0.0891098529,-0.1387198567,-0.0441237539,0.1827340573,-0.4613678753,-0.3021485507,-0.2280495763,0.0444356278,0.0629076734,0.2366307676,-0.2858436108,0.1709893048,0.0659947768,0.1328113973,-0.3080635071,-0.1222961098,-0.1796017885,0.4985628128,-0.2371805012,-0.2333060801,0.1059011444,0.130261451,0.4757525921,0.1674677283,-0.4497697651,0.179243952,-0.3459710777,0.1534799039,-0.0468465574,0.0758880824,0.3201242983,0.1453730166,-0.1137198061,-0.0569933727,-0.1610036939,-0.050038822,-0.3855092824,0.2330114543,-0.0367576629,0.2503889203,-0.1210072637,0.398199141,0.2551098466,0.07454741,0.3040669858,-0.1943264455,0.1306042373,-0.1279257536,-0.1889470071,-0.0984253138,0.1577033997,-0.116324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{"html_url":"https:\/\/github.com\/huggingface\/datasets\/issues\/2859","title":"Loading allenai\/c4 in streaming mode does too many HEAD requests","comments":"https:\/\/github.com\/huggingface\/datasets\/blob\/6c766f9115d686182d76b1b937cb27e099c45d68\/src\/datasets\/builder.py#L179-L186","body":"This does 60,000+ HEAD requests to get all the ETags of all the data files:\r\n```python\r\nfrom datasets import load_dataset\r\nload_dataset(\"allenai\/c4\", streaming=True)\r\n```\r\nIt makes loading the dataset completely impractical.\r\n\r\nThe ETags are used to compute the config id (it must depend on the data files being used).\r\nInstead of using the ETags, we could simply use the commit hash of the dataset repository on the hub, as well and the glob pattern used to resolve the files (here it's `*` by default, to load all the files of the repository)","comment_length":1,"text":"Loading allenai\/c4 in streaming mode does too many HEAD requests\nThis does 60,000+ HEAD requests to get all the ETags of all the data files:\r\n```python\r\nfrom datasets import load_dataset\r\nload_dataset(\"allenai\/c4\", streaming=True)\r\n```\r\nIt makes loading the dataset completely impractical.\r\n\r\nThe ETags are used to compute the config id (it must depend on the data files being used).\r\nInstead of using the ETags, we could simply use the commit hash of the dataset repository on the hub, as well and the glob pattern used to resolve the files (here it's `*` by default, to load all the files of the repository)\nhttps:\/\/github.com\/huggingface\/datasets\/blob\/6c766f9115d686182d76b1b937cb27e099c45d68\/src\/datasets\/builder.py#L179-L186","embeddings":[-0.5428535938,-0.0302166119,-0.1265247166,0.3286388516,0.256604135,-0.0104140276,0.197608754,0.3729439676,0.1844934821,0.2685716748,-0.1927886903,0.3850267828,0.0771837533,0.2340062857,-0.143449083,0.136008963,-0.1367563605,0.3763958514,0.1254129112,0.1078248546,-0.0619285703,0.0006759175,-0.0417456739,-0.0773263052,-0.2133178711,0.2759331763,0.1665377468,0.2257279307,-0.0134929046,-0.4966956973,0.240168795,0.2341412455,-0.0487481132,0.2447642982,-0.0001018645,0.1439338028,0.4033434391,-0.0089729438,-0.085589841,0.4268731475,-0.4157178402,-0.0739421472,0.0224096645,-0.3959259391,-0.2034465969,-0.2251017988,0.0632516742,-0.0980081558,0.2041195333,0.2827344537,0.260243088,0.4249944985,0.0377698317,-0.1970410794,0.2039705664,-0.316323489,0.0617868043,-0.056926813,0.0797685385,0.1593059301,-0.0834435821,0.2483358234,-0.0834733918,0.0802756175,0.2913605869,-0.1320183277,-0.0032377529,-0.2171800733,0.3939404786,0.2030942291,0.3443941772,-0.3286153674,-0.1332122386,-0.2533044219,0.0619671606,-0.1955298185,0.0476448163,0.1538827866,-0.1587007791,0.1192615852,-0.0326113962,-0.0544691943,-0.1602670252,-0.0494049191,-0.0776140019,0.089980714,-0.0840734094,0.1207940727,0.136279732,0.058017496,0.0522075854,-0.2175264806,-0.0846055672,-0.004862661,-0.3694218993,-0.0516511574,0.3653528988,0.4273298979,0.1478993595,0.1687032282,-0.1197926477,0.2871201038,0.0435164198,0.0685389638,0.0575072095,0.0231996793,-0.0176825654,-0.389597863,0.5508090258,0.0899150446,-0.1071902066,-0.2005494833,-0.0709187463,-0.1060602814,0.0840810388,-0.1210096851,0.1117649302,-0.2036800086,-0.0995911956,-0.1678549349,-0.0444756113,0.0720648617,0.1929066479,0.4125182927,-0.1070380881,0.2573559582,-0.2619225979,0.0917120874,-0.1934460849,-0.0925472528,-0.3160288334,-0.2928213477,-0.1694728285,0.0569894873,0.1707771719,-0.34016487,0.4697290957,-0.0191245824,0.3383053243,0.0339444056,0.0842757225,0.1081046611,-0.0809869394,0.4188500047,0.2263165563,-0.0787535161,-0.0641879365,-0.1923369914,-0.291814357,-0.1569433808,0.0312824622,-0.4784304202,0.1928419471,0.2877151072,-0.0427191779,0.1639586836,-0.3409174085,0.0171625558,0.0027538103,0.1268018633,-0.1082377136,-0.0361211002,0.1082094535,-0.2483724952,0.1618676782,0.4884290099,-0.0835868865,-0.0830648839,-0.039552927,-0.1286983341,-0.0772875845,0.3747945726,-0.3466023803,0.120490931,-0.0308591686,0.0409644805,0.032458812,-0.425350368,-0.6205683351,0.1140533686,0.0639335662,-0.0276992563,0.2386657745,0.3460126519,0.1039455682,-0.107216388,0.0222086739,0.1709899902,0.073792845,0.2256556302,-0.1190583929,-0.2345744073,-0.2999114394,0.3787722886,-0.1165996566,-0.1365634799,0.1347563714,-0.1876992285,0.2526454926,-0.3332684338,0.0380112678,-0.1584161669,0.0938546658,-0.0603501089,0.0127826966,0.2236943841,-0.4421748519,0.3059828579,0.2439787388,0.1666173935,-0.1472782493,-0.3505738974,0.0228462406,-0.0090298792,-0.3510281146,-0.0372455232,0.1835951656,0.0712855086,0.0677272379,-0.1504935324,-0.2270190865,0.2682373822,-0.3091340661,-0.0179539192,-0.4381204545,0.1312066019,0.0892122611,0.0468895361,0.2672764957,-0.1050158292,0.0074687083,0.0492864773,0.0621649623,0.1636259705,-0.0330553278,0.4530902505,0.0818722695,0.7110303044,0.1389819533,-0.2826481164,0.3203724325,0.2067766637,0.1654448509,-0.2394565195,-0.1613846868,0.4084748626,0.3778287172,0.3252526224,0.1585008949,-0.0329761878,0.410119772,-0.0721899271,-0.1317404956,-0.0330927819,-0.00823195,-0.0209802482,0.3309631944,-0.023005046,-0.2523918152,0.3234723508,0.3031021953,0.1111327931,-0.1492699087,-0.0652219951,-0.298992157,-0.1263385117,0.2080144435,-0.0855184197,0.2448243797,0.4307595193,-0.0929894522,0.1461498737,0.0120118773,-0.256077677,0.3098646998,0.0617690906,0.0520799011,0.0422569439,0.1629692465,-0.2966534495,-0.4100134969,-0.3106255233,-0.1572241336,0.1993097663,-0.2276444584,-0.0174489729,-0.0920694247,-0.3194893003,0.0963194221,-0.0115835099,0.0477257594,-0.2335455716,0.0685858503,0.2594055235,-0.2168204486,0.1381093711,-0.1156482473,0.2903750539,0.0892408416,-0.1132856086,-0.2681921422,-0.2419697493,-0.1095879748,0.1148864031,0.4646658599,0.0141319409,0.3488751948,-0.3224317729,0.1775819212,-0.3888134956,-0.1769560575,0.1015825272,-0.1227035671,0.0946206078,0.0673972368,0.1070112586,0.2800557911,-0.125384286,0.0769697279,-0.2286968827,-0.0885543525,0.1452503055,-0.154563725,-0.2497059852,0.0414588526,-0.3924611509,-0.0118674375,-0.3728982508,0.342957437,0.2925284505,0.2317622006,-0.0143444026,0.0886501297,0.003775317,0.0996953025,-0.221465528,-0.2427807301,-0.3542366624,0.1676867455,-0.1924644858,-0.1902693361,0.0533196107,0.1380496174,0.4981618226,-0.0522706732,-0.4013659656,-0.0050968095,-0.4799565375,0.4729455709,0.0018389857,0.1385525167,0.2309796959,0.1050632596,-0.2131042331,-0.003395986,0.0059769182,0.1519519687,-0.0271408688,-0.2601141036,-0.0332135223,0.1607500166,-0.14402548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{"html_url":"https:\/\/github.com\/huggingface\/datasets\/issues\/2846","title":"Negative timezone","comments":"Fixed by #2847.","body":"## Describe the bug\r\nThe load_dataset method do not accept a parquet file with a negative timezone, as it has the following regex:\r\n```\r\n\"^(s|ms|us|ns),\\s*tz=([a-zA-Z0-9\/_+:]*)$\"\r\n```\r\nSo a valid timestap ```timestamp[us, tz=-03:00]``` returns an error when loading parquet files.\r\n\r\n## Steps to reproduce the bug\r\n```python\r\n# Where the timestamp column has a tz of -03:00\r\ndatasets = load_dataset('parquet', data_files={'train': train_files, 'validation': validation_files,\r\n 'test': test_files}, cache_dir=\".\/cache_teste\/\")\r\n```\r\n\r\n## Expected results\r\nThe -03:00 is a valid tz so the regex should accept this without raising an error.\r\n\r\n## Actual results\r\nAs this regex disaproves a valid tz it raises the following error:\r\n```python\r\nraise ValueError(\r\n f\"{datasets_dtype} is not a validly formatted string representation of a pyarrow timestamp.\"\r\n f\"Examples include timestamp[us] or timestamp[us, tz=America\/New_York]\"\r\n f\"See: https:\/\/arrow.apache.org\/docs\/python\/generated\/pyarrow.timestamp.html#pyarrow.timestamp\"\r\n )\r\n```\r\n\r\n## Environment info\r\n\r\n- `datasets` version: 1.11.0\r\n- Platform: Ubuntu 20.04\r\n- Python version: 3.8\r\n- PyArrow version: 5.0.0\r\n","comment_length":3,"text":"Negative timezone\n## Describe the bug\r\nThe load_dataset method do not accept a parquet file with a negative timezone, as it has the following regex:\r\n```\r\n\"^(s|ms|us|ns),\\s*tz=([a-zA-Z0-9\/_+:]*)$\"\r\n```\r\nSo a valid timestap ```timestamp[us, tz=-03:00]``` returns an error when loading parquet files.\r\n\r\n## Steps to reproduce the bug\r\n```python\r\n# Where the timestamp column has a tz of -03:00\r\ndatasets = load_dataset('parquet', data_files={'train': train_files, 'validation': validation_files,\r\n 'test': test_files}, cache_dir=\".\/cache_teste\/\")\r\n```\r\n\r\n## Expected results\r\nThe -03:00 is a valid tz so the regex should accept this without raising an error.\r\n\r\n## Actual results\r\nAs this regex disaproves a valid tz it raises the following error:\r\n```python\r\nraise ValueError(\r\n f\"{datasets_dtype} is not a validly formatted string representation of a pyarrow timestamp.