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[ "Hi !\r\n\r\nGood catch, thanks for creating this PR :)\r\n\r\nCould you also regenerate the metadata for this dataset using \r\n```\r\ndatasets-cli test ./datasets/gap --save_infos --all_configs\r\n```\r\n\r\nThat'd be awesome", "@lhoestq Thank you for your revieing!!!\r\n\r\nI've performed it and have read CONTRIBUTING.md now!" ]

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[ "Good catch !\r\nIndeed the `@property` is missing.\r\n\r\nFeel free to open a PR :)", "Also not that C4 is a dataset that needs an Apache Beam runtime to be generated.\r\nFor example Dataflow, Spark, Flink etc.\r\n\r\nUsually we generate the dataset on our side once and for all, but we haven't done it for C4 yet.\r\nMore info about beam datasets [here](https://huggingface.co/docs/datasets/beam_dataset.html)\r\n\r\nLet me know if you have any questions" ]

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[ "The problem still exists after removing the cache files.", "Can you reproduce this example in a Colab so we can investigate? (or give more information on your software/hardware config)", "Thanks for reporting.\r\nI just found the issue, I'm creating a PR", "We'll do a release pretty soon to include the fix :)\r\nIn the meantime you can install the lib from source if you want to " ]

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[ "Yes you can have a look here: https://huggingface.co/docs/datasets/loading_datasets.html#csv-files", "No activity, closing" ]

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[ "I have the same issue. Tried to download a few of them and not a single one is downloaded successfully.\r\n\r\nThis is the output:\r\n```\r\n>>> dataset = load_dataset('blended_skill_talk', split='train')\r\nUsing custom data configuration default <-- This step never ends\r\n```", "This was fixed in #644 \r\nI'll do a new release soon :)\r\n\r\nIn the meantime you can run it by installing from source", "Closing since version 1.1.0 got released with Windows support :) \r\nLet me know if it works for you now" ]

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[ "Thanks for reporting !\r\nWe'll free some memory" ]

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[ "We should try to regenerate the data using the official script.\r\nBut iirc that's what we used in the first place, so not sure why it didn't match in the first place.\r\n\r\nI'll let you know when the dataset is updated", "Thanks, looking forward to hearing your update on this thread. \r\n\r\nThis is a blocking issue for us; would appreciate any progress on this front. We can also help with the fix, if you deem it appropriately. ", "I just started the generation on my side, I'll let you know how it goes :) ", "Hmm after a first run I'm still missing 136668/226711 urls.\r\nI'll relaunch it tomorrow to try to get the remaining ones.", "Update: I'm missing 36/226711 urls but I haven't managed to download them yet", "Thanks! That sounds like a reasonable number! ", "So I managed to download them all but when parsing only 226,181/226,711 worked.\r\nNot sure if it's worth digging and debugging parsing at this point :/ ", "Maybe @sshleifer can help, I think he's already played with xsum at one point", "Thanks @lhoestq\r\nIt would be great to improve coverage, but IDs are the really crucial part for us. We'd really appreciate an update to the dataset with IDs either way!", "I gave up at an even earlier point. The dataset I use has 204,017 train examples.", "@lhoestq @sshleifer like @jbragg said earlier, the main issue for us is that the current XSUM dataset (in your package) does not have IDs suggested by the original dataset ([here is the file](https://raw.githubusercontent.com/EdinburghNLP/XSum/master/XSum-Dataset/XSum-TRAINING-DEV-TEST-SPLIT-90-5-5.json).) Would appreciate if you update the XSUM dataset to include the instance IDs. \r\n\r\nThe missing instances is also a problem, but likely not worth pursuing given its relatively small scale. ", ">So I managed to download them all but when parsing only 226,181/226,711 worked.\r\n\r\n@lhoestq any chance we could update the HF-hosted dataset with the IDs in your new version? Happy to help if there's something I can do.", "Well I couldn't parse what I downloaded.\r\nUnfortunately I think I won't be able to take a look at it this week.\r\nI can try to send you what I got if you want to give it a shot @jbragg \r\nOtherwise feel free to re-run the xsum download script, maybe you'll be luckier than me" ]

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[ "Hi @xixiaoyao,\r\nDepending on what you want to do you can:\r\n- use a first step of `filter` to filter out the invalid examples: https://huggingface.co/docs/datasets/processing.html#filtering-rows-select-and-filter\r\n- or directly detect the invalid examples inside the callable used with `map` and return them unchanged or even remove them at the same time if you are using `map` in batched mode. Here is an example where we use `map` in batched mode to add new rows on the fly but you can also use it to remove examples on the fly (that's what `filter` actually do under-the-hood): https://huggingface.co/docs/datasets/processing.html#augmenting-the-dataset", "Closing this one.\r\nFeel free to re-open if you have other questions" ]

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[ "And I tested it on T5ForConditionalGeneration, that works no problem.", "Hi did you manage to fix your issue ?\r\n\r\nIf so feel free to share your fix and close this thread" ]

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[ "No they are other similar copies but they are not provided by the official Bert models authors." ]

