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[ "Hi @lithafnium, thanks for reporting.\r\n\r\nPlease note that you can use the \"Community\" tab in the corresponding dataset page to start any discussion: https://huggingface.co/datasets/glue/discussions\r\n\r\nIndeed this issue was already raised there (https://huggingface.co/datasets/glue/discussions/5) and answered: https://huggingface.co/datasets/glue/discussions/5#63907885937867f0cb3cde31\r\n> The test labels are not public.\r\n>\r\n> Note this dataset belongs to a benchmark: people send their predictions for the test split to GLUE (https://gluebenchmark.com/) and then they get a score in their leaderboard...\r\n" ]

"2023-03-09T14:47:18"

"2023-03-09T16:49:29"

"2023-03-09T16:49:29"

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"2023-03-09T08:46:01"

"2023-03-09T15:39:00"

"2023-03-09T15:31:59"

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"2023-03-08T12:30:23"

"2023-03-11T05:07:38"

"2023-03-11T05:07:38"

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"2023-03-08T10:22:50"

"2023-03-11T00:57:18"

"2023-03-11T00:49:56"

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"2023-03-07T13:31:53"

"2023-03-08T14:01:27"

"2023-03-08T13:54:22"

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"2023-03-07T13:22:41"

"2023-03-07T13:47:01"

"2023-03-07T13:39:02"

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"2023-03-07T08:41:51"

"2023-03-07T13:39:03"

"2023-03-07T13:39:03"

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"2023-03-07T08:18:20"

"2023-03-07T08:44:55"

"2023-03-07T08:37:28"

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"2023-03-07T08:06:39"

"2023-03-07T08:37:29"

"2023-03-07T08:37:29"

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[ "Hi! You can use `concatenate_datasets([ids1, ids2], axis=1)` to do this." ]

"2023-03-07T01:52:00"

"2023-03-09T15:24:05"

"2023-03-09T15:23:54"

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"2023-03-06T17:28:09"

"2023-03-07T13:27:50"

"2023-03-07T13:20:57"

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[ "Hi!\r\n\r\n> naming convention of mp3 files\r\n\r\nYes, this could be the problem. MP3 files should end with `.mp3`/`.MP3` to be recognized as audio files.\r\n\r\nIf the file names are not the culprit, can you paste the audio folder's directory structure to help us reproduce the error (e.g., by running the `tree \"x\"` command)?", "Hi! I'm sorry, I don't want to reveal my entire dataset, but here's a snippet (all of the mp3 files below are some of the ones not being recognized by audiofolder. Also, for another dataset, audiofolder loaded zero mp3 files because \"train\" was in the name of one of the mp3 files. \r\nmy_dataset\r\n├── data\r\n│   ├── VHA_Innovation_Stories_-_Day_2-123.mp3\r\n│   ├── VHA_Innovation_Stories_-_Day_2-124.mp3\r\n│   ├── ASSOCIATION_OF_GENERAL_PRACTITIONERS_OF_JAMAICA_NEPHROLOGY_CONFERENCE_-_JULY_3,_2022-93.mp3\r\n│   ├── ASSOCIATION_OF_GENERAL_PRACTITIONERS_OF_JAMAICA_NEPHROLOGY_CONFERENCE_-_JULY_3,_2022-94.mp3\r\n│   ├── ASSOCIATION_OF_GENERAL_PRACTITIONERS_OF_JAMAICA_NEPHROLOGY_CONFERENCE_-_JULY_3,_2022-95.mp3\r\n│   ├── Your_Impact\\357\\274\\232_Neurosurgery_equipment-5.mp3\r\n│   └── Your_Impact\\357\\274\\232_Neurosurgery_equipment-6.mp3\r\n└── metadata.csv\r\n\r\nHere's a few of the 13 files recognized by the dataset:\r\nBritish_Heart_Foundation_-_Your_guide_to_a_Coronary_Angiogram,_a_test_for_heart_disease-1.mp3\r\nBritish_Heart_Foundation_-_Your_guide_to_a_Coronary_Angiogram,_a_test_for_heart_disease-2.mp3\r\nBritish_Heart_Foundation_-_Your_guide_to_a_Coronary_Angiogram,_a_test_for_heart_disease-3.mp3\r\nIVP_⧸_IVU_test_Procedure_for_Kidneys_intravenous_pyelogram_-_medical_radiology_X-ray_ivp-1.mp3\r\nIVP_⧸_IVU_test_Procedure_for_Kidneys_intravenous_pyelogram_-_medical_radiology_X-ray_ivp-2.mp3" ]