\"\r\n f\"Examples include timestamp[us] or timestamp[us, tz=America\/New_York]\"\r\n f\"See: https:\/\/arrow.apache.org\/docs\/python\/generated\/pyarrow.timestamp.html#pyarrow.timestamp\"\r\n )\r\n```\r\n\r\n## Environment info\r\n\r\n- `datasets` version: 1.11.0\r\n- Platform: Ubuntu 20.04\r\n- Python version: 3.8\r\n- PyArrow version: 5.0.0\r\n\nFixed by #2847.","embeddings":[-0.401596576,0.1829235852,0.07779257,0.0459456369,0.0025876896,-0.1361358166,0.2783327103,0.1863403022,-0.2822602391,-0.1084523499,0.0049508214,0.4612381458,-0.0764548704,0.011837787,0.0862466618,-0.0383578874,0.011814937,0.2273899317,-0.0372114666,0.0899889767,-0.2281404585,0.1430076659,-0.1861973256,0.0171979833,-0.0101191457,0.0847581327,-0.135529995,0.2957262397,-0.0179393198,-0.3312743306,0.0218966678,-0.2963922024,-0.0028354791,0.4744252563,-0.0001110772,0.1515261084,0.2427365482,-0.0586084239,-0.1286713034,-0.0980418921,0.0726354867,-0.1844875515,0.1201160625,-0.1410159767,-0.2818562984,-0.4092908502,-0.1490833312,-0.2255629301,0.4855223894,0.7533420324,0.1939110011,0.5161628723,-0.1995947659,0.1261487156,0.5708992481,-0.1920128316,-0.0927384943,0.0550048351,0.2838503122,-0.0549646839,0.0217351187,-0.0753623918,-0.0667717829,-0.1474190205,-0.0274879858,0.1315036863,0.1412029564,0.1112895012,-0.1078112423,0.0587866716,0.5331471562,-0.3926923573,-0.3223041892,-0.0117041012,-0.1060191542,-0.4288388491,0.241237238,0.3066093624,0.124086462,0.4001655281,0.2249842137,0.2806190848,-0.3247011304,0.2575951517,-0.0456544571,0.4889696538,0.2003501654,0.0241223089,-0.4663511813,0.0521042608,0.1108724698,0.1738105118,0.1420491487,0.3028146625,-0.0712178871,0.1471797675,-0.144489795,0.0858040303,0.1398317665,0.1655455232,0.0636238679,0.053757105,0.1454739124,0.1479489505,0.1892784685,0.0139465919,-0.1422267556,0.0613142997,-0.013570535,-0.2705182135,0.1799209118,-0.1905653626,0.3304877281,-0.3920660615,0.2817029655,0.1169816926,0.3362499475,-0.2698073685,-0.2274588794,0.1621998847,-0.1237005219,0.0080517251,0.0145802246,0.0851220116,-0.2806506157,0.0991542861,-0.0242676027,0.2271328568,-0.2589269876,-0.3209973276,-0.302365303,0.0338018872,-0.3716346025,0.1070889086,0.0459969416,-0.0455009155,0.100954026,0.5322930217,-0.1443692893,0.1839753389,-0.003500768,-0.0746635497,0.1978344023,0.2002501637,-0.3000686467,0.0292879231,0.0530873127,-0.3285584748,-0.1837910563,0.0785339326,-0.3674685955,-0.3055340052,-0.2438785285,0.2281315029,-0.1431456208,-0.0010055227,-0.3128293753,-0.0223961119,0.2348175347,-0.333059907,-0.004378831,-0.0979190767,-0.0376100279,-0.1344065964,0.2565334141,0.1289270669,-0.5483288765,0.0963393524,-0.2633677721,-0.1224089786,0.4269274771,0.0700964183,-0.1817486882,-0.1433549374,-0.0769442394,0.2165022492,0.1412177533,-0.2336217314,-0.1102606952,0.2337683588,-0.3173281252,-0.1192938015,-0.0990622118,-0.231791079,0.1512234509,0.020114962,-0.0212348569,0.3360035121,0.1083586439,0.1227589697,-0.5359722376,0.0807048976,0.3973245323,0.2341749817,0.0942998827,-0.1980790943,0.0284822267,-0.1203887314,0.1557871401,0.1266277283,0.0885832459,0.3448267877,0.0967899412,0.1287433058,0.1226942837,-0.1904807985,-0.4723727703,0.1862957329,-0.1095412374,0.1620388627,-0.1045084372,-0.0073415767,-0.4141269624,0.1481197923,-0.096865952,-0.0436174348,0.1869332194,0.0512472242,0.2622552812,0.249815464,-0.0642117113,0.3733778894,0.0246363953,0.0647729114,-0.0456543528,0.5021957159,-0.1471415311,-0.3258623183,0.1392658502,0.0646996722,0.3012734354,-0.037828967,0.0434902348,0.3261065483,0.0395886116,0.2853697836,-0.3048215508,-0.0612492934,0.1293693781,-0.3858837187,-0.207326442,0.4110406637,0.1310723275,0.0106387213,-0.1130714864,0.4067870975,0.1465439796,-0.0204076506,-0.2243354172,-0.2361697406,0.1568973809,-0.0847803056,-0.229190886,-0.2032954246,0.0412322916,0.1294880956,0.1418613344,0.1586416364,-0.2724761665,0.0539743006,0.433634758,0.0812699646,-0.0223389436,0.1786754131,-0.0640674978,-0.3206694722,0.0971370861,0.0145480074,0.3622031212,0.3019005358,-0.0451056398,0.1357600987,-0.3348754644,-0.0767820105,0.0611638278,0.064834334,0.1726130247,0.2490399033,0.3392724097,0.1360774487,-0.3790141344,-0.2310668826,0.1009448841,0.3705965579,-0.4107787907,0.2328082919,-0.5654392242,0.0682446212,-0.1355815381,-0.0026652142,-0.2121079564,-0.2839313447,0.1905758828,-0.0255046487,-0.1566009223,0.1916968375,-0.0622645728,0.1792233735,0.042999357,-0.2946195304,-0.4987807274,-0.3976953924,-0.361844182,0.0726953521,0.0359174013,0.1968455762,0.1091454104,-0.3954747915,-0.267641753,-0.1785188019,-0.2480862737,0.0385909453,-0.0182739533,0.3933659196,-0.1042994037,0.3079154491,0.01678426,-0.1031174734,0.3731988072,-0.1594765782,-0.1894491464,0.1921914071,-0.0201293398,0.2859966755,-0.1708589047,-0.3231950104,-0.1922650784,-0.3542550206,-0.0851473436,0.1331684142,-0.0649012774,0.1285061538,0.0572508425,-0.0619811751,-0.3311877549,0.2320606112,-0.141649574,-0.2909179926,0.3566639721,-0.2580299377,-0.4099886417,-0.0560200587,0.0336855054,0.0038646574,0.1677842438,-0.1878620535,-0.1797359437,0.0439801328,0.0636100173,-0.1022412255,-0.3338511884,0.2544527352,-0.0195600595,-0.1497008204,-0.0398604199,-0.1063863933,-0.0719658583,0.2644098401,-0.0550280772,0.2287327051,0.2309377044,0.0191080179,0.5462126136,-0.1312200427,-0.1270393878,0.6752419472,-0.3085885644,0.2448028922,-0.1913854629,0.1500792205,-0.1392898709,0.1639823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{"html_url":"https:\/\/github.com\/huggingface\/datasets\/issues\/2842","title":"always requiring the username in the dataset name when there is one","comments":"From what I can understand, you want the saved arrow file directory to have username as well instead of just dataset name if it was downloaded with the user prefix?","body":"Me and now another person have been bitten by the `datasets`'s non-strictness on requiring a dataset creator's username when it's due.\r\n\r\nSo both of us started with `stas\/openwebtext-10k`, somewhere along the lines lost `stas\/` and continued using `openwebtext-10k` and it all was good until we published the software and things broke, since there is no `openwebtext-10k`\r\n\r\nSo this feature request is asking to tighten the checking and not allow dataset loading if it was downloaded with the user prefix, but then attempted to be used w\/o it.\r\n\r\nThe same in code:\r\n\r\n```\r\n# first run\r\npython -c \"from datasets import load_dataset; load_dataset('stas\/openwebtext-10k')\"\r\n# now run immediately\r\npython -c \"from datasets import load_dataset; load_dataset('openwebtext-10k')\"\r\n# the second command should fail, but it doesn't fail now.\r\n```\r\n\r\nPlease let me know if I explained myself clearly.\r\n\r\nThank you!","comment_length":30,"text":"always requiring the username in the dataset name when there is one\nMe and now another person have been bitten by the `datasets`'s non-strictness on requiring a dataset creator's username when it's due.\r\n\r\nSo both of us started with `stas\/openwebtext-10k`, somewhere along the lines lost `stas\/` and continued using `openwebtext-10k` and it all was good until we published the software and things broke, since there is no `openwebtext-10k`\r\n\r\nSo this feature request is asking to tighten the checking and not allow dataset loading if it was downloaded with the user prefix, but then attempted to be used w\/o it.\r\n\r\nThe same in code:\r\n\r\n```\r\n# first run\r\npython -c \"from datasets import load_dataset; load_dataset('stas\/openwebtext-10k')\"\r\n# now run immediately\r\npython -c \"from datasets import load_dataset; load_dataset('openwebtext-10k')\"\r\n# the second command should fail, but it doesn't fail now.\r\n```\r\n\r\nPlease let me know if I explained myself clearly.\r\n\r\nThank you!\nFrom what I can understand, you want the saved arrow file directory to have username as well instead of just dataset name if it was downloaded with the user prefix?","embeddings":[-0.1190379634,0.1679278612,-0.0471789353,0.083183147,0.0581347011,-0.1882434636,0.2695405185,0.0901198387,0.0316710472,0.300450474,0.1571788788,-0.0406884216,-0.0339300595,0.0137734013,-0.2211274058,0.0345355049,-0.037572436,0.4607703388,-0.1611818522,-0.0188344046,-0.2140433043,0.0586200505,0.0266007949,0.1466935724,0.092917338,0.058842849,0.3130096197,0.2868224382,-0.346788317,-0.4643648267,0.3015707433,0.2356016636,0.0029155812,0.2852761447,-0.0001282645,-0.0333672389,0.2038314193,-0.0626532808,-0.4011749923,-0.4367166162,-0.5333263278,-0.2659046352,0.3054177761,-0.3457644284,0.0780374855,-0.2222444117,0.0719516128,-0.7639085054,0.5935953856,0.173073858,0.0422012918,0.2400033772,-0.058429502,0.0676861182,0.0910241902,0.1828541756,-0.1983405799,0.0576104783,0.2272553295,0.1533893198,0.2952234745,0.2544378638,-0.1423670352,0.1849683672,0.4132423401,-0.2334423661,0.1443344504,-0.1818229556,-0.0621791855,0.4150875509,1.119764924,0.1163945422,-0.2934359312,-0.1209268048,-0.1289778799,0.0856915265,0.4294357896,-0.028681282,-0.2189789265,0.0680714622,0.049464494,-0.2302126884,-0.2342119068,0.0908740088,-0.2012763619,0.4058734477,-0.029753875,0.2471694201,0.0076992027,-0.1546998769,0.1335837245,0.0433808118,-0.0445635058,0.0337282456,-0.3212507665,-0.1692341864,0.090835236,-0.4048357606,-0.0476178452,0.5174331069,0.4020608962,0.0327300504,-0.3441611528,-0.0132745011,0.1745274514,0.190897122,0.6876799464,-0.0796756595,0.4477432966,-0.2132449448,0.1390418559,-0.1412651986,-0.2152579576,-0.2961265147,0.1313990057,0.0904852152,0.6935943961,-0.2948879004,-0.1523353606,0.0342134871,-0.1434054077,-0.2593048513,0.0486348569,0.0749922767,-0.0735887289,0.1086224318,0.1906049252,-0.0469755083,0.2274105549,0.0317912996,0.0382744707,-0.4006378651,-0.1337124705,-0.0184564274,0.2508587837,-0.3766240478,0.4235996902,-0.069896698,-0.0486346111,-0.0030262368,0.2811372578,0.0505682491,0.1693538427,0.1912305951,-0.0317290314,0.1745623946,0.0232970379,-0.2015249431,-0.2798311412,0.4101695418,-0.3928644657,-0.2087896764,-0.458591193,-0.0268326569,-0.2381677628,0.0136597864,-0.1689317673,-0.3450929523,-0.061842192,-0.0526142754,0.2095700502,0.2401569933,-0.061588455,-0.1526175439,-0.0715633631,0.3001872301,-0.2423455864,-0.1067422256,-0.2043185681,-0.0189731102,-0.158886537,0.1915175617,-0.4447416663,0.0448998325,-0.2232249677,0.1948380321,0.5952612162,-0.495640099,-0.0760816261,0.1809930801,-0.1531474739,0.0020545234,0.5405682325,-0.1667470187,-0.1783379763,-0.273727715,-0.5374604464,0.2701226771,0.2313288748,-0.0610755235,0.0770300031,-0.1622279584,0.0180178434,-0.2001359463,-0.237470299,0.0979825258,0.1585738361,-0.1249815747,0.3110917807,-0.2299071401,-0.0763425902,0.0260678306,0.280485779,0.1328603923,-0.0561477169,0.1408209652,-0.3887729943,0.209692806,0.0229794681,-0.0062835971,-0.1900233626,-0.3237221539,-0.2919830382,-0.0033373197,-0.1393821537,0.4665467143,-0.1532784253,0.2935825884,-0.2240790874,-0.1394454837,-0.1043931916,0.3928515017,-0.061280597,0.1351870298,-0.2509830892,-0.0169803556,0.136933744,0.2311661541,-0.049291268,-0.168064788,0.3726317585,-0.0220886804,-0.1133118421,0.5330518484,-0.0282319766,0.0319849886,-0.1048781797,0.1954684258,0.1114103943,-0.2514074445,0