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[ "Hi !\r\nIt works on my side with both the LongFormerTokenizer and the LongFormerTokenizerFast.\r\n\r\nWhich version of transformers/datasets are you using ?", "transformers and datasets are both the latest", "Then I guess you need to give us more informations on your setup (OS, python, GPU, etc) or a Google Colab reproducing the error for us to be able to debug this error.", "And your version of `dill` if possible :)", "I have the same issue with `transformers/BertJapaneseTokenizer`.\r\n\r\n\r\n\r\n```python\r\n# train_ds = Dataset(features: {\r\n# 'title': Value(dtype='string', id=None), \r\n# 'score': Value(dtype='float64', id=None)\r\n# }, num_rows: 99999)\r\n\r\nt = BertJapaneseTokenizer.from_pretrained('bert-base-japanese-whole-word-masking')\r\nencoded = train_ds.map(lambda examples: {'tokens': t.encode(examples['title'])}, batched=True)\r\n```\r\n\r\n<details><summary>Error Message</summary>\r\n\r\n```\r\n---------------------------------------------------------------------------\r\nTypeError Traceback (most recent call last)\r\n<ipython-input-35-2b7d66b291c1> in <module>\r\n 2 \r\n 3 encoded = train_ds.map(lambda examples:\r\n----> 4 {'tokens': t.encode(examples['title'])}, batched=True)\r\n\r\n/usr/local/lib/python3.6/site-packages/datasets/arrow_dataset.py in map(self, function, with_indices, input_columns, batched, batch_size, drop_last_batch, remove_columns, keep_in_memory, load_from_cache_file, cache_file_name, writer_batch_size, features, disable_nullable, fn_kwargs, num_proc, suffix_template, new_fingerprint)\r\n 1242 fn_kwargs=fn_kwargs,\r\n 1243 new_fingerprint=new_fingerprint,\r\n-> 1244 update_data=update_data,\r\n 1245 )\r\n 1246 else:\r\n\r\n/usr/local/lib/python3.6/site-packages/datasets/arrow_dataset.py in wrapper(*args, **kwargs)\r\n 151 \"output_all_columns\": self._output_all_columns,\r\n 152 }\r\n--> 153 out: Union[\"Dataset\", \"DatasetDict\"] = func(self, *args, **kwargs)\r\n 154 if new_format[\"columns\"] is not None:\r\n 155 new_format[\"columns\"] = list(set(new_format[\"columns\"]) & set(out.column_names))\r\n\r\n/usr/local/lib/python3.6/site-packages/datasets/fingerprint.py in wrapper(*args, **kwargs)\r\n 156 kwargs_for_fingerprint[\"fingerprint_name\"] = fingerprint_name\r\n 157 kwargs[fingerprint_name] = update_fingerprint(\r\n--> 158 self._fingerprint, transform, kwargs_for_fingerprint\r\n 159 )\r\n 160 \r\n\r\n/usr/local/lib/python3.6/site-packages/datasets/fingerprint.py in update_fingerprint(fingerprint, transform, transform_args)\r\n 103 for key in sorted(transform_args):\r\n 104 hasher.update(key)\r\n--> 105 hasher.update(transform_args[key])\r\n 106 return hasher.hexdigest()\r\n 107 \r\n\r\n/usr/local/lib/python3.6/site-packages/datasets/fingerprint.py in update(self, value)\r\n 55 def update(self, value):\r\n 56 self.m.update(f\"=={type(value)}==\".encode(\"utf8\"))\r\n---> 57 self.m.update(self.hash(value).encode(\"utf-8\"))\r\n 58 \r\n 59 def hexdigest(self):\r\n\r\n/usr/local/lib/python3.6/site-packages/datasets/fingerprint.py in hash(cls, value)\r\n 51 return cls.dispatch[type(value)](cls, value)\r\n 52 else:\r\n---> 53 return cls.hash_default(value)\r\n 54 \r\n 55 def update(self, value):\r\n\r\n/usr/local/lib/python3.6/site-packages/datasets/fingerprint.py in hash_default(cls, value)\r\n 44 @classmethod\r\n 45 def hash_default(cls, value):\r\n---> 46 return cls.hash_bytes(dumps(value))\r\n 47 \r\n 48 @classmethod\r\n\r\n/usr/local/lib/python3.6/site-packages/datasets/utils/py_utils.py in dumps(obj)\r\n 365 file = StringIO()\r\n 366 with _no_cache_fields(obj):\r\n--> 367 dump(obj, file)\r\n 368 return file.getvalue()\r\n 369 \r\n\r\n/usr/local/lib/python3.6/site-packages/datasets/utils/py_utils.py in dump(obj, file)\r\n 337 def dump(obj, file):\r\n 338 \"\"\"pickle an object to a file\"\"\"\r\n--> 339 Pickler(file, recurse=True).dump(obj)\r\n 340 return\r\n 341 \r\n\r\n/usr/local/lib/python3.6/site-packages/dill/_dill.py in dump(self, obj)\r\n 444 raise PicklingError(msg)\r\n 445 else:\r\n--> 446 StockPickler.dump(self, obj)\r\n 447 stack.clear() # clear record of 'recursion-sensitive' pickled objects\r\n 448 return\r\n\r\n/usr/local/lib/python3.6/pickle.py in dump(self, obj)\r\n 407 if self.proto >= 4:\r\n 408 self.framer.start_framing()\r\n--> 409 self.save(obj)\r\n 410 self.write(STOP)\r\n 411 self.framer.end_framing()\r\n\r\n/usr/local/lib/python3.6/pickle.py in save(self, obj, save_persistent_id)\r\n 474 f = self.dispatch.get(t)\r\n 475 if f is not None:\r\n--> 476 f(self, obj) # Call unbound method with explicit self\r\n 477 return\r\n 478 \r\n\r\n/usr/local/lib/python3.6/site-packages/dill/_dill.py in save_function(pickler, obj)\r\n 1436 globs, obj.__name__,\r\n 1437 obj.__defaults__, obj.__closure__,\r\n-> 1438 obj.__dict__, fkwdefaults), obj=obj)\r\n 1439 else:\r\n 1440 _super = ('super' in getattr(obj.func_code,'co_names',())) and (_byref is not None) and getattr(pickler, '_recurse', False)\r\n\r\n/usr/local/lib/python3.6/pickle.py in save_reduce(self, func, args, state, listitems, dictitems, obj)\r\n 608 else:\r\n 609 save(func)\r\n--> 610 save(args)\r\n 611 write(REDUCE)\r\n 612 \r\n\r\n/usr/local/lib/python3.6/pickle.py in save(self, obj, save_persistent_id)\r\n 474 f = self.dispatch.get(t)\r\n 475 if f is not None:\r\n--> 476 f(self, obj) # Call unbound method with explicit self\r\n 477 return\r\n 478 \r\n\r\n/usr/local/lib/python3.6/pickle.py in save_tuple(self, obj)\r\n 749 write(MARK)\r\n 750 for element in obj:\r\n--> 751 save(element)\r\n 752 \r\n 753 if id(obj) in memo:\r\n\r\n/usr/local/lib/python3.6/pickle.py in save(self, obj, save_persistent_id)\r\n 474 f = self.dispatch.get(t)\r\n 475 if f is not None:\r\n--> 476 f(self, obj) # Call unbound method with explicit self\r\n 477 return\r\n 478 \r\n\r\n/usr/local/lib/python3.6/site-packages/dill/_dill.py in save_module_dict(pickler, obj)\r\n 931 # we only care about session the first pass thru\r\n 932 pickler._session = False\r\n--> 933 StockPickler.save_dict(pickler, obj)\r\n 934 log.info(\"# D2\")\r\n 935 return\r\n\r\n/usr/local/lib/python3.6/pickle.py in save_dict(self, obj)\r\n 819 \r\n 820 self.memoize(obj)\r\n--> 821 self._batch_setitems(obj.items())\r\n 822 \r\n 823 dispatch[dict] = save_dict\r\n\r\n/usr/local/lib/python3.6/pickle.py in _batch_setitems(self, items)\r\n 850 k, v = tmp[0]\r\n 851 save(k)\r\n--> 852 save(v)\r\n 853 write(SETITEM)\r\n 854 # else tmp is empty, and we're done\r\n\r\n/usr/local/lib/python3.6/pickle.py in save(self, obj, save_persistent_id)\r\n 519 \r\n 520 # Save the reduce() output and finally memoize the object\r\n--> 521 self.save_reduce(obj=obj, *rv)\r\n 522 \r\n 523 def persistent_id(self, obj):\r\n\r\n/usr/local/lib/python3.6/pickle.py in save_reduce(self, func, args, state, listitems, dictitems, obj)\r\n 632 \r\n 633 if state is not None:\r\n--> 634 save(state)\r\n 635 write(BUILD)\r\n 636 \r\n\r\n/usr/local/lib/python3.6/pickle.py in save(self, obj, save_persistent_id)\r\n 474 f = self.dispatch.get(t)\r\n 475 if f is not None:\r\n--> 476 f(self, obj) # Call unbound method with explicit self\r\n 477 return\r\n 478 \r\n\r\n/usr/local/lib/python3.6/site-packages/dill/_dill.py in save_module_dict(pickler, obj)\r\n 931 # we only care about session the first pass thru\r\n 932 pickler._session = False\r\n--> 933 StockPickler.save_dict(pickler, obj)\r\n 934 log.info(\"# D2\")\r\n 935 return\r\n\r\n/usr/local/lib/python3.6/pickle.py in save_dict(self, obj)\r\n 819 \r\n 820 self.memoize(obj)\r\n--> 821 self._batch_setitems(obj.items())\r\n 822 \r\n 823 dispatch[dict] = save_dict\r\n\r\n/usr/local/lib/python3.6/pickle.py in _batch_setitems(self, items)\r\n 845 for k, v in tmp:\r\n 846 save(k)\r\n--> 847 save(v)\r\n 848 write(SETITEMS)\r\n 849 elif n:\r\n\r\n/usr/local/lib/python3.6/pickle.py in save(self, obj, save_persistent_id)\r\n 519 \r\n 520 # Save the reduce() output and finally memoize the object\r\n--> 521 self.save_reduce(obj=obj, *rv)\r\n 522 \r\n 523 def persistent_id(self, obj):\r\n\r\n/usr/local/lib/python3.6/pickle.py in save_reduce(self, func, args, state, listitems, dictitems, obj)\r\n 632 \r\n 633 if state is not None:\r\n--> 634 save(state)\r\n 635 write(BUILD)\r\n 636 \r\n\r\n/usr/local/lib/python3.6/pickle.py in save(self, obj, save_persistent_id)\r\n 474 f = self.dispatch.get(t)\r\n 475 if f is not None:\r\n--> 476 f(self, obj) # Call unbound method with explicit self\r\n 477 return\r\n 478 \r\n\r\n/usr/local/lib/python3.6/site-packages/dill/_dill.py in save_module_dict(pickler, obj)\r\n 931 # we only care about session the first pass thru\r\n 932 pickler._session = False\r\n--> 933 StockPickler.save_dict(pickler, obj)\r\n 934 log.info(\"# D2\")\r\n 935 return\r\n\r\n/usr/local/lib/python3.6/pickle.py in save_dict(self, obj)\r\n 819 \r\n 820 self.memoize(obj)\r\n--> 821 self._batch_setitems(obj.items())\r\n 822 \r\n 823 dispatch[dict] = save_dict\r\n\r\n/usr/local/lib/python3.6/pickle.py in _batch_setitems(self, items)\r\n 845 for k, v in tmp:\r\n 846 save(k)\r\n--> 847 save(v)\r\n 848 write(SETITEMS)\r\n 849 elif n:\r\n\r\n/usr/local/lib/python3.6/pickle.py in save(self, obj, save_persistent_id)\r\n 494 reduce = getattr(obj, \"__reduce_ex__\", None)\r\n 495 if reduce is not None:\r\n--> 496 rv = reduce(self.proto)\r\n 497 else:\r\n 498 reduce = getattr(obj, \"__reduce__\", None)\r\n\r\nTypeError: can't pickle Tagger objects\r\n```\r\n\r\n</details>\r\n\r\ntrainsformers: 2.10.0\r\ndatasets: 1.0.2\r\ndill: 0.3.2\r\npython: 3.6.8\r\n\r\nOS: ubuntu 16.04 (Docker Image) on [Deep Learning VM](https://console.cloud.google.com/marketplace/details/click-to-deploy-images/deeplearning) (GCP)\r\nGPU: Tesla P100 (CUDA 10)\r\n", "> I have the same issue with `transformers/BertJapaneseTokenizer`.\r\n\r\nIt looks like it this tokenizer is not supported unfortunately.\r\nThis is because `t.word_tokenizer.mecab` is a `fugashi.fugashi.GenericTagger` which is not compatible with pickle nor dill.\r\n\r\nWe need objects passes to `map` to be picklable for our caching system to work properly.\r\nHere it crashes because the caching system is not able to pickle the GenericTagger.\r\n\r\n\\> Maybe you can create an issue on [fugashi](https://github.com/polm/fugashi/issues) 's repo and ask to make `fugashi.fugashi.GenericTagger` compatible with pickle ?\r\n\r\nWhat you can do in the meantime is use a picklable wrapper of the tokenizer:\r\n\r\n\r\n```python\r\nfrom transformers import BertJapaneseTokenizer, MecabTokenizer\r\n\r\nclass PicklableTokenizer(BertJapaneseTokenizer):\r\n\r\n def __getstate__(self):\r\n state = dict(self.__dict__)\r\n state[\"do_lower_case\"] = self.word_tokenizer.do_lower_case\r\n state[\"never_split\"] = self.word_tokenizer.never_split \r\n del state[\"word_tokenizer\"]\r\n return state\r\n\r\n def __setstate__(self, state):\r\n do_lower_case = state.pop(\"do_lower_case\")\r\n never_split = state.pop(\"never_split\")\r\n self.__dict__ = state\r\n self.word_tokenizer = MecabTokenizer(\r\n do_lower_case=do_lower_case, never_split=never_split)\r\n )\r\n\r\nt = PicklableTokenizer.from_pretrained(\"cl-tohoku/bert-base-japanese-whole-word-masking\")\r\nencoded = train_ds.map(lambda examples: {'tokens': t.encode(examples['title'])}, batched=True) # it works\r\n```", "We can also update the `BertJapaneseTokenizer` in `transformers` as you just shown @lhoestq to make it compatible with pickle. It will be faster than asking on fugashi 's repo and good for the other users of `transformers` as well.\r\n\r\nI'm currently working on `transformers` I'll include it in the https://github.com/huggingface/transformers/pull/7141 PR and the next release of `transformers`.", "Thank you for the rapid and polite response!\r\n\r\n@lhoestq Thanks for the suggestion! I've passed the pickle phase, but another `ArrowInvalid` problem occored. I created another issue #687 .\r\n\r\n@thomwolf Wow, really fast work. I'm looking forward to the next release 🤗" ]

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[ "Hi !\r\nThanks for reporting.\r\nIt looks like no object inherits from `datasets.GeneratorBasedBuilder` (or more generally from `datasets.DatasetBuilder`) in your script.\r\n\r\nCould you check that there exist at least one dataset builder class ?", "Hi @xixiaoyao did you manage to fix your issue ?", "No activity, closing" ]

