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[ "I couldn't reproduce on my side.\r\nIt looks like you were not able to generate all the examples, and you have the problem for each split train-test-validation.\r\nCould you try to enable logging, try again and send the logs ?\r\n```python\r\nimport logging\r\nlogging.basicConfig(level=logging.INFO)\r\n```", "here's the log\r\n```\r\n>>> import nlp\r\nimport logging\r\nlogging.basicConfig(level=logging.INFO)\r\nnlp.load_dataset('cnn_dailymail', '3.0.0')\r\n>>> import logging\r\n>>> logging.basicConfig(level=logging.INFO)\r\n>>> nlp.load_dataset('cnn_dailymail', '3.0.0')\r\nINFO:nlp.load:Checking /u/jm8wx/.cache/huggingface/datasets/720d2e20d8dc6d98f21195a39cc934bb41dd0a40b57ea3d323661a7c5d70522c.d44c2417f4e0fe938ede0a684dcbb1fa9b4789de22e8a99c43103d4b4c374b3b.py for additional imports.\r\nINFO:filelock:Lock 140443095301136 acquired on /u/jm8wx/.cache/huggingface/datasets/720d2e20d8dc6d98f21195a39cc934bb41dd0a40b57ea3d323661a7c5d70522c.d44c2417f4e0fe938ede0a684dcbb1fa9b4789de22e8a99c43103d4b4c374b3b.py.lock\r\nINFO:nlp.load:Found main folder for dataset https://s3.amazonaws.com/datasets.huggingface.co/nlp/datasets/cnn_dailymail/cnn_dailymail.py at /p/qdata/jm8wx/datasets/nlp/src/nlp/datasets/cnn_dailymail\r\nINFO:nlp.load:Found specific version folder for dataset https://s3.amazonaws.com/datasets.huggingface.co/nlp/datasets/cnn_dailymail/cnn_dailymail.py at /p/qdata/jm8wx/datasets/nlp/src/nlp/datasets/cnn_dailymail/9645e0bc96f647decf46541f6f4bef6936ee82ace653ac362bab03309a46d4ad\r\nINFO:nlp.load:Found script file from https://s3.amazonaws.com/datasets.huggingface.co/nlp/datasets/cnn_dailymail/cnn_dailymail.py to /p/qdata/jm8wx/datasets/nlp/src/nlp/datasets/cnn_dailymail/9645e0bc96f647decf46541f6f4bef6936ee82ace653ac362bab03309a46d4ad/cnn_dailymail.py\r\nINFO:nlp.load:Updating dataset infos file from https://s3.amazonaws.com/datasets.huggingface.co/nlp/datasets/cnn_dailymail/dataset_infos.json to /p/qdata/jm8wx/datasets/nlp/src/nlp/datasets/cnn_dailymail/9645e0bc96f647decf46541f6f4bef6936ee82ace653ac362bab03309a46d4ad/dataset_infos.json\r\nINFO:nlp.load:Found metadata file for dataset https://s3.amazonaws.com/datasets.huggingface.co/nlp/datasets/cnn_dailymail/cnn_dailymail.py at /p/qdata/jm8wx/datasets/nlp/src/nlp/datasets/cnn_dailymail/9645e0bc96f647decf46541f6f4bef6936ee82ace653ac362bab03309a46d4ad/cnn_dailymail.json\r\nINFO:filelock:Lock 140443095301136 released on /u/jm8wx/.cache/huggingface/datasets/720d2e20d8dc6d98f21195a39cc934bb41dd0a40b57ea3d323661a7c5d70522c.d44c2417f4e0fe938ede0a684dcbb1fa9b4789de22e8a99c43103d4b4c374b3b.py.lock\r\nINFO:nlp.info:Loading Dataset Infos from /p/qdata/jm8wx/datasets/nlp/src/nlp/datasets/cnn_dailymail/9645e0bc96f647decf46541f6f4bef6936ee82ace653ac362bab03309a46d4ad\r\nINFO:nlp.builder:Generating dataset cnn_dailymail (/u/jm8wx/.cache/huggingface/datasets/cnn_dailymail/3.0.0/3.0.0)\r\nINFO:nlp.builder:Dataset not on Hf google storage. Downloading and preparing it from source\r\nDownloading and preparing dataset cnn_dailymail/3.0.0 (download: 558.32 MiB, generated: 1.26 GiB, total: 1.81 GiB) to /u/jm8wx/.cache/huggingface/datasets/cnn_dailymail/3.0.0/3.0.0...\r\nINFO:nlp.utils.info_utils:All the checksums matched successfully.\r\nINFO:nlp.builder:Generating split train\r\nINFO:nlp.arrow_writer:Done writing 285161 examples in 1240618482 bytes /u/jm8wx/.cache/huggingface/datasets/cnn_dailymail/3.0.0/3.0.0.incomplete/cnn_dailymail-train.arrow.\r\nINFO:nlp.builder:Generating split validation\r\nINFO:nlp.arrow_writer:Done writing 13255 examples in 56637485 bytes /u/jm8wx/.cache/huggingface/datasets/cnn_dailymail/3.0.0/3.0.0.incomplete/cnn_dailymail-validation.arrow.\r\nINFO:nlp.builder:Generating split test\r\nINFO:nlp.arrow_writer:Done writing 11379 examples in 48931393 bytes /u/jm8wx/.cache/huggingface/datasets/cnn_dailymail/3.0.0/3.0.0.incomplete/cnn_dailymail-test.arrow.\r\nTraceback (most recent call last):\r\n File \"<stdin>\", line 1, in <module>\r\n File \"/p/qdata/jm8wx/datasets/nlp/src/nlp/load.py\", line 520, in load_dataset\r\n builder_instance.download_and_prepare(\r\n File \"/p/qdata/jm8wx/datasets/nlp/src/nlp/builder.py\", line 431, in download_and_prepare\r\n self._download_and_prepare(\r\n File \"/p/qdata/jm8wx/datasets/nlp/src/nlp/builder.py\", line 488, in _download_and_prepare\r\n verify_splits(self.info.splits, split_dict)\r\n File \"/p/qdata/jm8wx/datasets/nlp/src/nlp/utils/info_utils.py\", line 70, in verify_splits\r\n raise NonMatchingSplitsSizesError(str(bad_splits))\r\nnlp.utils.info_utils.NonMatchingSplitsSizesError: [{'expected': SplitInfo(name='test', num_bytes=49424491, num_examples=11490, dataset_name='cnn_dailymail'), 'recorded': SplitInfo(name='test', num_bytes=48931393, num_examples=11379, dataset_name='cnn_dailymail')}, {'expected': SplitInfo(name='train', num_bytes=1249178681, num_examples=287113, dataset_name='cnn_dailymail'), 'recorded': SplitInfo(name='train', num_bytes=1240618482, num_examples=285161, dataset_name='cnn_dailymail')}, {'expected': SplitInfo(name='validation', num_bytes=57149241, num_examples=13368, dataset_name='cnn_dailymail'), 'recorded': SplitInfo(name='validation', num_bytes=56637485, num_examples=13255, dataset_name='cnn_dailymail')}]\r\n```", "> here's the log\r\n> \r\n> ```\r\n> >>> import nlp\r\n> import logging\r\n> logging.basicConfig(level=logging.INFO)\r\n> nlp.load_dataset('cnn_dailymail', '3.0.0')\r\n> >>> import logging\r\n> >>> logging.basicConfig(level=logging.INFO)\r\n> >>> nlp.load_dataset('cnn_dailymail', '3.0.0')\r\n> INFO:nlp.load:Checking /u/jm8wx/.cache/huggingface/datasets/720d2e20d8dc6d98f21195a39cc934bb41dd0a40b57ea3d323661a7c5d70522c.d44c2417f4e0fe938ede0a684dcbb1fa9b4789de22e8a99c43103d4b4c374b3b.py for additional imports.\r\n> INFO:filelock:Lock 140443095301136 acquired on /u/jm8wx/.cache/huggingface/datasets/720d2e20d8dc6d98f21195a39cc934bb41dd0a40b57ea3d323661a7c5d70522c.d44c2417f4e0fe938ede0a684dcbb1fa9b4789de22e8a99c43103d4b4c374b3b.py.lock\r\n> INFO:nlp.load:Found main folder for dataset https://s3.amazonaws.com/datasets.huggingface.co/nlp/datasets/cnn_dailymail/cnn_dailymail.py at /p/qdata/jm8wx/datasets/nlp/src/nlp/datasets/cnn_dailymail\r\n> INFO:nlp.load:Found specific version folder for dataset https://s3.amazonaws.com/datasets.huggingface.co/nlp/datasets/cnn_dailymail/cnn_dailymail.py at /p/qdata/jm8wx/datasets/nlp/src/nlp/datasets/cnn_dailymail/9645e0bc96f647decf46541f6f4bef6936ee82ace653ac362bab03309a46d4ad\r\n> INFO:nlp.load:Found script file from https://s3.amazonaws.com/datasets.huggingface.co/nlp/datasets/cnn_dailymail/cnn_dailymail.py to /p/qdata/jm8wx/datasets/nlp/src/nlp/datasets/cnn_dailymail/9645e0bc96f647decf46541f6f4bef6936ee82ace653ac362bab03309a46d4ad/cnn_dailymail.py\r\n> INFO:nlp.load:Updating dataset infos file from https://s3.amazonaws.com/datasets.huggingface.co/nlp/datasets/cnn_dailymail/dataset_infos.json to /p/qdata/jm8wx/datasets/nlp/src/nlp/datasets/cnn_dailymail/9645e0bc96f647decf46541f6f4bef6936ee82ace653ac362bab03309a46d4ad/dataset_infos.json\r\n> INFO:nlp.load:Found metadata file for dataset https://s3.amazonaws.com/datasets.huggingface.co/nlp/datasets/cnn_dailymail/cnn_dailymail.py at /p/qdata/jm8wx/datasets/nlp/src/nlp/datasets/cnn_dailymail/9645e0bc96f647decf46541f6f4bef6936ee82ace653ac362bab03309a46d4ad/cnn_dailymail.json\r\n> INFO:filelock:Lock 140443095301136 released on /u/jm8wx/.cache/huggingface/datasets/720d2e20d8dc6d98f21195a39cc934bb41dd0a40b57ea3d323661a7c5d70522c.d44c2417f4e0fe938ede0a684dcbb1fa9b4789de22e8a99c43103d4b4c374b3b.py.lock\r\n> INFO:nlp.info:Loading Dataset Infos from /p/qdata/jm8wx/datasets/nlp/src/nlp/datasets/cnn_dailymail/9645e0bc96f647decf46541f6f4bef6936ee82ace653ac362bab03309a46d4ad\r\n> INFO:nlp.builder:Generating dataset cnn_dailymail (/u/jm8wx/.cache/huggingface/datasets/cnn_dailymail/3.0.0/3.0.0)\r\n> INFO:nlp.builder:Dataset not on Hf google storage. Downloading and preparing it from source\r\n> Downloading and preparing dataset cnn_dailymail/3.0.0 (download: 558.32 MiB, generated: 1.26 GiB, total: 1.81 GiB) to /u/jm8wx/.cache/huggingface/datasets/cnn_dailymail/3.0.0/3.0.0...\r\n> INFO:nlp.utils.info_utils:All the checksums matched successfully.\r\n> INFO:nlp.builder:Generating split train\r\n> INFO:nlp.arrow_writer:Done writing 285161 examples in 1240618482 bytes /u/jm8wx/.cache/huggingface/datasets/cnn_dailymail/3.0.0/3.0.0.incomplete/cnn_dailymail-train.arrow.\r\n> INFO:nlp.builder:Generating split validation\r\n> INFO:nlp.arrow_writer:Done writing 13255 examples in 56637485 bytes /u/jm8wx/.cache/huggingface/datasets/cnn_dailymail/3.0.0/3.0.0.incomplete/cnn_dailymail-validation.arrow.\r\n> INFO:nlp.builder:Generating split test\r\n> INFO:nlp.arrow_writer:Done writing 11379 examples in 48931393 bytes /u/jm8wx/.cache/huggingface/datasets/cnn_dailymail/3.0.0/3.0.0.incomplete/cnn_dailymail-test.arrow.\r\n> Traceback (most recent call last):\r\n> File \"<stdin>\", line 1, in <module>\r\n> File \"/p/qdata/jm8wx/datasets/nlp/src/nlp/load.py\", line 520, in load_dataset\r\n> builder_instance.download_and_prepare(\r\n> File \"/p/qdata/jm8wx/datasets/nlp/src/nlp/builder.py\", line 431, in download_and_prepare\r\n> self._download_and_prepare(\r\n> File \"/p/qdata/jm8wx/datasets/nlp/src/nlp/builder.py\", line 488, in _download_and_prepare\r\n> verify_splits(self.info.splits, split_dict)\r\n> File \"/p/qdata/jm8wx/datasets/nlp/src/nlp/utils/info_utils.py\", line 70, in verify_splits\r\n> raise NonMatchingSplitsSizesError(str(bad_splits))\r\n> nlp.utils.info_utils.NonMatchingSplitsSizesError: [{'expected': SplitInfo(name='test', num_bytes=49424491, num_examples=11490, dataset_name='cnn_dailymail'), 'recorded': SplitInfo(name='test', num_bytes=48931393, num_examples=11379, dataset_name='cnn_dailymail')}, {'expected': SplitInfo(name='train', num_bytes=1249178681, num_examples=287113, dataset_name='cnn_dailymail'), 'recorded': SplitInfo(name='train', num_bytes=1240618482, num_examples=285161, dataset_name='cnn_dailymail')}, {'expected': SplitInfo(name='validation', num_bytes=57149241, num_examples=13368, dataset_name='cnn_dailymail'), 'recorded': SplitInfo(name='validation', num_bytes=56637485, num_examples=13255, dataset_name='cnn_dailymail')}]\r\n> ```\r\n\r\nWith `nlp == 0.3.0` version, I'm not able to reproduce this error on my side.\r\nWhich version are you using for reproducing your bug?\r\n\r\n```\r\n>> nlp.load_dataset('cnn_dailymail', '3.0.0')\r\n\r\n8.90k/8.90k [00:18<00:00, 486B/s]\r\n\r\nDownloading: 100%\r\n9.37k/9.37k [00:00<00:00, 234kB/s]\r\n\r\nDownloading and preparing dataset cnn_dailymail/3.0.0 (download: 558.32 MiB, generated: 1.26 GiB, total: 1.81 GiB) to /root/.cache/huggingface/datasets/cnn_dailymail/3.0.0/3.0.0...\r\nDownloading:\r\n159M/? [00:09<00:00, 16.7MB/s]\r\n\r\nDownloading:\r\n376M/? [00:06<00:00, 62.6MB/s]\r\n\r\nDownloading:\r\n2.11M/? [00:06<00:00, 333kB/s]\r\n\r\nDownloading:\r\n46.4M/? [00:02<00:00, 18.4MB/s]\r\n\r\nDownloading:\r\n2.43M/? [00:00<00:00, 2.62MB/s]\r\n\r\nDataset cnn_dailymail downloaded and prepared to /root/.cache/huggingface/datasets/cnn_dailymail/3.0.0/3.0.0. Subsequent calls will reuse this data.\r\n{'test': Dataset(schema: {'article': 'string', 'highlights': 'string'}, num_rows: 11490),\r\n 'train': Dataset(schema: {'article': 'string', 'highlights': 'string'}, num_rows: 287113),\r\n 'validation': Dataset(schema: {'article': 'string', 'highlights': 'string'}, num_rows: 13368)}\r\n\r\n>> ...\r\n\r\n```", "In general if some examples are missing after processing (hence causing the `NonMatchingSplitsSizesError `), it is often due to either\r\n1) corrupted cached files\r\n2) decoding errors\r\n\r\nI just checked the dataset script for code that could lead to decoding errors but I couldn't find any. Before we try to dive more into the processing of the dataset, could you try to clear your cache ? Just to make sure that it isn't 1)", "Yes thanks for the support! I cleared out my cache folder and everything works fine now" ]

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[ "Hi, can you give details on your environment/os/packages versions/etc?", "Environment is Ubuntu 18.04, Python 3.7.5, nlp==0.3.0, filelock=3.0.12.\r\n\r\nThe external volume is Amazon FSx for Lustre, and it by default creates files with limited permissions. My working theory is that FileLock creates a lockfile that isn't writable, and thus there's no way to acquire it by removing the .lock file. But Python is able to create new files and write to them outside of the FileLock package.\r\n\r\nWhen I attempt to use FileLock within a Docker container by writing to `/root/.cache/hello.txt`, it succeeds. So there's some permissions issue. But it's not a Docker configuration issue; I've replicated it without Docker.\r\n```bash\r\necho \"hello world\" >> hello.txt\r\nls -l\r\n\r\n-rw-rw-r-- 1 ubuntu ubuntu 10 Jun 30 19:52 hello.txt\r\n```", "Looks like the `flock` syscall does not work on Lustre filesystems by default: https://github.com/benediktschmitt/py-filelock/issues/67.\r\n\r\nI added the `-o flock` option when mounting the filesystem, as [described here](https://docs.aws.amazon.com/fsx/latest/LustreGuide/getting-started-step2.html), which fixed the issue.", "Awesome, thanks a lot for sharing your fix!" ]