.1943351328,0.1735413671,0.1979680359,-0.110180378,0.0263108853,0.1806216091,-0.0178217087,0.0831885189,0.0754430369,-0.1566095501,-0.0618395284,-0.2291377485,-0.3702287674,-0.1156575456,0.0781684741,0.028383391,0.1068357155,-0.0578241237,-0.2347726524,-0.2743072808,0.3148424625,0.0879814997,0.1735295206,-0.0414846316,0.0935693234,-0.0817902759,0.1874943078,0.0105516203,0.414855361,0.1526489854,0.1386745274,-0.2018697709,0.3261217773,-0.1958365887,0.4617404044,0.0994198173,-0.1915585101,0.0323105305,-0.2889240384,-0.000456579,-0.2048773319,-0.1207685098,0.1294811517,-0.1390759945,-0.5578092933,-0.1775350273,-0.1947994381,-0.177322939,-0.0711520389,-0.389251858,-0.1408149898,-0.0222053286,-0.0295884032,0.3878869712,-0.0663037002,0.3042517006,-0.2504909933,0.2322497219,-0.2513068914,-0.4720901251,-0.2935732305,0.1879668981,0.1781593114,-0.0206833128,0.1983250529,-0.0375114866,0.3403944969,0.0168807711,0.3036705256,-0.4464102685,-0.2624958158,-0.1112394556,-0.0941314921,0.0098525006,0.1129646525,0.2278973162,0.1429516822,-0.3494535685,-0.0963938758,-0.130720526,-0.140706256,0.0009915548,0.1941831559,-0.0720808953,0.0421999432,-0.0923184529,-0.0686048046,-0.1738803238,0.1846844405,0.0589118898,0.3326393366,0.4182104766,0.024567036,0.0229594819,0.0603554808,0.0939202309,-0.1221084967,-0.4152963758,0.2814902365,-0.221095562,-0.0365174599,0.1737717241,0.232513532,0.0525959171,0.0665095448,-0.2961342633,-0.0420534499,-0.2180652767,0.4774214327,-0.2487527877,0.2532512844,0.567563653,0.3703916371,-0.1215839982,-0.1645440161,-0.0928736031,0.1102988049,0.0533315688,0.1783058792,0.1550331414,-0.0761385411,-0.3044638634,0.702835381,-0.0307355858,0.3346610367,0.5488693714,0.1978102028,0.3732857704,0.0515313633,-0.3388848603,-0.3989714384,0.15294368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{"html_url":"https:\/\/github.com\/huggingface\/datasets\/issues\/2842","title":"always requiring the username in the dataset name when there is one","comments":"I don't think the user cares of how this is done, but the 2nd command should fail, IMHO, as its dataset name is invalid:\r\n```\r\n# first run\r\npython -c \"from datasets import load_dataset; load_dataset('stas\/openwebtext-10k')\"\r\n# now run immediately\r\npython -c \"from datasets import load_dataset; load_dataset('openwebtext-10k')\"\r\n# the second command should fail, but it doesn't fail now.\r\n```\r\n\r\nMoreover, if someone were to create `openwebtext-10k` w\/o the prefix, they will now get the wrong dataset, if they previously downloaded `stas\/openwebtext-10k`.\r\n\r\nAnd if there are 2 users with the same dataset name `foo\/ds` and `bar\/ds` - currently this won't work to get the correct dataset.\r\n\r\nSo really there 3 unrelated issues hiding in the current behavior.","body":"Me and now another person have been bitten by the `datasets`'s non-strictness on requiring a dataset creator's username when it's due.\r\n\r\nSo both of us started with `stas\/openwebtext-10k`, somewhere along the lines lost `stas\/` and continued using `openwebtext-10k` and it all was good until we published the software and things broke, since there is no `openwebtext-10k`\r\n\r\nSo this feature request is asking to tighten the checking and not allow dataset loading if it was downloaded with the user prefix, but then attempted to be used w\/o it.\r\n\r\nThe same in code:\r\n\r\n```\r\n# first run\r\npython -c \"from datasets import load_dataset; load_dataset('stas\/openwebtext-10k')\"\r\n# now run immediately\r\npython -c \"from datasets import load_dataset; load_dataset('openwebtext-10k')\"\r\n# the second command should fail, but it doesn't fail now.\r\n```\r\n\r\nPlease let me know if I explained myself clearly.\r\n\r\nThank you!","comment_length":115,"text":"always requiring the username in the dataset name when there is one\nMe and now another person have been bitten by the `datasets`'s non-strictness on requiring a dataset creator's username when it's due.\r\n\r\nSo both of us started with `stas\/openwebtext-10k`, somewhere along the lines lost `stas\/` and continued using `openwebtext-10k` and it all was good until we published the software and things broke, since there is no `openwebtext-10k`\r\n\r\nSo this feature request is asking to tighten the checking and not allow dataset loading if it was downloaded with the user prefix, but then attempted to be used w\/o it.\r\n\r\nThe same in code:\r\n\r\n```\r\n# first run\r\npython -c \"from datasets import load_dataset; load_dataset('stas\/openwebtext-10k')\"\r\n# now run immediately\r\npython -c \"from datasets import load_dataset; load_dataset('openwebtext-10k')\"\r\n# the second command should fail, but it doesn't fail now.\r\n```\r\n\r\nPlease let me know if I explained myself clearly.\r\n\r\nThank you!\nI don't think the user cares of how this is done, but the 2nd command should fail, IMHO, as its dataset name is invalid:\r\n```\r\n# first run\r\npython -c \"from datasets import load_dataset; load_dataset('stas\/openwebtext-10k')\"\r\n# now run immediately\r\npython -c \"from datasets import load_dataset; load_dataset('openwebtext-10k')\"\r\n# the second command should fail, but it doesn't fail now.\r\n```\r\n\r\nMoreover, if someone were to create `openwebtext-10k` w\/o the prefix, they will now get the wrong dataset, if they previously downloaded `stas\/openwebtext-10k`.\r\n\r\nAnd if there are 2 users with the same dataset name `foo\/ds` and `bar\/ds` - currently this won't work to get the correct dataset.\r\n\r\nSo really there 3 unrelated issues hiding in the current behavior.","embeddings":[-0.0112160547,0.1447476298,-0.039360974,0.0958193168,0.0011399824,-0.21814017,0.4295825958,0.1111663133,0.1069055125,0.3369613886,0.1158719733,-0.0281225555,0.012058734,0.0387605466,-0.1626236439,0.15405114,-0.0621639118,0.3452987075,-0.1390585154,0.0082569737,-0.1663803458,-0.0051505985,0.0509563424,0.0512073487,0.1581596732,0.0974032432,0.3119137287,0.1957424432,-0.2975040972,-0.4824235439,0.3003055453,0.3615881801,-0.0639261305,0.4107335508,-0.0001179238,-0.0590646155,0.2076957226,0.0588892549,-0.5383250713,-0.4455489516,-0.4917058647,-0.2482866049,0.2102007866,-0.3056302369,0.0239957049,-0.0770888329,0.0180540141,-0.7141695619,0.4935949147,0.1425914317,0.1235402897,0.4247736037,-0.0561008267,-0.0546838194,0.0977879092,0.0445507616,-0.1430555433,0.0673177168,0.2824338078,0.1683505923,0.3565243781,0.2082258761,-0.2156513482,0.1110855341,0.3078671992,-0.297311604,0.1378888935,-0.1850421876,-0.0196566731,0.4707064927,1.0010164976,0.1690532565,-0.4670833349,-0.1757358611,-0.1048424318,0.0698701069,0.4081428647,0.1350550354,-0.1117157638,0.1679628044,-0.0835038573,-0.0350223184,-0.0327410065,-0.0183726028,-0.2701371312,0.463075012,-0.0382801704,0.2346923351,-0.126982227,-0.1310454905,0.1787928343,-0.0570791326,0.0163661037,-0.0085559338,-0.4005134404,-0.1691485345,0.1516158283,-0.4226424098,0.0428753421,0.3494897187,0.2651739419,0.0839743465,-0.3256967366,-0.0293966886,0.2081443816,0.2185648829,0.5727408528,-0.1588097215,0.4727654457,-0.2071726769,0.2513425052,-0.0986485332,-0.0985091105,-0.1068097875,0.0423010625,0.0020768261,0.5621961951,-0.2326089889,-0.2164977491,0.0521594696,-0.0958086252,-0.2232666016,0.0548773855,0.0210059099,-0.1488406956,0.1968661994,0.084558256,-0.0511213467,0.1819668114,-0.1001203507,-0.0620180219,-0.3828084171,-0.1790466756,0.0472363196,0.2418147027,-0.4406692982,0.3566652834,0.0988026187,-0.114215441,0.0436313599,0.1698496491,0.0908757001,-0.0172396172,0.190460071,-0.0580173172,0.1688790023,-0.0506810285,-0.2279134095,-0.2390823513,0.3085813522,-0.390411824,-0.2045666426,-0.4336534142,0.1097150519,-0.2935425639,0.066132538,-0.0812161043,-0.2224237472,-0.0917920545,-0.0883756503,0.1027795821,0.1798978746,-0.1870242059,-0.1584708095,-0.0523947552,0.3964365125,-0.1756321788,-0.0502207018,-0.1365360767,0.029464392,-0.1163142025,0.1394523829,-0.4678532481,0.1224646345,-0.1508981884,-0.0489705689,0.3005219698,-0.5388397574,-0.0999098346,0.2233630568,-0.1950829178,0.2370555699,0.515113771,-0.1638976336,-0.1690343618,-0.3403670788,-0.38592875,0.2341131121,0.1532597393,0.0108526954,-0.0428429842,-0.2005463839,0.1890724003,-0.0827106163,-0.1046393141,0.1461027265,0.1451874524,0.0288746953,0.2422480285,-0.2445032746,-0.1366341412,-0.1447138786,0.2217039317,0.0762651041,-0.1103753522,0.0332951918,-0.4837906957,0.3472396135,0.1079088002,0.2498538941,-0.0062445723,-0.3379675746,-0.2230136245,-0.0397204049,-0.0805989653,0.3676057756,-0.0520953275,0.2502617836,-0.1500829607,-0.1581352502,-0.0297868438,0.5793233514,-0.1334762275,0.1298864037,-0.3625038862,-0.1167224422,0.0630655214,0.3549443483,-0.0472435132,-0.0926375985,0.3039370775,-0.0752965957,-0.032413844,0.4737949967,0.1176315397,-0.163392067,-0.2493177205,0.2496862262,0.2226758897,-0.0240225773,0.1172254756,0.0922882482,0.3392164409,-0.1424901634,-0.0014673754,0.2404722124,-0.0266190972,0.0524699837,0.0816142038,-0.2937871218,0.0200445335,-0.2223831415,-0.3907833099,-0.1125158742,0.0381230712,0.0196339674,0.0680680349,0.0361104049,-0.1875891834,-0.2777249813,0.2804121077,0.0181260668,0.0682562515,-0.0759634972,0.2254895121,-0.1054001525,0.2398348451,-0.136148423,0.3855210245,0.2088996172,0.059797354,-0.2161650807,0.2650007308,-0.2814117074,0.3381402791,0.0773393586,-0.3269681633,-0.0710419416,-0.1665609181,0.1119682863,-0.1857122034,-0.1120791584,0.2062527388,-0.144352138,-0.5838578939,-0.1948499233,-0.1586288065,-0.0823749378,-0.0362053961,-0.3682667911,-0.2206246853,-0.1217518374,0.0648033395,0.3796051443,-0.1369002163,0.1926307976,-0.2595559359,0.2505998909,-0.387212038,-0.3063415289,-0.2005381584,0.1017693058,0.0251546223,0.0505025312,0.2794833779,-0.0904417783,0.4055159688,-0.1425036788,0.0767796338,-0.284624368,-0.4424414039,-0.0563289598,-0.1785662323,0.1260644644,0.0438687876,-0.0142092248,0.2531159818,-0.3800748587,-0.1869846284,-0.1745903641,-0.0908754691,0.0464824699,0.1490806341,-0.2406558543,-0.0902432725,-0.1752282232,-0.0152530055,-0.2159507722,0.0440757349,-0.0034682797,0.2913244665,0.5777443051,-0.1085720286,-0.092646189,0.0440641046,0.2822085023,-0.241314888,-0.4220570326,0.1361677647,-0.1282571852,-0.1011924967,0.1170816571,0.2195591033,0.1298719347,0.1245224699,-0.3237330914,-0.0901662037,-0.2222660482,0.4953493774,-0.1317195296,0.2276004553,0.4823291898,0.3495170772,-0.1312914342,-0.1352271438,-0.110282965,0.0061191386,-0.0955961198,0.1160098016,0.1497413665,-0.1784745306,-0.2178835422,0.5322826505,-0.0081038838,0.1795237362,0.4474458992,0.2515381873,0.4841234088,-0.0553931706,-0.3686906993,-0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{"html_url":"https:\/\/github.com\/huggingface\/datasets\/issues\/2839","title":"OpenWebText: NonMatchingSplitsSizesError","comments":"Thanks for reporting, I'm updating the verifications metadata","body":"## Describe the bug\r\n\r\nWhen downloading `openwebtext`, I'm getting:\r\n```\r\ndatasets.utils.info_utils.NonMatchingSplitsSizesError: [{'expected': SplitInfo(name='train', num_bytes=39769494896, num_examples=8013769, dataset_name='openwebtext'), 'recorded': SplitInfo(name='train', num_bytes=39611023912, num_examples=7982430, dataset_name='openwebtext')}]\r\n```\r\n\r\nI suspect that the file we download from has changed since the size doesn't look like to match with documentation\r\n\r\n`Downloading: 0%| | 0.00\/12.9G [00:00, ?B\/s]` This suggest the total size is 12.9GB, whereas the one documented mentions `Size of downloaded dataset files: 12283.35 MB`.