"2023-03-05T00:14:45"

"2023-03-12T00:02:57"

"2023-03-12T00:02:57"

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[ "We won't support in-place modifications since `datasets` is based on the Apache Arrow format which doesn't support in-place modifications.\r\n\r\nIn your case the old dataset is garbage collected pretty quickly so you won't have memory issues.\r\n\r\nNote that datasets loaded from disk (memory mapped) are not loaded in memory, and therefore the new dataset actually use the same buffers as the old one.", "Thank you for your detailed reply.\r\n\r\n> In your case the old dataset is garbage collected pretty quickly so you won't have memory issues.\r\n\r\nI understand this, but it still copies the old dataset to create the new one, is this correct? So maybe it is not memory-consuming, but time-consuming?", "Indeed, and because of that it is more efficient to add multiple rows at once instead of one by one, using `concatenate_datasets` for example." ]

"2023-03-01T12:58:18"

"2023-03-02T13:30:41"

"2023-03-02T03:47:00"

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[ "Apparently some JSON objects have a `\"labels\"` field. Since this field is not present in every object, you must specify all the fields types in the README.md\r\n\r\nEDIT: actually specifying the feature types doesn’t solve the issue, it raises an error because “labels” is missing in the data", "We've updated the dataset to remove the extra `labels` field from some files, closing this issue. Thanks!" ]

"2023-03-01T12:53:08"

"2023-03-02T11:12:11"

"2023-03-02T11:12:11"

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[ "Thanks for reporting and thanks @mariosasko for fixing ! We just did a patch release `2.10.1` with the fix" ]

"2023-02-28T12:18:09"

"2023-02-28T18:17:26"

"2023-02-28T17:21:59"

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[ "Hi ! No the cache is not transportable in general. It will work on a shared filesystem if you use the same python environment, but not across machines/os/environments.\r\n\r\nIn particular, reloading cached datasets does work, but reloading cached processed datasets (e.g. from `map`) may not work. This is because some hashes used by caching are based on pickle dumps of the function you pass to `map`.\r\n\r\nFinally you may copy the cache to another machine, but all the `cached-*.arrow` files are unlikely to be reloaded.", "OK good to know. Thanks @lhoestq !" ]

"2023-02-28T00:53:06"

"2023-02-28T21:26:52"

"2023-02-28T21:26:52"

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[ "Hi @manuaero \r\n\r\nThank you for your interest in the COYO dataset.\r\n\r\nOur dataset provides the img-url and alt-text in the form of a parquet, so to utilize the coyo dataset you will need to download it directly.\r\n\r\nWe provide a [guide](https://github.com/kakaobrain/coyo-dataset/blob/main/download/README.md) to download, so check it out.\r\n\r\nThank you." ]

"2023-02-27T19:35:03"

"2023-02-28T07:27:59"

"2023-02-28T07:27:58"

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"2023-02-27T17:04:46"

"2023-02-28T13:52:15"

"2023-02-28T13:44:04"

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[ "Thanks for reporting!" ]

"2023-02-27T08:03:09"

"2023-02-28T19:19:17"

"2023-02-28T19:19:17"

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"2023-02-27T04:06:05"

"2023-03-11T01:02:49"

"2023-03-11T00:55:40"

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"2023-02-24T15:37:43"

"2023-02-27T17:28:25"

"2023-02-27T17:21:09"

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[ "Hi! I've merged a PR to use `int32` instead of `int8` for `reddit_scores`, so it should work now.\r\n\r\n" ]

"2023-02-24T13:01:48"

"2023-02-24T14:01:09"

"2023-02-24T14:01:09"

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[ "Duplicated issue." ]

"2023-02-24T12:57:49"

"2023-02-24T12:58:31"

"2023-02-24T12:58:18"