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[ "To be able to generate the Arrow dataset you need to either use our csv or json utilities `load_dataset(\"json\", data_files=my_json_files)` OR write your own custom dataset script (you can find some inspiration from the [squad](https://github.com/huggingface/nlp/blob/master/datasets/squad/squad.py) script for example). Custom dataset scripts can be called locally with `nlp.load_dataset(path_to_my_script_directory)`.\r\n\r\nThis should help you get what you call \"Dataset1\".\r\n\r\nThen using some dataset transforms like `.map` for example you can get to \"DatasetNER\" and \"DatasetREL\".\r\n", "Thanks for the helpful advice, @lhoestq -- I wasn't quite able to get the json recipe working - \r\n\r\n```\r\n~/.virtualenvs/inv-text2struct/lib/python3.6/site-packages/pyarrow/ipc.py in __init__(self, source)\r\n 60 \r\n 61 def __init__(self, source):\r\n---> 62 self._open(source)\r\n 63 \r\n 64 \r\n~/.virtualenvs/inv-text2struct/lib/python3.6/site-packages/pyarrow/ipc.pxi in pyarrow.lib._RecordBatchStreamReader._open()\r\n~/.virtualenvs/inv-text2struct/lib/python3.6/site-packages/pyarrow/error.pxi in pyarrow.lib.pyarrow_internal_check_status()\r\n~/.virtualenvs/inv-text2struct/lib/python3.6/site-packages/pyarrow/error.pxi in pyarrow.lib.check_status()\r\nArrowInvalid: Tried reading schema message, was null or length 0\r\n```\r\n\r\nBut I'm going to give the generator_dataset_builder a try.\r\n\r\n1 more quick question -- can .map be used to output different length mappings -- could I skip one, or yield 2, can you map_batch ", "You can use `.map(my_func, batched=True)` and return less examples, or more examples if you want", "Thanks this answers my question. I think the issue I was having using the json loader were due to using gzipped jsonl files.\r\n\r\nThe error I get now is :\r\n\r\n```\r\n\r\nUsing custom data configuration test\r\n---------------------------------------------------------------------------\r\n\r\nValueError Traceback (most recent call last)\r\n\r\n<ipython-input-38-29082a31e5b2> in <module>\r\n 5 print(ner_datafiles)\r\n 6 \r\n----> 7 ds = nlp.load_dataset(\"json\", \"test\", data_files=ner_datafiles[0])\r\n 8 \r\n\r\n~/.virtualenvs/inv-text2struct/lib/python3.6/site-packages/nlp/load.py in load_dataset(path, name, version, data_dir, data_files, split, cache_dir, download_config, download_mode, ignore_verifications, save_infos, **config_kwargs)\r\n 522 download_mode=download_mode,\r\n 523 ignore_verifications=ignore_verifications,\r\n--> 524 save_infos=save_infos,\r\n 525 )\r\n 526 \r\n\r\n~/.virtualenvs/inv-text2struct/lib/python3.6/site-packages/nlp/builder.py in download_and_prepare(self, download_config, download_mode, ignore_verifications, save_infos, try_from_hf_gcs, dl_manager, **download_and_prepare_kwargs)\r\n 430 verify_infos = not save_infos and not ignore_verifications\r\n 431 self._download_and_prepare(\r\n--> 432 dl_manager=dl_manager, verify_infos=verify_infos, **download_and_prepare_kwargs\r\n 433 )\r\n 434 # Sync info\r\n\r\n~/.virtualenvs/inv-text2struct/lib/python3.6/site-packages/nlp/builder.py in _download_and_prepare(self, dl_manager, verify_infos, **prepare_split_kwargs)\r\n 481 try:\r\n 482 # Prepare split will record examples associated to the split\r\n--> 483 self._prepare_split(split_generator, **prepare_split_kwargs)\r\n 484 except OSError:\r\n 485 raise OSError(\"Cannot find data file. \" + (self.manual_download_instructions or \"\"))\r\n\r\n~/.virtualenvs/inv-text2struct/lib/python3.6/site-packages/nlp/builder.py in _prepare_split(self, split_generator)\r\n 736 schema_dict[field.name] = Value(str(field.type))\r\n 737 \r\n--> 738 parse_schema(writer.schema, features)\r\n 739 self.info.features = Features(features)\r\n 740 \r\n\r\n~/.virtualenvs/inv-text2struct/lib/python3.6/site-packages/nlp/builder.py in parse_schema(schema, schema_dict)\r\n 734 parse_schema(field.type.value_type, schema_dict[field.name])\r\n 735 else:\r\n--> 736 schema_dict[field.name] = Value(str(field.type))\r\n 737 \r\n 738 parse_schema(writer.schema, features)\r\n\r\n<string> in __init__(self, dtype, id, _type)\r\n\r\n~/.virtualenvs/inv-text2struct/lib/python3.6/site-packages/nlp/features.py in __post_init__(self)\r\n 55 \r\n 56 def __post_init__(self):\r\n---> 57 self.pa_type = string_to_arrow(self.dtype)\r\n 58 \r\n 59 def __call__(self):\r\n\r\n~/.virtualenvs/inv-text2struct/lib/python3.6/site-packages/nlp/features.py in string_to_arrow(type_str)\r\n 32 if str(type_str + \"_\") not in pa.__dict__:\r\n 33 raise ValueError(\r\n---> 34 f\"Neither {type_str} nor {type_str + '_'} seems to be a pyarrow data type. \"\r\n 35 f\"Please make sure to use a correct data type, see: \"\r\n 36 f\"https://arrow.apache.org/docs/python/api/datatypes.html#factory-functions\"\r\n\r\nValueError: Neither list<item: int64> nor list<item: int64>_ seems to be a pyarrow data type. Please make sure to use a correct data type, see: https://arrow.apache.org/docs/python/api/datatypes.html#factory-functions.\r\n```\r\n\r\nIf I just create a pa- table manually like is done in the jsonloader -- it seems to work fine. Ths JSON I'm trying to load isn't overly complex - 1 integer field, the rest text fields with a nested list of objects with text fields .", "I'll close this -- It's still unclear how to go about troubleshooting the json example as I mentioned above. If I decide it's worth the trouble, I'll create another issue, or wait for a better support for using nlp for making custom data-loaders." ]

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[ "I'm pretty sure you can get some inspiration from the squad_v2 script. It looks like the dataset is quite big so it will take some time for the users to generate it, but it should be reasonable.\r\n\r\nAlso you are saying that you are still making the dataset grow in size right ?\r\nIt's probably good practice to let the users do their training/evaluations with the exact same version of the dataset.\r\nWe allow for each dataset to specify a version (ex: 1.0.0) and increment this number every time there are new samples in the dataset for example. Does it look like a good solution for you ? Or would you rather have one final version with the full dataset ?", "It would also be good if there is any possibility for versioning, I think this way is much better than the dynamic way.\nIf you mean that part to put the tiles into one is the generation it would take up to 15-20 minutes on home computer hardware.\nAre there any compression or optimization algorithms while generating the dataset ?\nOtherwise the hardware limit is around 32 GB ram at the moment.\nIf everything works well we will add some more gigabytes of data in future what would make it pretty memory costly.", "15-20 minutes is fine !\r\nAlso there's no RAM limitations as we save to disk every 1000 elements while generating the dataset by default.\r\nAfter generation, the dataset is ready to use with (again) no RAM limitations as we do memory-mapping.", "Wow, that sounds pretty cool.\nActually I have the problem of running out of memory while tokenization on our local machine.\nThat wouldn't happen again, would it ?", "You can do the tokenization step using `my_tokenized_dataset = my_dataset.map(my_tokenize_function)` that writes the tokenized texts on disk as well. And then `my_tokenized_dataset` will be a memory-mapped dataset too, so you should be fine :)", "Does it have an affect to the trainings speed ?", "In your training loop, loading the tokenized texts is going to be fast and pretty much negligible compared to a forward pass. You shouldn't expect any slow down.", "Closing this one. Feel free to re-open if you have other questions" ]

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[ "The glue metric for cola is a metric for classification. It expects label ids as integers as inputs.", "I want to evaluate a sentence pair whether they are semantically equivalent, so I used MRPC and it gives the same error, does that mean we have to encode the sentences and parse as input?\r\n\r\nusing BertTokenizer;\r\n```\r\nencoded_reference=tokenizer.encode(reference, add_special_tokens=False)\r\nencoded_prediction=tokenizer.encode(prediction, add_special_tokens=False)\r\n```\r\n\r\n`glue_score = glue_metric.compute(encoded_prediction, encoded_reference)`\r\n```\r\n\r\nValueError Traceback (most recent call last)\r\n<ipython-input-9-4c3a3ce7b583> in <module>()\r\n----> 1 glue_score = glue_metric.compute(encoded_prediction, encoded_reference)\r\n\r\n6 frames\r\n/usr/local/lib/python3.6/dist-packages/nlp/metric.py in compute(self, predictions, references, timeout, **metrics_kwargs)\r\n 198 predictions = self.data[\"predictions\"]\r\n 199 references = self.data[\"references\"]\r\n--> 200 output = self._compute(predictions=predictions, references=references, **metrics_kwargs)\r\n 201 return output\r\n 202 \r\n\r\n/usr/local/lib/python3.6/dist-packages/nlp/metrics/glue/27b1bc63e520833054bd0d7a8d0bc7f6aab84cc9eed1b576e98c806f9466d302/glue.py in _compute(self, predictions, references)\r\n 101 return pearson_and_spearman(predictions, references)\r\n 102 elif self.config_name in [\"mrpc\", \"qqp\"]:\r\n--> 103 return acc_and_f1(predictions, references)\r\n 104 elif self.config_name in [\"sst2\", \"mnli\", \"mnli_mismatched\", \"mnli_matched\", \"qnli\", \"rte\", \"wnli\", \"hans\"]:\r\n 105 return {\"accuracy\": simple_accuracy(predictions, references)}\r\n\r\n/usr/local/lib/python3.6/dist-packages/nlp/metrics/glue/27b1bc63e520833054bd0d7a8d0bc7f6aab84cc9eed1b576e98c806f9466d302/glue.py in acc_and_f1(preds, labels)\r\n 60 def acc_and_f1(preds, labels):\r\n 61 acc = simple_accuracy(preds, labels)\r\n---> 62 f1 = f1_score(y_true=labels, y_pred=preds)\r\n 63 return {\r\n 64 \"accuracy\": acc,\r\n\r\n/usr/local/lib/python3.6/dist-packages/sklearn/metrics/_classification.py in f1_score(y_true, y_pred, labels, pos_label, average, sample_weight, zero_division)\r\n 1097 pos_label=pos_label, average=average,\r\n 1098 sample_weight=sample_weight,\r\n-> 1099 zero_division=zero_division)\r\n 1100 \r\n 1101 \r\n\r\n/usr/local/lib/python3.6/dist-packages/sklearn/metrics/_classification.py in fbeta_score(y_true, y_pred, beta, labels, pos_label, average, sample_weight, zero_division)\r\n 1224 warn_for=('f-score',),\r\n 1225 sample_weight=sample_weight,\r\n-> 1226 zero_division=zero_division)\r\n 1227 return f\r\n 1228 \r\n\r\n/usr/local/lib/python3.6/dist-packages/sklearn/metrics/_classification.py in precision_recall_fscore_support(y_true, y_pred, beta, labels, pos_label, average, warn_for, sample_weight, zero_division)\r\n 1482 raise ValueError(\"beta should be >=0 in the F-beta score\")\r\n 1483 labels = _check_set_wise_labels(y_true, y_pred, average, labels,\r\n-> 1484 pos_label)\r\n 1485 \r\n 1486 # Calculate tp_sum, pred_sum, true_sum ###\r\n\r\n/usr/local/lib/python3.6/dist-packages/sklearn/metrics/_classification.py in _check_set_wise_labels(y_true, y_pred, average, labels, pos_label)\r\n 1314 raise ValueError(\"Target is %s but average='binary'. Please \"\r\n 1315 \"choose another average setting, one of %r.\"\r\n-> 1316 % (y_type, average_options))\r\n 1317 elif pos_label not in (None, 1):\r\n 1318 warnings.warn(\"Note that pos_label (set to %r) is ignored when \"\r\n\r\nValueError: Target is multiclass but average='binary'. Please choose another average setting, one of [None, 'micro', 'macro', 'weighted'].\r\n\r\n```", "MRPC is also a binary classification task, so its metric is a binary classification metric.\r\n\r\nTo evaluate if pairs of sentences are semantically equivalent, maybe you could take a look at models that compute if one sentence entails the other or not (typically the kinds of model that could work well on the MRPC task).", "Closing this one. Feel free to re-open if you have other questions :)" ]