\r\n\r\n## Steps to reproduce the bug\r\n```python\r\nfrom datasets import load_dataset\r\n\r\nload_dataset(\"openwebtext\", download_mode=\"force_redownload\")\r\n```\r\n\r\n## Expected results\r\n\r\nLoading is successful\r\n\r\n## Actual results\r\n\r\nLoading throws above error.\r\n\r\n## Environment info\r\n\r\n- `datasets` version: 1.10.2\r\n- Platform: linux (Redhat version 8.1)\r\n- Python version: 3.8\r\n- PyArrow version: 4.0.1\r\n","comment_length":8,"text":"OpenWebText: NonMatchingSplitsSizesError\n## Describe the bug\r\n\r\nWhen downloading `openwebtext`, I'm getting:\r\n```\r\ndatasets.utils.info_utils.NonMatchingSplitsSizesError: [{'expected': SplitInfo(name='train', num_bytes=39769494896, num_examples=8013769, dataset_name='openwebtext'), 'recorded': SplitInfo(name='train', num_bytes=39611023912, num_examples=7982430, dataset_name='openwebtext')}]\r\n```\r\n\r\nI suspect that the file we download from has changed since the size doesn't look like to match with documentation\r\n\r\n`Downloading: 0%| | 0.00\/12.9G [00:00, ?B\/s]` This suggest the total size is 12.9GB, whereas the one documented mentions `Size of downloaded dataset files: 12283.35 MB`.\r\n\r\n## Steps to reproduce the bug\r\n```python\r\nfrom datasets import load_dataset\r\n\r\nload_dataset(\"openwebtext\", download_mode=\"force_redownload\")\r\n```\r\n\r\n## Expected results\r\n\r\nLoading is successful\r\n\r\n## Actual results\r\n\r\nLoading throws above error.\r\n\r\n## Environment info\r\n\r\n- `datasets` version: 1.10.2\r\n- Platform: linux (Redhat version 8.1)\r\n- Python version: 3.8\r\n- PyArrow version: 4.0.1\r\n\nThanks for reporting, I'm updating the verifications 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{"html_url":"https:\/\/github.com\/huggingface\/datasets\/issues\/2839","title":"OpenWebText: NonMatchingSplitsSizesError","comments":"I just regenerated the verifications metadata and noticed that nothing changed: the data file is fine (the checksum didn't change), and the number of examples is still 8013769. Not sure how you managed to get 7982430 examples.\r\n\r\nCan you try to delete your cache ( by default at `~\/.cache\/huggingface\/datasets`) and try again please ?\r\nAlso, on which platform are you (linux\/macos\/windows) ?","body":"## Describe the bug\r\n\r\nWhen downloading `openwebtext`, I'm getting:\r\n```\r\ndatasets.utils.info_utils.NonMatchingSplitsSizesError: [{'expected': SplitInfo(name='train', num_bytes=39769494896, num_examples=8013769, dataset_name='openwebtext'), 'recorded': SplitInfo(name='train', num_bytes=39611023912, num_examples=7982430, dataset_name='openwebtext')}]\r\n```\r\n\r\nI suspect that the file we download from has changed since the size doesn't look like to match with documentation\r\n\r\n`Downloading: 0%| | 0.00\/12.9G [00:00, ?B\/s]` This suggest the total size is 12.9GB, whereas the one documented mentions `Size of downloaded dataset files: 12283.35 MB`.\r\n\r\n## Steps to reproduce the bug\r\n```python\r\nfrom datasets import load_dataset\r\n\r\nload_dataset(\"openwebtext\", download_mode=\"force_redownload\")\r\n```\r\n\r\n## Expected results\r\n\r\nLoading is successful\r\n\r\n## Actual results\r\n\r\nLoading throws above error.\r\n\r\n## Environment info\r\n\r\n- `datasets` version: 1.10.2\r\n- Platform: linux (Redhat version 8.1)\r\n- Python version: 3.8\r\n- PyArrow version: 4.0.1\r\n","comment_length":62,"text":"OpenWebText: NonMatchingSplitsSizesError\n## Describe the bug\r\n\r\nWhen downloading `openwebtext`, I'm getting:\r\n```\r\ndatasets.utils.info_utils.NonMatchingSplitsSizesError: [{'expected': SplitInfo(name='train', num_bytes=39769494896, num_examples=8013769, dataset_name='openwebtext'), 'recorded': SplitInfo(name='train', num_bytes=39611023912, num_examples=7982430, dataset_name='openwebtext')}]\r\n```\r\n\r\nI suspect that the file we download from has changed since the size doesn't look like to match with documentation\r\n\r\n`Downloading: 0%| | 0.00\/12.9G [00:00, ?B\/s]` This suggest the total size is 12.9GB, whereas the one documented mentions `Size of downloaded dataset files: 12283.35 MB`.\r\n\r\n## Steps to reproduce the bug\r\n```python\r\nfrom datasets import load_dataset\r\n\r\nload_dataset(\"openwebtext\", download_mode=\"force_redownload\")\r\n```\r\n\r\n## Expected results\r\n\r\nLoading is successful\r\n\r\n## Actual results\r\n\r\nLoading throws above error.\r\n\r\n## Environment info\r\n\r\n- `datasets` version: 1.10.2\r\n- Platform: linux (Redhat version 8.1)\r\n- Python version: 3.8\r\n- PyArrow version: 4.0.1\r\n\nI just regenerated the verifications metadata and noticed that nothing changed: the data file is fine (the checksum didn't change), and the number of examples is still 8013769. Not sure how you managed to get 7982430 examples.\r\n\r\nCan you try to delete your cache ( by default at `~\/.cache\/huggingface\/datasets`) and try again please ?\r\nAlso, on which platform are you (linux\/macos\/windows) ?","embeddings":[-0.2325653881,-0.1025962085,0.0594487116,0.4710067809,0.099145472,0.0983288512,0.001799254,0.5494816303,-0.0201674923,0.1316695958,-0.1176461726,0.0032898269,-0.1183154136,0.091308251,-0.0851507634,0.1036110669,-0.1839791536,0.1432445645,-0.2236059755,0.0837038904,-0.1660715044,0.2510773838,-0.2940531671,-0.136175856,-0.2839494944,-0.0273162425,-0.2651298642,0.2377939373,-0.2557264864,-0.4160161018,0.1758898795,-0.3987876177,-0.0372202508,0.5550357699,-0.0001198019,0.053337954,0.2946435809,-0.2280974835,-0.130750522,-0.1020404696,-0.4944339693,-0.3940509856,-0.1452365369,-0.1462632567,0.1926995069,-0.128324911,0.0508886799,-0.2793247402,0.45934093,0.4411795735,0.1552283317,0.397815913,0.2411608696,0.0371606573,0.3650759459,-0.2219923884,-0.131810233,-0.0362073407,0.0326931886,0.0405232348,0.0806393847,0.1172676533,0.010848294,0.208711639,-0.0005465942,-0.04086693,0.2286907285,-0.2362536341,0.1534994245,0.4934612215,0.6223924756,-0.136980623,-0.3544664681,-0.3586990833,-0.4166816473,0.0730490908,0.2820883095,0.4564271271,0.0207831394,0.2088509351,-0.3920858502,0.0627977401,0.0887393206,0.1010190621,-0.219721511,0.1737966686,-0.0613732822,0.0936699957,0.0337460227,0.1205220819,0.2228565961,-0.1298710406,-0.0552684665,0.130376339,-0.2470706999,-0.0399980396,0.0465838946,-0.1719465107,0.3725028634,0.3157573938,0.3307099342,-0.2253412157,-0.2792927325,-0.0260545649,0.1021305472,0.486409694,-0.0030222856,0.181135416,-0.0994259194,0.153441906,0.0841908902,-0.038324248,-0.0627070367,-0.3573563993,0.0782192796,0.0519293919,0.5506390929,-0.313762635,-0.5638862252,0.1355626136,-0.271795541,-0.2499685287,-0.2926042974,-0.0598699637,-0.3987960219,0.21013017,0.1625957936,-0.1578373164,-0.3149763942,-0.3888362348,-0.274458766,-0.0227039084,-0.1876686811,0.1219247207,0.0013910971,-0.1314144135,0.4226850271,-0.0792298242,-0.0405353419,-0.2938423455,0.16571486,0.011818096,0.0518370979,0.1675295681,-0.2069491893,0.1224142388,0.0686967969,0.0914774612,-0.2862041891,0.3105949759,-0.2906442583,-0.4093714058,-0.1381831467,0.1050514579,-0.3705904186,-0.160875082,0.3073712587,-0.0599066541,0.3331327736,-0.1210963205,-0.0874515325,-0.1715030372,-0.1489326507,-0.1385395974,0.136148721,0.4009007215,-0.2815036774,-0.1870612502,0.2009663135,-0.1067375243,0.2657059729,0.4419420362,0.0383116044,0.1077955142,-0.2697066367,0.1994151175,0.1035528705,-0.3222500086,-0.6370256543,0.3166928291,-0.0614389703,0.1890935451,0.1131784543,-0.2998300493,0.0997247845,-0.1133560538,-0.1074843481,0.1425459236,-0.1855406165,0.0653884783,-0.6042557955,-0.7404735684,0.4050538242,-0.0152202202,0.2199621052,-0.2009830177,0.0258024931,0.2023736984,0.4867910445,-0.0068813604,0.0082399799,0.1832945794,0.1684482396,0.0200096425,-0.0262848958,-0.3034965396,-0.1270769089,0.2578174174,-0.1189626977,-0.0252107475,0.2395293266,-0.1284899116,-0.5201492906,-0.2797634602,-0.027436683,-0.2890127301,-0.0011977525,0.0537340343,0.3314978778,0.070443891,0.1006710827,0.3630115986,0.0587042272,0.1622793376,-0.3886450529,0.2116378248,-0.0419298112,-0.1590762138,0.147472024,-0.0411890857,0.157839641,-0.1788159907,-0.3645519018,0.6570310593,0.1383420527,0.02780932,-0.0206335951,0.1761494279,0.2389790714,-0.232609421,0.1361853629,0.3155079186,-0.0367029049,-0.0358329862,0.0357469767,-0.0957837403,0.1101791039,-0.027026739,-0.0629577935,0.0031620259,-0.0677025169,-0.2016023397,-0.0996729061,-0.1194978952,0.0788498223,-0.0529226586,-0.1825649738,0.0738681331,0.1595273167,-0.1473862082,0.7876149416,-0.0265092328,-0.0810908601,0.2676542401,-0.2830883563,-0.1285564452,-0.183779493,0.1333068758,0.5281811357,0.2116181999,0.1979825199,0.2271813452,0.0595819056,-0.0967165157,0.1486262977,0.2788825929,0.2119769752,0.301492095,0.0253650583,0.0782746673,-0.1882289052,0.1017744318,0.116242066,0.3736377954,-0.4455088973,-0.1506477296,-0.499330163,0.0167021099,-0.2890394628,0.1519827545,-0.1953706294,-0.2340987176,-0.0569427386,0.0578246228,-0.0190754719,0.2200787216,-0.1844465733,0.1744143516,0.177943483,0.2221967876,0.0834514573,-0.0005307973,-0.5165869594,0.0194541551,0.3351318538,-0.249814257,0.4144765437,-0.5614904165,-0.0921603888,-0.201660797,-0.2970202863,-0.0893634483,0.0089835012,0.1129095405,0.292773813,0.2110989392,0.0249922592,-0.0226391368,0.1041431502,-0.320573926,-0.2713530958,-0.0989822373,0.0973132402,0.2661294639,-0.0641359314,-0.5627799034,-0.216370061,-0.2757206857,0.326700151,0.1139719784,0.202980265,0.0925925523,0.0694469884,0.1050399691,-0.0523555838,0.1160848886,-0.2542546391,-0.5217090845,0.3188512623,-0.0026786192,-0.3333176374,-0.0070148506,0.1511377543,-0.0392282307,0.0255860798,-0.6288368106,-0.2420555353,-0.4020483494,-0.0416228026,-0.1522629708,0.2767257392,0.4846800268,-0.030819742,-0.0632560626,-0.1522991359,-0.3440429568,-0.0237116944,0.1624833941,0.6210068464,-0.1053256467,0.1852699518,0.4056099951,0.4629566371,0.5385028124,0.0481480174,-0.0136958975,-0.0321367905,0.1083640978,-0.1104782,-0.0401165858,0.2920781076,-0.1999634057,0.0356370732,0.29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{"html_url":"https:\/\/github.com\/huggingface\/datasets\/issues\/2839","title":"OpenWebText: NonMatchingSplitsSizesError","comments":"I'll try without deleting the whole cache (we have large datasets already stored). I was under the impression that `download_mode=\"force_redownload\"` would bypass cache.