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[ "Also encountering this issue for every dataset I try to stream! Installed datasets from main:\r\n```\r\n- `datasets` version: 2.10.1.dev0\r\n- Platform: macOS-13.1-arm64-arm-64bit\r\n- Python version: 3.9.13\r\n- PyArrow version: 10.0.1\r\n- Pandas version: 1.5.2\r\n```\r\n\r\nRepro:\r\n```python\r\nfrom datasets import load_dataset\r\n\r\nspigi = load_dataset(\"kensho/spgispeech\", \"dev\", split=\"validation\", streaming=True, use_auth_token=True)\r\nsample = next(iter(spigi))\r\n```\r\n\r\n<details>\r\n<summary> Traceback </summary>\r\n\r\n```python\r\n---------------------------------------------------------------------------\r\nClientResponseError Traceback (most recent call last)\r\nFile ~/venv/lib/python3.9/site-packages/fsspec/implementations/http.py:407, in HTTPFileSystem._info(self, url, **kwargs)\r\n 405 try:\r\n 406 info.update(\r\n--> 407 await _file_info(\r\n 408 self.encode_url(url),\r\n 409 size_policy=policy,\r\n 410 session=session,\r\n 411 **self.kwargs,\r\n 412 **kwargs,\r\n 413 )\r\n 414 )\r\n 415 if info.get(\"size\") is not None:\r\n\r\nFile ~/venv/lib/python3.9/site-packages/fsspec/implementations/http.py:792, in _file_info(url, session, size_policy, **kwargs)\r\n 791 async with r:\r\n--> 792 r.raise_for_status()\r\n 794 # TODO:\r\n 795 # recognise lack of 'Accept-Ranges',\r\n 796 # or 'Accept-Ranges': 'none' (not 'bytes')\r\n 797 # to mean streaming only, no random access => return None\r\n\r\nFile ~/venv/lib/python3.9/site-packages/aiohttp/client_reqrep.py:1005, in ClientResponse.raise_for_status(self)\r\n 1004 self.release()\r\n-> 1005 raise ClientResponseError(\r\n 1006 self.request_info,\r\n 1007 self.history,\r\n 1008 status=self.status,\r\n 1009 message=self.reason,\r\n 1010 headers=self.headers,\r\n 1011 )\r\n\r\nClientResponseError: 403, message='Forbidden', url=URL('[https://cdn-lfs.huggingface.co/repos/e2/89/e28905247d6f48bb4edad5baf9b1bb4158e897a13fdf18bf3b8ee89ff8387ab8/46eca7431a7b6bad344bf451800e5b10cea1dd168f26d1027a6d9eb374b7fac3?response-content-disposition=attachment%3B+filename*%3DUTF-8''dev.csv%3B+filename%3D%22dev.csv%22%3B&response-content-type=text/csv&Expires=1677494732&Policy=eyJTdGF0ZW1lbnQiOlt7IlJlc291cmNlIjoiaHR0cHM6Ly9jZG4tbGZzLmh1Z2dpbmdmYWNlLmNvL3JlcG9zL2UyLzg5L2UyODkwNTI0N2Q2ZjQ4YmI0ZWRhZDViYWY5YjFiYjQxNThlODk3YTEzZmRmMThiZjNiOGVlODlmZjgzODdhYjgvNDZlY2E3NDMxYTdiNmJhZDM0NGJmNDUxODAwZTViMTBjZWExZGQxNjhmMjZkMTAyN2E2ZDllYjM3NGI3ZmFjMz9yZXNwb25zZS1jb250ZW50LWRpc3Bvc2l0aW9uPSomcmVzcG9uc2UtY29udGVudC10eXBlPXRleHQlMkZjc3YiLCJDb25kaXRpb24iOnsiRGF0ZUxlc3NUaGFuIjp7IkFXUzpFcG9jaFRpbWUiOjE2Nzc0OTQ3MzJ9fX1dfQ__&Signature=EzQB9f7xPckvqfFB6LzcyR-wzTnQCqtPDdWtQUzZ3QJ-gY-IHG5mxQITJgMr1nVTbJZrPmGAaDngMcPFUfSQa8RmCqYH~dZl-UGE8CO4neKNUT1DvA2WEvLDS4WaAJ3SN-9rX0uFb03~c1QS78cIgIRboYvf6ugKiJz86Bd7Vs~tcp201JFR0A6jIMseqApOnkb9d8dHMP3Ny~F6gO3Qf2QpEWM-QsDIyw2Kz2QV55nq8TsDpRYZCZo50~WwD~73Hej0PoDhEA1K37d19pa0CQhkaN-gjCrbT9xLabbvhJWa~ZkWcMdD0teCgjYqv1wKyvFXDAxukxLGEc7OBXVbYw__&Key-Pair-Id=KVTP0A1DKRTAX](https://cdn-lfs.huggingface.co/repos/e2/89/e28905247d6f48bb4edad5baf9b1bb4158e897a13fdf18bf3b8ee89ff8387ab8/46eca7431a7b6bad344bf451800e5b10cea1dd168f26d1027a6d9eb374b7fac3?response-content-disposition=attachment%3B+filename*%3DUTF-8%27%27dev.csv%3B+filename%3D%22dev.csv%22%3B&response-content-type=text/csv&Expires=1677494732&Policy=eyJTdGF0ZW1lbnQiOlt7IlJlc291cmNlIjoiaHR0cHM6Ly9jZG4tbGZzLmh1Z2dpbmdmYWNlLmNvL3JlcG9zL2UyLzg5L2UyODkwNTI0N2Q2ZjQ4YmI0ZWRhZDViYWY5YjFiYjQxNThlODk3YTEzZmRmMThiZjNiOGVlODlmZjgzODdhYjgvNDZlY2E3NDMxYTdiNmJhZDM0NGJmNDUxODAwZTViMTBjZWExZGQxNjhmMjZkMTAyN2E2ZDllYjM3NGI3ZmFjMz9yZXNwb25zZS1jb250ZW50LWRpc3Bvc2l0aW9uPSomcmVzcG9uc2UtY29udGVudC10eXBlPXRleHQlMkZjc3YiLCJDb25kaXRpb24iOnsiRGF0ZUxlc3NUaGFuIjp7IkFXUzpFcG9jaFRpbWUiOjE2Nzc0OTQ3MzJ9fX1dfQ__&Signature=EzQB9f7xPckvqfFB6LzcyR-wzTnQCqtPDdWtQUzZ3QJ-gY-IHG5mxQITJgMr1nVTbJZrPmGAaDngMcPFUfSQa8RmCqYH~dZl-UGE8CO4neKNUT1DvA2WEvLDS4WaAJ3SN-9rX0uFb03~c1QS78cIgIRboYvf6ugKiJz86Bd7Vs~tcp201JFR0A6jIMseqApOnkb9d8dHMP3Ny~F6gO3Qf2QpEWM-QsDIyw2Kz2QV55nq8TsDpRYZCZo50~WwD~73Hej0PoDhEA1K37d19pa0CQhkaN-gjCrbT9xLabbvhJWa~ZkWcMdD0teCgjYqv1wKyvFXDAxukxLGEc7OBXVbYw__&Key-Pair-Id=KVTP0A1DKRTAX)')\r\n\r\nThe