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[ "It looks like it comes from `mwparserfromhell`.\r\n\r\nWould it be possible to get the bad `section` that causes this issue ? The `section` string is from `datasets/wikipedia.py:L548` ? You could just add a `try` statement and print the section if the line `section_text.append(section.strip_code().strip())` crashes.\r\n\r\nIt will help us know if we have to fix it on our side or if it is a `mwparserfromhell` issue.", "Hi, \r\n\r\nThank you for you answer.\r\nI have try to print the bad section using `try` and `except`, but it is a bit weird as the error seems to appear 3 times for instance, but the two first error does not print anything (as if the function did not go in the `except` part).\r\nFor the third one, I got that (I haven't display the entire text) :\r\n\r\n> error : ==== Parque nacional Cajas ====\r\n> {{AP|Parque nacional Cajas}}\r\n> [[Archivo:Ecuador cajas national park.jpg|thumb|left|300px|Laguna del Cajas]]\r\n> El parque nacional Cajas está situado en los [[Cordillera de los Andes|Andes]], al sur del [[Ecuador]], en la provincia de [[Provincia de Azuay|Azuay]], a 33\r\n> [[km]] al noroccidente de la ciudad de [[Cuenca (Ecuador)|Cuenca]]. Los accesos más comunes al parque inician todos en Cuenca: Desde allí, la vía Cuenca-Mol\r\n> leturo atraviesa en Control de [[Surocucho]] en poco más de 30 minutos de viaje; más adelante, esta misma carretera pasa a orillas de la laguna La Toreadora donde están el Centro Administrativo y de Información del parque. Siguiendo de largo hacia [[Molleturo]], por esta vía se conoce el sector norte del Cajas y se serpentea entre varias lagunas mayores y menores.\r\n> Para acceder al parque desde la costa, la vía Molleturo-Cuenca es también la mejor opción.\r\n\r\nHow can I display the link instead of the text ? I suppose it will help you more ", "The error appears several times as Apache Beam retries to process examples up to 4 times irc.\r\n\r\nI just tried to run this text into `mwparserfromhell` but it worked without the issue.\r\n\r\nI used this code (from the `wikipedia.py` script):\r\n```python\r\nimport mwparserfromhell as parser\r\nimport re\r\nimport six\r\n\r\nraw_content = r\"\"\"==== Parque nacional Cajas ====\r\n{{AP|Parque nacional Cajas}}\r\n[[Archivo:Ecuador cajas national park.jpg|thumb|left|300px|Laguna del Cajas]]\r\nEl parque nacional Cajas está situado en los [[Cordillera de los Andes|Andes]], al sur del [[Ecuador]], en la provincia de [[Provincia de Azuay|Azuay]], a 33\r\n[[km]] al noroccidente de la ciudad de [[Cuenca (Ecuador)|Cuenca]]. Los accesos más comunes al parque inician todos en Cuenca: Desde allí, la vía Cuenca-Mol\r\nleturo atraviesa en Control de [[Surocucho]] en poco más de 30 minutos de viaje; más adelante, esta misma carretera pasa a orillas de la laguna La Toreadora donde están el Centro Administrativo y de Información del parque. Siguiendo de largo hacia [[Molleturo]], por esta vía se conoce el sector norte del Cajas y se serpentea entre varias lagunas mayores y menores.\r\n\"\"\"\r\n\r\nwikicode = parser.parse(raw_content)\r\n\r\n# Filters for references, tables, and file/image links.\r\nre_rm_wikilink = re.compile(\"^(?:File|Image|Media):\", flags=re.IGNORECASE | re.UNICODE)\r\n\r\ndef rm_wikilink(obj):\r\n return bool(re_rm_wikilink.match(six.text_type(obj.title)))\r\n\r\ndef rm_tag(obj):\r\n return six.text_type(obj.tag) in {\"ref\", \"table\"}\r\n\r\ndef rm_template(obj):\r\n return obj.name.lower() in {\"reflist\", \"notelist\", \"notelist-ua\", \"notelist-lr\", \"notelist-ur\", \"notelist-lg\"}\r\n\r\ndef try_remove_obj(obj, section):\r\n try:\r\n section.remove(obj)\r\n except ValueError:\r\n # For unknown reasons, objects are sometimes not found.\r\n pass\r\n\r\nsection_text = []\r\nfor section in wikicode.get_sections(flat=True, include_lead=True, include_headings=True):\r\n for obj in section.ifilter_wikilinks(matches=rm_wikilink, recursive=True):\r\n try_remove_obj(obj, section)\r\n for obj in section.ifilter_templates(matches=rm_template, recursive=True):\r\n try_remove_obj(obj, section)\r\n for obj in section.ifilter_tags(matches=rm_tag, recursive=True):\r\n try_remove_obj(obj, section)\r\n\r\n section_text.append(section.strip_code().strip())\r\n```", "Not sure why we're having this issue. Maybe could you get also the file that's causing that ?", "thanks for your answer.\r\nHow can I know which file is causing the issue ? \r\nI am trying to load the spanish wikipedia data. ", "Because of the way Apache Beam works we indeed don't have access to the file name at this point in the code.\r\nWe'll have to use some tricks I think :p \r\n\r\nYou can append `filepath` to `title` in `wikipedia.py:L512` for example. [[EDIT: it's L494 my bad]]\r\nThen just do `try:...except:` on the call of `_parse_and_clean_wikicode` L500 I guess.\r\n\r\nThanks for diving into this ! I tried it myself but I run out of memory on my laptop\r\nAs soon as we have the name of the file it should be easier to find what's wrong.", "Thanks for your help.\r\n\r\nI tried to print the \"title\" of the document inside the` except (mwparserfromhell.parser.ParserError) as e`,the title displayed was : \"Campeonato Mundial de futsal de la AMF 2015\". (Wikipedia ES) Is it what you were looking for ?", "Thanks a lot @Shiro-LK !\r\n\r\nI was able to reproduce the issue. It comes from [this table on wikipedia](https://es.wikipedia.org/wiki/Campeonato_Mundial_de_futsal_de_la_AMF_2015#Clasificados) that can't be parsed.\r\n\r\nThe file in which the problem occurs comes from the wikipedia dumps, and it can be downloaded [here](https://dumps.wikimedia.org/eswiki/20200501/eswiki-20200501-pages-articles-multistream6.xml-p6424816p7924815.bz2)\r\n\r\nParsing the file this way raises the parsing issue:\r\n\r\n```python\r\nimport mwparserfromhell as parser\r\nfrom tqdm.auto import tqdm\r\nimport bz2\r\nimport six\r\nimport logging\r\nimport codecs\r\nimport xml.etree.cElementTree as etree\r\n\r\nfilepath = \"path/to/eswiki-20200501-pages-articles-multistream6.xml-p6424816p7924815.bz2\"\r\n\r\ndef _extract_content(filepath):\r\n \"\"\"Extracts article content from a single WikiMedia XML file.\"\"\"\r\n logging.info(\"generating examples from = %s\", filepath)\r\n with open(filepath, \"rb\") as f:\r\n f = bz2.BZ2File(filename=f)\r\n if six.PY3:\r\n # Workaround due to:\r\n # https://github.com/tensorflow/tensorflow/issues/33563\r\n utf_f = codecs.getreader(\"utf-8\")(f)\r\n else:\r\n utf_f = f\r\n # To clear root, to free-up more memory than just `elem.clear()`.\r\n context = etree.iterparse(utf_f, events=(\"end\",))\r\n context = iter(context)\r\n unused_event, root = next(context)\r\n for unused_event, elem in tqdm(context, total=949087):\r\n if not elem.tag.endswith(\"page\"):\r\n continue\r\n namespace = elem.tag[:-4]\r\n title = elem.find(\"./{0}title\".format(namespace)).text\r\n ns = elem.find(\"./{0}ns\".format(namespace)).text\r\n id_ = elem.find(\"./{0}id\".format(namespace)).text\r\n # Filter pages that are not in the \"main\" namespace.\r\n if ns != \"0\":\r\n root.clear()\r\n continue\r\n raw_content = elem.find(\"./{0}revision/{0}text\".format(namespace)).text\r\n root.clear()\r\n\r\n if \"Campeonato Mundial de futsal de la AMF 2015\" in title:\r\n yield (id_, title, raw_content)\r\n\r\nfor id_, title, raw_content in _extract_content(filepath):\r\n wikicode = parser.parse(raw_content)\r\n```\r\n\r\nThe copied the raw content that can't be parsed [here](https://pastebin.com/raw/ZbmevLyH).\r\n\r\nThe minimal code to reproduce is:\r\n```python\r\nimport mwparserfromhell as parser\r\nimport requests\r\n\r\nraw_content = requests.get(\"https://pastebin.com/raw/ZbmevLyH\").content.decode(\"utf-8\")\r\nwikicode = parser.parse(raw_content)\r\n\r\n```\r\n\r\nI will create an issue on mwparserfromhell's repo to see if we can fix that\r\n", "This going to be fixed in the next `mwparserfromhell` release :)" ]

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[ "I wonder if this means downloading failed? That corpus has a really slow server.", "This dataset seems to have a decoding problem that results in inconsistencies in the number of generated examples.\r\nSee #215.\r\nThat's why we end up with a `NonMatchingSplitsSizesError `." ]

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[ "Oh yes, this is a backward compatibility feature with tensorflow_dataset in which a `Sequence` or `dict` is converted in a `dict` of `lists`, unfortunately it is not very intuitive, see here: https://github.com/huggingface/nlp/blob/master/src/nlp/features.py#L409\r\n\r\nTo avoid this behavior, you can just define the list in the feature with a simple list or a tuple (which is also simpler to write).\r\nIn your case, the features could be as follow:\r\n``` python\r\n...\r\nfeatures=nlp.Features({\r\n \"title\": nlp.Value(\"string\"),\r\n \"vertexSet\": [[{\r\n \"name\": nlp.Value(\"string\"),\r\n \"sent_id\": nlp.Value(\"int32\"),\r\n \"pos\": nlp.features.Sequence(nlp.Value(\"int32\")),\r\n \"type\": nlp.Value(\"string\"),\r\n }]],\r\n ...\r\n }),\r\n...\r\n```" ]

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[ "For one dataset you can have different configurations that each have their own `nlp.Features`.\r\nWe imagine having one configuration per subtask for example.\r\nThey are loaded with `nlp.load_dataset(\"my_dataset\", \"my_config\")`.\r\n\r\nFor example the `glue` dataset has many configurations. It is a bit different from your case though because each configuration is a dataset by itself (sst2, mnli).\r\nAnother example is `wikipedia` that has one configuration per language." ]

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[ "Update: I think I've found a solution.\r\n\r\n```python\r\noutput_types = {\"input_ids\": tf.int64, \"token_type_ids\": tf.int64, \"attention_mask\": tf.int64}\r\ndef train_dataset_gen():\r\n for i in range(len(train_dataset)):\r\n yield train_dataset[i]\r\ntf_dataset = tf.data.Dataset.from_generator(train_dataset_gen, output_types=output_types)\r\n```\r\n\r\nloads WikiText-2 in 20 ms, and WikiText-103 in 20 ms. It appears to be lazily loading via indexing train_dataset.", "Yes this is the current best solution. We should probably show it in the tutorial notebook.\r\n\r\nNote that this solution unfortunately doesn't allow to train on TPUs (yet). See #193 ", "This approach still seems quite slow. When using TFRecords with a similar training loop, I get ~3.0-3.5 it/s on multi-node, multi-GPU training. I notice a pretty severe performance regression when scaling, with observed performance numbers. Since the allreduce step takes less than 100ms/it and I've achieved 80% scaling efficiency up to 64 GPUs, it must be the data pipeline.\r\n\r\n| Nodes | GPUs | Iterations/Second |\r\n| --- | --- | --- |\r\n| 1 | 2 | 2.01 |\r\n| 1 | 8 | 0.81 |\r\n| 2 | 16 | 0.37 |\r\n\r\nHere are performance metrics over 10k steps. The iteration speed appears to follow some sort of caching pattern. I would love to use `nlp` in my project, but a slowdown from 3.0 it/s to 0.3 it/s is too great to stomach.\r\n\r\n<img width=\"1361\" alt=\"Screen Shot 2020-07-02 at 8 29 22 AM\" src=\"https://user-images.githubusercontent.com/4564897/86378156-2f8d3900-bc3e-11ea-918b-c395c3df5377.png\">\r\n", "An interesting alternative to investigate here would be to use the tf.io library which has some support for Arrow to TF conversion: https://www.tensorflow.org/io/api_docs/python/tfio/arrow/ArrowDataset\r\n\r\nThere are quite a few types supported, including lists so if the unsupported columns are dropped then we could maybe have a zero-copy mapping from Arrow to TensorFlow, including tokenized inputs and 1D tensors like the ones we mostly use in NLP: https://github.com/tensorflow/io/blob/322b3170c43ecac5c6af9e39dbd18fd747913e5a/tensorflow_io/arrow/python/ops/arrow_dataset_ops.py#L44-L72\r\n\r\nHere is an introduction on Arrow to TF using tf.io: https://medium.com/tensorflow/tensorflow-with-apache-arrow-datasets-cdbcfe80a59f", "Interesting. There's no support for strings, but it does enable int and floats so that would work for tokenized inputs. \r\n\r\nArrowStreamDataset requires loading from a \"record batch iterator\", which can be instantiated from in-memory arrays as described here: https://arrow.apache.org/docs/python/ipc.html. \r\n\r\nBut the nlp.Dataset stores its data as a `pyarrow.lib.Table`, and the underlying features are `pyarrow.lib.ChunkedArray`. I can't find any documentation about lazily creating a record batch iterator from a ChunkedArray or a Table. Have you had any success?\r\n\r\nI can't find [any uses](https://grep.app/search?q=ArrowDataset&filter[lang][0]=Python) of tfio.arrow.ArrowDataset on GitHub.", "You can use `to_batches` maybe?\r\nhttps://arrow.apache.org/docs/python/generated/pyarrow.Table.html#pyarrow.Table.to_batches", "Also note that since #322 it is now possible to do\r\n```python\r\nids = [1, 10, 42, 100]\r\nbatch = dataset[ids]\r\n```\r\nFrom my experience it is quite fast but it can take lots of memory for large batches (haven't played that much with it).\r\nLet me know if you think there could be a better way to implement it. (current code is [here](https://github.com/huggingface/nlp/blob/78628649962671b4aaa31a6b24e7275533416845/src/nlp/arrow_dataset.py#L463))", "Thanks @lhoestq! That format is much better to work with.\r\n\r\nI put together a benchmarking script. This doesn't measure the CPU-to-GPU efficiency, nor how it scales with multi-GPU multi-node training where many processes are making the same demands on the same dataset. But it does show some interesting results:\r\n\r\n```python\r\nimport nlp\r\nimport numpy as np\r\nimport tensorflow as tf\r\nimport time\r\n\r\ndset = nlp.load_dataset(\"wikitext\", \"wikitext-2-raw-v1\", split=\"train\")\r\ndset = dset.filter(lambda ex: len(ex[\"text\"]) > 0)\r\nbsz = 1024\r\nn_batches = 100\r\n\r\ndef single_item_gen():\r\n for i in range(len(dset)):\r\n yield dset[i]\r\n\r\ndef sequential_batch_gen():\r\n for i in range(0, len(dset), bsz):\r\n yield dset[i:i+bsz]\r\n\r\ndef random_batch_gen():\r\n for i in range(len(dset)):\r\n indices = list(np.random.randint(len(dset), size=(bsz,)))\r\n yield dset[indices]\r\n\r\noutput_types = {\"text\": tf.string}\r\nsingle_item = tf.data.Dataset.from_generator(single_item_gen, output_types=output_types).batch(bsz)\r\ninterleaved = tf.data.Dataset.range(10).interleave(\r\n lambda idx: tf.data.Dataset.from_generator(single_item_gen, output_types=output_types),\r\n cycle_length=10,\r\n)\r\nsequential_batch = tf.data.Dataset.from_generator(sequential_batch_gen, output_types=output_types)\r\nrandom_batch = tf.data.Dataset.from_generator(random_batch_gen, output_types=output_types)\r\n\r\ndef iterate(tf_dset):\r\n start = time.perf_counter()\r\n for i, batch in enumerate(tf_dset.take(n_batches)):\r\n pass\r\n elapsed = time.perf_counter() - start\r\n print(f\"{tf_dset} took {elapsed:.3f} secs\")\r\n\r\niterate(single_item)\r\niterate(interleaved)\r\niterate(sequential_batch)\r\niterate(random_batch)\r\n```\r\n\r\nResults:\r\n```\r\n<BatchDataset shapes: {text: <unknown>}, types: {text: tf.string}> took 23.005 secs\r\n<InterleaveDataset shapes: {text: <unknown>}, types: {text: tf.string}> took 0.135 secs\r\n<FlatMapDataset shapes: {text: <unknown>}, types: {text: tf.string}> took 0.074 secs\r\n<FlatMapDataset shapes: {text: <unknown>}, types: {text: tf.string}> took 0.550 secs\r\n```\r\n\r\n- Batching a generator which fetches a single item is terrible.\r\n- Interleaving performs well on a single process, but doesn't scale well to multi-GPU training. I believe the bottleneck here is in Arrow dataset locking or something similar. The numbers from the table above are with interleaving.\r\n- The sequential access dominates the random access (7x faster). Is there any way to bring random access times closer to sequential access? Maybe re-indexing the dataset after shuffling each pass over the data.", "Hey @jarednielsen \r\n\r\nThanks for this very interesting analysis!! IMHO to read text data one should use `tf.data.TextLineDataset`. It would be interesting to compare what you have done with simply load with a `TextLineDataset` and see if there is a difference.\r\n\r\nA good example can be found here https://www.tensorflow.org/tutorials/load_data/text", "Thanks! I'm not actually loading in raw text data, that was just the synthetic data I created for this benchmark. A more realistic use case would be a dataset of tokenized examples, which would be a dict of lists of integers. TensorFlow's TextLineDataset greedily loads the dataset into the graph itself, which can lead to out-of-memory errors - one of the main reason I'm so drawn to the `nlp` library is its zero-copy no-RAM approach to dataset loading and mapping. \r\n\r\nIt's quite helpful for running a preprocessing pipeline - a sample ELECTRA pipeline I've built is here: https://github.com/jarednielsen/deep-learning-models/blob/nlp/models/nlp/common/preprocess.py.", "Sorry, I think I badly expressed myself, my bad. What I suggested is to compare with the usual loading textual data in pure TF with `TextLineDataset` with `nlp`. I know it is not recommended with very large datasets to use it, but I was curious to see how it behaves compared to a processing with `nlp` on smaller datasets.\r\n\r\nBTW your script looks very interesting, thanks for sharing!!" ]

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[ "Hi Jared,\r\nInteresting, thanks for raising this question. You can also do that after loading with `dataset.select()` or `dataset.filter()` which let you keep only a specific subset of rows in a dataset.\r\nWhat is your use-case for sharding?", "Thanks for the pointer to those functions! It's still a little more verbose since you have to manually calculate which ids each rank would keep, but definitely works.\r\n\r\nMy use case is multi-node, multi-GPU training and avoiding global batches of duplicate elements. I'm using horovod. You can shuffle indices, or set random seeds, but explicitly sharding the dataset up front is the safest and clearest way I've found to do so." ]

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[ "Even more complicating - \r\n\r\nAs I see it, datasets can have \"addons\".\r\nFor example, the WebNLG dataset is a dataset for data-to-text. However, a work of mine and other works enriched this dataset with text plans / underlying text structures. In that case, I see a need to load the dataset \"WebNLG\" with \"plans\" addon.\r\n\r\nSame for sign language - if there is a dataset of videos, one addon can be to run OpenPose, another to run ARKit4 pose estimation, and another to run PoseNet, or even just a video embedding addon. (which are expensive to run individually for everyone who wants to use these data)\r\n\r\nThis is something I dabbled with my own implementation to a [research datasets library](https://github.com/AmitMY/meta-scholar/) and I love to get the discussion going on these topics.", "This is a really cool idea !\r\nThe example for data objects you gave for the RWTH-PHOENIX-Weather 2014 T dataset can totally fit inside the library.\r\n\r\nFor your point about formats like `ilex`, `eaf`, or `srt`, it is possible to use any library in your dataset script.\r\nHowever most user probably won't need these libraries, as most datasets don't need them, and therefore it's unlikely that we will have them in the minimum requirements to use `nlp` (we want to keep it as light-weight as possible). If a user wants to load your dataset and doesn't have the libraries you need, an error is raised asking the user to install them.\r\n\r\nMore generally, we plan to have something like a `requirements.txt` per dataset. This could also be a place for addons as you said. What do you think ?", "Thanks, Quentin, I think a `requirements.txt` per dataset will be a good thing.\r\nI will work on adding this dataset next week, and once we sort all of the kinks, I'll add more." ]