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[ "Adding a direct link to the rendered markdown:\r\nhttps://github.com/mcmillanmajora/datasets/blob/add_dataset_documentation/datasets/snli/README.md\r\n", "It would be amazing if we ended up with this much information on all of our datasets :) \r\n\r\nI don't think there's too much repetition, everything that is in here is relevant. The main challenge will be to figure out how to structure the sheet so that all of the information can be presented without overwhelming the reader. We'll also want to have as much of it as possible in structured form so it can be easily navigated.", "@mcmillanmajora for now can you remove the prompts / quoted blocks so we can see what the datasheet would look like on its own?\r\n\r\nWould also love to hear if @sgugger has some first impressions", "I removed the prompts. It's definitely a little easier to read without them!", "Should we name the file `README.md` for consistency with models?", "Asked @sleepinyourhat for some insights too :) ", "Thank you for taking the time to look through the card and for all your comments @sleepinyourhat ! I've incorporated them in the latest update. ", "Be careful to keep the ‘sa’ term in the license. It’s something we\ninherited from the Flickr captions.\n\nOn Thu, Oct 1, 2020 at 10:09 AM Julien Chaumond <notifications@github.com>\nwrote:\n\n> *@julien-c* commented on this pull request.\n> ------------------------------\n>\n> In datasets/snli/README.md\n> <https://urldefense.proofpoint.com/v2/url?u=https-3A__github.com_huggingface_datasets_pull_663-23discussion-5Fr498273172&d=DwMFaQ&c=slrrB7dE8n7gBJbeO0g-IQ&r=sCzLyHdE8zgQwk2-sKwA1w&m=PHPCew9Xj3CBQrudcaii70ln-wpRtbngE_tj3Ioy3NI&s=WbEkKXCbL6j5Ui3sox_WqvzrbShbJn2WW-51SENL2ZQ&e=>\n> :\n>\n> > +---\n> +language:\n> +- en\n> +task:\n> +- text-classification\n> +purpose:\n> +- NLI\n> +size:\n> +- \">100k\"\n> +language producers:\n> +- crowdsourced\n> +annotation:\n> +- crowdsourced\n> +tags:\n> +- extended-from-other-datasets\n> +license: \"CC BY-SA 4.0\"\n>\n> ⬇️ Suggested change\n>\n> -license: \"CC BY-SA 4.0\"\n> +license: cc-by-4.0\n>\n> For models (documented at\n> https://huggingface.co/docs#what-metadata-can-i-add-to-my-model-card\n> <https://urldefense.proofpoint.com/v2/url?u=https-3A__huggingface.co_docs-23what-2Dmetadata-2Dcan-2Di-2Dadd-2Dto-2Dmy-2Dmodel-2Dcard&d=DwMFaQ&c=slrrB7dE8n7gBJbeO0g-IQ&r=sCzLyHdE8zgQwk2-sKwA1w&m=PHPCew9Xj3CBQrudcaii70ln-wpRtbngE_tj3Ioy3NI&s=ck3x8c_ujrwKReDTSGuWWgD9W6REHEPbZaO7S4GFRd4&e=>)\n> we use the License keywords listed by GitHub at\n> https://docs.github.com/en/free-pro-team@latest/github/creating-cloning-and-archiving-repositories/licensing-a-repository#searching-github-by-license-type\n> <https://urldefense.proofpoint.com/v2/url?u=https-3A__docs.github.com_en_free-2Dpro-2Dteam-40latest_github_creating-2Dcloning-2Dand-2Darchiving-2Drepositories_licensing-2Da-2Drepository-23searching-2Dgithub-2Dby-2Dlicense-2Dtype&d=DwMFaQ&c=slrrB7dE8n7gBJbeO0g-IQ&r=sCzLyHdE8zgQwk2-sKwA1w&m=PHPCew9Xj3CBQrudcaii70ln-wpRtbngE_tj3Ioy3NI&s=dWBP-ZvtMErD-egoBiBTCKA4500mjDXVSk03oW1g16U&e=>\n>\n> (Hopefully we'll plug some sort of form validation for users at some point)\n>\n> —\n> You are receiving this because you were mentioned.\n> Reply to this email directly, view it on GitHub\n> <https://urldefense.proofpoint.com/v2/url?u=https-3A__github.com_huggingface_datasets_pull_663-23pullrequestreview-2D500386385&d=DwMFaQ&c=slrrB7dE8n7gBJbeO0g-IQ&r=sCzLyHdE8zgQwk2-sKwA1w&m=PHPCew9Xj3CBQrudcaii70ln-wpRtbngE_tj3Ioy3NI&s=HU2Hwi7HH9W2NtMoCIiQlhXxxEULLi8L9gnWU5PBAPY&e=>,\n> or unsubscribe\n> <https://urldefense.proofpoint.com/v2/url?u=https-3A__github.com_notifications_unsubscribe-2Dauth_AAJZSWL63W2LB7SBICA2GMTSISEPZANCNFSM4RWKAZRA&d=DwMFaQ&c=slrrB7dE8n7gBJbeO0g-IQ&r=sCzLyHdE8zgQwk2-sKwA1w&m=PHPCew9Xj3CBQrudcaii70ln-wpRtbngE_tj3Ioy3NI&s=086__lKQLxTanHfjE8kOIpaJbaWPzBB9gGIt_prWeH8&e=>\n> .\n>\n", "@sleepinyourhat You're right, wrong copy/paste", "Question: Where does this standard come from? It looks similar to both\n'Data Statements' and 'Datasheets for Datasets', but it doesn't look quite\nlike either.\n\nOn Mon, Oct 12, 2020 at 4:27 PM Yacine Jernite <notifications@github.com>\nwrote:\n\n> Merged #663\n> <https://urldefense.proofpoint.com/v2/url?u=https-3A__github.com_huggingface_datasets_pull_663&d=DwMCaQ&c=slrrB7dE8n7gBJbeO0g-IQ&r=sCzLyHdE8zgQwk2-sKwA1w&m=D34WbiHBTYHOdXsI9JV9wJqSieP6zAPGqGKDziM5uKU&s=s4_X-BSEnTKgGg9rPLBt3cyVptyMX_iWD5Ql3UMBi-I&e=>\n> into master.\n>\n> —\n> You are receiving this because you were mentioned.\n> Reply to this email directly, view it on GitHub\n> <https://urldefense.proofpoint.com/v2/url?u=https-3A__github.com_huggingface_datasets_pull_663-23event-2D3868180429&d=DwMCaQ&c=slrrB7dE8n7gBJbeO0g-IQ&r=sCzLyHdE8zgQwk2-sKwA1w&m=D34WbiHBTYHOdXsI9JV9wJqSieP6zAPGqGKDziM5uKU&s=elcM4umqReQfIrgHhpey9W_wPaq5QRgq7xNlubM47QI&e=>,\n> or unsubscribe\n> <https://urldefense.proofpoint.com/v2/url?u=https-3A__github.com_notifications_unsubscribe-2Dauth_AAJZSWJVGQRCR4OTTV27VTTSKNRBXANCNFSM4RWKAZRA&d=DwMCaQ&c=slrrB7dE8n7gBJbeO0g-IQ&r=sCzLyHdE8zgQwk2-sKwA1w&m=D34WbiHBTYHOdXsI9JV9wJqSieP6zAPGqGKDziM5uKU&s=NB6nEROnTPgwNyF3ZklOmHnvP7kOkOm7sEa740KbVCs&e=>\n> .\n>\n", "@sleepinyourhat The schema is definitely drawing from Data Statements and Datasheets for Datasets but we also wanted to include some more general information to introduce the dataset to new users. If you have any suggestions for changes to the schema itself, please let us know!" ]

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[ "Resubmitting on a new fork" ]

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[ "BTW, is there a one-line command to make our building scripts pass flake8 test? (included code quality test), I got like trailing space or mixed space and tab warning and error, and fixed them manually.", "> BTW, is there a one-line command to make our building scripts pass flake8 test? (included code quality test), I got like trailing space or mixed space and tab warning and error, and fixed them manually.\r\n\r\nI don't think so.\r\nWe have a command for black and isort but not flake8 as far as I know.", "Thanks for your awesome work too.\r\nBTW a little reminder, this solves #132 " ]

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[ "Closing this one in favor of #670 \r\n\r\nThanks again for reporting the issue and proposing this fix !\r\nLet me know if you have other remarks" ]

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[ "Thanks for reporting !\r\nThere indeed a mismatch between the features and the kwargs description\r\n\r\nI believe `answer_start` was added to match the squad dataset format for consistency, even though it is not used in the metric computation. I think I'd rather keep it this way, so that you can just give `references=squad[\"answers\"]` to `.compute()`.\r\nMaybe we can just fix the description then.", "But then providing the `answer_start` becomes mandatory since the format of the features is checked against the one provided in the squad [file](https://github.com/huggingface/datasets/pull/658/files)." ]

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[ "We can just install multiprocess actually, I'll change that", "Just an FYI: I remember that I wanted to try pathos a couple of years back and I ran into issues considering cross-platform; the code would just break on Windows. If I can verify this PR by running CPU tests on Windows, let me know!", "That's good to know thanks\r\nI guess we can just wait for #644 to be merged first. I'm working on fixing the tests for windows", "Looks like all the CI jobs on windows passed !\r\nI also tested locally on my windows and it works great :) \r\n\r\nI think this is ready to merge, let me know if you have any remarks @thomwolf @BramVanroy " ]

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[ "To fix the CI you just have to copy the dummy data to the 1.1.0 folder, and maybe create the dummy ones for the `debiased` configuration", "Fixed! Thanks @lhoestq " ]

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[ "Currently the `json` dataset doesn't support this format unfortunately.\r\nHowever you could load it with\r\n```python\r\nfrom datasets import Dataset\r\nimport pandas as pd\r\n\r\ndf = pd.read_json(\"path_to_local.json\", orient=\"index\")\r\ndataset = Dataset.from_pandas(df)\r\n```", "or you can make a custom dataset script as explained in doc here: https://huggingface.co/docs/datasets/add_dataset.html" ]

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[ "Hi :) \r\nIn your dummy data zip file you can just have `subset000.xz` as directories instead of compressed files.\r\nLet me know if it helps", "Thanks for your comment @lhoestq ,\r\nJust for confirmation, changing dummy data like this won't make dummy test test the functionality to extract `subsetxxx.xz` but actually kind of circumvent it. But since we will test the real data so it is ok ?", "Yes it's fine for now. We plan to add a job for slow tests.\r\nAnd at one point we'll also do another pass on the dummy data handling and consider extracting files.", "Thanks for the confirmation.\r\nAlso the suggestion works. Thank you." ]

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[ "Thanks for reporting !\r\n\r\nThis issue must have appeared when we refactored type inference in `nlp`\r\nBy default the library tries to keep the same feature types when applying `map` but apparently it has troubles with nested structures. I'll try to fix that next week" ]

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[ "This should be fixed with #645 ", "Feel free to re-open if it still occurs" ]

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[ "Thanks for reporting !\r\nWe should add support for servers without internet connection indeed\r\nI'll do that early next week", "Thanks, @lhoestq !\r\nPlease let me know when it is available. ", "Right now the recommended way is to create the dataset on a server with internet connection and then to save it and copy the serialized dataset to the server without internet connection.", "#652 should allow you to load text/json/csv/pandas datasets without an internet connection **IF** you've the dataset script locally.\r\n\r\nExample: \r\nIf you have `datasets/text/text.py` locally, then you can do `load_dataset(\"./datasets/text\", data_files=...)`" ]