above exception was the direct cause of the following exception:\r\n\r\nFileNotFoundError Traceback (most recent call last)\r\nCell In[5], line 4\r\n 1 from datasets import load_dataset\r\n 3 spigi = load_dataset(\"kensho/spgispeech\", \"dev\", split=\"validation\", streaming=True)\r\n----> 4 sample = next(iter(spigi))\r\n\r\nFile ~/datasets/src/datasets/iterable_dataset.py:937, in IterableDataset.__iter__(self)\r\n 934 yield from self._iter_pytorch(ex_iterable)\r\n 935 return\r\n--> 937 for key, example in ex_iterable:\r\n 938 if self.features:\r\n 939 # `IterableDataset` automatically fills missing columns with None.\r\n 940 # This is done with `_apply_feature_types_on_example`.\r\n 941 yield _apply_feature_types_on_example(\r\n 942 example, self.features, token_per_repo_id=self._token_per_repo_id\r\n 943 )\r\n\r\nFile ~/datasets/src/datasets/iterable_dataset.py:113, in ExamplesIterable.__iter__(self)\r\n 112 def __iter__(self):\r\n--> 113 yield from self.generate_examples_fn(**self.kwargs)\r\n\r\nFile ~/.cache/huggingface/modules/datasets_modules/datasets/kensho--spgispeech/5fbf75dd9ef795a9b5a673457d2cbaf0b8fa0de8fb62acbd1da338d83a41e2f0/spgispeech.py:186, in Spgispeech._generate_examples(self, local_extracted_archive_paths, archives, meta_path)\r\n 183 dict_keys = [\"wav_filename\", \"wav_filesize\", \"transcript\"]\r\n 185 logging.info(\"Reading metadata...\")\r\n--> 186 with open(meta_path, encoding=\"utf-8\") as f:\r\n 187 csvreader = csv.DictReader(f, delimiter=\"|\")\r\n 188 metadata = {x[\"wav_filename\"]: dict((k, x[k]) for k in dict_keys) for x in csvreader}\r\n\r\nFile ~/datasets/src/datasets/streaming.py:70, in extend_module_for_streaming.<locals>.wrap_auth.<locals>.wrapper(*args, **kwargs)\r\n 68 @wraps(function)\r\n 69 def wrapper(*args, **kwargs):\r\n---> 70 return function(*args, use_auth_token=use_auth_token, **kwargs)\r\n\r\nFile ~/datasets/src/datasets/download/streaming_download_manager.py:495, in xopen(file, mode, use_auth_token, *args, **kwargs)\r\n 493 kwargs = {**kwargs, **new_kwargs}\r\n 494 try:\r\n--> 495 file_obj = fsspec.open(file, mode=mode, *args, **kwargs).open()\r\n 496 except ValueError as e:\r\n 497 if str(e) == \"Cannot seek streaming HTTP file\":\r\n\r\nFile ~/venv/lib/python3.9/site-packages/fsspec/core.py:135, in OpenFile.open(self)\r\n 128 def open(self):\r\n 129 \"\"\"Materialise this as a real open file without context\r\n 130 \r\n 131 The OpenFile object should be explicitly closed to avoid enclosed file\r\n 132 instances persisting. You must, therefore, keep a reference to the OpenFile\r\n 133 during the life of the file-like it generates.\r\n 134 \"\"\"\r\n--> 135 return self.__enter__()\r\n\r\nFile ~/venv/lib/python3.9/site-packages/fsspec/core.py:103, in OpenFile.__enter__(self)\r\n 100 def __enter__(self):\r\n 101 mode = self.mode.replace(\"t\", \"\").replace(\"b\", \"\") + \"b\"\r\n--> 103 f = self.fs.open(self.path, mode=mode)\r\n 105 self.fobjects = [f]\r\n 107 if self.compression is not None:\r\n\r\nFile ~/venv/lib/python3.9/site-packages/fsspec/spec.py:1106, in AbstractFileSystem.open(self, path, mode, block_size, cache_options, compression, **kwargs)\r\n 1104 else:\r\n 1105 ac = kwargs.pop(\"autocommit\", not self._intrans)\r\n-> 1106 f = self._open(\r\n 1107 path,\r\n 1108 mode=mode,\r\n 1109 block_size=block_size,\r\n 1110 autocommit=ac,\r\n 1111 cache_options=cache_options,\r\n 1112 **kwargs,\r\n 1113 )\r\n 1114 if compression is not None:\r\n 1115 from fsspec.compression import compr\r\n\r\nFile ~/venv/lib/python3.9/site-packages/fsspec/implementations/http.py:346, in HTTPFileSystem._open(self, path, mode, block_size, autocommit, cache_type, cache_options, size, **kwargs)\r\n 344 kw[\"asynchronous\"] = self.asynchronous\r\n 345 kw.update(kwargs)\r\n--> 346 size = size or self.info(path, **kwargs)[\"size\"]\r\n 347 session = sync(self.loop, self.set_session)\r\n 348 if block_size and size:\r\n\r\nFile ~/venv/lib/python3.9/site-packages/fsspec/asyn.py:113, in sync_wrapper.