\r\nSorry plateform should be linux (Redhat version 8.1)","body":"## Describe the bug\r\n\r\nWhen downloading `openwebtext`, I'm getting:\r\n```\r\ndatasets.utils.info_utils.NonMatchingSplitsSizesError: [{'expected': SplitInfo(name='train', num_bytes=39769494896, num_examples=8013769, dataset_name='openwebtext'), 'recorded': SplitInfo(name='train', num_bytes=39611023912, num_examples=7982430, dataset_name='openwebtext')}]\r\n```\r\n\r\nI suspect that the file we download from has changed since the size doesn't look like to match with documentation\r\n\r\n`Downloading: 0%| | 0.00\/12.9G [00:00, ?B\/s]` This suggest the total size is 12.9GB, whereas the one documented mentions `Size of downloaded dataset files: 12283.35 MB`.\r\n\r\n## Steps to reproduce the bug\r\n```python\r\nfrom datasets import load_dataset\r\n\r\nload_dataset(\"openwebtext\", download_mode=\"force_redownload\")\r\n```\r\n\r\n## Expected results\r\n\r\nLoading is successful\r\n\r\n## Actual results\r\n\r\nLoading throws above error.\r\n\r\n## Environment info\r\n\r\n- `datasets` version: 1.10.2\r\n- Platform: linux (Redhat version 8.1)\r\n- Python version: 3.8\r\n- PyArrow version: 4.0.1\r\n","comment_length":31,"text":"OpenWebText: NonMatchingSplitsSizesError\n## Describe the bug\r\n\r\nWhen downloading `openwebtext`, I'm getting:\r\n```\r\ndatasets.utils.info_utils.NonMatchingSplitsSizesError: [{'expected': SplitInfo(name='train', num_bytes=39769494896, num_examples=8013769, dataset_name='openwebtext'), 'recorded': SplitInfo(name='train', num_bytes=39611023912, num_examples=7982430, dataset_name='openwebtext')}]\r\n```\r\n\r\nI suspect that the file we download from has changed since the size doesn't look like to match with documentation\r\n\r\n`Downloading: 0%| | 0.00\/12.9G [00:00, ?B\/s]` This suggest the total size is 12.9GB, whereas the one documented mentions `Size of downloaded dataset files: 12283.35 MB`.\r\n\r\n## Steps to reproduce the bug\r\n```python\r\nfrom datasets import load_dataset\r\n\r\nload_dataset(\"openwebtext\", download_mode=\"force_redownload\")\r\n```\r\n\r\n## Expected results\r\n\r\nLoading is successful\r\n\r\n## Actual results\r\n\r\nLoading throws above error.\r\n\r\n## Environment info\r\n\r\n- `datasets` version: 1.10.2\r\n- Platform: linux (Redhat version 8.1)\r\n- Python version: 3.8\r\n- PyArrow version: 4.0.1\r\n\nI'll try without deleting the whole cache (we have large datasets already stored). I was under the impression that `download_mode=\"force_redownload\"` would bypass cache.\r\nSorry plateform should be linux (Redhat version 8.1)","embeddings":[-0.3607826829,-0.239854753,0.0516973063,0.4607081115,-0.0456267372,0.1327427775,0.066052109,0.6086549163,0.1439708173,0.1374783516,-0.2283655256,0.1216922924,-0.0511874668,0.24087888,0.0173501056,0.0554530993,-0.1288683265,0.209086746,0.0327513143,0.0964000374,-0.2407321781,0.165215224,-0.3307679594,-0.0877942294,-0.0919432417,0.0468051881,-0.0785730928,0.2525186837,-0.1423963159,-0.3720365465,0.3101969957,-0.3209818304,0.0526632667,0.4554589093,-0.0001236256,0.058411181,0.3002471924,-0.2492422163,-0.16436176,-0.0202188827,-0.4952323437,-0.4582964778,-0.1188494414,-0.1141500697,0.184963122,-0.0521582067,0.0923798084,-0.2381899059,0.3523260951,0.1760241985,0.1076085791,0.2688066065,0.1909793615,-0.0141789233,0.3994997442,-0.288500309,-0.2018007636,-0.026038751,0.2025960684,0.2345232815,-0.0004501377,0.2051186413,0.0155433789,0.3176065981,0.1230182052,-0.0852457508,0.1734517515,-0.223595798,0.1765745282,0.4632994831,0.7116635442,-0.2908660173,-0.3333330154,-0.4549492896,-0.4818343818,-0.0767983198,0.3402119577,0.3721658885,0.0110199759,0.2835704088,-0.2383954376,-0.0144351302,0.0867683887,0.0248056687,-0.2652613223,0.1660380363,-0.0655873343,0.0525521077,-0.0053646825,0.1384350061,0.2647070587,-0.0735872909,0.0548304617,0.1759223789,-0.2408612818,-0.0740365535,0.0165432449,-0.2352584451,0.3727194965,0.3354320526,0.3635959923,-0.1954646856,-0.3061286211,-0.0127849197,0.202025786,0.4631952047,-0.1391366273,0.0402779281,-0.1740580797,0.0575832874,0.111650236,-0.1003556475,-0.0169872101,-0.3424649537,0.1643748879,0.0634849742,0.6322448254,-0.3608763218,-0.5638146996,0.0620397069,-0.2243329287,-0.2449309826,-0.2871406674,-0.0788733289,-0.3087580204,0.3446002007,0.1083343402,-0.1410493702,-0.250838846,-0.4097034633,-0.2513109446,-0.1282017827,-0.1443449259,0.0415491126,0.0319383219,-0.138546139,0.2920025587,0.0044018342,-0.0483897887,-0.1885268092,0.1772954911,-0.0495248884,0.0395917334,0.2417313159,-0.0637856051,0.2164584994,0.0311726723,0.1261219084,-0.3001105189,0.3434175551,-0.3207673132,-0.3199581802,-0.0984371156,0.0781468749,-0.2246572673,-0.1677706391,0.1053349003,-0.047128059,0.3671295941,-0.2251332849,-0.0991620123,-0.1212022156,-0.1245307252,-0.1096512154,0.0449683294,0.5349205136,-0.4528928995,-0.1801821291,0.185575366,-0.1425025314,0.3732614815,0.3822270036,0.0877135545,0.0240434296,-0.2359959036,0.0825527161,0.0203862526,-0.4213356078,-0.8024846911,0.4292296767,-0.063970007,0.1961514801,0.1568332464,-0.1446538568,0.1170874834,-0.1508550644,0.057605356,0.1717762351,-0.2016098052,0.0442328788,-0.629065454,-0.8196583986,0.3851351142,-0.0254817307,0.2239643931,-0.1056236476,0.0249994807,0.2732915282,0.368870616,0.1013237163,0.0660170689,0.1658230275,0.0039065285,0.0954902247,-0.0445364453,-0.2933856547,-0.3191492856,0.2778995037,-0.2501968443,-0.1287551522,0.3011008203,-0.1411423236,-0.5185483098,-0.2888207138,-0.0862790644,-0.0926862955,-0.0315534584,0.0389909446,0.2252077758,0.0351627357,0.0298353322,0.3739862442,0.116735056,0.1384649873,-0.2917839289,0.1245396957,-0.0396486931,-0.0166225843,0.0037614766,-0.1315949559,0.189512223,-0.1118277237,-0.3834511042,0.6283049583,-0.0011398828,0.1150420979,-0.090775691,-0.0132446112,0.2655419707,-0.1831507087,0.1516956985,0.3305782974,-0.0388676003,-0.1138824895,0.0377740338,-0.1808829308,0.1214482114,0.0156273618,-0.1123446822,0.0120707881,-0.0592523217,-0.1683395058,0.0520322248,-0.1470990628,0.041033186,-0.132320255,0.1531793624,-0.058693409,0.089498423,-0.0775124133,0.6427761316,-0.0461898148,-0.0024128065,0.2773858607,-0.31353724,-0.1165984571,-0.149243772,0.3093588948,0.4315313101,0.2351769954,0.1816487759,0.214854151,-0.0480404198,-0.0663257837,0.0623969994,0.2788154185,0.1103492528,0.278703928,0.0314646326,0.0604010746,-0.2338596284,0.1105607077,0.1959756166,0.3361184299,-0.4241134524,-0.1764782369,-0.4222451448,-0.0984081179,-0.2253969163,0.1779851317,-0.1684412062,-0.228486225,-0.0869306996,0.0673669577,-0.0112149464,0.18163158,-0.2745671272,0.1598093063,0.139281556,0.0402397811,0.0125676868,0.0235119388,-0.4680196047,-0.0232420117,0.3328770697,-0.314013958,0.3491358757,-0.535438776,-0.0107724341,-0.0256563537,-0.1533284187,0.0009073421,0.082536146,-0.0696029216,0.2342223376,0.2336276174,0.0563837104,0.1101676375,0.1208643317,-0.270549655,-0.2377162576,-0.1075411141,0.1214817017,0.3437823653,-0.0233938284,-0.5561491847,-0.2882370651,-0.3905978799,0.2452228218,0.1488028616,0.2224647105,0.3056887388,0.081562236,0.1264165938,0.1255260706,0.086612016,-0.2552498579,-0.3846787512,0.27545017,0.0955306813,-0.2659647465,-0.0228641741,0.2200033963,-0.0600050166,0.1335960776,-0.5093490481,-0.1830071062,-0.4022891521,-0.2084587067,-0.1236945465,0.2260227352,0.4829974174,0.0146279922,-0.0300645493,-0.1376815438,-0.330088079,0.0368260704,0.1950068027,0.4865730703,-0.1092981622,0.238016054,0.3572044671,0.4601337314,0.468192935,0.1195461452,0.0832175612,0.0417757891,0.2068868279,-0.1063739881,0.0597097725,0.1245609075,-0.2355274707,-0.0282634329,0.2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{"html_url":"https:\/\/github.com\/huggingface\/datasets\/issues\/2839","title":"OpenWebText: NonMatchingSplitsSizesError","comments":"Hi @thomasw21 , are you still having this issue after clearing your cache ?","body":"## Describe the bug\r\n\r\nWhen downloading `openwebtext`, I'm getting:\r\n```\r\ndatasets.utils.info_utils.NonMatchingSplitsSizesError: [{'expected': SplitInfo(name='train', num_bytes=39769494896, num_examples=8013769, dataset_name='openwebtext'), 'recorded': SplitInfo(name='train', num_bytes=39611023912, num_examples=7982430, dataset_name='openwebtext')}]\r\n```\r\n\r\nI suspect that the file we download from has changed since the size doesn't look like to match with documentation\r\n\r\n`Downloading: 0%| | 0.00\/12.9G [00:00, ?B\/s]` This suggest the total size is 12.9GB, whereas the one documented mentions `Size of downloaded dataset files: 12283.35 MB`.\r\n\r\n## Steps to reproduce the bug\r\n```python\r\nfrom datasets import load_dataset\r\n\r\nload_dataset(\"openwebtext\", download_mode=\"force_redownload\")\r\n```\r\n\r\n## Expected results\r\n\r\nLoading is successful\r\n\r\n## Actual results\r\n\r\nLoading throws above error.\r\n\r\n## Environment info\r\n\r\n- `datasets` version: 1.10.2\r\n- Platform: linux (Redhat version 8.1)\r\n- Python version: 3.8\r\n- PyArrow version: 4.0.1\r\n","comment_length":14,"text":"OpenWebText: NonMatchingSplitsSizesError\n## Describe the bug\r\n\r\nWhen downloading `openwebtext`, I'm getting:\r\n```\r\ndatasets.utils.info_utils.NonMatchingSplitsSizesError: [{'expected': SplitInfo(name='train', num_bytes=39769494896, num_examples=8013769, dataset_name='openwebtext'), 'recorded': SplitInfo(name='train', num_bytes=39611023912, num_examples=7982430, dataset_name='openwebtext')}]\r\n```\r\n\r\nI suspect that the file we download from has changed since the size doesn't look like to match with documentation\r\n\r\n`Downloading: 0%| | 0.00\/12.9G [00:00, ?B\/s]` This suggest the total size is 12.9GB, whereas the one documented mentions `Size of downloaded dataset files: 12283.35 MB`.