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[ "I tried lower batch sizes and it didn't accelerate filter (quite the opposite actually).\r\nThe slow-down also appears for pyarrow 0.17.1 for some reason, not sure it comes from these changes", "I just checked the benchmarks of other PRs and some of them had 300s (!!) for filter. This needs some investigation..", "Merging this one since it's not the cause of the the slow down" ]

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[ "This PR is ready :)\r\nIt brings official support for windows.\r\n\r\nSome tests `AWSDatasetTest` are failing.\r\nThis is because I had to fix a few datasets that were not compatible with windows.\r\nThese test will pass once they got merged on master :)" ]

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[ "Thanks for reporting !\r\nIt uses a temporary file to write the data.\r\nHowever it looks like the temporary file is not placed in the right directory during the processing", "Well actually I just tested and the temporary file is placed in the same directory, so it should work as expected.\r\nWhich version of `datasets` are you using ?", "`datasets-1.0.1`\r\nHere you can reproduce it here:\r\nhttps://colab.research.google.com/drive/1O0KcepTFsmpkBbrbLLMq42iwTKmQh8d5?usp=sharing\r\n", "It looks like a pyarrow issue with google colab.\r\nFor some reason this code increases the disk usage of google colab while it actually writes into google drive:\r\n\r\n```python\r\nimport pyarrow as pa\r\n\r\nstream = pa.OSFile(\"/content/drive/My Drive/path/to/file.arrow\", \"wb\")\r\nwriter = pa.RecordBatchStreamWriter(stream, schema=pa.schema({\"text\": pa.string()}))\r\nwriter.write_table(pa.Table.from_pydict({\"text\": [\"a\"*511 + \"\\n\"] * ((1 << 30) // 512)})) # 1GiB\r\nwriter.close()\r\nstream.close()\r\n```\r\n\r\nMoreover if I `rm` the file on google drive, it frees disk space on google colab.", "It looks like replacing `pa.OSFile` by `open` fixes it, I'm going to open a PR", "Ok. Thank you so much!", "Actually I did more tests it doesn't >.<\r\nI'll let you know if I find a way to fix that", "Actually I also have the issue when writing a regular text file\r\n\r\n```python\r\nf = open(\"/content/drive/My Drive/path/to/file\", \"w\")\r\nf.write((\"a\"*511 + \"\\n\") * ((1 << 30) // 512)) # 1GiB\r\nf.close()\r\n```\r\n\r\nIs that supposed to happen ?", "The code you wrote should write a 1GB file in the Google Drive folder. Doesn't it? ", "Yes it does, but the disk usage of google colab also increases by 1GB", "I could check it and as you say as I write to te Drive disk the colab disk also increases...", "To reproduce it: \r\n```bash\r\n!df -h | grep sda1\r\n```\r\n```python\r\nf = open(\"/content/drive/My Drive/test_to_remove.txt\", \"w\")\r\nf.write((\"a\"*511 + \"\\n\") * ((1 << 30) // 512)) # 1GiB\r\nf.write((\"a\"*511 + \"\\n\") * ((1 << 30) // 512)) # 1GiB\r\nf.close()\r\n```\r\n```bash\r\n!ls -lh /content/drive/My\\ Drive/test_to_remove.txt\r\n\r\n!df -h | grep sda1\r\n\r\n!rm -rf /content/drive/My\\ Drive/test_to_remove.txt\r\n\r\n```\r\n[Colab](https://colab.research.google.com/drive/1D0UiweCYQwwWZ65EEhuqqbaDDbhJYXfm?usp=sharing)\r\n\r\n\r\n", "Apparently, Colab uses a local cache of the data files read/written from Google Drive. See:\r\n- https://github.com/googlecolab/colabtools/issues/2087#issuecomment-860818457\r\n- https://github.com/googlecolab/colabtools/issues/1915#issuecomment-804234540\r\n- https://github.com/googlecolab/colabtools/issues/2147#issuecomment-885052636" ]

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[ "Hi @joeddav thanks for adding this! (I did a long webarchive.org session to actually find that dataset a while ago).\r\n\r\nOne question: should we manually correct the labeling scheme to (at least) IOB1?\r\n\r\nThat means \"LOC\" will be converted to \"I-LOC\". IOB1 is not explict. mentioned in the paper, but it is used in the documentation:\r\n\r\nhttps://polyglot.readthedocs.io/en/latest/NamedEntityRecognition.html", "@stefan-it I went back and forth on this. My biggest problem with it is that once you are in IOB, there is the expectation that the beginning of new entities are marked with a `B-` (at least in the case of two back-to-back entities):\r\n```\r\nToday O\r\nAlice I-PER\r\nBob B-PER\r\nand O\r\nI O \r\nate O\r\nlasagna O\r\n```\r\nIf we just prepend `I-` to everything, `Bob` would be incorrectly tagged `I-PER`, meaning `Bob Alice` is a single entity. The current format is bad but is at least clear that it does not contain that information.\r\n\r\nBut I could go either way if someone has a strong opinion.", "Indeed I'm not sure we can convert them to IOB because of this issue. I'm fine with keeping it like that", "I'll do a release later today, hopefully we can include this dataset in the release :)\r\n\r\nLet me know if you need help with the dummy data", "@lhoestq cool thanks, I think I've got it right now – just zipped them wrong. I'm running tests locally now and then will push.", "@lhoestq set to merge?", "@joeddav I'm fine with keeping the original labeling scheme :) " ]

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[ "Hi ! Sure I'll take a look" ]

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[ "I think it's ready now @stas00, did you want to add something else ?\r\nThis PR includes your changes but with the level set to warning", "LGTM, thank you, @lhoestq " ]