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[ "Whoops didn't mean to close this one.\r\nI did some changes, could you try to run it from the master branch ?", "Now it works, thanks!" ]

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[ "Thanks for reporting these errors :)\r\n\r\nI can actually see two issues here.\r\n\r\nFirst, datasets like `natural_questions` require apache_beam to be processed. Right now the import is not at the right place so we have this error message. However, even the imports are fixed, the nlp viewer doesn't actually have the resources to process NQ right now so we'll have to wait until we have a version that we've already processed on our google storage (that's what we've done for wikipedia for example).\r\n\r\nSecond, datasets like `newsroom` require manual downloads as we're not allowed to redistribute the data ourselves (if I'm not wrong). An error message should be displayed saying that we're not allowed to show the dataset.\r\n\r\nI can fix the first issue with the imports but for the second one I think we'll have to see with @srush to show a message for datasets that require manual downloads (it can be checked whether a dataset requires manual downloads if `dataset_builder_instance.manual_download_instructions is not None`).\r\n\r\n", "I added apache-beam to the viewer. We can think about how to add newsroom. ", "We don't plan to host the source files of newsroom ourselves for now.\r\nYou can still get the dataset if you follow the download instructions given by `dataset = load_dataset('newsroom')` though.\r\nThe viewer also shows the instructions now.\r\n\r\nClosing this one. If you have other questions, feel free to re-open :)" ]

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[ "@jxmorris12 , we use `-1` to label examples for which `gold label` is missing (`gold label = -` in the original dataset). ", "Thanks @mariamabarham! so the original dataset is missing some labels? That is weird. Is standard practice just to discard those examples training/eval?", "Yes the original dataset is missing some labels maybe @sleepinyourhat , @gangeli can correct me if I'm wrong \r\nFor my personal opinion at least if you want your model to learn to predict no answer (-1) you can leave it their but otherwise you can discard them. ", "thanks @mariamabarham :)" ]

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[ "I couldn't replicate this issue on my macbook :/\r\nCould you try to play with different encodings in `with open(path, encoding=...) as f` in scientific_papers.py:L108 ?", "I was able to track down the file causing the problem by adding the following to `scientific_papers.py` (starting at line 116):\r\n\r\n```python\r\n from json import JSONDecodeError\r\n try:\r\n d = json.loads(line)\r\n summary = \"\\n\".join(d[\"abstract_text\"])\r\n except JSONDecodeError:\r\n print(path, line)\r\n```\r\n\r\n\r\n\r\nFor me it was at: `/Users/johngiorgi/.cache/huggingface/datasets/f87fd498c5003cbe253a2af422caa1e58f87a4fd74cb3e67350c635c8903b259/arxiv-dataset/train.txt` with `\"article_id\": \"1407.3051\"`.\r\n\r\nNot really 100% sure at the moment, but it looks like this specific substring from `\"article_text\"` may be causing the problem?\r\n\r\n```\r\n\"after the missing - mass scale adjustment , the validity of the corrections was tested in the @xmath85 productions at 1.69 gev/@xmath1 . in fig . [\", \"fig : calibrations ] ( a ) , we show the missing - mass spectrum in the @xmath86 region in the @xmath87 reaction at 1.69 gev/@xmath1 . a fitting result with a lorentzian function for the @xmath86 ( dashed line ) and the three - body phas\r\n```\r\n\r\nperhaps because it appears to be truncated. I (think) I can recreate the problem by doing the following:\r\n\r\n```python\r\nimport json\r\n\r\n# A minimal example of the json file that causes the error\r\ninvalid_json = '{\"article_id\": \"1407.3051\", \"article_text\": [\"the missing - mass resolution was obtained to be 2.8 @xmath3 0.1 mev/@xmath4 ( fwhm ) , which corresponds to the missing - mass resolution of 3.2 @xmath3 0.2 mev/@xmath4 ( fwhm ) at the @xmath6 cusp region in the @xmath0 reaction .\", \"this resolution is at least by a factor of 2 better than the previous measurement with the same reaction ( 3.2@xmath595.5 mev/@xmath4 in @xmath84 ) @xcite .\", \"after the missing - mass scale adjustment , the validity of the corrections was tested in the @xmath85 productions at 1.69 gev/@xmath1 . in fig . [\", \"fig : calibrations ] ( a ) , we show the missing - mass spectrum in the @xmath86 region in the @xmath87 reaction at 1.69 gev/@xmath1 . a fitting result with a lorentzian function for the @xmath86 ( dashed line ) and the three - body phas' \r\n# The line of code from `scientific_papers.py` which appears to cause the error\r\njson.loads(invalid_json)\r\n```\r\n\r\nThis is as far as I get before I am stumped.", "I just checked inside `train.txt` and this line isn't truncated for me (line 163577).\r\nCould you try to clear your cache and re-download the dataset ?", "Ah the turn-it-off-turn-it-on again solution! That did it, thanks a lot :) " ]

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[ "It should ne fixed now, thanks for reporting this one :)\r\nIt was an issue on our google storage.\r\n\r\nLet me now if you're still facing this issue.", "It works now, thanks for prompt help!" ]

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[ "It looks like the bug comes from `dill`. Which version of `dill` are you using ?", "Thank you. It is version 0.2.6, which version is better?", "0.2.6 is three years old now, maybe try a more recent one, e.g. the current 0.3.2 if you can?", "Thanks guys! I upgraded dill and it works.", "Awesome" ]

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[ "Hi @MFreidank, you should already be able to load a dataset from local sources, indeed. (ping @lhoestq and @jplu)\r\n\r\nWe're also thinking about the ability to host private datasets on a hosted bucket with permission management, but that's further down the road.", "Hi @MFreidank, it is possible to load a dataset from your local storage, but only CSV/TSV and JSON are supported. To load a dataset in JSON format:\r\n\r\n```\r\nnlp.load_dataset(path=\"json\", data_files={nlp.Split.TRAIN: [\"path/to/train.json\"], nlp.Split.TEST: [\"path/to/test.json\"]})\r\n```\r\n\r\nFor CSV/TSV datasets, you have to replace `json` by `csv`.", "Hi @julien-c @jplu,\r\nThanks for sharing this solution with me, it helps, this is what I was looking for. \r\nIf not already there and only missed by me, this could be a great addition in the docs.\r\n\r\nClosing my issue as resolved, thanks again." ]

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[ "When you're processing a dataset with `.map`, it checks whether it has already done this computation using a hash based on the function and the input (using some fancy serialization with `dill`). If you found that it doesn't work as expected in some cases, let us know !\r\n\r\nGiven that, you can still force to re-process using `.map(my_func, load_from_cache_file=False)` if you want to.\r\n\r\nI am curious about the problem you have with splits. It makes me think about #160 that was an issue of version 0.1.0. What version of `nlp` are you running ? Could you give me more details ?", "Thanks, that's helpful! I was running 0.1.0, but since upgraded to 0.2.1. I can't reproduce the issue anymore as I've cleared the cache & everything now seems to be running fine since the upgrade. I've added some checks to my code, so if I do encounter it again I will reopen this issue.", "Just checking in, the cache sometimes still does not work when I make changes in my processing function in version `1.2.1`. The changes made to my data processing function only propagate to the dataset when I use `load_from_cache_file=False` or clear the cache. Is this a system-specific issue?", "Hi @sarahwie \r\nThe data are reloaded from the cache if the hash of the function you provide is the same as a computation you've done before. The hash is computed by recursively looking at the python objects of the function you provide.\r\n\r\nIf you think there's an issue, can you share the function you used or a google colab please ?", "I can't reproduce it, so I'll close for now." ]

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[ "Hi !\r\n\r\nAs you noticed, \"big\" datasets like Wikipedia require apache beam to be processed.\r\nHowever users usually don't have an apache beam runtime available (spark, dataflow, etc.) so our goal for this library is to also make available processed versions of these datasets, so that users can just download and use them right away.\r\n\r\nThis is the case for english and french wikipedia right now: we've processed them ourselves and now they are available from our google storage. However we've not processed the german one (yet).", "Hi @lhoestq \r\n\r\nThank you for your quick reply. I thought this might be the case, that the processing was done for some languages and not for others. Is there any set timeline for when other languages (German, Italian) will be processed?\r\n\r\nGiven enough memory, is it possible to process the data ourselves by specifying the `beam_runner`?", "Adding them is definitely in our short term objectives. I'll be working on this early next week :)\r\n\r\nAlthough if you have an apache beam runtime feel free to specify the beam runner. You can find more info [here](https://github.com/huggingface/nlp/blob/master/docs/beam_dataset.md) on how to make it work on Dataflow but you can adapt it for Spark or any other beam runtime (by changing the `runner`).\r\n\r\nHowever if you don't have a beam runtime and even if you have enough memory, I discourage you to use the `DirectRunner` on the german or italian wikipedia. According to Apache Beam documentation it was made for testing purposes and therefore it is memory-inefficient.", "German is [almost] done @gregburman", "I added the German and the Italian Wikipedia to our google cloud storage:\r\nFirst update the `nlp` package to 0.3.0:\r\n```bash\r\npip install nlp --upgrade\r\n```\r\nand then\r\n```python\r\nfrom nlp import load_dataset\r\nwiki_de = load_dataset(\"wikipedia\", \"20200501.de\")\r\nwiki_it = load_dataset(\"wikipedia\", \"20200501.it\")\r\n```\r\nThe datasets are downloaded and directly ready to use (no processing).", "Hi @lhoestq \r\n\r\nWow, thanks so much, that's **really** incredible! I was considering looking at creating my own Beam Dataset, as per the doc you linked, but instead opted to process the data myself using `wikiextractor`. However, now that this is available, I'll definitely switch across and use it.\r\n\r\nThanks so much for the incredible work, this really helps out our team considerably!\r\n\r\nHave a great (and well-deserved ;) weekend ahead!\r\n\r\nP.S. I'm not sure if I should close the issue here - if so I'm happy to do so.", "Thanks for your message, glad I could help :)\r\nClosing this one." ]

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[ "We are only wrapping the original dataset.\r\n\r\nMaybe try to ask on the GLUE mailing list or reach out to the original authors?", "Tanks for the suggestion, I'll try to ask GLUE benchmark.\r\nI'll first close the issue, post the following up here afterwards, and reopen the issue if needed. " ]

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[ "For some reason the files are not available for unauthenticated users right now (like the download service of this package). Instead of downloading the right files, it downloads the html of the error.\r\nAccording to the error it should be back again in 24h.\r\n\r\n\r\n" ]

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[ "Sounds good! Do you want to give it a try?", "Ok, I'll see if I can figure it out tomorrow!", "Got around to this today, and so far so good, I'm able to download and load pg19 locally. However, I think there may be an issue with the dummy data, and testing in general.\r\n\r\nThe problem lies in the fact that each book from pg19 actually resides as its own text file in a google cloud folder that denotes the split, where the book id is the name of the text file. https://console.cloud.google.com/storage/browser/deepmind-gutenberg/train/ I don't believe there's anywhere else (even in the supplied metadata), where the mapping of id -> split can be found.\r\n\r\nTherefore I end up making a network call `tf.io.gfile.listdir` to get all the files within each of the split directories. https://github.com/lucidrains/nlp/commit/adbacbd85decc80db2347d0882e7dab4faa6fd03#diff-cece8f166a85dd927caf574ba303d39bR78\r\n\r\nDoes this network call need to be eventually stubbed out for testing?", "Ohh nevermind, I think I can use `download_custom` here with `listdir` as the custom function. Ok, I'll keep trying to make the dummy data work!" ]

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[ "C4 is too large to be shown in the viewer" ]

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[ "I will take a look :-) " ]

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[ "Thanks a lot, @aleSuglia for the very detailed and introductive feature request.\r\nIt seems like we could build something pretty useful here indeed.\r\n\r\nOne of the questions here is that Arrow doesn't have built-in support for generic \"tensors\" in records but there might be ways to do that in a clean way. We'll probably try to tackle this during the summer.", "I was looking into Facebook MMF and apparently they decided to use LMDB to store additional features associated with every example: https://github.com/facebookresearch/mmf/blob/master/mmf/datasets/databases/features_database.py\r\n\r\n", "I saw the Mozilla common_voice dataset in model hub, which has mp3 audio recordings as part it. It's use predominantly maybe in ASR and TTS, but dataset is a Language + Voice Dataset similar to @aleSuglia's point about Language + Vision. \r\n\r\nhttps://huggingface.co/datasets/common_voice" ]

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[ "When you install `nlp` for the first time on a Colab runtime, it updates the `pyarrow` library that was already on colab. This update shows this message on colab:\r\n```\r\nWARNING: The following packages were previously imported in this runtime:\r\n [pyarrow]\r\nYou must restart the runtime in order to use newly installed versions.\r\n```\r\nYou just have to restart the runtime and it should be fine.\r\nIf you don't restart, then it breaks like in your message.", "Yeah, that worked! Thanks :) " ]

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[ "this seems to work for my specific problem:\r\n\r\n`self.train_ds, self.test_ds, self.val_ds = map(_prepare_ds, ('train', 'test[:25%]+test[50%:75%]', 'test[75%:]'))`", "Currently you can indeed split a dataset using `ds_test = nlp.load_dataset('imdb, split='test[:5000]')` (works also with percentages).\r\n\r\nHowever right now we don't have a way to shuffle a dataset but we are thinking about it in the discussion in #166. Feel free to share your thoughts about it.\r\n\r\nOne trick that you can do until we have a better solution is to shuffle and split the indices of your dataset:\r\n```python\r\nimport nlp\r\nfrom sklearn.model_selection import train_test_split\r\n\r\nimdb = nlp.load_dataset('imbd', split='test')\r\ntest_indices, val_indices = train_test_split(range(len(imdb)))\r\n```\r\n\r\nand then to iterate each split:\r\n```python\r\nfor i in test_indices:\r\n example = imdb[i]\r\n ...\r\n```\r\n", "I added a small guide [here](https://github.com/huggingface/nlp/tree/master/docs/splits.md) that explains how to split a dataset. It is very similar to the tensorflow datasets guide, as we kept the same logic.", "Thanks a lot, the new explanation is very helpful!\r\n\r\nAbout using train_test_split from sklearn: I stumbled across the [same error message as this user ](https://github.com/huggingface/nlp/issues/147 )and thought it can't be used at the moment in this context. Will check it out again.\r\n\r\nOne of the problems is how to shuffle very large datasets, which don't fit into the memory. Well, one strategy could be shuffling data in sections. But in a case where the data is sorted by the labels you have to swap larger sections first. \r\n", "We added a way to shuffle datasets (shuffle the indices and then reorder to make a new dataset).\r\nYou can do `shuffled_dset = dataset.shuffle(seed=my_seed)`. It shuffles the whole dataset.\r\nThere is also `dataset.train_test_split()` which if very handy (with the same signature as sklearn).\r\n\r\nClosing this issue as we added the docs for splits and tools to split datasets. Thanks again for your feedback !" ]

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[ "Hi ! This is because `.map` added the new column `input_ids` to the dataset, and so all the other columns were kept. Therefore the dataset size increased a lot.\r\n If you want to only keep the `input_ids` column, you can stash the other ones by specifying `remove_columns=[\"title\", \"text\"]` in the arguments of `.map`", "Hi ! Thanks for your reply.\r\n\r\nBut since size of `input_ids` < size of `text`, I am wondering why\r\nsize of `input_ids` + `text` > 2x the size of `text` 🤔", "Hard to tell... This is probably related to the way apache arrow compresses lists of integers, that may be different from the compression of strings.", "Thanks for your point. 😀, It might be answer.\r\nSince this is hard to know, I'll close this issue.\r\nBut if somebody knows more details, please comment below ~ 😁" ]

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[ "Yes, the new release of tokenizers solves this and should be out soon.\r\nIn the meantime, you can install it with `pip install tokenizers==0.8.0-dev2`", "If others run into this issue, a quick fix is to use python 3.6 instead of 3.7+. Serialization differences between the 3rd party `dataclasses` package for 3.6 and the built in `dataclasses` in 3.7+ cause the issue.\r\n\r\nProbably a dumb fix, but it works for me." ]