\r\n\r\n## Steps to reproduce the bug\r\n```python\r\nfrom datasets import load_dataset\r\n\r\nload_dataset(\"openwebtext\", download_mode=\"force_redownload\")\r\n```\r\n\r\n## Expected results\r\n\r\nLoading is successful\r\n\r\n## Actual results\r\n\r\nLoading throws above error.\r\n\r\n## Environment info\r\n\r\n- `datasets` version: 1.10.2\r\n- Platform: linux (Redhat version 8.1)\r\n- Python version: 3.8\r\n- PyArrow version: 4.0.1\r\n\nHi @thomasw21 , are you still having this issue after clearing your cache ?","embeddings":[-0.3699499965,-0.1261989325,0.0325992927,0.4002228081,0.0890990272,0.1182889119,-0.0533089116,0.6034545302,-0.0335419439,0.1091241091,-0.1687633097,0.0282330066,-0.0962741897,0.0463814177,-0.1560756266,0.0372467712,-0.1961586177,0.2053611428,-0.2641817033,0.1242605746,-0.2632367015,0.2561306357,-0.2909330726,-0.0491796099,-0.1904989481,0.0199674014,-0.1706748307,0.1613520384,-0.221933037,-0.4056100547,0.2906131744,-0.3588837385,0.0469210073,0.5125917792,-0.000114122,0.0556628332,0.3204028606,-0.1646400541,-0.1723579913,-0.1874836534,-0.4532294571,-0.3731660545,-0.0388759747,-0.112672627,0.1566630751,-0.1037599817,0.1046383306,-0.3784665465,0.3734263182,0.330273509,0.2179907411,0.3707486689,0.2069821954,0.0058436338,0.4057067931,-0.3193127215,-0.2311393917,-0.1044008136,0.1066185907,0.0624603406,-0.0348753482,0.0600125566,0.0215322468,0.3120785952,0.077384375,-0.0503110252,0.3094576597,-0.2281142473,0.1407149136,0.3834085763,0.7641160488,-0.2195935696,-0.283985287,-0.3051543236,-0.3677956462,-0.0343249999,0.2471122444,0.483710438,-0.020346934,0.2292669863,-0.3121861517,0.0388080738,0.0425118431,0.1139018014,-0.2064270824,0.2632624805,-0.0242877677,0.1047253832,-0.0661162212,0.0650689006,0.3052019179,-0.1170110404,-0.0257206298,0.1538783908,-0.2919646204,-0.0112022916,0.0328578874,-0.2262672037,0.2527976036,0.3594455421,0.3436971903,-0.2234082371,-0.1035509557,-0.0320001915,0.2006347477,0.4552832246,-0.0448409393,0.1968113631,-0.0917967036,-0.0303141531,0.0936576501,-0.0837954581,-0.0554752834,-0.3193314373,0.0959796682,0.0045037735,0.5925169587,-0.2778443098,-0.6033614874,0.0504921898,-0.2114330232,-0.2764321566,-0.3233760893,-0.1093845218,-0.3041671515,0.2534267902,0.1797761768,-0.1391205937,-0.3311912417,-0.3503164649,-0.3089872301,-0.1209257022,-0.1772671044,0.032847248,-0.0027473106,-0.0529400334,0.3967550993,-0.1295852065,-0.0087198969,-0.2121687531,0.2079534382,-0.0091037238,-0.0446107127,0.1839971989,-0.1260671616,0.1843945831,0.0385377072,0.1092341244,-0.3352973461,0.3740764558,-0.2801018655,-0.4088790119,-0.1939189583,0.189780131,-0.3035056293,-0.1956293732,0.3018482625,-0.0626553893,0.2803269625,-0.1122081727,-0.1693761051,-0.1711202711,-0.1893253773,-0.1533607095,0.0882912874,0.3502240479,-0.257031709,-0.1984224617,0.2106315792,-0.0680614114,0.3246780038,0.4196591675,-0.0076634637,0.0039387178,-0.2138770074,0.1999334693,0.1432218552,-0.2937815785,-0.6871965528,0.3707432449,-0.114377968,0.1523077488,0.0933001712,-0.1211867109,0.1059551015,-0.1198136285,-0.0463212542,0.2644116879,-0.2024022788,0.0870940834,-0.5790618062,-0.7767994404,0.3755401075,-0.0242120661,0.2111732364,-0.1639950573,0.006461286,0.2177522928,0.4441138506,0.0989785343,0.0500627458,0.1430724412,0.0939549729,0.0429766849,-0.061436139,-0.279608041,-0.221799925,0.2446716726,-0.1376405358,-0.0303829275,0.2486105412,-0.1314045936,-0.5314114094,-0.2417081743,-0.1061991081,-0.1340866238,0.0522408374,-0.0163130574,0.2626656592,0.0801130608,0.080397673,0.3879548609,0.0932108238,0.1257617027,-0.2942942381,0.2135344148,-0.0467306525,-0.1446321309,0.178412959,-0.0720042661,0.1614710093,-0.0529614687,-0.4088436663,0.656724751,-0.0448579974,0.1105701253,-0.0014170395,0.0634532049,0.2236272097,-0.2137767524,0.1476328522,0.3671287,-0.0286879614,-0.0925415456,0.1367339641,-0.1318332106,0.1761523336,0.0378681459,-0.1045079455,-0.0272626355,-0.0866388455,-0.1919618994,0.0127834119,-0.1355392635,0.121220693,-0.0005551229,-0.1029190198,0.0522346124,0.1520875394,-0.1716966927,0.7244181037,-0.0214816853,-0.0341246277,0.2815185785,-0.334707588,-0.0916437805,-0.1870049685,0.1764208823,0.4675450623,0.2792059779,0.1903891414,0.1980343014,0.0257366169,-0.1207682714,0.1171555221,0.2820116878,0.2443300039,0.2476788908,0.0440932438,0.0549047031,-0.2191032767,0.127850458,0.0708637387,0.3302167356,-0.3441953063,-0.2616869509,-0.4838453829,-0.0104162619,-0.2235880345,0.1671076715,-0.156897679,-0.3010331094,-0.0594916195,0.0940519497,-0.0327388905,0.2392505854,-0.2775541842,0.1586039364,0.1916082054,0.1540816426,0.0908739641,-0.0944102034,-0.5167679787,0.0587307364,0.2828665674,-0.3290631771,0.4073050916,-0.5408995748,0.0238137655,-0.0917047858,-0.1395531148,-0.1037957743,0.0741046965,0.0165922679,0.1612576097,0.1679470241,0.0378816687,-0.0143355317,0.1781898737,-0.3044912815,-0.2063006163,-0.0532360338,0.1252015829,0.2432821542,-0.0463878065,-0.6165706515,-0.291761905,-0.3603462577,0.2076623291,0.0761469081,0.1756553352,0.0283724535,0.0443828218,-0.0005369574,0.0527454726,0.0789406672,-0.2449579984,-0.4761647284,0.3822352588,-0.0113914954,-0.2909473777,0.0234442204,0.2967124879,-0.0753872171,0.0756534189,-0.6022984385,-0.1599006504,-0.4078228176,-0.0391741544,-0.108949624,0.2167443335,0.4038220942,0.0629792586,-0.0997803211,-0.1050412282,-0.3229443431,-0.0342823453,0.2531094849,0.4984205365,-0.075255096,0.2646891475,0.3646192551,0.4557607174,0.4932022393,0.0393025428,0.0251377858,-0.0237086453,0.1077448577,-0.1484603286,0.0608350486,0.1915815771,-0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{"html_url":"https:\/\/github.com\/huggingface\/datasets\/issues\/2839","title":"OpenWebText: NonMatchingSplitsSizesError","comments":"Sorry I haven't had time to work on this. I'll close and re-open if I can't figure out why I'm having this issue. Thanks for taking a look !","body":"## Describe the bug\r\n\r\nWhen downloading `openwebtext`, I'm getting:\r\n```\r\ndatasets.utils.info_utils.NonMatchingSplitsSizesError: [{'expected': SplitInfo(name='train', num_bytes=39769494896, num_examples=8013769, dataset_name='openwebtext'), 'recorded': SplitInfo(name='train', num_bytes=39611023912, num_examples=7982430, dataset_name='openwebtext')}]\r\n```\r\n\r\nI suspect that the file we download from has changed since the size doesn't look like to match with documentation\r\n\r\n`Downloading: 0%| | 0.00\/12.9G [00:00, ?B\/s]` This suggest the total size is 12.9GB, whereas the one documented mentions `Size of downloaded dataset files: 12283.35 MB`.\r\n\r\n## Steps to reproduce the bug\r\n```python\r\nfrom datasets import load_dataset\r\n\r\nload_dataset(\"openwebtext\", download_mode=\"force_redownload\")\r\n```\r\n\r\n## Expected results\r\n\r\nLoading is successful\r\n\r\n## Actual results\r\n\r\nLoading throws above error.\r\n\r\n## Environment info\r\n\r\n- `datasets` version: 1.10.2\r\n- Platform: linux (Redhat version 8.1)\r\n- Python version: 3.8\r\n- PyArrow version: 4.0.1\r\n","comment_length":29,"text":"OpenWebText: NonMatchingSplitsSizesError\n## Describe the bug\r\n\r\nWhen downloading `openwebtext`, I'm getting:\r\n```\r\ndatasets.utils.info_utils.NonMatchingSplitsSizesError: [{'expected': SplitInfo(name='train', num_bytes=39769494896, num_examples=8013769, dataset_name='openwebtext'), 'recorded': SplitInfo(name='train', num_bytes=39611023912, num_examples=7982430, dataset_name='openwebtext')}]\r\n```\r\n\r\nI suspect that the file we download from has changed since the size doesn't look like to match with documentation\r\n\r\n`Downloading: 0%| | 0.00\/12.9G [00:00, ?B\/s]` This suggest the total size is 12.9GB, whereas the one documented mentions `Size of downloaded dataset files: 12283.35 MB`.\r\n\r\n## Steps to reproduce the bug\r\n```python\r\nfrom datasets import load_dataset\r\n\r\nload_dataset(\"openwebtext\", download_mode=\"force_redownload\")\r\n```\r\n\r\n## Expected results\r\n\r\nLoading is successful\r\n\r\n## Actual results\r\n\r\nLoading throws above error.\r\n\r\n## Environment info\r\n\r\n- `datasets` version: 1.10.2\r\n- Platform: linux (Redhat version 8.1)\r\n- Python version: 3.8\r\n- PyArrow version: 4.0.1\r\n\nSorry I haven't had time to work on this. I'll close and re-open if I can't figure out why I'm having this issue. Thanks for taking a look !","embeddings":[-0.3353740871,-0.18841286,0.0487625636,0.4328111112,0.0591351986,0.1115414873,-0.0852611586,0.5720363855,-0.0169446841,0.1263842881,-0.1240672097,0.0030228929,-0.0780212209,0.0841974169,-0.1284149289,0.028532682,-0.1261273474,0.1875724196,-0.2567581236,0.1037800536,-0.289475292,0.2635649145,-0.3147125542,0.0043351529,-0.1761120707,0.0403093137,-0.1997045577,0.1530286819,-0.2597185671,-0.3653438091,0.2717806399,-0.3823173642,0.0590334535,0.4936454296,-0.0001182356,0.0295847729,0.3093135059,-0.2089018524,-0.0846336931,-0.1598861217,-0.4955505431,-0.4291180968,-0.0661034137,-0.1210819632,0.1902426779,-0.1171414033,0.126828596,-0.3275127709,0.3349781334,0.3231547475,0.178308785,0.4126829505,0.2072996944,0.0013006629,0.3759169579,-0.2919977009,-0.2213473916,-0.0704078153,0.0860572755,0.0268128999,0.0031038474,0.0268938132,0.0232798997,0.2867479622,0.1630622447,-0.0368349515,0.3667397797,-0.2062833607,0.1011864692,0.421010673,0.7274805903,-0.215399608,-0.2575476766,-0.3073272705,-0.3769135177,0.0297992546,0.2773856521,0.4866474867,0.0031598224,0.1984095871,-0.3036155999,0.057540372,0.038236998,0.1616750956,-0.2182648033,0.2967064977,-0.0009074002,0.1527033448,-0.0929155275,0.0851415694,0.2732588649,-0.1273201704,0.0084036961,0.1611264944,-0.2615137696,0.0117760235,-0.028219644,-0.2475543171,0.2653850913,0.3092905581,0.3690367043,-0.3131037354,-0.1379642636,-0.0037725242,0.239037782,0.379905194,-0.0437345132,0.1846986711,-0.1888067573,0.0326588899,0.1132931337,-0.0755623877,-0.0282561257,-0.3621478677,0.0659071729,-0.0113287931,0.6134747267,-0.2487007678,-0.6051997542,0.0658672377,-0.2793935835,-0.2565443218,-0.3295621574,-0.1635749787,-0.3112907708,0.2582394481,0.1950361133,-0.0819413513,-0.3276638687,-