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[ "Not sure what could cause that on the `datasets` side. Could this be a `Trainer` issue ? cc @julien-c @sgugger ?", "There was a memory leak issue fixed recently in master. You should install from source and see if it fixes your problem.", "@lhoestq @sgugger Thanks for your comments. I have install from source code as you told, but the problem is still there.\r\nTo reproduce the issue, just replace [these lines](https://github.com/huggingface/transformers/blob/master/examples/language-modeling/run_language_modeling.py#L241-L258) with: \r\n(load_dataset and DataCollatorForDatasetsLanguageModeling as [above mentioned](https://github.com/huggingface/datasets/issues/633#issue-702440484))\r\n```python\r\n dataset = load_dataset(\"bookcorpus\")\r\n dataset = dataset.train_test_split(test_size=0.1)\r\n train_dataset = dataset['train']\r\n eval_dataset = dataset['test'] if training_args.do_eval else None\r\n\r\n data_collator = DataCollatorForDatasetsLanguageModeling(\r\n tokenizer=tokenizer,\r\n mlm=data_args.mlm,\r\n mlm_probability=data_args.mlm_probability,\r\n block_size=data_args.block_size\r\n )\r\n```\r\nand run by:\r\n```bash\r\npython run_language_modeling.py\r\n--output_dir=output \\\r\n--model_type=bert \\\r\n--model_name_or_path=bert-base-uncased \\\r\n--do_train \\\r\n--do_eval \\\r\n--mlm \r\n```", "Same here. Pre-training on wikitext-103 to do some test. At the end of the training it takes 32GB of RAM + ~30GB of SWAP. I installed dataset==1.1.0, not built from source. I will try uninstalling and building from source when it finish.", "This seems to be on the `transformers` library side.\r\n\r\nIf you have more informations (pip env) or even better, a colab reproducing the error we can investigate.", "It seems like it's solved with freshed versions of transformers. I have tried to replicate the error doing a fresh pip install transformers & datasets on colab and the error doesn't continue. On colab it keeps stable on 5GB! (Y)\r\n\r\nEdit: **Thanks for your great work**. Have a good day.", "@gaceladri witch version transformers and datasets are you using now? I want to try again. Thanks.", "transformers==3.3.1\r\ndatasets==1.1.0\r\ntokenizers==0.8.1rc2\r\n", "doing some modifications to mobilebert\r\nhttps://colab.research.google.com/drive/1ba09ZOpyHGAOQLcsxiQAHRXl10qnMU5o?usp=sharing ", "It does not happen to me anymore. Can we close? @leethu2012 ", "It's happening to me again. After 4 hours of pre-training, my ram memory gets full and the kernel dies. I am using the last transformers version as today. 4.4.0 and the last version of datasets 1.2.1, both installed from master. The memory consumption keeps increasing.", "It looks like it is something from pytorch/python itself :face_with_head_bandage: https://github.com/pytorch/pytorch/issues/13246 ", "Thanks for the investigation @gaceladri \r\n\r\nApparently this happens when `num_workers>0` and has to do with objects being copied-on-write.\r\nDid you try setting num_workers to 0 @gaceladri ?\r\nIf the issue doesn't happen with `num_workers=0` then this would confirm that it's indeed related to this python/pytorch issue.\r\n\r\nSince a `Dataset` object is a wrapper of a pyarrow Table, we should investigate if the data being copied comes from the Table itself or from metadata in the `Dataset` object. If it comes from the metadata in the `Dataset` object, we should be able to implement a workaround. But if it comes from the Table, we'll need to see with the pyarrow team what we can do... ", "@lhoestq I have tried and it keeps increasing also with `dataloader_num_workers=0`", "Hmmm so this might come from another issue...\r\nSince it doesn't seem to be related to multiprocessing it should be easier to investigate though.\r\nDo you have some ideas @gaceladri ?", "@lhoestq I looked quickly to a previously spoted bug in my env wandb /sdk/interface/interface.py, because sometimes when I load the dataset I got a multiprocessing error at line 510 in wandb...interface.py\r\n\r\nThis bug is reported here https://github.com/huggingface/datasets/issues/847\r\n\r\n```\r\n---------------------------------------------------------------------------\r\nAssertionError Traceback (most recent call last)\r\n<timed eval> in <module>\r\n\r\n~/anaconda3/envs/tfm/lib/python3.6/site-packages/transformers/trainer.py in train(self, model_path, trial)\r\n 877 print(len(epoch_iterator))\r\n 878 \r\n--> 879 for step, inputs in enumerate(epoch_iterator):\r\n 880 \r\n 881 start_step = time.time()\r\n\r\n~/anaconda3/envs/tfm/lib/python3.6/site-packages/torch/utils/data/dataloader.py in __next__(self)\r\n 433 if self._sampler_iter is None:\r\n 434 self._reset()\r\n--> 435 data = self._next_data()\r\n 436 self._num_yielded += 1\r\n 437 if self._dataset_kind == _DatasetKind.Iterable and \\\r\n\r\n~/anaconda3/envs/tfm/lib/python3.6/site-packages/torch/utils/data/dataloader.py in _next_data(self)\r\n 1083 else:\r\n 1084 del self._task_info[idx]\r\n-> 1085 return self._process_data(data)\r\n 1086 \r\n 1087 def _try_put_index(self):\r\n\r\n~/anaconda3/envs/tfm/lib/python3.6/site-packages/torch/utils/data/dataloader.py in _process_data(self, data)\r\n 1109 self._try_put_index()\r\n 1110 if isinstance(data, ExceptionWrapper):\r\n-> 1111 data.reraise()\r\n 1112 return data\r\n 1113 \r\n\r\n~/anaconda3/envs/tfm/lib/python3.6/site-packages/torch/_utils.py in reraise(self)\r\n 426 # have message field\r\n 427 raise self.exc_type(message=msg)\r\n--> 428 raise self.exc_type(msg)\r\n 429 \r\n 430 \r\n\r\nAssertionError: Caught AssertionError in DataLoader worker process 0.\r\nOriginal Traceback (most recent call last):\r\n File \"/home/ad/anaconda3/envs/tfm/lib/python3.6/site-packages/torch/utils/data/_utils/worker.py\", line 198, in _worker_loop\r\n data = fetcher.fetch(index)\r\n File \"/home/ad/anaconda3/envs/tfm/lib/python3.6/site-packages/torch/utils/data/_utils/fetch.py\", line 44, in fetch\r\n data = [self.dataset[idx] for idx in possibly_batched_index]\r\n File \"/home/ad/anaconda3/envs/tfm/lib/python3.6/site-packages/torch/utils/data/_utils/fetch.py\", line 44, in <listcomp>\r\n data = [self.dataset[idx] for idx in possibly_batched_index]\r\n File \"/home/ad/anaconda3/envs/tfm/lib/python3.6/site-packages/datasets/arrow_dataset.py\", line 1083, in __getitem__\r\n format_kwargs=self._format_kwargs,\r\n File \"/home/ad/anaconda3/envs/tfm/lib/python3.6/site-packages/datasets/arrow_dataset.py\", line 1070, in _getitem\r\n format_kwargs=format_kwargs,\r\n File \"/home/ad/anaconda3/envs/tfm/lib/python3.6/site-packages/datasets/arrow_dataset.py\", line 886, in _convert_outputs\r\n v = map_nested(command, v, **map_nested_kwargs)\r\n File \"/home/ad/anaconda3/envs/tfm/lib/python3.6/site-packages/datasets/utils/py_utils.py\", line 216, in map_nested\r\n return function(data_struct)\r\n File \"/home/ad/anaconda3/envs/tfm/lib/python3.6/site-packages/datasets/arrow_dataset.py\", line 847, in command\r\n return torch.tensor(x, **format_kwargs)\r\n File \"/home/ad/anaconda3/envs/tfm/lib/python3.6/warnings.py\", line 101, in _showwarnmsg\r\n _showwarnmsg_impl(msg)\r\n File \"/home/ad/anaconda3/envs/tfm/lib/python3.6/warnings.py\", line 30, in _showwarnmsg_impl\r\n file.write(text)\r\n File \"/home/ad/anaconda3/envs/tfm/lib/python3.6/site-packages/wandb/sdk/lib/redirect.py\", line 100, in new_write\r\n cb(name, data)\r\n File \"/home/ad/anaconda3/envs/tfm/lib/python3.6/site-packages/wandb/sdk/wandb_run.py\", line 729, in _console_callback\r\n self._backend.interface.publish_output(name, data)\r\n File \"/home/ad/anaconda3/envs/tfm/lib/python3.6/site-packages/wandb/sdk/interface/interface.py\", line 186, in publish_output\r\n self._publish_output(o)\r\n File \"/home/ad/anaconda3/envs/tfm/lib/python3.6/site-packages/wandb/sdk/interface/interface.py\", line 191, in _publish_output\r\n self._publish(rec)\r\n File \"/home/ad/anaconda3/envs/tfm/lib/python3.6/site-packages/wandb/sdk/interface/interface.py\", line 510, in _publish\r\n if self._process and not self._process.is_alive():\r\n File \"/home/ad/anaconda3/envs/tfm/lib/python3.6/multiprocessing/process.py\", line 134, in is_alive\r\n assert self._parent_pid == os.getpid(), 'can only test a child process'\r\nAssertionError: can only test a child process\r\n```\r\n\r\nMy workaround was to just comment those lines without looking to much into consecuences:\r\n\r\n```\r\ndef _publish(self, record: pb.Record, local: bool = None) -> None:\r\n #if self._process and not self._process.is_alive():\r\n # raise Exception(\"The wandb backend process has shutdown\")\r\n```\r\n\r\nIt worked so far... I need to try running without wandb and see if it could be causing something wrong with multiprocessing. I am going to try to launch the training setting wandb to false and I will let you know again.", "@lhoestq But despite this, I got lost into the [class Dataset()](https://huggingface.co/docs/datasets/_modules/datasets/arrow_dataset.html#Dataset) reading the pyarrow files.\r\n\r\nEdit: but you should be rigth, that it does not have to be related to multiprocessing since it keeps happening when `num_workers=0` ", "Or maybe wandb uses multiprocessing ? One process for wandb logging and one for actual training ? If this is the case then even setting `num_workers=0` would cause the process to be forked for wandb and therefore cause the memory issue.", "@lhoestq could be, but if we set wandb to false this should not happen. I am going to try.", "@lhoestq It keeps happening. I have uninstalled wandb from my env, setted `%env WANDB_DISABLED=true` on my notebook, and commented this func:\r\n\r\n```\r\ndef get_available_reporting_integrations():\r\n integrations = []\r\n if is_azureml_available():\r\n integrations.append(\"azure_ml\")\r\n if is_comet_available():\r\n integrations.append(\"comet_ml\")\r\n if is_mlflow_available():\r\n integrations.append(\"mlflow\")\r\n if is_tensorboard_available():\r\n integrations.append(\"tensorboard\")\r\n # if is_wandb_available():\r\n # integrations.append(\"wandb\")\r\n return integrations\r\n```\r\nAs a fast test and it keeps increasing the ram memory. Wandb could not be the blameworthy here.", "Thanks for checking @gaceladri . Let's investigate the single process setting then.\r\nIf you have some sort of colab notebook with a minimal code example that shows this behavior feel free to share it @gaceladri so that we can play around with it to find what causes this. Otherwise I'll probably try to reproduce on my side at one point", "@lhoestq sure. Here you have https://colab.research.google.com/drive/1ba09ZOpyHGAOQLcsxiQAHRXl10qnMU5o?usp=sharing let me know if the link works and it reproduces the issue. To me, it reproduces the issue, since if you start the training the ram memory keeps increasing.\r\n\r\nLet me know. Thanks!", "Could the bug be comming from tokenizers?\r\n\r\nI got this warning at the terminal from my jupyter notebook: \r\n```\r\nhuggingface/tokenizers: The current process just got forked, after parallelism has already been used. Disabling parallelism to avoid deadlocks...\r\nTo disable this warning, you can either:\r\n\t- Avoid using `tokenizers` before the fork if possible\r\n\t- Explicitly set the environment variable TOKENIZERS_PARALLELISM=(true | false)\r\n```", "I've never experienced memory issues with tokenizers so I don't know\r\nCc @n1t0 are you aware of any issue that would cause memory to keep increasing when the tokenizer is used in the Data Collator for language modeling ?", "@lhoestq Thanks for pointing to n1t0, just to clarify. That warning was doing fine-tuning, without collator:\r\n```\r\n\r\nfrom datasets import load_dataset, load_metric\r\nimport numpy as np\r\n\r\nGLUE_TASKS = [\r\n \"cola\",\r\n \"mnli\",\r\n \"mnli-mm\",\r\n \"mrpc\",\r\n \"qnli\",\r\n \"qqp\",\r\n \"rte\",\r\n \"sst2\",\r\n \"stsb\",\r\n \"wnli\",\r\n]\r\ntask = \"mnli\"\r\nactual_task = \"mnli\" if task == \"mnli-mm\" else task\r\ndataset = load_dataset(\"glue\", actual_task)\r\nmetric = load_metric(\"glue\", actual_task)\r\nbatch_size = 16\r\nattention_type = \"linear\"\r\n\r\nfrom transformers.models.mobilebert_mod import (\r\n MobileBertForSequenceClassification,\r\n MobileBertTokenizerFast,\r\n)\r\nfrom transformers.models.mobilebert_mod.configuration_mobilebert import (\r\n MobileBertConfigMod,\r\n)\r\nfrom transformers import TrainingArguments, Trainer\r\n\r\nnum_labels = 3 if task.startswith(\"mnli\") else 1 if task == \"stsb\" else 2\r\ntokenizer = MobileBertTokenizerFast.from_pretrained(\r\n \"/media/ad/00b5422b-9d54-4449-8b5d-08eab5cdac8c/training_trfm/big_linear_layerdrop_shared/checkpoint-23000/\",\r\n max_len=512,\r\n)\r\nmodel = MobileBertForSequenceClassification.from_pretrained(\r\n \"/media/ad/00b5422b-9d54-4449-8b5d-08eab5cdac8c/training_trfm/big_linear_layerdrop_shared/checkpoint-23000/\",\r\n num_labels=num_labels,\r\n)\r\nprint(model.num_parameters())\r\n\r\ntask_to_keys = {\r\n \"cola\": (\"sentence\", None),\r\n \"mnli\": (\"premise\", \"hypothesis\"),\r\n \"mnli-mm\": (\"premise\", \"hypothesis\"),\r\n \"mrpc\": (\"sentence1\", \"sentence2\"),\r\n \"qnli\": (\"question\", \"sentence\"),\r\n \"qqp\": (\"question1\", \"question2\"),\r\n \"rte\": (\"sentence1\", \"sentence2\"),\r\n \"sst2\": (\"sentence\", None),\r\n \"stsb\": (\"sentence1\", \"sentence2\"),\r\n \"wnli\": (\"sentence1\", \"sentence2\"),\r\n}\r\n\r\nsentence1_key, sentence2_key = task_to_keys[task]\r\nif sentence2_key is None:\r\n print(f\"Sentence: {dataset['train'][0][sentence1_key]}\")\r\nelse:\r\n print(f\"Sentence 1: {dataset['train'][0][sentence1_key]}\")\r\n print(f\"Sentence 2: {dataset['train'][0][sentence2_key]}\")\r\n\r\n\r\ndef preprocess_function(examples):\r\n if sentence2_key is None:\r\n return tokenizer(examples[sentence1_key], truncation=True)\r\n return tokenizer(examples[sentence1_key], examples[sentence2_key], truncation=True)\r\n\r\n\r\nencoded_dataset = dataset.map(preprocess_function, batched=True)\r\nmetric_name = (\r\n \"pearson\"\r\n if task == \"stsb\"\r\n else \"matthews_correlation\"\r\n if task == \"cola\"\r\n else \"accuracy\"\r\n)\r\n\r\nargs = TrainingArguments(\r\n f\"test-glue/{task}_{attention_type}\",\r\n evaluation_strategy=\"steps\",\r\n learning_rate=1e-5,\r\n per_device_train_batch_size=batch_size,\r\n per_device_eval_batch_size=batch_size,\r\n logging_steps=200,\r\n num_train_epochs=5,\r\n gradient_accumulation_steps=1,\r\n warmup_steps=10000,\r\n fp16=True,\r\n dataloader_num_workers=10,\r\n weight_decay=0.1,\r\n load_best_model_at_end=True,\r\n metric_for_best_model=metric_name,\r\n)\r\n\r\n\r\ndef compute_metrics(eval_pred):\r\n predictions, labels = eval_pred\r\n if task != \"stsb\":\r\n predictions = np.argmax(predictions, axis=1)\r\n else:\r\n predictions = predictions[:, 0]\r\n return metric.compute(predictions=predictions, references=labels)\r\n\r\n\r\nvalidation_key = (\r\n \"validation_mismatched\"\r\n if task == \"mnli-mm\"\r\n else \"validation_matched\"\r\n if task == \"mnli\"\r\n else \"validation\"\r\n)\r\n\r\ntrainer = Trainer(\r\n model,\r\n args,\r\n train_dataset=encoded_dataset[\"train\"],\r\n eval_dataset=encoded_dataset[validation_key],\r\n tokenizer=tokenizer,\r\n compute_metrics=compute_metrics,\r\n)\r\n\r\ntrainer.train()\r\n```\r\n\r\nNow, I have come back to pre-training. The changes that I think I have done are: not formatting the dataset to torch: ~~`big_dataset.set_format(type='torch', columns=[\"text\", \"input_ids\", \"attention_mask\", \"token_type_ids\"])`~~ so maybe some column is dropped and not freezed in memory and now I have not setted any validation dataset in the trainer. \r\n\r\nMy validation dataset before:\r\n```\r\nbook_corpus_eval = load_dataset(\r\n \"bookcorpus\",\r\n \"plain_text\",\r\n cache_dir=\"/home/ad/Desktop/bookcorpus\",\r\n split=\"train[98:99%]\",\r\n)\r\nbook_corpus_eval = book_corpus_eval.map(encode, batched=True)\r\nbook_corpus_eval.set_format(\r\n type=\"torch\", columns=[\"text\", \"input_ids\", \"attention_mask\", \"token_type_ids\"]\r\n)\r\n**book_corpus_eval = book_corpus_eval.select([i for i in range(1500)])**\r\n```\r\nMaybe _selecting_ or indexing the dataset before feeding it to the trainer, do something strange.\r\n\r\nMy trainer now:\r\n```\r\n\r\nbig_dataset = load_from_disk(\"/home/ad/Desktop/35percent_data.arrow/\")\r\n\r\nfrom transformers import DataCollatorForWholeWordMask\r\n\r\ndata_collator = DataCollatorForWholeWordMask(\r\n tokenizer=tokenizer, mlm=True, mlm_probability=0.15)\r\n\r\nfrom transformers import Trainer, TrainingArguments\r\n\r\ntraining_args = TrainingArguments(\r\n output_dir=\"./big_linear_layerdrop_shared_silu_secondtry\",\r\n overwrite_output_dir=True,\r\n per_device_train_batch_size=60,\r\n per_device_eval_batch_size=60,\r\n save_steps=500,\r\n save_total_limit=10,\r\n logging_first_step=True,\r\n logging_steps=100,\r\n# evaluation_strategy='steps',\r\n# eval_steps=250,\r\n gradient_accumulation_steps=8,\r\n fp16=True,\r\n dataloader_num_workers=10,\r\n warmup_steps=15000,\r\n learning_rate=6e-4,\r\n adam_epsilon=1e-6,\r\n adam_beta2=0.98,\r\n weight_decay=0.01,\r\n max_grad_norm=1.0,\r\n max_steps=500000, \r\n)\r\n\r\ntrainer = Trainer(\r\n model=model,\r\n args=training_args,\r\n data_collator=data_collator,\r\n train_dataset=big_dataset,\r\n# eval_dataset=book_corpus_eval,\r\n tokenizer=tokenizer)\r\n\r\nimport wandb\r\nwandb.login()\r\n\r\ntrainer.train()\r\n```\r\n\r\nAnd surprisingly, the ram now keeps going up and down. The training is up now for 12h without collapse the ram. I don't know what could cause the leakage. :mag: \r\n\r\nEdit: I didn't see the swap memory, that keeps increasing. So the problem persist. ", "Thanks for sharing your results.\r\nSo you still had the issue for fine-tuning ?\r\nAnd the issue still appears with a bare-bone dataset from an arrow file...", "Yes, on both cases. Fine-tuning a pre-trained model and pre-training from scratch with a local arrow file already pre-processed." ]