<locals>.wrapper(*args, **kwargs)\r\n 110 @functools.wraps(func)\r\n 111 def wrapper(*args, **kwargs):\r\n 112 self = obj or args[0]\r\n--> 113 return sync(self.loop, func, *args, **kwargs)\r\n\r\nFile ~/venv/lib/python3.9/site-packages/fsspec/asyn.py:98, in sync(loop, func, timeout, *args, **kwargs)\r\n 96 raise FSTimeoutError from return_result\r\n 97 elif isinstance(return_result, BaseException):\r\n---> 98 raise return_result\r\n 99 else:\r\n 100 return return_result\r\n\r\nFile ~/venv/lib/python3.9/site-packages/fsspec/asyn.py:53, in _runner(event, coro, result, timeout)\r\n 51 coro = asyncio.wait_for(coro, timeout=timeout)\r\n 52 try:\r\n---> 53 result[0] = await coro\r\n 54 except Exception as ex:\r\n 55 result[0] = ex\r\n\r\nFile ~/venv/lib/python3.9/site-packages/fsspec/implementations/http.py:420, in HTTPFileSystem._info(self, url, **kwargs)\r\n 417 except Exception as exc:\r\n 418 if policy == \"get\":\r\n 419 # If get failed, then raise a FileNotFoundError\r\n--> 420 raise FileNotFoundError(url) from exc\r\n 421 logger.debug(str(exc))\r\n 423 return {\"name\": url, \"size\": None, **info, \"type\": \"file\"}\r\n\r\nFileNotFoundError: https://huggingface.co/datasets/kensho/spgispeech/resolve/main/data/meta/dev.csv\r\n```\r\n</details>", "Hi ! We're investigating this issue, sorry for the inconvenience", "This has been resolved ! Thanks for reporting", "Wow, thanks for the very quick fix!", "This problem now appears again, this time with an underlying HTTP 502 status code:\r\n\r\n```\r\naiohttp.client_exceptions.ClientResponseError: 502, message='Bad Gateway', url=URL('https://huggingface.co/datasets/allenai/c4/resolve/1ddc917116b730e1859edef32896ec5c16be51d0/en/c4-validation.00002-of-00008.json.gz')\r\n```", "Re-executing a minute later, the underlying cause is an HTTP 403 status code, as reported yesterday:\r\n\r\n```\r\naiohttp.client_exceptions.ClientResponseError: 403, message='Forbidden', url=URL('https://cdn-lfs.huggingface.co/datasets/allenai/c4/4bf6b248b0f910dcde2cdf2118d6369d8208c8f9515ec29ab73e531f380b18e2?response-content-disposition=attachment%3B+filename*%3DUTF-8''c4-validation.00002-of-00008.json.gz%3B+filename%3D%22c4-validation.00002-of-00008.json.gz%22%3B&response-content-type=application/gzip&Expires=1677571273&Policy=eyJTdGF0ZW1lbnQiOlt7IlJlc291cmNlIjoiaHR0cHM6Ly9jZG4tbGZzLmh1Z2dpbmdmYWNlLmNvL2RhdGFzZXRzL2FsbGVuYWkvYzQvNGJmNmIyNDhiMGY5MTBkY2RlMmNkZjIxMThkNjM2OWQ4MjA4YzhmOTUxNWVjMjlhYjczZTUzMWYzODBiMThlMj9yZXNwb25zZS1jb250ZW50LWRpc3Bvc2l0aW9uPSomcmVzcG9uc2UtY29udGVudC10eXBlPWFwcGxpY2F0aW9uJTJGZ3ppcCIsIkNvbmRpdGlvbiI6eyJEYXRlTGVzc1RoYW4iOnsiQVdTOkVwb2NoVGltZSI6MTY3NzU3MTI3M319fV19&Signature=WW42NOKkLuX~xVB1QfbkqzdvGo2AOXpgbF3PjTXy6iKd~ffilr1N9ScPXfvTXqy5yvdhJg1G0xJy1zYtUjGAL8GEx3Av-0vIhpWMGYTM8XKEU5gYA9qt30oVtNph6TkTYSABrsYTaj-hzQL9WCgyapmjvG69ETMh4wj44r2rcbk4T3j0l6l4u76Gh~lyRSll3aK4qycdUwcyL7FECDu~0W1mJIJwKkCrWHhSpHJSshb-0ElwG71pq4eyQ5g2uxHdK6JbRF7loxUpRQQJ1vlk0EHXdw0wTMaQ9tqHy6xcrQd8Ep0Yvx3tUD8MR0vWOcbQKnL6LwPQByc8tkChlpjnig__&Key-Pair-Id=KVTP0A1DKRTAX')\r\n```", "I'm facing the same problem. Interestingly using `wget` I can download the file. ", "It's been resolved again ;)", "> It's been resolved again ;)\r\n\r\nI'm experiencing the same issue when trying to load this dataset, `FileNotFoundError: https://huggingface.co/datasets/allenai/c4/resolve/1ddc917116b730e1859edef32896ec5c16be51d0/realnewslike/c4-train.00000-of-00512.json.gz`" ]