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[ "Do you have an example of what you would like to do? (you can just add a field in the output of the unction you give to map and this will add this field in the output table)", "Given another source of data loaded in, I want to pre-add it to the dataset so that it aligns with the indices of the arrow dataset prior to performing map.\r\n\r\nE.g. \r\n```\r\nnew_info = list of length dataset['train']\r\n\r\ndataset['train'] = dataset['train'].map(lambda x: some_function(x, new_info[index of x]))\r\n\r\ndef some_function(x, new_info_x):\r\n # adds new_info[index of x] as a field to x\r\n x['new_info'] = new_info_x\r\n return x\r\n```\r\nI was thinking to instead create a new field in the arrow dataset so that instance x contains all the necessary information when map function is applied (since I don't have index information to pass to map function).", "This is what I have so far: \r\n\r\n```\r\nimport pyarrow as pa\r\nfrom nlp.arrow_dataset import Dataset\r\n\r\naug_dataset = dataset['train'][:]\r\naug_dataset['new_info'] = new_info\r\n\r\n#reformat as arrow-table\r\nschema = dataset['train'].schema\r\n\r\n# this line doesn't work:\r\nschema.append(pa.field('new_info', pa.int32()))\r\n\r\ntable = pa.Table.from_pydict(\r\n aug_dataset,\r\n schema=schema\r\n)\r\ndataset['train'] = Dataset(table) \r\n```", "Maybe you can use `with_indices`?\r\n\r\n```python\r\nnew_info = list of length dataset['train']\r\n\r\ndef some_function(indice, x):\r\n # adds new_info[index of x] as a field to x\r\n x['new_info'] = new_info_x[indice]\r\n return x\r\n\r\ndataset['train'] = dataset['train'].map(some_function, with_indices=True)\r\n```", "Oh great. That should work. I missed that in the documentation- thanks :) " ]

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[ "Oh yes you can now do that with the `dataset.filter()` method that was added in #214 " ]

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[ "I just tried on my side and I didn't encounter your problem.\r\nApparently the script doesn't generate all the examples on your side.\r\n\r\nCan you provide the version of `nlp` you're using ?\r\nCan you try to clear your cache and re-run the code ?", "I updated it, that was it, thanks!", "Hello, I am facing the same problem... how do you clear the huggingface cache?", "Hi ! The cache is at ~/.cache/huggingface\r\nYou can just delete this folder if needed :)" ]

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[ "Thanks for noticing all these :) They should be easy to fix indeed", "Alright I think I fixed all the problems you mentioned. Thanks again, that will be useful for many people.\r\nThere is still more work needed for point 7. but we plan to have some nice docs soon." ]

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[ "Everything is already cached by default in 🤗nlp (in particular dataset\nloading and all the “map()” operations) so I don’t think you need to do any\nspecific caching in streamlit.\n\nTell us if you feel like it’s not the case.\n\nOn Sat, 6 Jun 2020 at 13:02, Fabrizio Milo <notifications@github.com> wrote:\n\n> For example if I want to use streamlit with a nlp dataset:\n>\n> @st.cache\n> def load_data():\n> return nlp.load_dataset('squad')\n>\n> This code raises the error \"uncachable object\"\n>\n> Right now I just fixed with a constant for my specific case:\n>\n> @st.cache(hash_funcs={pyarrow.lib.Buffer: lambda b: 0})\n>\n> But I was curious to know what is the best way in general\n>\n> —\n> You are receiving this because you are subscribed to this thread.\n> Reply to this email directly, view it on GitHub\n> <https://github.com/huggingface/nlp/issues/246>, or unsubscribe\n> <https://github.com/notifications/unsubscribe-auth/ABYDIHKAKO7CWGX2QY55UXLRVIO3ZANCNFSM4NV333RQ>\n> .\n>\n", "Closing this one. Feel free to re-open if you have other questions !" ]

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[ "this also happened to me with `nlp.load_dataset('glue', 'mnli')`", "Yes, this is because the test sets for glue are hidden so the labels are\nnot publicly available. You can read the glue paper for more details.\n\nOn Sat, 6 Jun 2020 at 18:16, Jack Morris <notifications@github.com> wrote:\n\n> this also happened to me with nlp.load_datasets('glue', 'mnli')\n>\n> —\n> You are receiving this because you are subscribed to this thread.\n> Reply to this email directly, view it on GitHub\n> <https://github.com/huggingface/nlp/issues/245#issuecomment-640083980>,\n> or unsubscribe\n> <https://github.com/notifications/unsubscribe-auth/ABYDIHMVQD2EDX2HTZUXG5DRVJTWRANCNFSM4NVG3AKQ>\n> .\n>\n", "Thanks @thomwolf!", "@thomwolf shouldn't this be visible in the .info and/or in the .features?", "It should be in the datasets card (the README.md and on the hub) in my opinion. What do you think @yjernite?", "I checked both before I got to looking at issues, so that would be fine as well.\r\n\r\nSome additional thoughts on this: Is there a specific reason why the \"test\" split even has a \"label\" column if it isn't tagged. Shouldn't there just not be any. Seems more transparent", "I'm a little confused with the data size.\r\n`sst2` dataset is referenced to `Recursive Deep Models for Semantic Compositionality Over a Sentiment Treebank` and the link of the dataset in the paper is https://nlp.stanford.edu/sentiment/index.html which is often shown in GLUE/SST2 reference.\r\nFrom the original data, the standard train/dev/test splits split is 6920/872/1821 for binary classification. \r\nWhy in GLUE/SST2 the train/dev/test split is 67,349/872/1,821 ? \r\n\r\n", "> I'm a little confused with the data size.\r\n> `sst2` dataset is referenced to `Recursive Deep Models for Semantic Compositionality Over a Sentiment Treebank` and the link of the dataset in the paper is https://nlp.stanford.edu/sentiment/index.html which is often shown in GLUE/SST2 reference.\r\n> From the original data, the standard train/dev/test splits split is 6920/872/1821 for binary classification.\r\n> Why in GLUE/SST2 the train/dev/test split is 67,349/872/1,821 ?\r\n\r\nHave you figured out this problem? AFAIK, the original sst-2 dataset is totally different from the GLUE/sst-2. Do you think so?", "@yc1999 Sorry, I didn't solve this conflict. In the end, I just use a local data file provided by the previous work I followed(for consistent comparison), not use `datasets` package.\r\n\r\nRelated information: https://github.com/thunlp/OpenAttack/issues/146#issuecomment-766323571", "@yc1999 I find that the original SST-2 dataset (6,920/872/1,821) can be loaded from https://huggingface.co/datasets/gpt3mix/sst2 or built with SST data and the scripts in https://github.com/prrao87/fine-grained-sentiment/tree/master/data/sst.\r\nThe GLUE/SST-2 dataset (67,349/872/1,821) should be a completely different version.\r\n" ]

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[ "It should be good now, thanks for noticing and fixing it ! I would say that it was because you are on windows but not 100% sure", "On Windows Python supports Unicode almost everywhere, but one of the notable exceptions is open() where it uses the locale encoding schema. So platform independent python scripts would always set the encoding='utf-8' in calls to open explicitly. \r\nIn the meantime: since Python 3.7 Windows users can set the default encoding for everything including open() to Unicode by setting this environment variable: set PYTHONUTF8=1 (details can be found in [PEP 540](https://www.python.org/dev/peps/pep-0540/))\r\n\r\nFor me this fixed the problem described by the OP." ]

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[ "Yes good point !", "I think using `sorted(glob.glob())` would actually solve this problem. Can you think of other reasons why dataset loading might not be deterministic? @mariamabarham @yjernite @lhoestq @thomwolf . \r\n\r\nI can do a sweep through the dataset scripts and fix the glob.glob() if you guys are ok with it", "I'm pretty sure it would solve the problem too.\r\n\r\nThe only other dataset that is not deterministic right now is `blog_authorship_corpus` (see #215) but this is a problem related to string encodings.", "I think we should do the same also for `os.list_dir`" ]

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[ "I think you can just `rm` this directory and it should be good :)", "@lhoestq - this seems to happen quite often (already the 2nd issue). Can we maybe delete this automatically?", "Yes I have an idea of what's going on. I'm sure I can fix that", "Hi, I rebase my local copy to `fix-empty-cache-dir`, and try to run again `python nlp-cli test datasets/bookcorpus --save_infos --all_configs`.\r\n\r\nI got this, \r\n```\r\nTraceback (most recent call last):\r\n File \"nlp-cli\", line 10, in <module>\r\n from nlp.commands.run_beam import RunBeamCommand\r\n File \"/home/yisiang/nlp/src/nlp/commands/run_beam.py\", line 6, in <module>\r\n import apache_beam as beam\r\nModuleNotFoundError: No module named 'apache_beam'\r\n```\r\nAnd after I installed it. I got this\r\n```\r\nFile \"/home/yisiang/nlp/src/nlp/datasets/bookcorpus/aea0bd5142d26df645a8fce23d6110bb95ecb81772bb2a1f29012e329191962c/bookcorpus.py\", line 88, in _split_generators\r\n downloaded_path_or_paths = dl_manager.download_custom(_GDRIVE_FILE_ID, download_file_from_google_drive)\r\n File \"/home/yisiang/nlp/src/nlp/utils/download_manager.py\", line 128, in download_custom\r\n downloaded_path_or_paths = map_nested(url_to_downloaded_path, url_or_urls)\r\n File \"/home/yisiang/nlp/src/nlp/utils/py_utils.py\", line 172, in map_nested\r\n return function(data_struct)\r\n File \"/home/yisiang/nlp/src/nlp/utils/download_manager.py\", line 126, in url_to_downloaded_path\r\n return os.path.join(self._download_config.cache_dir, hash_url_to_filename(url))\r\n File \"/home/yisiang/miniconda3/envs/nlppr/lib/python3.7/posixpath.py\", line 80, in join\r\n a = os.fspath(a)\r\n```\r\nThe problem is when I print `self._download_config.cache_dir` using pdb, it is `None`.\r\n\r\nDid I miss something ? Or can you provide a workaround first so I can keep testing my script ?", "I'll close this issue because I brings more reports in another issue #249 ." ]

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[ "This print statement comes from the official implementation of bert_score (see [here](https://github.com/Tiiiger/bert_score/blob/master/bert_score/utils.py#L343)). The warning shows up only if the attention mask outputs no candidate.\r\nRight now we want to only use official code for metrics to have fair evaluations, so I'm not sure we can do anything about it. Maybe you can try to create an issue on their [repo](https://github.com/Tiiiger/bert_score) ?" ]

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[ "You should use `load_dataset('glue', 'mnli')`", "Thanks! I thought I had to use the same code displayed in the live viewer:\r\n```python\r\n!pip install nlp\r\nfrom nlp import load_dataset\r\ndataset = load_dataset('multi_nli', 'plain_text')\r\n```\r\nYour suggestion works, even if then I got a different issue (#242). ", "Glad it helps !\nThough I am not one of hf team, but maybe you should close this issue first." ]

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[ "What do you mean 'custom' ? You may want to elaborate on it when ask a question.\r\n\r\nAnyway, there are two things you may interested\r\n`nlp.Dataset.from_file` and `load_dataset(..., cache_dir=)`", "To load a dataset you need to have a script that defines the format of the examples, the splits and the way to generate examples. As your dataset has the same format of squad, you can just copy the squad script (see the [datasets](https://github.com/huggingface/nlp/tree/master/datasets) forlder) and just replace the url to load the data to your local or remote path.\r\n\r\nThen what you can do is `load_dataset(<path/to/your/script>)`", "Also if you want to upload your script, you should be able to use the `nlp-cli`.\r\n\r\nUnfortunately the upload feature was not shipped in the latest version 0.2.0. so right now you can either clone the repo to use it or wait for the next release. We will add some docs to explain how to upload datasets.\r\n", "Since the latest release 0.2.1 you can use \r\n```bash\r\nnlp-cli upload_dataset <path/to/dataset>\r\n```\r\nwhere `<path/to/dataset>` is a path to a folder containing your script (ex: `squad.py`).\r\nThis will upload the script under your namespace on our S3.\r\n\r\nOptionally the folder can also contain `dataset_infos.json` generated using\r\n```bash\r\nnlp-cli test <path/to/dataset> --all_configs --save_infos\r\n```\r\n\r\nThen you should be able to do\r\n```python\r\nnlp.load_dataset(\"my_namespace/dataset_name\")\r\n```" ]

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[ "Hello ! Thanks for noticing this bug, let me fix that.\r\n\r\nAlso for information, as specified in the changelog of the latest release, C4 currently needs to have a runtime for apache beam to work on. Apache beam is used to process this very big dataset and it can work on dataflow, spark, flink, apex, etc. You can find more info on beam datasets [here](https://github.com/huggingface/nlp/blob/master/docs/beam_dataset.md).\r\n\r\nOur goal in the future is to make available an already-processed version of C4 (as we do for wikipedia for example) so that users without apache beam runtimes can load it.", "@lhoestq I am facing `IsADirectoryError` while downloading with this command.\r\nCan you pls look into it & help me.\r\nI'm using version 0.4.0 of `nlp`.\r\n\r\n```\r\ndataset = load_dataset(\"c4\", 'en', data_dir='.', beam_runner='DirectRunner')\r\n```\r\n\r\nHere's the complete stack trace.\r\n\r\n```\r\nDownloading and preparing dataset c4/en (download: Unknown size, generated: Unknown size, post-processed: Unknown sizetotal: Unknown size) to /home/devops/.cache/huggingface/datasets/c4/en/2.3.0/096df5a27756d51957c959a2499453e60a08154971fceb017bbb29f54b11bef7...\r\n\r\n---------------------------------------------------------------------------\r\nIsADirectoryError Traceback (most recent call last)\r\n<ipython-input-11-f622e6705e03> in <module>\r\n----> 1 dataset = load_dataset(\"c4\", 'en', data_dir='.', beam_runner='DirectRunner')\r\n\r\n/data/anaconda/envs/hf/lib/python3.6/site-packages/nlp/load.py in load_dataset(path, name, version, data_dir, data_files, split, cache_dir, features, download_config, download_mode, ignore_verifications, save_infos, **config_kwargs)\r\n 547 # Download and prepare data\r\n 548 builder_instance.download_and_prepare(\r\n--> 549 download_config=download_config, download_mode=download_mode, ignore_verifications=ignore_verifications,\r\n 550 )\r\n 551 \r\n\r\n/data/anaconda/envs/hf/lib/python3.6/site-packages/nlp/builder.py in download_and_prepare(self, download_config, download_mode, ignore_verifications, try_from_hf_gcs, dl_manager, **download_and_prepare_kwargs)\r\n 461 if not downloaded_from_gcs:\r\n 462 self._download_and_prepare(\r\n--> 463 dl_manager=dl_manager, verify_infos=verify_infos, **download_and_prepare_kwargs\r\n 464 )\r\n 465 # Sync info\r\n\r\n/data/anaconda/envs/hf/lib/python3.6/site-packages/nlp/builder.py in _download_and_prepare(self, dl_manager, verify_infos)\r\n 964 pipeline = beam_utils.BeamPipeline(runner=beam_runner, options=beam_options,)\r\n 965 super(BeamBasedBuilder, self)._download_and_prepare(\r\n--> 966 dl_manager, verify_infos=False, pipeline=pipeline,\r\n 967 ) # TODO handle verify_infos in beam datasets\r\n 968 # Run pipeline\r\n\r\n/data/anaconda/envs/hf/lib/python3.6/site-packages/nlp/builder.py in _download_and_prepare(self, dl_manager, verify_infos, **prepare_split_kwargs)\r\n 516 split_dict = SplitDict(dataset_name=self.name)\r\n 517 split_generators_kwargs = self._make_split_generators_kwargs(prepare_split_kwargs)\r\n--> 518 split_generators = self._split_generators(dl_manager, **split_generators_kwargs)\r\n 519 # Checksums verification\r\n 520 if verify_infos:\r\n\r\n/data/anaconda/envs/hf/lib/python3.6/site-packages/nlp/datasets/c4/096df5a27756d51957c959a2499453e60a08154971fceb017bbb29f54b11bef7/c4.py in _split_generators(self, dl_manager, pipeline)\r\n 187 if self.config.realnewslike:\r\n 188 files_to_download[\"realnews_domains\"] = _REALNEWS_DOMAINS_URL\r\n--> 189 file_paths = dl_manager.download_and_extract(files_to_download)\r\n 190 \r\n 191 if self.config.webtextlike:\r\n\r\n/data/anaconda/envs/hf/lib/python3.6/site-packages/nlp/utils/download_manager.py in download_and_extract(self, url_or_urls)\r\n 218 extracted_path(s): `str`, extracted paths of given URL(s).\r\n 219 \"\"\"\r\n--> 220 return self.extract(self.download(url_or_urls))\r\n 221 \r\n 222 def get_recorded_sizes_checksums(self):\r\n\r\n/data/anaconda/envs/hf/lib/python3.6/site-packages/nlp/utils/download_manager.py in download(self, url_or_urls)\r\n 156 lambda url: cached_path(url, download_config=self._download_config,), url_or_urls,\r\n 157 )\r\n--> 158 self._record_sizes_checksums(url_or_urls, downloaded_path_or_paths)\r\n 159 return downloaded_path_or_paths\r\n 160 \r\n\r\n/data/anaconda/envs/hf/lib/python3.6/site-packages/nlp/utils/download_manager.py in _record_sizes_checksums(self, url_or_urls, downloaded_path_or_paths)\r\n 106 flattened_downloaded_path_or_paths = flatten_nested(downloaded_path_or_paths)\r\n 107 for url, path in zip(flattened_urls_or_urls, flattened_downloaded_path_or_paths):\r\n--> 108 self._recorded_sizes_checksums[url] = get_size_checksum_dict(path)\r\n 109 \r\n 110 def download_custom(self, url_or_urls, custom_download):\r\n\r\n/data/anaconda/envs/hf/lib/python3.6/site-packages/nlp/utils/info_utils.py in get_size_checksum_dict(path)\r\n 77 \"\"\"Compute the file size and the sha256 checksum of a file\"\"\"\r\n 78 m = sha256()\r\n---> 79 with open(path, \"rb\") as f:\r\n 80 for chunk in iter(lambda: f.read(1 << 20), b\"\"):\r\n 81 m.update(chunk)\r\n\r\nIsADirectoryError: [Errno 21] Is a directory: '/'\r\n\r\n```\r\n\r\nCan anyone please try to see what I am doing wrong or is this a bug?", "I have the same problem as @prashant-kikani", "Looks like a bug in the dataset script, can you open an issue ?", "I see the same issue as @prashant-kikani. I'm using `datasets` version 1.2.0 to download C4." ]