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[ "thanks!" ]

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[ "Which OS are you using ?@abhi1nandy2", "> Which OS are you using ?\r\n\r\nPRETTY_NAME=\"Debian GNU/Linux 9 (stretch)\"\r\nNAME=\"Debian GNU/Linux\"\r\nVERSION_ID=\"9\"\r\nVERSION=\"9 (stretch)\"\r\nVERSION_CODENAME=stretch\r\nID=debian\r\nHOME_URL=\"https://www.debian.org/\"\r\nSUPPORT_URL=\"https://www.debian.org/support\"\r\nBUG_REPORT_URL=\"https://bugs.debian.org/\"", "Do you mind sharing the data you used (or part of it), so I can try to reproduce ?\r\nOr at least some info about the text file you're using ? (size, n of lines, encoding)", "Lot of data, difficult to share. There are 46 shards, each having about 256000 lines. using `file` command gives this - `ASCII text, with very long lines`.", "Ok I see, no problem :) \r\nI'll see what I can do\r\n\r\nCould you just test with one single dummy text file with a few lines to see if you're having the issue ?\r\nAlso which version of `datasets` do you have ?" ]

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[ "Seems like it works when setting ```block_size=2100000000``` or something arbitrarily large though.", "Can you give us some stats on the data files you use as inputs?", "Basically ~600MB txt files(UTF-8) * 59. \r\ncontents like ```안녕하세요, 이것은 예제로 한번 말해보는 텍스트입니다. 그냥 이렇다고요.<|endoftext|>\\n```\r\n\r\nAlso, it gets stuck for a loooong time at ```Testing the mapped function outputs```, for more than 12 hours(currently ongoing)", "It gets stuck while doing `.map()` ? Are you using multiprocessing ?\r\nIf you could provide a code snippet it could be very useful", "From transformers/examples/language-modeling/run-language-modeling.py :\r\n```\r\ndef get_dataset(\r\n args: DataTrainingArguments,\r\n tokenizer: PreTrainedTokenizer,\r\n evaluate: bool = False,\r\n cache_dir: Optional[str] = None,\r\n):\r\n file_path = args.eval_data_file if evaluate else args.train_data_file\r\n if True:\r\n dataset = load_dataset(\"text\", data_files=glob.glob(file_path), split='train', use_threads=True, \r\n ignore_verifications=True, save_infos=True, block_size=104857600)\r\n dataset = dataset.map(lambda ex: tokenizer(ex[\"text\"], add_special_tokens=True,\r\n truncation=True, max_length=args.block_size), batched=True)\r\n dataset.set_format(type='torch', columns=['input_ids'])\r\n return dataset\r\n if args.line_by_line:\r\n return LineByLineTextDataset(tokenizer=tokenizer, file_path=file_path, block_size=args.block_size)\r\n else:\r\n return TextDataset(\r\n tokenizer=tokenizer,\r\n file_path=file_path,\r\n block_size=args.block_size,\r\n overwrite_cache=args.overwrite_cache,\r\n cache_dir=cache_dir,\r\n )\r\n```\r\n\r\nNo, I'm not using multiprocessing.", "I am not able to reproduce on my side :/\r\n\r\nCould you send the version of `datasets` and `pyarrow` you're using ?\r\nCould you try to update the lib and try again ?\r\nOr do you think you could try to reproduce it on google colab ?", "Huh, weird. It's fixed on my side too.\r\nBut now ```Caching processed dataset``` is taking forever - how can I disable it? Any flags?", "Right after `Caching processed dataset`, your function is applied to the dataset and there's a progress bar that shows how much time is left. How much time does it take for you ?\r\n\r\nAlso caching isn't supposed to slow down your processing. But if you still want to disable it you can do `.map(..., load_from_cache_file=False)`", "Ah, it’s much faster now(Takes around 15~20min). \r\nBTW, any way to set default tensor output as plain tensors with distributed training? The ragged tensors are incompatible with tpustrategy :(", "> Ah, it’s much faster now(Takes around 15~20min).\r\n\r\nGlad to see that it's faster now. What did you change exactly ?\r\n\r\n> BTW, any way to set default tensor output as plain tensors with distributed training? The ragged tensors are incompatible with tpustrategy :(\r\n\r\nOh I didn't know about that. Feel free to open an issue to mention that.\r\nI guess what you can do for now is set the dataset format to numpy instead of tensorflow, and use a wrapper of the dataset that converts the numpy arrays to tf tensors.\r\n\r\n", ">>> Glad to see that it's faster now. What did you change exactly ?\r\nI don't know, it just worked...? Sorry I couldn't be more helpful.\r\n\r\nSetting with numpy array is a great idea! Thanks." ]

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[ "sorry it's an apache arrow issue." ]

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[ "Thanks!" ]