"2023-02-24T07:57:32"

"2023-03-06T13:14:11"

"2023-02-27T04:03:38"

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"2023-02-23T19:19:44"

"2023-02-28T20:25:14"

"2023-02-28T20:16:02"

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"2023-02-23T17:28:11"

"2023-02-23T18:03:55"

"2023-02-23T18:03:55"

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[ "Hi! \r\n\r\nYou need to pass an input json file explicitly as `data_files` to `load_dataset` to avoid this error:\r\n```python\r\n ds = load_dataset(\"json\", data_files=args.input_json)\r\n```\r\n\r\n", "Thanks it worked!" ]

"2023-02-23T16:50:11"

"2023-02-24T13:21:47"

"2023-02-24T13:21:47"

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"2023-02-23T16:06:04"

"2023-02-24T14:06:37"

"2023-02-23T18:15:16"

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[ "Hi ! Oh good catch. I think the features should be passed to `IterableDataset.from_generator()` in `to_iterable_dataset()` indeed.\r\n\r\nSetting this as a good first issue if someone would like to contribute, otherwise we can take care of it :)", "#self-assign", "seems like the feature parameter is missing from `return IterableDataset.from_generator(Dataset._iter_shards, gen_kwargs={\"shards\": shards})` hence it defaults to None." ]

"2023-02-23T13:45:33"

"2023-02-24T13:22:36"

"2023-02-24T13:22:36"