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[ "Added pull request to change the name of the file from dataset_infos.json to dataset_info.json", "Thanks for reporting this bug !\r\nAs it seems to be just a cache problem, I closed your PR.\r\nI think we might just need to clear and reload the `xnli` cache @srush ? ", "Update: The dataset_info.json error is gone, but we have a new one instead:\r\n```\r\nConnectionError: Couldn't reach https://www.nyu.edu/projects/bowman/xnli/XNLI-1.0.zip\r\n```\r\nI am not able to reproduce on my side unfortunately. Any idea @srush ?", "xnli is now properly shown in the viewer.\r\nClosing this one." ]

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[ "Thanks for your message 😊 \r\nIndeed users shouldn't have to install those dependencies", "Got it, feel free to close this issue when you think it’s resolved.", "It should be good now :)" ]

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[ "@Colanim unfortunately there are different implementations of the ROUGE metric floating around online which yield different results, and we had to chose one for the package :) We ended up including the one from the google-research repository, which does minimal post-processing before computing the P/R/F scores. If I recall correctly, files2rouge relies on the Perl, script, which among other things normalizes all numbers to a special token: in the case you presented, this should account for a good chunk of the difference.\r\n\r\nWe may end up adding in more versions of the metric, but probably not for a while (@lhoestq correct me if I'm wrong). However, feel free to take a stab at adding it in yourself and submitting a PR if you're interested!", "Thank you for your kind answer.\r\n\r\nAs a side question : Isn't it better to have a package that normalize more ?\r\n\r\nI understand to idea of having a package that does minimal post-processing for transparency.\r\n\r\nBut it means that people using different architecture (with different tokenizers for example) will have difference in ROUGE scores even if their predictions are actually similar. \r\nThe goal of `nlp` is to have _one package to rule them all_, right ?\r\n\r\nI will look into it but I'm not sure I have the required skill for this ^^ ", "You're right, there's a pretty interesting trade-off here between robustness and sensitivity :) The flip side of your argument is that we also still want the metric to be sensitive to model mistakes. How we think about number normalization for example has evolved a fair bit since the Perl script was written: at the time, ROUGE was used mostly to evaluate short-medium text summarization systems, where there were only a few numbers in the input and it was assumed that the most popular methods in use at the time would get those right. However, as your example showcases, that assumption does not hold any more, and we do want to be able to penalize a model that generates a wrong numerical value.\r\n\r\nAlso, we think that abstracting away tokenization differences is the role of the model/tokenizer: if you use the 🤗Tokenizers library for example, it will handle that for you ;)\r\n\r\nFinally, there is a lot of active research on developing model-powered metrics that are both more sensitive and more robust than ROUGE. Check out for example BERTscore, which is implemented in this library!" ]

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[ "Is there any update on this? \r\n\r\nThanks!", "Hitting this error when using bleurt with PyTorch ...\r\n\r\n```\r\nUnrecognizedFlagError: Unknown command line flag 'f'\r\n```\r\n... and I'm assuming because it was built for TF specifically. Is there a way to use this metric in PyTorch?", "We currently provide a wrapper on the TensorFlow implementation: https://huggingface.co/metrics/bleurt\r\n\r\nWe have long term plans to better handle model-based metrics, but they probably won't be implemented right away\r\n\r\n@adamwlev it would still be cool to add the BLEURT checkpoints to the transformers repo if you're interested, but that would best be discussed there :) \r\n\r\nclosing for now", "Hi there. We ran into the same problem this year (converting BLEURT to PyTorch) and thanks to @adamwlev found his colab notebook which didn't work but served as a good starting point. Finally, we **made it work** by doing just two simple conceptual fixes: \r\n\r\n1. Transposing 'kernel' layers instead of 'dense' ones when copying params from the original model;\r\n2. Taking pooler_output as a cls_state in forward function of the BleurtModel class.\r\n\r\nPlus few minor syntactical fixes for the outdated parts. The result is still not exactly the same, but is very close to the expected one (1.0483 vs 1.0474).\r\n\r\nFind the fixed version here (fixes are commented): https://colab.research.google.com/drive/1KsCUkFW45d5_ROSv2aHtXgeBa2Z98r03?usp=sharing \r\n" ]

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[ "Hi @lbourdois, yes please share it with us", "@mariamabarham \r\nI put all the datasets on this drive: https://1drv.ms/u/s!Ao2Rcpiny7RFinDypq7w-LbXcsx9?e=iVsEDh\r\n\r\n\r\nSome information : \r\n• For FLUE, the quote used is\r\n\r\n> @misc{le2019flaubert,\r\n> title={FlauBERT: Unsupervised Language Model Pre-training for French},\r\n> author={Hang Le and Loïc Vial and Jibril Frej and Vincent Segonne and Maximin Coavoux and Benjamin Lecouteux and Alexandre Allauzen and Benoît Crabbé and Laurent Besacier and Didier Schwab},\r\n> year={2019},\r\n> eprint={1912.05372},\r\n> archivePrefix={arXiv},\r\n> primaryClass={cs.CL}\r\n> }\r\n\r\n• The Github repo of FLUE is avaible here : https://github.com/getalp/Flaubert/tree/master/flue\r\n\r\n\r\n\r\nInformation related to the different tasks of FLUE : \r\n\r\n**1. Classification**\r\nThree dataframes are available: \r\n- Book\r\n- DVD\r\n- Music\r\nFor each of these dataframes is available a set of training and test data, and a third one containing unlabelled data.\r\n\r\nCitation : \r\n>@dataset{prettenhofer_peter_2010_3251672,\r\n author = {Prettenhofer, Peter and\r\n Stein, Benno},\r\n title = {{Webis Cross-Lingual Sentiment Dataset 2010 (Webis- \r\n CLS-10)}},\r\n month = jul,\r\n year = 2010,\r\n publisher = {Zenodo},\r\n doi = {10.5281/zenodo.3251672},\r\n url = {https://doi.org/10.5281/zenodo.3251672}\r\n}\r\n\r\n\r\n**2. Paraphrasing** \r\nFrench part of the PAWS-X dataset (https://github.com/google-research-datasets/paws).\r\nThree dataframes are available: \r\n- train\r\n- dev\r\n- test \r\n\r\nCitation : \r\n> @InProceedings{pawsx2019emnlp,\r\n> title = {{PAWS-X: A Cross-lingual Adversarial Dataset for Paraphrase Identification}},\r\n> author = {Yang, Yinfei and Zhang, Yuan and Tar, Chris and Baldridge, Jason},\r\n> booktitle = {Proc. of EMNLP},\r\n> year = {2019}\r\n> }\r\n\r\n\r\n\r\n**3. Natural Language Inference**\r\nFrench part of the XNLI dataset (https://github.com/facebookresearch/XNLI).\r\nThree dataframes are available: \r\n- train\r\n- dev\r\n- test \r\n\r\nFor the dev and test datasets, extra columns compared to the train dataset were available so I left them in the dataframe (I didn't know if these columns could be useful for other tasks or not). \r\nIn the context of the FLUE benchmark, only the columns gold_label, sentence1 and sentence2 are useful.\r\n\r\n\r\nCitation : \r\n\r\n> @InProceedings{conneau2018xnli,\r\n> author = \"Conneau, Alexis\r\n> and Rinott, Ruty\r\n> and Lample, Guillaume\r\n> and Williams, Adina\r\n> and Bowman, Samuel R.\r\n> and Schwenk, Holger\r\n> and Stoyanov, Veselin\",\r\n> title = \"XNLI: Evaluating Cross-lingual Sentence Representations\",\r\n> booktitle = \"Proceedings of the 2018 Conference on Empirical Methods\r\n> in Natural Language Processing\",\r\n> year = \"2018\",\r\n> publisher = \"Association for Computational Linguistics\",\r\n> location = \"Brussels, Belgium\",\r\n\r\n\r\n**4. Parsing**\r\nThe dataset used by the FLUE authors for this task is not freely available.\r\nUsers of your library will therefore not be able to access it.\r\nNevertheless, I think maybe it is useful to add a link to the site where to request this dataframe: http://ftb.linguist.univ-paris-diderot.fr/telecharger.php?langue=en \r\n(personally it was sent to me less than 48 hours after I requested it).\r\n\r\n\r\n**5. Word Sense Disambiguation Tasks**\r\n5.1 Verb Sense Disambiguation\r\n\r\nTwo dataframes are available: train and test\r\nFor both dataframes, 4 columns are available: document, sentence, lemma and word.\r\nI created the document column thinking that there were several documents in the dataset but afterwards it turns out that there were not: several sentences but only one document. It's up to you to keep it or not when importing these two dataframes.\r\n\r\nThe sentence column is used to determine to which sentence the word in the word column belongs. It is in the form of a dictionary {'id': 'd000.s001', 'idx': '1'}. I thought for a while to keep only the idx because the id doesn't matter any more information. Nevertheless for the test dataset, the dictionary has an extra value indicating the source of the sentence. I don't know if it's useful or not, that's why I left the dictionary just in case. The user is free to do what he wants with it.\r\n\r\nCitation : \r\n\r\n> Segonne, V., Candito, M., and Crabb ́e, B. (2019). Usingwiktionary as a resource for wsd: the case of frenchverbs. InProceedings of the 13th International Confer-ence on Computational Semantics-Long Papers, pages259–270\r\n\r\n5.2 Noun Sense Disambiguation\r\nTwo dataframes are available: 2 train and 1 test\r\n\r\nI confess I didn't fully understand the procedure for this task.\r\n\r\nCitation : \r\n\r\n> @dataset{loic_vial_2019_3549806,\r\n> author = {Loïc Vial},\r\n> title = {{French Word Sense Disambiguation with Princeton \r\n> WordNet Identifiers}},\r\n> month = nov,\r\n> year = 2019,\r\n> publisher = {Zenodo},\r\n> version = {1.0},\r\n> doi = {10.5281/zenodo.3549806},\r\n> url = {https://doi.org/10.5281/zenodo.3549806}\r\n> }\r\n\r\nFinally, additional information about FLUE is available in the FlauBERT publication : \r\nhttps://arxiv.org/abs/1912.05372 (p. 4).\r\n\r\n\r\nHoping to have provided you with everything you need to add this benchmark :) \r\n", "https://github.com/huggingface/datasets/pull/943" ]

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[ "The notebook forces version 0.1.0. If I use the latest, things work, I'll run the whole notebook and create a PR.\r\n\r\nBut in the meantime, this issue gets fixed by changing:\r\n`!pip install nlp==0.1.0`\r\nto\r\n`!pip install nlp`", "It still breaks very near the end\r\n\r\n\r\n", "When you install `nlp` for the first time on a Colab runtime, it updates the `pyarrow` library that was already on colab. This update shows this message on colab:\r\n```\r\nWARNING: The following packages were previously imported in this runtime:\r\n [pyarrow]\r\nYou must restart the runtime in order to use newly installed versions.\r\n```\r\nYou just have to restart the runtime and it should be fine.\r\nIf you don't restart, then it breaks like in your first message ", "Thanks for reporting the second one ! We'll update the notebook to fix this one :)", "This trick from @thomwolf seems to be the most reliable solution to fix this colab notebook issue:\r\n\r\n```python\r\n# install nlp\r\n!pip install -qq nlp==0.2.0\r\n\r\n# Make sure that we have a recent version of pyarrow in the session before we continue - otherwise reboot Colab to activate it\r\nimport pyarrow\r\nif int(pyarrow.__version__.split('.')[1]) < 16:\r\n import os\r\n os.kill(os.getpid(), 9)\r\n```", "The second part got fixed here: 2cbc656d6fc4b18ce57eac070baec05b31180d39\r\n\r\nThanks! I'm then closing this issue." ]