0.3721604943,-0.2869725823,-0.1174980029,-0.1620406508,0.0461693704,-0.104130201,-0.0131914904,0.3832283616,-0.0587805249,-0.0423808992,-0.2699390352,0.1448607892,0.0324001946,-0.0699524432,0.150408566,-0.163368687,0.1828367412,0.0472206064,0.084708415,-0.356433779,0.3897977173,-0.2615618706,-0.3636491001,-0.2217823118,0.1456974,-0.3320489228,-0.2151723057,0.3394041359,-0.0417384505,0.2415718585,-0.2057961076,-0.1408115029,-0.2203736901,-0.2054502219,-0.1400124133,0.0715175718,0.3740119636,-0.2969316244,-0.218428269,0.2159541398,-0.0582996793,0.4005611539,0.4265969098,0.0306359641,0.0068852236,-0.1978981346,0.251529634,0.1011661589,-0.2532138526,-0.6780224442,0.4099708498,-0.1634244025,0.1814756691,0.1188286543,-0.0990723297,0.0737057328,-0.131755352,-0.0850457847,0.2120765597,-0.2106408179,0.0707255006,-0.5871102214,-0.7869003415,0.3780463934,-0.0584511533,0.1627453417,-0.1797488481,-0.0278539229,0.2599582672,0.4960872531,0.1067426801,0.0147278421,0.1068839282,0.0614952855,0.0681387484,-0.0840681568,-0.2501348555,-0.1588561684,0.2199242413,-0.1815293133,-0.0562988445,0.3016198874,-0.1083739847,-0.5671508908,-0.205630973,-0.0594865642,-0.0970369652,0.0005349878,0.0013908687,0.1822182983,0.0878181085,0.1549400836,0.3509557545,0.0925608948,0.1153081357,-0.3258306086,0.2187996805,-0.042067606,-0.1144090295,0.1862325072,-0.0807211772,0.1894461066,-0.0506584011,-0.4008100629,0.6621257663,-0.0508649424,0.1304217279,-0.0485616773,0.0759109631,0.208716765,-0.1647550017,0.1396922916,0.4040056467,-0.0078118043,-0.0893657655,0.1285756379,-0.1466649324,0.1681570411,0.0385813452,-0.1399838179,0.0219172258,-0.1082341596,-0.1906882823,-0.0218825191,-0.1363818944,0.1214792728,-0.0143770063,-0.1486689448,0.0490543544,0.1307161152,-0.1663719416,0.735114038,-0.0395391472,-0.0493242703,0.3084146678,-0.3009499013,-0.0696082339,-0.1869142652,0.2009025812,0.5170977712,0.2373419106,0.1705388427,0.2232394367,0.0059838467,-0.1573754251,0.1209169701,0.298640728,0.2252731025,0.2332677841,-0.0040379744,0.0442569368,-0.1431460232,0.1358934343,0.054894004,0.3265087605,-0.4214719534,-0.2221026719,-0.4799452126,-0.0070513934,-0.332985729,0.185629949,-0.1814439297,-0.3045488298,-0.0691658854,0.0729711354,-0.0868554711,0.1772733927,-0.2617514133,0.1166194379,0.1726839989,0.1684265584,0.1131397113,-0.0296697319,-0.5200146437,0.0163424835,0.2846112549,-0.2646366358,0.3774171472,-0.553154707,0.0195056908,-0.0720698833,-0.1443551183,-0.1200665608,0.0596865043,0.0407049321,0.1904513389,0.1615996957,0.0788518563,-0.0324806385,0.1823148876,-0.2791775167,-0.1931467801,-0.0665636063,0.1417395473,0.2153919935,-0.0159327202,-0.5946922898,-0.2640362382,-0.3566413522,0.2469536364,0.0686296374,0.1870593131,0.0002924684,0.082385689,0.0191314239,0.042840708,0.0874992684,-0.2150725871,-0.4413594306,0.3366400301,0.0674884766,-0.3040826321,-0.0139753176,0.2830919027,-0.0750178322,0.0852657259,-0.6037398577,-0.1121485978,-0.3842428923,-0.1020442545,-0.1089007631,0.1909778714,0.4259518683,0.0320949741,-0.0522993766,-0.1426744163,-0.3267737031,-0.0091122296,0.2860001922,0.5351224542,-0.0486740395,0.2700761557,0.3071492612,0.437469542,0.536436379,0.0791866481,0.0161778517,-0.038535431,0.0828423277,-0.1028325781,0.053590294,0.191081807,-0.2176369429,0.0492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{"html_url":"https:\/\/github.com\/huggingface\/datasets\/issues\/2837","title":"prepare_module issue when loading from read-only fs","comments":"Hello, I opened #2887 to fix this.","body":"## Describe the bug\r\n\r\nWhen we use prepare_module from a readonly file system, we create a FileLock using the `local_path`.\r\nThis path is not necessarily writable.\r\n\r\n`lock_path = local_path + \".lock\"`\r\n\r\n\r\n## Steps to reproduce the bug\r\n\r\nRun `load_dataset` on a readonly python loader file.\r\n```python\r\nds = load_dataset(\r\n python_loader, data_files={\"train\": train_path, \"test\": test_path}\r\n )\r\n```\r\n\r\nwhere `python_loader` is a path to a file located in a readonly folder.\r\n\r\n## Expected results\r\nThis should work I think?\r\n\r\n## Actual results\r\n\r\n```python\r\n return load_dataset(\r\n File \"\/usr\/local\/lib\/python3.8\/dist-packages\/datasets\/load.py\", line 711, in load_dataset\r\n module_path, hash, resolved_file_path = prepare_module(\r\n File \"\/usr\/local\/lib\/python3.8\/dist-packages\/datasets\/load.py\", line 465, in prepare_module\r\n with FileLock(lock_path):\r\n File \"\/usr\/local\/lib\/python3.8\/dist-packages\/datasets\/utils\/filelock.py\", line 314, in __enter__\r\n self.acquire()\r\n File \"\/usr\/local\/lib\/python3.8\/dist-packages\/datasets\/utils\/filelock.py\", line 263, in acquire\r\n self._acquire()\r\n File \"\/usr\/local\/lib\/python3.8\/dist-packages\/datasets\/utils\/filelock.py\", line 378, in _acquire\r\n fd = os.open(self._lock_file, open_mode)\r\nOSError: [Errno 30] Read-only file system: 'YOUR_FILE.py.lock'\r\n```\r\n\r\n## Environment info\r\n\r\n- `datasets` version: 1.7.0\r\n- Platform: macOS-10.15.7-x86_64-i386-64bit\r\n- Python version: 3.8.8\r\n- PyArrow version: 3.0.0\r\n","comment_length":7,"text":"prepare_module issue when loading from read-only fs\n## Describe the bug\r\n\r\nWhen we use prepare_module from a readonly file system, we create a FileLock using the `local_path`.\r\nThis path is not necessarily writable.\r\n\r\n`lock_path = local_path + \".lock\"`\r\n\r\n\r\n## Steps to reproduce the bug\r\n\r\nRun `load_dataset` on a readonly python loader file.\r\n```python\r\nds = load_dataset(\r\n python_loader, data_files={\"train\": train_path, \"test\": test_path}\r\n )\r\n```\r\n\r\nwhere `python_loader` is a path to a file located in a readonly folder.\r\n\r\n## Expected results\r\nThis should work I think?\r\n\r\n## Actual results\r\n\r\n```python\r\n return load_dataset(\r\n File \"\/usr\/local\/lib\/python3.8\/dist-packages\/datasets\/load.py\", line 711, in load_dataset\r\n module_path, hash, resolved_file_path = prepare_module(\r\n File \"\/usr\/local\/lib\/python3.8\/dist-packages\/datasets\/load.py\", line 465, in prepare_module\r\n with FileLock(lock_path):\r\n File \"\/usr\/local\/lib\/python3.8\/dist-packages\/datasets\/utils\/filelock.py\", line 314, in __enter__\r\n self.acquire()\r\n File \"\/usr\/local\/lib\/python3.8\/dist-packages\/datasets\/utils\/filelock.py\", line 263, in acquire\r\n self._acquire()\r\n File \"\/usr\/local\/lib\/python3.8\/dist-packages\/datasets\/utils\/filelock.py\", line 378, in _acquire\r\n fd = os.open(self._lock_file, open_mode)\r\nOSError: [Errno 30] Read-only file system: 'YOUR_FILE.py.lock'\r\n```\r\n\r\n## Environment info\r\n\r\n- `datasets` version: 1.7.0\r\n- Platform: macOS-10.15.7-x86_64-i386-64bit\r\n- Python version: 3.8.8\r\n- PyArrow version: 3.0.0\r\n\nHello, I opened #2887 to fix 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{"html_url":"https:\/\/github.com\/huggingface\/datasets\/issues\/2831","title":"ArrowInvalid when mapping dataset with missing values","comments":"Hi ! It fails because of the feature type inference.\r\n\r\nBecause the first 1000 examples all have null values in the \"match\" field, then it infers that the type for this field is `null` type before writing the data on disk. But as soon as it tries to map an example with a non-null \"match\" field, then it fails.\r\n\r\nTo fix that you can either:\r\n- increase the writer_batch_size to >2000 (default is 1000) so that some non-null values will be in the first batch written to disk\r\n```python\r\ndatasets = datasets.map(lambda e: {'labels': e['match']}, remove_columns=['id'], writer_batch_size=2000)\r\n```\r\n- OR force the feature type with:\r\n```python\r\nfrom datasets import Features, Value\r\n\r\nfeatures = Features({\r\n 'conflict': Value('int64'),\r\n 'date': Value('string'),\r\n 'headline': Value('string'),\r\n 'match': Value('float64'),\r\n 'label': Value('float64')\r\n})\r\ndatasets = datasets.map(lambda e: {'labels': e['match']}, remove_columns=['id'], features=features)\r\n```","body":"## Describe the bug\r\nI encountered an `ArrowInvalid` when mapping dataset with missing values. \r\nHere are the files for a minimal example. The exception is only thrown when the first line in the csv has a missing value (if you move the last line to the top it isn't thrown).\r\n[data_small.csv](https:\/\/github.com\/huggingface\/datasets\/files\/7037838\/data_small.csv)\r\n[data.csv](https:\/\/github.com\/huggingface\/datasets\/files\/7037842\/data.csv)\r\n\r\n## Steps to reproduce the bug\r\n```python\r\nfrom datasets import load_dataset\r\n\r\ndatasets = load_dataset(\"csv\", data_files=['data_small.csv'])\r\n\r\ndatasets = datasets.map(lambda e: {'labels': e['match']},\r\n remove_columns=['id'])\r\n```\r\n\r\n## Expected results\r\nNo error\r\n\r\n## Actual results\r\n```\r\nFile \"pyarrow\/error.pxi\", line 84, in pyarrow.lib.check_status\r\npyarrow.lib.ArrowInvalid: Invalid null value\r\n```\r\n\r\n## Environment info\r\n- `datasets` version: 1.5.0\r\n- Platform: Linux-5.11.0-25-generic-x86_64-with-glibc2.29\r\n- Python version: 3.8.10\r\n- PyTorch version (GPU?): 1.7.1+cpu (False)\r\n- Tensorflow version (GPU?): 2.4.1 (False)\r\n- Using GPU in script?: no\r\n- Using distributed or parallel set-up in script?: no\r\n","comment_length":134,"text":"ArrowInvalid when mapping dataset with missing values\n## Describe the bug\r\nI encountered an `ArrowInvalid` when mapping dataset with missing values. \r\nHere are the files for a minimal example. The exception is only thrown when the first line in the csv has a missing value (if you move the last line to the top it isn't thrown).\r\n[data_small.csv](https:\/\/github.com\/huggingface\/datasets\/files\/7037838\/data_small.csv)\r\n[data.csv](https:\/\/github.com\/huggingface\/datasets\/files\/7037842\/data.csv)\r\n\r\n## Steps to reproduce the bug\r\n```python\r\nfrom datasets import load_dataset\r\n\r\ndatasets = load_dataset(\"csv\", data_files=['data_small.csv'])\r\n\r\ndatasets = datasets.map(lambda e: {'labels': e['match']},\r\n remove_columns=['id'])\r\n```\r\n\r\n## Expected results\r\nNo error\r\n\r\n## Actual results\r\n```\r\nFile \"pyarrow\/error.pxi\", line 84, in pyarrow.lib.check_status\r\npyarrow.lib.ArrowInvalid: Invalid null value\r\n```\r\n\r\n## Environment info\r\n- `datasets` version: 1.5.0\r\n- Platform: Linux-5.11.0-25-generic-x86_64-with-glibc2.29\r\n- Python version: 3.8.10\r\n- PyTorch version (GPU?): 1.7.1+cpu (False)\r\n- Tensorflow version (GPU?): 2.4.1 (False)\r\n- Using GPU in script?: no\r\n- Using distributed or parallel set-up in script?: no\r\n\nHi ! It fails because of the feature type inference.