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[ "Indeed we convert tensors to list to be able to write in arrow format. Because of this conversion we lose the dtype information. We should add the dtype detection when we do type inference. However it would require a bit of refactoring since currently the conversion happens before the type inference..\r\n\r\nAnd then for your information, when reading from arrow format we have to cast from arrow to numpy (which is fast since pyarrow has a numpy integration), and then to torch.\r\n\r\nHowever there's one thing that can help you: we make sure that the dtypes correspond to what is defined in `features`.\r\nTherefore what you can do is provide `features` in `.map(preprocess, feature=...)` to specify the output types.\r\n\r\nFor example in your case:\r\n```python\r\nfrom datasets import Features, Value, Sequence\r\n\r\nfeatures = Features({\r\n \"input_ids\": Sequence(Value(\"int32\")),\r\n \"sembedding\": Sequence(Value(\"float32\"))\r\n})\r\npreprocessed_dataset = dataset.map(preprocess, features=features)\r\n\r\npreprocessed_dataset.set_format(\"torch\", columns=[\"input_ids\", \"sembedding\"])\r\nprint(preprocessed_dataset[0][\"sembedding\"].dtype)\r\n# \"torch.float32\"\r\n```\r\n\r\nLet me know if it helps", "If the arrow format is basically lists, why is the intermediate step to numpy necessary? I am a bit confused about that part.\r\n\r\nThanks for your suggestion. as I have currently implemented this, I cast to torch.Tensor in my collate_fn to save disk space (so I do not have to save padded tensors to max_len but can pad up to max batch len in collate_fn) at the cost of a bit slower processing. So for me this is not relevant anymore, but I am sure it is for others!", "I'm glad you managed to figure something out :)\r\n\r\nCasting from arrow to numpy can be 100x faster than casting from arrow to list.\r\nThis is because arrow has an integration with numpy that allows it to instantiate numpy arrays with zero-copy from arrow.\r\nOn the other hand to create python lists it is slow since it has to recreate the list object by iterating through each element in python.", "Ah that is interesting. I have no direct experience with arrow so I didn't know. ", "I encountered a simliar issue: `datasets` converted my float numpy array to `torch.float64` tensors, while many pytorch operations require `torch.float32` inputs and it's very troublesome. \r\n\r\nI tried @lhoestq 's solution, but since it's mixed with the preprocess function, it's not very intuitive. \r\n\r\nI just want to share another possible simpler solution: directly cast the dtype of the processed dataset.\r\n\r\nNow I want to change the type of `labels` in `train_dataset` from float64 to float32, I can do this.\r\n\r\n```\r\nfrom datasets import Value, Sequence, Features\r\nfeats = train_dataset.features.copy()\r\nfeats['labels'].feature = Value(dtype='float32')\r\nfeats = Features(feats)\r\ntrain_dataset.cast_(feats)\r\n```\r\n", "Reopening since @bhavitvyamalik started looking into it !\r\n\r\nAlso I'm posting here a function that could be helpful to support preserving the dtype of tensors.\r\n\r\nIt's used to build a pyarrow array out of a numpy array and:\r\n- it doesn't convert the numpy array to a python list\r\n- it keeps the precision of the numpy array for the pyarrow array\r\n- it works with multidimensional arrays (while `pa.array` can only take a 1D array as input)\r\n- it builds the pyarrow ListArray from offsets created on-the-fly and values that come from the flattened numpy array\r\n\r\n```python\r\nfrom functools import reduce\r\nfrom operator import mul\r\n\r\nimport numpy as np\r\nimport pyarrow as pa\r\n\r\ndef pa_ndarray(a):\r\n \"\"\"Build a PyArrow ListArray from a multidimensional NumPy array\"\"\"\r\n values = pa.array(a.flatten()) \r\n for i in range(a.ndim - 1): \r\n n_offsets = reduce(mul, a.shape[:a.ndim - i - 1], 1) \r\n step_offsets = a.shape[a.ndim - i - 1] \r\n offsets = pa.array(np.arange(n_offsets + 1) * step_offsets, type=pa.int32()) \r\n values = pa.ListArray.from_arrays(offsets, values) \r\n return values \r\n\r\nnarr = np.arange(42).reshape(7, 2, 3).astype(np.uint8)\r\nparr = pa_ndarray(narr)\r\nassert isinstance(parr, pa.Array)\r\nassert parr.type == pa.list_(pa.list_(pa.uint8()))\r\nassert narr.tolist() == parr.to_pylist()\r\n```\r\n\r\nThe only costly operation is the offsets computations. Since it doesn't iterate on the numpy array values this function is pretty fast.", "@lhoestq Have you thought about this further?\r\n\r\nWe have a use case where we're attempting to load data containing numpy arrays using the `datasets` library.\r\n\r\nWhen using one of the \"standard\" methods (`[Value(...)]` or `Sequence()`) we see ~200 samples processed per second during the call to `_prepare_split`. This slowdown is caused by the vast number of calls to `encode_nested_example` (each sequence is converted to a list, and each element in the sequence...). \r\n\r\nUsing the `Feature` `ArrayND` improves this somewhat to ~500/s as it now uses numpy's `tolist()` rather than iterating over each value in the array and converting them individually.\r\n\r\nHowever, it's still pretty slow and in theory it should be possible to avoid the `numpy -> python -> arrow` dance altogether. To demonstrate this, if you keep the `Feature` set to an `ArrayND` but instead return a `pa_ndarray(...)` in `_generate_examples` it skips the conversion (`return obj, False`) and hits ~11_000/s. Two orders of magnitude speed up! The problem is this then fails later on when the `ArrowWriter` tries to write the examples to disk :-( \r\n\r\nIt would be nice to have first-class support for user-defined PyArrow objects. Is this a possibility? We have _large_ datasets where even an order of magnitude difference is important so settling on the middle ~500/s is less than ideal! \r\n\r\nIs there a workaround for this or another method that should be used instead that gets near-to or equal performance to returning PyArrow arrays?", "Note that manually generating the table using `pyarrow` achieves ~30_000/s", "Hi !\r\n\r\nIt would be awesome to achieve this speed for numpy arrays !\r\nFor now we have to use `encode_nested_example` to convert numpy arrays to python lists since pyarrow doesn't support multidimensional numpy arrays (only 1D).\r\n\r\nMaybe let's start a new PR from your PR @bhavitvyamalik (idk why we didn't answer your PR at that time, sorry about that).\r\nBasically the idea is to allow `TypedSequence` to support numpy arrays as you did, and remove the numpy->python casting in `_cast_to_python_objects`.\r\n\r\nThis is really important since we are starting to have a focus on other modalities than text as well (audio, images).\r\n\r\nThough until then @samgd, there is another feature that may interest you and that may give you the speed you want:\r\n\r\nIn a dataset script you can subclass either a GeneratorBasedBuilder (with the `_generate_examples ` method) or an ArrowBasedBuilder if you want. the ArrowBasedBuilder allows to yield arrow data by implementing the `_generate_tables` method (it's the same as `_generate_examples` except you must yield arrow tables). Since the data are already in arrow format, it doesn't call `encode_nested_example`. Let me know if that helps." ]

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[ "Currently `csv` doesn't support the `features` attribute (unlike `json`).\r\nWhat you can do for now is cast the features using the in-place transform `cast_`\r\n\r\n```python\r\nfrom datasets import load_dataset\r\n\r\ndataset = load_dataset('csv', data_files=file_dict, delimiter=';', column_names=['text', 'label'])\r\ndataset.cast_(emotion_features)\r\n```\r\n", "Thanks for the clarification!", "Hi @lhoestq we've tried out your suggestion but are now running into the following error:\r\n\r\n```\r\n---------------------------------------------------------------------------\r\nValueError Traceback (most recent call last)\r\n<ipython-input-163-81ffd5ac18c9> in <module>\r\n----> 1 dataset.cast_(emotion_features)\r\n\r\n/usr/local/lib/python3.6/dist-packages/datasets/dataset_dict.py in cast_(self, features)\r\n 125 self._check_values_type()\r\n 126 for dataset in self.values():\r\n--> 127 dataset.cast_(features=features)\r\n 128 \r\n 129 def remove_columns_(self, column_names: Union[str, List[str]]):\r\n\r\n/usr/local/lib/python3.6/dist-packages/datasets/fingerprint.py in wrapper(*args, **kwargs)\r\n 161 # Call actual function\r\n 162 \r\n--> 163 out = func(self, *args, **kwargs)\r\n 164 \r\n 165 # Update fingerprint of in-place transforms + update in-place history of transforms\r\n\r\n/usr/local/lib/python3.6/dist-packages/datasets/arrow_dataset.py in cast_(self, features)\r\n 602 self._info.features = features\r\n 603 schema = pa.schema(features.type)\r\n--> 604 self._data = self._data.cast(schema)\r\n 605 \r\n 606 @fingerprint(inplace=True)\r\n\r\n/usr/local/lib/python3.6/dist-packages/pyarrow/table.pxi in pyarrow.lib.Table.cast()\r\n\r\nValueError: Target schema's field names are not matching the table's field names: ['text', 'label'], ['label', 'text']\r\n```\r\n\r\nLooking at the types in `emotion_features` we see that `label` and `text` appear to be swapped in the Arrow table:\r\n\r\n```\r\nemotion_features.type\r\nStructType(struct<label: int64, text: string>)\r\n```\r\n\r\nDid we define the `emotion_features` incorrectly? We just followed the instructions from the [docs](https://huggingface.co/docs/datasets/features.html?highlight=features#dataset-features), but perhaps we misunderstood something 😬 \r\n\r\n", "In general, I don't think there is any hard reason we don't allow to use `features` in the csv script, right @lhoestq?\r\n\r\nShould I add it?", "> In general, I don't think there is any hard reason we don't allow to use `features` in the csv script, right @lhoestq?\r\n> \r\n> Should I add it?\r\n\r\nSure let's add it. Setting the convert options should do the job\r\n\r\n> Hi @lhoestq we've tried out your suggestion but are now running into the following error:\r\n> \r\n> ```\r\n> ---------------------------------------------------------------------------\r\n> ValueError Traceback (most recent call last)\r\n> <ipython-input-163-81ffd5ac18c9> in <module>\r\n> ----> 1 dataset.cast_(emotion_features)\r\n>\r\n> /usr/local/lib/python3.6/dist-packages/pyarrow/table.pxi in pyarrow.lib.Table.cast()\r\n> \r\n> ValueError: Target schema's field names are not matching the table's field names: ['text', 'label'], ['label', 'text']\r\n> ```\r\n>\r\n> Did we define the `emotion_features` incorrectly? We just followed the instructions from the [docs](https://huggingface.co/docs/datasets/features.html?highlight=features#dataset-features), but perhaps we misunderstood something 😬\r\n\r\nThanks for reporting, that's a bug :) I'm fixing it right now", "PR is open for the `ValueError: Target schema's field names are not matching the table's field names` error.\r\n\r\nI'm adding the features parameter to csv", "Thanks a lot for the PR and quick fix @lhoestq!" ]