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[ "I like this feature! I think the first question we should decide on is how to convert all datasets into the same format. In T5, the authors decided to format every dataset into a text-to-text format. If the dataset had \"multiple\" inputs like MNLI, the inputs were concatenated. So in MNLI the input:\r\n\r\n> - **Hypothesis**: The St. Louis Cardinals have always won.\r\n> \r\n> - **Premise**: yeah well losing is i mean i’m i’m originally from Saint Louis and Saint Louis Cardinals when they were there were uh a mostly a losing team but \r\n\r\nwas flattened to a single input:\r\n\r\n> mnli hypothesis: The St. Louis Cardinals have always won. premise:\r\n> yeah well losing is i mean i’m i’m originally from Saint Louis and Saint Louis Cardinals\r\n> when they were there were uh a mostly a losing team but.\r\n\r\nThis flattening is actually a very simple operation in `nlp` already. You would just need to do the following:\r\n\r\n```python \r\ndef flatten_inputs(example):\r\n return {\"input\": \"mnli hypothesis: \" + example['hypothesis'] + \" premise: \" + example['premise']}\r\n\r\nt5_ready_mnli_ds = mnli_ds.map(flatten_inputs, remove_columns=[<all columns except output>])\r\n```\r\n\r\nSo I guess converting the datasets into the same format can be left to the user for now. \r\nThen the question is how we can merge the datasets. I would probably be in favor of a simple \r\n\r\n```python \r\ndataset.add()\r\n```\r\n\r\nfunction that checks if the dataset is of the same format and if yes merges the two datasets. Finally, how should the sampling be implemented? **Examples-proportional mixing** corresponds to just merging the datasets and shuffling. For the other two sampling approaches we would need some higher-level features, maybe even a `dataset.sample()` function for merged datasets. \r\n\r\nWhat are your thoughts on this @thomwolf @lhoestq @ghomasHudson @enzoampil ?", "I agree that we should leave the flattening of the dataset to the user for now. Especially because although the T5 framing seems obvious, there are slight variations on how the T5 authors do it in comparison to other approaches such as gpt-3 and decaNLP.\r\n\r\nIn terms of sampling, Examples-proportional mixing does seem the simplest to implement so would probably be a good starting point.\r\n\r\nTemperature-scaled mixing would probably most useful, offering flexibility as it can simulate the other 2 methods by setting the temperature parameter. There is a [relevant part of the T5 repo](https://github.com/google-research/text-to-text-transfer-transformer/blob/03c94165a7d52e4f7230e5944a0541d8c5710788/t5/data/utils.py#L889-L1118) which should help with implementation.\r\n\r\nAccording to the T5 authors, equal-mixing performs worst. Among the other two methods, tuning the K value (the artificial dataset size limit) has a large impact.\r\n", "I agree with going with temperature-scaled mixing for its flexibility!\r\n\r\nFor the function that combines the datasets, I also find `dataset.add()` okay while also considering that users may want it to be easy to combine a list of say 10 data sources in one go.\r\n\r\n`dataset.sample()` should also be good. By the looks of it, we're planning to have as main parameters: `temperature`, and `K`.\r\n\r\nOn converting the datasets to the same format, I agree that we can leave these to the users for now. But, I do imagine it'd be an awesome feature for the future to have this automatically handled, based on a chosen *approach* to formatting :smile: \r\n\r\nE.g. T5, GPT-3, decaNLP, original raw formatting, or a contributed way of formatting in text-to-text. ", "This is an interesting discussion indeed and it would be nice to make multi-task easier.\r\n\r\nProbably the best would be to have a new type of dataset especially designed for that in order to easily combine and sample from the multiple datasets.\r\n\r\nThis way we could probably handle the combination of datasets with differing schemas as well (unlike T5).", "@thomwolf Are you suggesting making a wrapper class which can take existing datasets as arguments and do all the required sampling/combining, to present the same interface as a normal dataset?\r\n\r\nThat doesn't seem too complicated to implement.\r\n", "I guess we're looking at the end user writing something like:\r\n``` python\r\nds = nlp.load_dataset('multitask-t5',datasets=[\"squad\",\"cnn_dm\",...], k=1000, t=2.0)\r\n```\r\nUsing the t5 method of combining here (or this could be a function passed in as an arg) \r\n\r\nPassing kwargs to each 'sub-dataset' might become tricky.", "From thinking upon @thomwolf 's suggestion, I've started experimenting:\r\n```python\r\nclass MultitaskDataset(DatasetBuilder):\r\n def __init__(self, *args, **kwargs):\r\n super(MultitaskDataset, self).__init__(*args, **kwargs)\r\n self._datasets = kwargs.get(\"datasets\")\r\n\r\n def _info(self):\r\n return nlp.DatasetInfo(\r\n description=_DESCRIPTION,\r\n features=nlp.Features({\r\n \"source\": nlp.Value(\"string\"),\r\n \"target\": nlp.Sequence(nlp.Value(\"string\"))\r\n })\r\n )\r\n\r\n def _get_common_splits(self):\r\n '''Finds the common splits present in all self._datasets'''\r\n min_set = None\r\n for dataset in self._datasets:\r\n if min_set != None:\r\n min_set.intersection(set(dataset.keys()))\r\n else:\r\n min_set = set(dataset.keys())\r\n return min_set\r\n\r\n....\r\n\r\n# Maybe this?:\r\nsquad = nlp.load_dataset(\"squad\")\r\ncnn_dm = nlp.load_dataset(\"cnn_dailymail\",\"3.0.0\")\r\nmultitask_dataset = nlp.load_dataset(\r\n 'multitask_dataset',\r\n datasets=[squad,cnn_dailymail], \r\n k=1000, \r\n t=2.0\r\n)\r\n\r\n```\r\n\r\nDoes anyone know what methods of `MultitaskDataset` I would need to implement? Maybe `as_dataset` and `download_and_prepare`? Most of these should be just calling the methods of the sub-datasets. \r\n\r\nI'm assuming DatasetBuilder is better than the more specific `GeneratorBasedBuilder`, `BeamBasedBuilder`, etc....\r\n\r\nOne of the other problems is that the dataset size is unknown till you construct it (as you can pick the sub-datasets). Am hoping not to need to make changes to `nlp.load_dataset` just for this class.\r\n\r\nI'd appreciate it if anyone more familiar with nlp's internal workings could tell me if I'm on the right track!", "I think I would probably go for a `MultiDataset` wrapper around a list of `Dataset`.\r\n\r\nI'm not sure we need to give it `k` and `t` parameters at creation, it can maybe be something along the lines of:\r\n```python\r\nsquad = nlp.load_dataset(\"squad\")\r\ncnn_dm = nlp.load_dataset(\"cnn_dailymail\",\"3.0.0\")\r\n\r\nmultitask_dataset = nlp.MultiDataset(squad, cnn_dm)\r\n\r\nbatch = multitask_dataset.sample(10, temperature=2.0, k=1000)\r\n```\r\n\r\nThe first proof-of-concept for multi-task datasets could definitely require that the provided datasets have the same name/type for columns (if needed you easily rename/cast a column prior to instantiating the `MultiDataset`).\r\n\r\nIt's good to think about it for some time though and don't overfit too much on the T5 examples (in particular for the ways/kwargs for sampling among datasets).", "The problem with changing `k` and `t` per sampling is that you'd have to somehow remember which examples you'd already returned while re-weighting the remaining examples based on the new `k` and `t`values. It seems possible but complicated (I can't really see a reason why you'd want to change the weighting of datasets after you constructed the multidataset).\r\n\r\nWouldn't it be convenient if it implemented the dataset interface? Then if someone has code using a single nlp dataset, they can replace it with a multitask combination of more datasets without having to change other code. We would at least need to be able to pass it into a `DataLoader`.\r\n\r\n", "A very janky (but working) implementation of `multitask_dataset.sample()` could be something like this:\r\n```python\r\nimport nlp\r\nimport torch\r\n\r\nclass MultiDataset():\r\n def __init__(self, *args, temperature=2.0, k=1000, maximum=None, scale=1):\r\n self.datasets = args\r\n self._dataloaders = {}\r\n for split in self._get_common_splits():\r\n split_datasets = [ds[split] for ds in self.datasets]\r\n mixing_rates = self._calc_mixing_rates(split_datasets,temperature, k, maximum, scale)\r\n weights = []\r\n for i in range(len(self.datasets)):\r\n weights += [mixing_rates[i]]*len(self.datasets[i][split])\r\n self._dataloaders[split] = torch.utils.data.DataLoader(torch.utils.data.ConcatDataset(split_datasets),\r\n sampler=torch.utils.data.sampler.WeightedRandomSampler(\r\n num_samples=len(weights),\r\n weights = weights,\r\n replacement=True),\r\n shuffle=False)\r\n\r\n def _get_common_splits(self):\r\n '''Finds the common splits present in all self.datasets'''\r\n min_set = None\r\n for dataset in self.datasets:\r\n if min_set != None:\r\n min_set.intersection(set(dataset.keys()))\r\n else:\r\n min_set = set(dataset.keys())\r\n return min_set\r\n\r\n\r\n def _calc_mixing_rates(self,datasets, temperature=2.0, k=1000, maximum=None, scale=1):\r\n '''Work out the weighting of each dataset based on t and k'''\r\n mixing_rates = []\r\n for dataset in datasets:\r\n rate = len(dataset)\r\n rate *= scale\r\n if maximum:\r\n rate = min(rate, maximum)\r\n if temperature != 1.0:\r\n rate = rate ** (1.0/temperature)\r\n mixing_rates.append(rate)\r\n return mixing_rates\r\n\r\n def sample(self,n,split):\r\n batch = []\r\n for example in self._dataloaders[split]:\r\n batch.append(example)\r\n n -= 1\r\n if n == 0:\r\n return batch\r\n\r\n\r\ndef flatten(dataset,flatten_fn):\r\n for k in dataset.keys():\r\n if isinstance(dataset[k],nlp.Dataset):\r\n dataset[k] = dataset[k].map(flatten_fn,remove_columns=dataset[k].column_names)\r\n\r\n# Squad\r\ndef flatten_squad(example):\r\n return {\"source\": \"squad context: \" + example['context'] + \" question: \" + example['question'],\"target\":example[\"answers\"][\"text\"]}\r\nsquad = nlp.load_dataset(\"squad\")\r\nflatten(squad,flatten_squad)\r\n\r\n# CNN_DM\r\ndef flatten_cnn_dm(example):\r\n return {\"source\": \"cnn_dm: \" + example['article'],\"target\":[example[\"highlights\"]]}\r\ncnn_dm = nlp.load_dataset(\"cnn_dailymail\", \"3.0.0\")\r\nflatten(cnn_dm,flatten_cnn_dm)\r\n\r\nmultitask_dataset = MultiDataset(squad, cnn_dm)\r\nbatch = multitask_dataset.sample(100,\"train\")\r\n```\r\n\r\nThere's definitely a more sensible way than embedding `DataLoader`s inside. ", "There is an interesting related investigation by @zphang here https://colab.research.google.com/github/zphang/zphang.github.io/blob/master/files/notebooks/Multi_task_Training_with_Transformers_NLP.ipynb", "Good spot! Here are my thoughts:\r\n\r\n- Aside: Adding `MultitaskModel` to transformers might be a thing to raise - even though having task-specific heads has become unfashionable in recent times in favour of text-to-text type models.\r\n- Adding the task name as an extra field also seems useful for these kind of models which have task-specific heads\r\n- There is some validation of our approach that the user should be expected to `map` datasets into a common form.\r\n- The size-proportional sampling (also called \"Examples-proportional mixing\") used here doesn't perform too badly in the T5 paper (it's comparable to temperature-scaled mixing in many cases but less flexible. This is only reasonable with a `K` maximum size parameter to prevent very large datasets dominating). This might be good for a first prototype using:\r\n ```python\r\n def __iter__(self):\r\n \"\"\"\r\n For each batch, sample a task, and yield a batch from the respective\r\n task Dataloader.\r\n\r\n We use size-proportional sampling, but you could easily modify this\r\n to sample from some-other distribution.\r\n \"\"\"\r\n task_choice_list = []\r\n for i, task_name in enumerate(self.task_name_list):\r\n task_choice_list += [i] * self.num_batches_dict[task_name]\r\n task_choice_list = np.array(task_choice_list)\r\n np.random.shuffle(task_choice_list)\r\n\r\n dataloader_iter_dict = {\r\n task_name: iter(dataloader) \r\n for task_name, dataloader in self.dataloader_dict.items()\r\n }\r\n for task_choice in task_choice_list:\r\n task_name = self.task_name_list[task_choice]\r\n yield next(dataloader_iter_dict[task_name]) \r\n ```\r\n We'd just need to pull samples from the raw datasets and not from `DataLoader`s for each task. We can assume the user has done `dataset.shuffle()` if they want to.\r\n\r\n Other sampling methods can later be implemented by changing how the `task_choice_list` is generated. This should allow more flexibility and not tie us to specific methods for sampling among datasets.\r\n", "Another thought: Multitasking over benchmarks (represented as Meta-datasets in nlp) is probably a common use case. Would be nice to pass an entire benchmark to our `MultiDataset` wrapper rather than having to pass individual components.", "Here's a fully working implementation based on the `__iter__` function of @zphang.\r\n\r\n- I've generated the task choice list in the constructor as it allows us to index into the MultiDataset just like a normal dataset. I'm changing `task_choice_list` into a list of `(dataset_idx, example_idx)` so each entry references a unique dataset example. The shuffling has to be done before this as we don't want to shuffle within each task (we assume this is done by the user if this is what they intend).\r\n- I'm slightly concerned this list could become very large if many large datasets were used. Can't see a way round it at the moment though.\r\n- I've used `task.info.builder_name` as the dataset name. Not sure if this is correct.\r\n- I'd love to add some of the other `Dataset` methods (map, slicing by column, etc...). Would be great to implement the whole interface so a single dataset can be simply replaced by this.\r\n- This does everything on the individual example-level. If some application required batches all from a single task in turn we can't really do that.\r\n\r\n```python\r\nimport nlp\r\nimport numpy as np\r\n\r\nclass MultiDataset:\r\n def __init__(self,tasks):\r\n self.tasks = tasks\r\n\r\n # Create random order of tasks\r\n # Using size-proportional sampling\r\n task_choice_list = []\r\n for i, task in enumerate(self.tasks):\r\n task_choice_list += [i] * len(task)\r\n task_choice_list = np.array(task_choice_list)\r\n np.random.shuffle(task_choice_list)\r\n\r\n # Add index into each dataset\r\n # - We don't want to shuffle within each task\r\n counters = {}\r\n self.task_choice_list = []\r\n for i in range(len(task_choice_list)):\r\n idx = counters.get(task_choice_list[i],0)\r\n self.task_choice_list.append((task_choice_list[i],idx))\r\n counters[task_choice_list[i]] = idx + 1\r\n\r\n\r\n def __len__(self):\r\n return np.sum([len(t) for t in self.tasks])\r\n\r\n def __repr__(self):\r\n task_str = \", \".join([str(t) for t in self.tasks])\r\n return f\"MultiDataset(tasks: {task_str})\"\r\n\r\n def __getitem__(self,key):\r\n if isinstance(key, int):\r\n task_idx, example_idx = self.task_choice_list[key]\r\n task = self.tasks[task_idx]\r\n example = task[example_idx]\r\n example[\"task_name\"] = task.info.builder_name\r\n return example\r\n elif isinstance(key, slice):\r\n raise NotImplementedError()\r\n\r\n def __iter__(self):\r\n for i in range(len(self)):\r\n yield self[i]\r\n\r\n\r\ndef load_multitask(*datasets):\r\n '''Create multitask datasets per split'''\r\n\r\n def _get_common_splits(datasets):\r\n '''Finds the common splits present in all self.datasets'''\r\n min_set = None\r\n for dataset in datasets:\r\n if min_set != None:\r\n min_set.intersection(set(dataset.keys()))\r\n else:\r\n min_set = set(dataset.keys())\r\n return min_set\r\n\r\n common_splits = _get_common_splits(datasets)\r\n out = {}\r\n for split in common_splits:\r\n out[split] = MultiDataset([d[split] for d in datasets])\r\n return out\r\n\r\n\r\n##########################################\r\n# Dataset Flattening\r\n\r\ndef flatten(dataset,flatten_fn):\r\n for k in dataset.keys():\r\n if isinstance(dataset[k],nlp.Dataset):\r\n dataset[k] = dataset[k].map(flatten_fn,remove_columns=dataset[k].column_names)\r\n\r\n# Squad\r\ndef flatten_squad(example):\r\n return {\"source\": \"squad context: \" + example['context'] + \" question: \" + example['question'],\r\n \"target\":example[\"answers\"][\"text\"]}\r\nsquad = nlp.load_dataset(\"squad\")\r\nflatten(squad,flatten_squad)\r\n\r\n# CNN_DM\r\ndef flatten_cnn_dm(example):\r\n return {\"source\": \"cnn_dm: \" + example['article'],\"target\":[example[\"highlights\"]]}\r\ncnn_dm = nlp.load_dataset(\"cnn_dailymail\", \"3.0.0\")\r\nflatten(cnn_dm,flatten_cnn_dm)\r\n\r\n#############################################\r\n\r\nmtds = load_multitask(squad,cnn_dm)\r\n\r\nfor example in mtds[\"train\"]:\r\n print(example[\"task_name\"],example[\"target\"])\r\n```\r\nLet me know if you have any thoughts. I've started using this in some of my projects and it seems to work. If people are happy with the general approach for a first version, I can make a pull request.", "Hey! Happy to jump into the discussion here. I'm still getting familiar with bits of this code, but the reasons I sampled over data loaders rather than datasets is 1) ensuring that each sampled batch corresponds to only 1 task (in case of different inputs formats/downstream models) and 2) potentially having different batch sizes per task (e.g. some tasks have very long/short inputs). How are you currently dealing with these in your PR?", "The short answer is - I'm not! Everything is currently on a per-example basis. It would be fairly simple to add a `batch_size` argument which would ensure that every `batch_size` examples come from the same task. That should suit most use-cases (unless you wanted to ensure batches all came from the same task and apply something like `SortishSampler` on each task first)\r\n\r\nYour notebook was really inspiring by the way - thanks!", "@zphang is having different batch sizes per task actually helpful? Would be interesting to know as it's not something I've come across as a technique used by any MTL papers.", "mt-dnn's [batcher.py](https://github.com/namisan/mt-dnn/blob/master/mt_dnn/batcher.py) might be worth looking at.", "> @zphang is having different batch sizes per task actually helpful? Would be interesting to know as it's not something I've come across as a technique used by any MTL papers.\r\n\r\nI think having different batch sizes per task is particularly helpful in some scenarios where each task has different amount of data. For example, the problem I'm currently facing is one task has tens of thousands of samples while one task has a couple hundreds. I think in this case different batch size could help. But if using the same batch size is a lot simpler to implement, I guess it makes sense to go with that.", "I think that instead of proportional to size sampling you should specify weights or probabilities for drawing a batch from each dataset. We should also ensure that the smaller datasets are repeated so that the encoder layer doesn't overtrain on the largest dataset.", "Are there any references for people doing different batch sizes per task in the literature? I've only seen constant batch sizes with differing numbers of batches for each task which seems sufficient to prevent the impact of large datasets (Read 3.5.3 of the [T5 paper](https://arxiv.org/pdf/1910.10683.pdf) for example).\r\n\r\n", "Hi,\r\nregarding building T5 dataset , I think we can use datasets https://github.com/huggingface/datasets and then need something similar to tf.data.experimental.sample_from_datasets, do you know if similar functionality exist in pytorch? Which can sample multiple datasets with the given rates. thanks. " ]

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[ "ROUGE-1 and ROUGE-L shouldn't return the same thing. This is weird", "For the rouge2 metric you can do\r\n\r\n```python\r\nrouge = nlp.load_metric('rouge')\r\nwith open(\"pred.txt\") as p, open(\"ref.txt\") as g:\r\n for lp, lg in zip(p, g):\r\n rouge.add(lp, lg)\r\nscore = rouge.compute(rouge_types=[\"rouge2\"])\r\n```\r\n\r\nNote that I just did a PR to have both `.add` and `.add_batch` for metrics, that's why now this is `rouge.add(lp, lg)` and not `rouge.add([lp], [lg])`", "Well I just tested with the official script and both rouge1 and rougeL return exactly the same thing for the input you gave, so this is actually fine ^^\r\n\r\nI hope it helped :)" ]