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"2023-02-22T15:09:30"

"2023-03-10T13:53:03"

"2023-03-10T13:45:43"

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"2023-02-18T20:57:40"

"2023-02-21T16:10:55"

"2023-02-21T16:04:03"

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[ "Thanks for reporting, @wjfwzzc.\r\n\r\nI am transferring this issue to the corresponding dataset on the Hub: https://huggingface.co/datasets/bookcorpusopen/discussions/1", "Thank you. All fixes are done:\r\n- [x] https://huggingface.co/datasets/bookcorpusopen/discussions/2\r\n- [x] https://huggingface.co/datasets/the_pile/discussions/1\r\n- [x] https://huggingface.co/datasets/the_pile_books3/discussions/1\r\n- [x] https://huggingface.co/datasets/the_pile_openwebtext2/discussions/2\r\n- [x] https://huggingface.co/datasets/the_pile_stack_exchange/discussions/2" ]

"2023-02-17T08:40:11"

"2023-02-21T06:37:00"

"2023-02-20T08:41:33"

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"2023-02-17T01:52:38"

"2023-02-17T19:20:49"

"2023-02-17T11:12:32"

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[ "Running the script above on the branch https://github.com/huggingface/datasets/pull/5542 results in the expected behaviour:\r\n```\r\nNum chunks for original ds: 1\r\nOriginal ds save/load\r\nsave_to_disk -- RAM memory used: 0.671875 MB -- Total time: 0.255265 s\r\nload_from_disk -- RAM memory used: 42.796875 MB -- Total time: 0.014899 s\r\nNum chunks for original ds after reloading: 5000\r\n\r\nNum chunks for selected ds: 1\r\nflatten_indices -- RAM memory used: 42.546875 MB -- Total time: 23.735089 s\r\nNum chunks for selected ds after flattening: 5000\r\n\r\nSelected ds save/load\r\nsave_to_disk -- RAM memory used: 0.0 MB -- Total time: 0.287112 s\r\nload_from_disk -- RAM memory used: 38.84375 MB -- Total time: 0.014772 s\r\nNum chunks for selected ds after reloading: 5000\r\n```", "Wouahouh super cool @marioga thanks a lot!", "We just released `datasets==2.10.0` with this big improvement, thanks again @marioga " ]

"2023-02-17T01:52:24"

"2023-02-22T13:15:20"

"2023-02-17T11:12:33"

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"2023-02-16T22:09:35"

"2023-02-17T18:50:46"

"2023-02-17T18:41:28"

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[ "Hi! The `set_transform` does not apply a custom formatting transform on a single example but the entire batch, so the fixed version of your transform would look as follows:\r\n```python\r\nfrom datasets import load_dataset\r\nimport torch\r\n\r\ndataset = load_dataset(\"lambdalabs/pokemon-blip-captions\", split='train')\r\ndef t(batch):\r\n return {\"test\": torch.tensor([1] * len(batch[next(iter(batch))]))}\r\n \r\ndataset.set_transform(t)\r\nd_0 = dataset[0]\r\n```\r\n\r\nStill, the formatter's error message should mention that a dict of **sequences** is expected as the returned value (not just a dict) to make debugging easier.", "I can take this", "Fixed in #5553 ", "> Hi! The `set_transform` does not apply a custom formatting transform on a single example but the entire batch, so the fixed version of your transform would look as follows:\r\n> \r\n> ```python\r\n> from datasets import load_dataset\r\n> import torch\r\n> \r\n> dataset = load_dataset(\"lambdalabs/pokemon-blip-captions\", split='train')\r\n> def t(batch):\r\n> return {\"test\": torch.tensor([1] * len(batch[next(iter(batch))]))}\r\n> \r\n> dataset.set_transform(t)\r\n> d_0 = dataset[0]\r\n> ```\r\n> \r\n> Still, the formatter's error message should mention that a dict of **sequences** is expected as the returned value (not just a dict) to make debugging easier.\r\n\r\nok, will change it according to suggestion. Thanks for the reply!" ]

"2023-02-16T16:08:51"

"2023-02-22T10:30:30"

"2023-02-21T13:03:57"

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[ "Hi! `seaborn`'s `load_dataset` pulls datasets from [here](https://github.com/mwaskom/seaborn-data) and not from our Hub, so this issue is not related to our library in any way and should be reported in their repo instead." ]

"2023-02-16T14:01:58"

"2023-02-16T14:44:36"

"2023-02-16T14:44:36"