\r\n\r\nBecause the first 1000 examples all have null values in the \"match\" field, then it infers that the type for this field is `null` type before writing the data on disk. But as soon as it tries to map an example with a non-null \"match\" field, then it fails.\r\n\r\nTo fix that you can either:\r\n- increase the writer_batch_size to >2000 (default is 1000) so that some non-null values will be in the first batch written to disk\r\n```python\r\ndatasets = datasets.map(lambda e: {'labels': e['match']}, remove_columns=['id'], writer_batch_size=2000)\r\n```\r\n- OR force the feature type with:\r\n```python\r\nfrom datasets import Features, Value\r\n\r\nfeatures = Features({\r\n 'conflict': Value('int64'),\r\n 'date': Value('string'),\r\n 'headline': Value('string'),\r\n 'match': Value('float64'),\r\n 'label': Value('float64')\r\n})\r\ndatasets = datasets.map(lambda e: {'labels': e['match']}, remove_columns=['id'], features=features)\r\n```","embeddings":[-0.0327268355,-0.1531262994,0.1040895432,0.2286237329,0.0726004243,0.1366074681,0.3329794109,0.4905757904,-0.0207695644,0.2300736755,0.2338506132,0.4390177727,0.0271520261,-0.1494164914,-0.1128671244,-0.0434916839,0.036237888,0.1175362915,-0.0013278106,-0.0447311327,-0.378742218,0.0844402388,-0.3389022946,0.0575111024,-0.3012346625,-0.1077298597,-0.0090291733,0.0034838135,0.1298887879,-0.4910511672,0.2923193276,-0.5293874741,-0.1505012959,0.1626194119,-0.0001158711,-0.0339868702,0.2518496215,0.0519128256,-0.0784546211,-0.2758250237,-0.1568179429,-0.2185934782,-0.0141735272,-0.1653571576,0.1762025803,-0.0375164859,0.0760230497,-0.1961084604,-0.0201382134,0.2709065378,0.1460493654,0.4126615822,0.217869252,0.0892388672,0.4545611143,0.0507913604,-0.2142468095,0.3872686923,0.150176838,-0.2261426896,0.1055756882,0.3145453632,-0.0312069766,0.0160816535,0.3101096451,0.176736027,-0.0633132234,-0.2293644398,-0.0956780314,0.1412264705,0.121313028,-0.4014458358,-0.430261761,-0.2851817012,0.0836299956,-0.3390862942,0.3403961062,0.168567881,-0.1564158648,0.1623672247,0.1155143604,0.2014273405,-0.192848891,0.1499879211,-0.1042830124,0.0833818689,0.0503504686,0.2609374523,-0.0560738258,-0.2023003697,-0.2976311743,-0.1810262501,0.0063066739,0.2630151212,-0.3672583699,-0.2198308855,-0.1504853517,-0.1560584158,0.1592742801,-0.2276551127,0.2990320325,-0.1067543626,0.0777331293,0.4128786623,0.0608040318,-0.1506104469,0.1527083069,0.108326219,0.1780028194,-0.4061113,0.2636467516,-0.0342918187,0.1340156496,-0.5859510303,0.5141164064,0.2606024444,0.6728469133,-0.1126297936,-0.4689024091,0.1276107281,-0.7892329097,0.3337589502,-0.135780707,0.1627140641,0.0130745769,0.0863919556,0.0484194085,0.2774145603,-0.1620678008,-0.2743251026,0.0147257512,-0.0746785253,-0.0540683046,-0.0446719974,0.3431840837,-0.189890027,0.2458141446,0.1759653986,-0.1122789308,-0.0045988979,0.2626313269,-0.3046376705,0.1480023861,0.1737484187,-0.4271009266,0.2794370651,0.0387199447,-0.146589011,-0.1892317235,0.2775795162,-0.1205456331,-0.1588583738,-0.2165751904,0.1482956111,0.0022299229,-0.2128342688,-0.0540005676,-0.0019999465,0.242374599,-0.173490569,0.1846102625,-0.219979018,0.0187497139,-0.3760899603,-0.0113685085,0.3736341894,-0.6385915279,0.070836775,0.1267937571,-0.0185744036,-0.0337321162,0.3634842634,-0.1870690137,-0.013671454,-0.0591617823,0.5252671838,-0.0086465375,-0.1326466799,-0.4373976886,0.131386131,-0.074395977,-0.2444548905,0.0401039533,0.2435707748,0.1417698264,-0.1496624351,-0.01733483,-0.1213846132,-0.1300859749,0.2243183255,-0.3260702193,-0.0411789082,0.1720778644,0.0506165735,-0.1469437182,-0.2109326124,0.1945364773,-0.6835855842,0.0329079628,-0.2068118751,0.2666673362,0.3962259293,0.4044350684,-0.0465761051,0.1552023888,-0.1333547831,-0.8110236526,0.1985868216,0.100847736,-0.1821604669,-0.4811460078,-0.4098103642,-0.1061270237,0.3230985403,0.169046849,0.1539756507,0.0751769021,-0.0067508742,-0.122685194,-0.0598925687,-0.2849860191,-0.298654139,-0.2172655761,0.1865486056,-0.2249355167,0.2575946748,-0.013920987,-0.2167794704,-0.0960751399,0.0344255976,0.1761991829,-0.1273743063,0.013591324,0.4820096791,0.1067622378,0.1463912725,-0.1182710975,0.0272354595,0.3414061964,-0.4875248373,-0.2777242064,0.3717956841,0.3092466295,0.0654949769,-0.1880118698,0.367030859,-0.0085762525,0.142661944,-0.243231982,-0.0240107384,0.0476458743,-0.0050884029,-0.1706681848,-0.0740773231,-0.0837703869,-0.0132249454,0.2308350503,0.1707009226,-0.648462832,0.0064496514,0.1777651906,-0.1262826473,0.1471400559,0.0463455319,-0.2821579576,0.3659059703,0.3918358088,0.0322905146,0.3437526524,0.197664246,-0.0775276646,0.0050951457,-0.1521711648,-0.0824590325,0.3486306369,-0.0050706733,0.0830795988,0.1302965432,0.1771517992,0.2778594494,-0.1754120141,0.0187847074,0.2271973938,0.1906450242,-0.5900598764,-0.0668672845,-0.1082044542,0.0791586116,-0.3532581627,-0.1602440476,-0.0837709084,-0.2679263949,-0.0238328073,0.1787119508,-0.4540548921,0.2962757349,-0.0503031909,0.0645017549,0.2097569555,-0.2873474061,-0.1986872554,-0.1805445403,-0.206972599,0.0005408119,0.1689912528,-0.0692994669,0.2951590121,0.2671799064,-0.105649069,-0.5031932592,-0.4341720939,0.0272096228,-0.228622973,0.1287030727,0.1987026632,0.2116244137,-0.0771777779,-0.3438918293,0.1970792711,0.0353334136,-0.2919718623,0.1465553045,-0.0171925724,-0.0445718765,-0.1751176119,-0.4850569963,-0.038820982,-0.228159681,0.2234225869,-0.3103996813,0.0502251722,0.163063243,0.3292430937,0.1380271018,-0.1170724183,-0.1576803476,0.1402166784,0.2776943147,0.5713930726,-0.0197698306,-0.3324659467,0.2355109453,-0.1852093339,-0.0800568014,0.4506347477,-0.2126406878,-0.0239198282,-0.1131706908,0.6397444606,-0.0702653602,-0.0265891757,0.3226291835,0.2941908538,0.0133324713,-0.1408387423,-0.3169084787,0.062488731,-0.0236808807,0.2940809429,0.0561678745,0.3970873654,0.0143699041,0.7554779053,0.1163189709,-0.2453815788,0.1562790424,-0.2184980959,-0.0008914333,0.1268464029,-0.1204357743,-0.117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{"html_url":"https:\/\/github.com\/huggingface\/datasets\/issues\/2826","title":"Add a Text Classification dataset: KanHope","comments":"Hi ! In your script it looks like you're trying to load the dataset `bn_hate_speech,`, not KanHope.\r\n\r\nMoreover the error `KeyError: ' '` means that you have a feature of type ClassLabel, but for a certain example of the dataset, it looks like the label is empty (it's just a string with a space). Can you make sure that the data don't have missing labels, and that your dataset script parses the labels correctly ?","body":"## Adding a Dataset\r\n- **Name:** *KanHope*\r\n- **Description:** *A code-mixed English-Kannada dataset for Hope speech detection*\r\n- **Paper:** *https:\/\/arxiv.org\/abs\/2108.04616* (I am the author of the paper}\r\n- **Author:** *[AdeepH](https:\/\/github.com\/adeepH)*\r\n- **Data:** *https:\/\/github.com\/adeepH\/KanHope\/tree\/main\/dataset*\r\n- **Motivation:** *The dataset is amongst the very few resources available for code-mixed Dravidian languages*\r\n\r\n- I tried following the steps as per the instructions. However, could not resolve an error. Any help would be appreciated.\r\n\r\n- The dataset card and the scripts for the dataset *https:\/\/github.com\/adeepH\/datasets\/tree\/multilingual-hope-speech\/datasets\/mhs_eval*\r\n\r\n```\r\nUsing custom data configuration default\r\nDownloading and preparing dataset bn_hate_speech\/default (download: Unknown size, generated: Unknown size, post-processed: Unknown size, total: Unknown size) to \/root\/.cache\/huggingface\/datasets\/bn_hate_speech\/default\/0.0.0\/5f417ddc89777278abd29988f909f39495f0ec802090f7d8fa63b5bffb121762...\r\n---------------------------------------------------------------------------\r\nKeyError Traceback (most recent call last)\r\n in ()\r\n 1 from datasets import load_dataset\r\n 2 \r\n----> 3 data = load_dataset('\/content\/bn')\r\n\r\n9 frames\r\n\/usr\/local\/lib\/python3.7\/dist-packages\/datasets\/load.py in load_dataset(path, name, data_dir, data_files, split, cache_dir, features, download_config, download_mode, ignore_verifications, keep_in_memory, save_infos, script_version, use_auth_token, task, streaming, **config_kwargs)\r\n 850 ignore_verifications=ignore_verifications,\r\n 851 try_from_hf_gcs=try_from_hf_gcs,\r\n--> 852 use_auth_token=use_auth_token,\r\n 853 )\r\n 854 \r\n\r\n\/usr\/local\/lib\/python3.7\/dist-packages\/datasets\/builder.py in download_and_prepare(self, download_config, download_mode, ignore_verifications, try_from_hf_gcs, dl_manager, base_path, use_auth_token, **download_and_prepare_kwargs)\r\n 614 if not downloaded_from_gcs:\r\n 615 self._download_and_prepare(\r\n--> 616 dl_manager=dl_manager, verify_infos=verify_infos, **download_and_prepare_kwargs\r\n 617 )\r\n 618 # Sync info\r\n\r\n\/usr\/local\/lib\/python3.7\/dist-packages\/datasets\/builder.py in _download_and_prepare(self, dl_manager, verify_infos, **prepare_split_kwargs)\r\n 691 try:\r\n 692 # Prepare split will record examples associated to the split\r\n--> 693 self._prepare_split(split_generator, **prepare_split_kwargs)\r\n 694 except OSError as e:\r\n 695 raise OSError(\r\n\r\n\/usr\/local\/lib\/python3.7\/dist-packages\/datasets\/builder.py in _prepare_split(self, split_generator)\r\n 1107 disable=bool(logging.get_verbosity() == logging.NOTSET),\r\n 1108 ):\r\n-> 1109 example = self.info.features.encode_example(record)\r\n 1110 writer.write(example, key)\r\n 1111 finally:\r\n\r\n\/usr\/local\/lib\/python3.7\/dist-packages\/datasets\/features.py in encode_example(self, example)\r\n 1015 \"\"\"\r\n 1016 example = cast_to_python_objects(example)\r\n-> 1017 return encode_nested_example(self, example)\r\n 1018 \r\n 1019 def encode_batch(self, batch):\r\n\r\n\/usr\/local\/lib\/python3.7\/dist-packages\/datasets\/features.py in encode_nested_example(schema, obj)\r\n 863 if isinstance(schema, dict):\r\n 864 return {\r\n--> 865 k: encode_nested_example(sub_schema, sub_obj) for k, (sub_schema, sub_obj) in utils.zip_dict(schema, obj)\r\n 866 }\r\n 867 elif isinstance(schema, (list, tuple)):\r\n\r\n\/usr\/local\/lib\/python3.7\/dist-packages\/datasets\/features.py in