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[ "I just ran it on colab and got this\r\n```\r\n[{'expected': SplitInfo(name='train', num_bytes=610252351, num_examples=532812,\r\ndataset_name='blog_authorship_corpus'), 'recorded': SplitInfo(name='train',\r\nnum_bytes=611607465, num_examples=533285, dataset_name='blog_authorship_corpus')},\r\n{'expected': SplitInfo(name='validation', num_bytes=37500394, num_examples=31277,\r\ndataset_name='blog_authorship_corpus'), 'recorded': SplitInfo(name='validation',\r\nnum_bytes=35652716, num_examples=30804, dataset_name='blog_authorship_corpus')}]\r\n```\r\nwhich is different from the `dataset_infos.json` and also different from yours.\r\n\r\nIt looks like the script for generating examples is not consistent", "The files provided by the authors are corrupted and the script seems to ignore the xml files that can't be decoded (it does `try:... except UnicodeDecodeError`). Maybe depending of the environment some files can be opened and some others don't but not sure why", "Feel free to do `ignore_verifications=True` for now... The verifications only include a check on the checksums of the downloaded files, and a check on the number of examples in each splits.", "I'm getting this same issue when loading the `imdb` corpus via `dataset = load_dataset(\"imdb\")`. When I try `ignore_verifications=True`, no examples are read into the `train` portion of the dataset. ", "> I'm getting this same issue when loading the `imdb` corpus via `dataset = load_dataset(\"imdb\")`. When I try `ignore_verifications=True`, no examples are read into the `train` portion of the dataset.\r\n\r\nWhen the checksums don't match, it may mean that the file you downloaded is corrupted. In this case you can try to load the dataset again `load_dataset(\"imdb\", download_mode=\"force_redownload\")`\r\n\r\nAlso I just checked on my side and it worked fine:\r\n\r\n```python\r\nfrom datasets import load_dataset\r\n\r\ndataset = load_dataset(\"imdb\")\r\nprint(len(dataset[\"train\"]))\r\n# 25000\r\n```\r\n\r\nLet me know if redownloading fixes your issue @EmilyAlsentzer .\r\nIf not, feel free to open a separate issue.", "It doesn't seem to fix the problem. I'll open a separate issue. Thanks. ", "I wasn't aware of the \"force_redownload\" option and manually removed the '/home/me/.cache/huggingface/datasets/' dir, this worked for me (dataset 'cnn_dailymail')", "Yes I think this might not be documented well enough. Let’s add it to the doc @lhoestq @SBrandeis.\r\nAnd everything on how to control the cache behavior better (removing, overriding, changing the path, etc)" ]

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[ "Here the full error trace:\r\n\r\n```\r\nArrowInvalid Traceback (most recent call last)\r\n<ipython-input-1-7aaf3f011358> in <module>\r\n 1 import nlp\r\n 2 ds = nlp.load_dataset(\"trivia_qa\", \"rc\", split=\"validation[:1%]\") # this might take 2.3 min to download but it's cached afterwards...\r\n----> 3 ds.map(lambda x: x, load_from_cache_file=False)\r\n\r\n~/python_bin/nlp/arrow_dataset.py in map(self, function, with_indices, batched, batch_size, remove_columns, keep_in_memory, load_from_cache_file, cache_file_name, writer_batch_size, arrow_schema, disable_nullable)\r\n 549\r\n 550 if update_data:\r\n--> 551 writer.finalize() # close_stream=bool(buf_writer is None)) # We only close if we are writing in a file\r\n 552\r\n 553 # Create new Dataset from buffer or file\r\n\r\n~/python_bin/nlp/arrow_writer.py in finalize(self, close_stream)\r\n 182 def finalize(self, close_stream=True):\r\n 183 if self.pa_writer is not None:\r\n--> 184 self.write_on_file()\r\n 185 self.pa_writer.close()\r\n 186 if close_stream:\r\n\r\n~/python_bin/nlp/arrow_writer.py in write_on_file(self)\r\n 104 \"\"\"\r\n 105 if self.current_rows:\r\n--> 106 pa_array = pa.array(self.current_rows, type=self._type)\r\n 107 first_example = pa.array(self.current_rows[0:1], type=self._type)[0]\r\n 108 # Sanity check\r\n\r\n~/hugging_face/venv_3.7/lib/python3.7/site-packages/pyarrow/array.pxi in pyarrow.lib.array()\r\n\r\n~/hugging_face/venv_3.7/lib/python3.7/site-packages/pyarrow/array.pxi in pyarrow.lib._sequence_to_array()\r\n\r\n~/hugging_face/venv_3.7/lib/python3.7/site-packages/pyarrow/error.pxi in pyarrow.lib.check_status()\r\n\r\nArrowInvalid: Could not convert TagMe with type str: converting to null type\r\n```", "Actually thinking a bit more about it, it's probably a data sample that is not correct in `trivia_qa`. But I'm a bit surprised though that we managed to write it in .arrow format and now cannot write it anymore after an \"identity\" mapping.", "I don't have this error :x", "Interesting, maybe I have a very old cache of trivia_qa...thanks for checking", "I'm running it right now on colab to double check", "Actually, I know what the problem is...I'm quite sure it's a bug. Here we take some test inputs: https://github.com/huggingface/nlp/blob/0e0ef12c14d2175e0b0bd7d8aa814b09e2cd7e1f/src/nlp/arrow_dataset.py#L472\r\n\r\nIt might be that in the test inputs, a `Sequence` type value is an emtpy list. So in my case I have `ds[0][\"entity_pages'][\"wiki_context\"] = []`. => this leads to an `arrow_schema` equal to `null` for `[\"entity_pages'][\"wiki_context\"]` => see line: https://github.com/huggingface/nlp/blob/0e0ef12c14d2175e0b0bd7d8aa814b09e2cd7e1f/src/nlp/arrow_dataset.py#L501 instead of list of string which it should for other examples. \r\n\r\nGuess it's an edge case, but it can happen.", "Good point, I think the schema should be infered at the writing stage where we have a `writer_batch_size` number of examples (typically 10k) so it's even less likely to run into this scenario." ]

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[ "~is the viewer also open source?~\r\n[is a streamlit app!](https://docs.streamlit.io/en/latest/getting_started.html)", "Appears that [two thirds of those polled on Twitter](https://twitter.com/srush_nlp/status/1265734497632477185) are in favor of _some_ mechanism for averting eyeballs from the test data." ]

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[ "We are thinking about ways to combine datasets for T5 in #217, feel free to share your thoughts about this.", "Ok great! I will look at it. Thanks" ]

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[ "Indeed, good catch ! thanks\r\nFixing it right now" ]

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[ "Sounds like something related to the nlp viewer @srush ", "Fixed. " ]

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[ "Hi so there are indeed two configurations in the datasets as you can see [here](https://github.com/huggingface/nlp/blob/master/datasets/scientific_papers/scientific_papers.py#L81-L82).\r\n\r\nYou can load either one with:\r\n```python\r\ndataset = nlp.load_dataset('scientific_papers', 'pubmed')\r\ndataset = nlp.load_dataset('scientific_papers', 'arxiv')\r\n```\r\n\r\nThis issues is actually related to a similar user-experience issue with GLUE. When several configurations are available and the first configuration is loaded by default (see issue #152 and #130), it seems to be unexpected for users.\r\n\r\nI think we should maybe raise a (very explicit) error when there are several configurations available and the user doesn't specify one.\r\n\r\nWhat do you think @lhoestq @patrickvonplaten @mariamabarham ?", "Yes, it looks like the right thing to do ", "Now if you don't specify which part you want, it raises an error:\r\n```\r\nValueError: Config name is missing.\r\nPlease pick one among the available configs: ['pubmed', 'arxiv']\r\nExample of usage:\r\n\t`load_dataset('scientific_papers', 'pubmed')`\r\n```" ]

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[ "I guess we can use `tf.data.Dataset.from_generator` instead. I'll give it a try.", "Is `tf.data.Dataset.from_generator` working on TPU ?", "`from_generator` is not working on TPU, I met the following error :\r\n\r\n```\r\nFile \"/usr/local/lib/python3.6/contextlib.py\", line 88, in __exit__\r\n next(self.gen)\r\n File \"/home/usr/.venv/bart/lib/python3.6/site-packages/tensorflow_core/python/eager/context.py\", line 1900, in execution_mode\r\n executor_new.wait()\r\n File \"/home/usr/.venv/bart/lib/python3.6/site-packages/tensorflow_core/python/eager/executor.py\", line 67, in wait\r\n pywrap_tensorflow.TFE_ExecutorWaitForAllPendingNodes(self._handle)\r\ntensorflow.python.framework.errors_impl.NotFoundError: No registered 'PyFunc' OpKernel for 'CPU' devices compatible with node {{node PyFunc}}\r\n . Registered: <no registered kernels>\r\n\r\n [[PyFunc]]\r\n```\r\n\r\n---\r\n\r\n@lhoestq It seems you merged some changes that allow lazy-loading. **Can you give an example of how to use ?** Maybe the Colab notebook should be updated with this method as well.", "Could you send me the code you used to run create the dataset using `.from_generator` ? What version of tensorflow are you using ?", "I'm using TF2.2\r\n\r\nHere is my code :\r\n```\r\nimport nlp\r\nfrom transformers import BartTokenizer\r\n\r\ntokenizer = BartTokenizer.from_pretrained('bart-large')\r\n\r\ndef encode(sample):\r\n article_inputs = tokenizer.encode_plus(sample[\"article\"], max_length=tokenizer.model_max_length, pad_to_max_length=True)\r\n summary_inputs = tokenizer.encode_plus(sample[\"highlights\"], max_length=tokenizer.model_max_length, pad_to_max_length=True)\r\n\r\n article_inputs.update({\"lm_labels\": summary_inputs['input_ids']})\r\n return article_inputs\r\n\r\ncnn_dm = nlp.load_dataset('cnn_dailymail', '3.0.0', split='test')\r\ncnn_dm = cnn_dm.map(encode)\r\n\r\ndef gen():\r\n for sample in cnn_dm:\r\n s = {}\r\n s['input_ids'] = sample['input_ids']\r\n s['attention_mask'] = sample['attention_mask']\r\n s['lm_labels'] = sample['lm_labels']\r\n yield s\r\n\r\ndataset = tf.data.Dataset.from_generator(gen, output_types={k: tf.int32 for k in ['input_ids', 'attention_mask', 'lm_labels']}, output_shapes={k: tf.TensorShape([tokenizer.model_max_length]) for k in ['input_ids', 'attention_mask', 'lm_labels']}\r\n```", "Apparently we'll have to wait for the next tensorflow release to use `.from_generator` and TPU. See https://github.com/tensorflow/tensorflow/issues/34346#issuecomment-598262489", "Fixed by https://github.com/huggingface/datasets/pull/339" ]

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[ "We store every dataset in the Arrow format. This is convenient as it supports nested types and memory mapping. If you are curious feel free to check the [pyarrow documentation](https://arrow.apache.org/docs/python/)\r\n\r\nYou can use this library to load your covid papers by creating a dataset script. You can find inspiration from the ones we've already written in `/datasets`. Here is a link to the steps to [add a dataset](https://github.com/huggingface/nlp/blob/master/CONTRIBUTING.md#how-to-add-a-dataset)", "Hello @mrm8488 and @lhoestq \r\n\r\nIs there a way to convert a dataset to Apache arrow format (locally/personal use) & use it before sending it to hugging face?\r\n\r\nThanks :)", "> Is there a way to convert a dataset to Apache arrow format (locally/personal use) & use it before sending it to hugging face?\r\n\r\nSure, to get a dataset in arrow format you can either:\r\n- [load from local files (txt, json, csv)](https://huggingface.co/nlp/loading_datasets.html?highlight=csv#from-local-files)\r\n- OR [load from python data (dict, pandas)](https://huggingface.co/nlp/loading_datasets.html?highlight=csv#from-in-memory-data)\r\n- OR [create your own dataset script](https://huggingface.co/nlp/loading_datasets.html?highlight=csv#using-a-custom-dataset-loading-script)\r\n", "> > Is there a way to convert a dataset to Apache arrow format (locally/personal use) & use it before sending it to hugging face?\r\n> \r\n> Sure, to get a dataset in arrow format you can either:\r\n> \r\n> * [load from local files (txt, json, csv)](https://huggingface.co/nlp/loading_datasets.html?highlight=csv#from-local-files)\r\n> \r\n> * OR [load from python data (dict, pandas)](https://huggingface.co/nlp/loading_datasets.html?highlight=csv#from-in-memory-data)\r\n> \r\n> * OR [create your own dataset script](https://huggingface.co/nlp/loading_datasets.html?highlight=csv#using-a-custom-dataset-loading-script)\r\n\r\nLinks were broken. \r\n\r\nUpdated links provided as below\r\n- [load from local files (txt, json, csv)](https://huggingface.co/docs/datasets/loading_datasets.html#from-local-or-remote-files)\r\n- [load from python data (dict, pandas)](https://huggingface.co/docs/datasets/loading_datasets.html#from-in-memory-data)\r\n- [create your own dataset script](https://huggingface.co/docs/datasets/loading_datasets.html#using-a-custom-dataset-loading-script)\r\n" ]

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[ "Hi @sarahwie, can you details this a little more?\r\n\r\nI'm not sure I understand what you refer to and what you mean when you say \"Previously, this was done by passing a list of InputFeatures to the dataloader instead of a list of InputFeature\"", "I think I've resolved it. For others' reference: to convert from using the [`MultipleChoiceDataset` class](https://github.com/huggingface/transformers/blob/a34a9896ac2a4a33ff9cd805c76eed914c8d8965/examples/multiple-choice/utils_multiple_choice.py#L82)/[`run_multiple_choice.py`](https://github.com/huggingface/transformers/blob/a34a9896ac2a4a33ff9cd805c76eed914c8d8965/examples/multiple-choice/run_multiple_choice.py) script in Huggingface Transformers, I've done the following for hellaswag:\r\n\r\n1. converted the `convert_examples_to_features()` function to only take one input and return a dictionary rather than a list:\r\n```\r\ndef convert_examples_to_features(example, tokenizer, max_length):\r\n\r\n choices_inputs = defaultdict(list)\r\n for ending_idx, ending in enumerate(example['endings']['ending']):\r\n text_a = example['ctx']\r\n text_b = ending\r\n\r\n inputs = tokenizer.encode_plus(\r\n text_a,\r\n text_b,\r\n add_special_tokens=True,\r\n max_length=max_length,\r\n pad_to_max_length=True,\r\n return_overflowing_tokens=True,\r\n )\r\n if \"num_truncated_tokens\" in inputs and inputs[\"num_truncated_tokens\"] > 0:\r\n logger.info(\r\n \"Attention! you are cropping tokens (swag task is ok). \"\r\n \"If you are training ARC and RACE and you are poping question + options,\"\r\n \"you need to try to use a bigger max seq length!\"\r\n )\r\n\r\n for key in inputs:\r\n choices_inputs[key].append(inputs[key])\r\n \r\n choices_inputs['label'] = int(example['label'])\r\n\r\n return choices_inputs\r\n```\r\n2. apply this directly (instance-wise) to dataset, convert dataset to torch tensors. Dataset is then ready to be passed to `Trainer` instance.\r\n\r\n```\r\ndataset['train'] = dataset['train'].map(lambda x: convert_examples_to_features(x, tokenizer, max_length), batched=False)\r\ncolumns = ['input_ids', 'token_type_ids', 'attention_mask', 'label']\r\ndataset['train'].set_format(type='torch', columns=columns)\r\n```" ]

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[ "On a similar note it would be nice to differentiate between train-easy, train-medium, and train-hard", "Hi @tylerroost, we don't have a timeline for this at the moment.\r\nIf you want to give it a look we would be happy to review a PR on it.\r\nAlso, the library is one week old so everything is quite barebones, in particular the doc.\r\nYou should expect some bumps on the road.\r\n\r\nTo get you started, you can check the datasets scripts in the `./datasets` folder on the repo and find the one on math_datasets that will need to be modified. Then you should check the original repository on the math_dataset to see where the other files to download are located and what is the expected format for the various parts of the dataset.\r\n\r\nTo get a general overview on how datasets scripts are written and used, you can read the nice tutorial on how to add a new dataset for TensorFlow Dataset [here](https://www.tensorflow.org/datasets/add_dataset), our API is not exactly identical but it can give you a high-level overview.", "Thanks I'll give it a look" ]

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[ "I have seen that somebody is hard working on easierly loadable wikipedia. #129 \r\nMaybe I should wait a few days for that version ?", "Yes we (well @lhoestq) are very actively working on this." ]

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[ "Looks like a duplicate of #120.\r\nThis is already fixed on master. We'll do a new release on pypi soon", "Good catch, it looks fixed.\r\n" ]

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[ "This is the test set given by the Glue benchmark. The labels are not provided, and therefore set to -1.", "Ah, yeah. Why it didn’t occur to me. 😂\nThank you for your comment." ]

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