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[ "Hi ! `enc` is not hashable:\r\n```python\r\nimport tiktoken\r\nfrom datasets.fingerprint import Hasher\r\n\r\nenc = tiktoken.get_encoding(\"gpt2\")\r\nHasher.hash(enc)\r\n# raises TypeError: cannot pickle 'builtins.CoreBPE' object\r\n```\r\nIt happens because it's not picklable, and because of that it's not possible to cache the result of `map`, hence the warning message.\r\n\r\nYou can find more details about caching here: https://huggingface.co/docs/datasets/about_cache\r\n\r\nYou can also provide your own unique hash in `map` if you want, with the `new_fingerprint` argument.\r\nOr disable caching using\r\n```python\r\nimport datasets\r\ndatasets.disable_caching()\r\n```", "@lhoestq Thank you for the explanation and advice. Will relay all of this to the repo where this (non)issue arose. \r\n\r\nGreat job with huggingface! ", "We made tiktoken tokenizers hashable in #5552, which is included in today's release `datasets==2.10.0`", "Just a heads up that when I'm trying to use TikToken along with the a given Dataset `.map()` method, I am still met with the following error :\r\n\r\n```\r\n File \"/opt/conda/lib/python3.8/site-packages/dill/_dill.py\", line 388, in save\r\n StockPickler.save(self, obj, save_persistent_id)\r\n File \"/opt/conda/lib/python3.8/pickle.py\", line 578, in save\r\n rv = reduce(self.proto)\r\nTypeError: cannot pickle 'builtins.CoreBPE' object\r\n```\r\n\r\nMy current environment is running datasets v2.10.0.", "cc @mariosasko " ]

"2023-02-16T03:12:07"

"2023-02-28T10:21:28"

"2023-02-16T14:56:41"

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"2023-02-15T20:35:11"

"2023-02-20T10:39:42"

"2023-02-20T10:32:39"

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"2023-02-15T13:44:01"

"2023-02-28T14:46:13"

"2023-02-28T14:46:12"

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[ "Hi! You can get this behavior by specifying `stratify_by_column=\"label\"` in `train_test_split`.\r\n\r\nThis is the full example:\r\n```python\r\nimport numpy as np\r\nfrom datasets import Dataset, ClassLabel\r\n\r\ndata = [\r\n {'label': 0, 'text': \"example1\"},\r\n {'label': 1, 'text': \"example2\"},\r\n {'label': 1, 'text': \"example3\"},\r\n {'label': 1, 'text': \"example4\"},\r\n {'label': 0, 'text': \"example5\"},\r\n {'label': 1, 'text': \"example6\"},\r\n {'label': 2, 'text': \"example7\"},\r\n {'label': 2, 'text': \"example8\"}\r\n]\r\n\r\nfor _ in range(10):\r\n data_set = Dataset.from_list(data)\r\n data_set = data_set.cast_column(\"label\", ClassLabel(num_classes=3))\r\n data_set = data_set.train_test_split(test_size=0.5, stratify_by_column=\"label\")\r\n unique_labels_train = np.unique(data_set[\"train\"][:][\"label\"])\r\n unique_labels_test = np.unique(data_set[\"test\"][:][\"label\"])\r\n assert len(unique_labels_train) >= len(unique_labels_test) \r\n```\r\n" ]

"2023-02-14T16:52:29"

"2023-02-15T16:09:19"

"2023-02-15T16:09:19"

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"2023-02-13T19:33:23"

"2023-02-14T14:40:41"

"2023-02-14T12:23:58"

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"2023-02-13T14:54:55"

"2023-02-23T18:14:32"

"2023-02-23T18:05:26"

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"2023-02-12T11:51:25"

"2023-02-13T10:29:03"

"2023-02-13T09:24:16"

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[ "Hi @lithafnium, thanks for reporting.\r\n\r\nPlease note that you can use the \"Community\" tab in the corresponding dataset page to start any discussion: https://huggingface.co/datasets/glue/discussions\r\n\r\nIndeed this issue was already raised there (https://huggingface.co/datasets/glue/discussions/5) and answered: https://huggingface.co/datasets/glue/discussions/5#63907885937867f0cb3cde31\r\n> The test labels are not public.\r\n>\r\n> Note this dataset belongs to a benchmark: people send their predictions for the test split to GLUE (https://gluebenchmark.com/) and then they get a score in their leaderboard...\r\n" ]

"2023-03-09T14:47:18"

"2023-03-09T16:49:29"

"2023-03-09T16:49:29"

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"2023-03-09T08:46:01"

"2023-03-09T15:39:00"

"2023-03-09T15:31:59"

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