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[ "Can you give us more information on your os and pip environments (pip list)?", "@thomwolf Sure. I'll try downgrading to 3.7 now even though Arrow say they support >=3.5.\r\n\r\nLinux (Ubuntu 18.04) - Python 3.8\r\n======================\r\nPackage - Version\r\n---------------------\r\ncertifi 2020.6.20\r\nchardet 3.0.4\r\nclick 7.1.2\r\ndatasets 1.0.1\r\ndill 0.3.2\r\nfasttext 0.9.2\r\nfilelock 3.0.12\r\nfuture 0.18.2\r\nidna 2.10\r\njoblib 0.16.0\r\nnltk 3.5\r\nnumpy 1.19.1\r\npackaging 20.4\r\npandas 1.1.2\r\npip 20.0.2\r\nprotobuf 3.13.0\r\npyarrow 1.0.1\r\npybind11 2.5.0\r\npyparsing 2.4.7\r\npython-dateutil 2.8.1\r\npytz 2020.1\r\nregex 2020.7.14\r\nrequests 2.24.0\r\nsacremoses 0.0.43\r\nscikit-learn 0.23.2\r\nscipy 1.5.2\r\nsentence-transformers 0.3.6\r\nsentencepiece 0.1.91\r\nsetuptools 46.1.3\r\nsix 1.15.0\r\nstanza 1.1.1\r\nthreadpoolctl 2.1.0\r\ntokenizers 0.8.1rc2\r\ntorch 1.6.0+cu101\r\ntqdm 4.48.2\r\ntransformers 3.1.0\r\nurllib3 1.25.10\r\nwheel 0.34.2\r\nxxhash 2.0.0\r\n\r\nWindows 10 - Python 3.8\r\n================\r\nPackage - Version\r\n----------------------------\r\ncertifi 2020.6.20\r\nchardet 3.0.4\r\nclick 7.1.2\r\ndatasets 1.0.1\r\ndill 0.3.2\r\nfasttext 0.9.2\r\nfilelock 3.0.12\r\nfuture 0.18.2\r\nidna 2.10\r\njoblib 0.16.0\r\nnlp 0.4.0\r\nnltk 3.5\r\nnumpy 1.19.1\r\npackaging 20.4\r\npandas 1.1.1\r\npip 20.0.2\r\nprotobuf 3.13.0\r\npyarrow 1.0.1\r\npybind11 2.5.0\r\npyparsing 2.4.7\r\npython-dateutil 2.8.1\r\npytz 2020.1\r\nregex 2020.7.14\r\nrequests 2.24.0\r\nsacremoses 0.0.43\r\nscikit-learn 0.23.2\r\nscipy 1.5.2\r\nsentence-transformers 0.3.5.1\r\nsentencepiece 0.1.91\r\nsetuptools 46.1.3\r\nsix 1.15.0\r\nstanza 1.1.1\r\nthreadpoolctl 2.1.0\r\ntokenizers 0.8.1rc1\r\ntorch 1.6.0+cu101\r\ntqdm 4.48.2\r\ntransformers 3.0.2\r\nurllib3 1.25.10\r\nwheel 0.34.2\r\nxxhash 2.0.0", "Downgrading to 3.7 does not help. Here is a dummy text file:\r\n\r\n```text\r\nVerzekering weigert vaker te betalen\r\nBedrijven van verzekeringen erkennen steeds minder arbeidsongevallen .\r\nIn 2012 weigerden de bedrijven te betalen voor 21.055 ongevallen op het werk .\r\nDat is 11,8 % van alle ongevallen op het werk .\r\nNog nooit weigerden verzekeraars zoveel zaken .\r\nIn 2012 hadden 135.118 mensen een ongeval op het werk .\r\nDat zijn elke werkdag 530 mensen .\r\nBij die ongevallen stierven 67 mensen .\r\nBijna 12.000 hebben een handicap na het ongeval .\r\nGeen echt arbeidsongeval Bedrijven moeten een verzekering hebben voor hun werknemers .\r\n```\r\n\r\nA temporary work around for the \"text\" type, is\r\n\r\n```python\r\ndataset = Dataset.from_dict({\"text\": Path(dataset_f).read_text().splitlines()})\r\n```", "\r\n\r\neven i am facing the same issue.", "@banunitte Please do not post screenshots in the future but copy-paste your code and the errors. That allows others to copy-and-paste your code and test it. You may also want to provide the Python version that you are using.", "I have the exact same problem in Windows 10, Python 3.8.\r\n", "I have the same problem on Linux of the script crashing with a CSV error. This may be caused by 'CRLF', when changed 'CRLF' to 'LF', the problem solved.", "I pushed a fix for `pyarrow.lib.ArrowInvalid: CSV parse error`. Let me know if you still have this issue.\r\n\r\nNot sure about the windows one yet", "To complete what @lhoestq is saying, I think that to use the new version of the `text` processing script (which is on master right now) you need to either specify the version of the script to be the `master` one or to install the lib from source (in which case it uses the `master` version of the script by default):\r\n```python\r\ndataset = load_dataset('text', script_version='master', data_files=XXX)\r\n```\r\nWe do versioning by default, i.e. your version of the dataset lib will use the script with the same version by default (i.e. only the `1.0.1` version of the script if you have the PyPI version `1.0.1` of the lib).", "\r\nwin10, py3.6\r\n\r\n\r\n```\r\nfrom datasets import Features, Value, ClassLabel, load_dataset\r\n\r\n\r\nfeatures = Features({'text': Value('string'), 'ctext': Value('string')})\r\nfile_dict = {'train': PATH/'summary.csv'}\r\n\r\ndataset = load_dataset('csv', data_files=file_dict, script_version='master', delimiter='\\t', column_names=['text', 'ctext'], features=features)\r\n```", "```python\r\nTraceback` (most recent call last):\r\n File \"main.py\", line 281, in <module>\r\n main()\r\n File \"main.py\", line 190, in main\r\n train_data, test_data = data_factory(\r\n File \"main.py\", line 129, in data_factory\r\n train_data = load_dataset('text', \r\n File \"/home/me/Downloads/datasets/src/datasets/load.py\", line 608, in load_dataset\r\n builder_instance.download_and_prepare(\r\n File \"/home/me/Downloads/datasets/src/datasets/builder.py\", line 468, in download_and_prepare\r\n self._download_and_prepare(\r\n File \"/home/me/Downloads/datasets/src/datasets/builder.py\", line 546, in _download_and_prepare\r\n self._prepare_split(split_generator, **prepare_split_kwargs)\r\n File \"/home/me/Downloads/datasets/src/datasets/builder.py\", line 888, in _prepare_split\r\n for key, table in utils.tqdm(generator, unit=\" tables\", leave=False, disable=not_verbose):\r\n File \"/home/me/.local/lib/python3.8/site-packages/tqdm/std.py\", line 1130, in __iter__\r\n for obj in iterable:\r\n File \"/home/me/.cache/huggingface/modules/datasets_modules/datasets/text/512f465342e4f4cd07a8791428a629c043bb89d55ad7817cbf7fcc649178b014/text.py\", line 103, in _generate_tables\r\n pa_table = pac.read_csv(\r\n File \"pyarrow/_csv.pyx\", line 617, in pyarrow._csv.read_csv\r\n File \"pyarrow/error.pxi\", line 123, in pyarrow.lib.pyarrow_internal_check_status\r\n File \"pyarrow/error.pxi\", line 85, in pyarrow.lib.check_status\r\npyarrow.lib.ArrowInvalid: CSV parse error: Expected 1 columns, got 2\r\n```\r\n\r\nUnfortunately i am still getting this issue on Linux. I installed datasets from source and specified script_version to master.\r\n\r\n", "> \r\n> win10, py3.6\r\n> \r\n> ```\r\n> from datasets import Features, Value, ClassLabel, load_dataset\r\n> \r\n> \r\n> features = Features({'text': Value('string'), 'ctext': Value('string')})\r\n> file_dict = {'train': PATH/'summary.csv'}\r\n> \r\n> dataset = load_dataset('csv', data_files=file_dict, script_version='master', delimiter='\\t', column_names=['text', 'ctext'], features=features)\r\n> ```\r\n\r\nSince #644 it should now work on windows @ScottishFold007 \r\n\r\n> Trying the following snippet, I get different problems on Linux and Windows.\r\n> \r\n> ```python\r\n> dataset = load_dataset(\"text\", data_files=\"data.txt\")\r\n> # or \r\n> dataset = load_dataset(\"text\", data_files=[\"data.txt\"])\r\n> ```\r\n>\r\n> Windows just seems to get stuck. Even with a tiny dataset of 10 lines, it has been stuck for 15 minutes already at this message:\r\n> \r\n> ```\r\n> Checking C:\\Users\\bramv\\.cache\\huggingface\\datasets\\b1d50a0e74da9a7b9822cea8ff4e4f217dd892e09eb14f6274a2169e5436e2ea.30c25842cda32b0540d88b7195147decf9671ee442f4bc2fb6ad74016852978e.py for additional imports.\r\n> Found main folder for dataset https://raw.githubusercontent.com/huggingface/datasets/1.0.1/datasets/text/text.py at C:\\Users\\bramv\\.cache\\huggingface\\modules\\datasets_modules\\datasets\\text\r\n> Found specific version folder for dataset https://raw.githubusercontent.com/huggingface/datasets/1.0.1/datasets/text/text.py at C:\\Users\\bramv\\.cache\\huggingface\\modules\\datasets_modules\\datasets\\text\\7e13bc0fa76783d4ef197f079dc8acfe54c3efda980f2c9adfab046ede2f0ff7\r\n> Found script file from https://raw.githubusercontent.com/huggingface/datasets/1.0.1/datasets/text/text.py to C:\\Users\\bramv\\.cache\\huggingface\\modules\\datasets_modules\\datasets\\text\\7e13bc0fa76783d4ef197f079dc8acfe54c3efda980f2c9adfab046ede2f0ff7\\text.py\r\n> Couldn't find dataset infos file at https://raw.githubusercontent.com/huggingface/datasets/1.0.1/datasets/text\\dataset_infos.json\r\n> Found metadata file for dataset https://raw.githubusercontent.com/huggingface/datasets/1.0.1/datasets/text/text.py at C:\\Users\\bramv\\.cache\\huggingface\\modules\\datasets_modules\\datasets\\text\\7e13bc0fa76783d4ef197f079dc8acfe54c3efda980f2c9adfab046ede2f0ff7\\text.json\r\n> Using custom data configuration default\r\n> ```\r\n\r\nSame for you @BramVanroy .\r\n\r\nNot sure about the one on linux though", "> To complete what @lhoestq is saying, I think that to use the new version of the `text` processing script (which is on master right now) you need to either specify the version of the script to be the `master` one or to install the lib from source (in which case it uses the `master` version of the script by default):\r\n> \r\n> ```python\r\n> dataset = load_dataset('text', script_version='master', data_files=XXX)\r\n> ```\r\n> \r\n> We do versioning by default, i.e. your version of the dataset lib will use the script with the same version by default (i.e. only the `1.0.1` version of the script if you have the PyPI version `1.0.1` of the lib).\r\n\r\nLinux here:\r\n\r\nI was using the 0.4.0 nlp library load_dataset to load a text dataset of 9-10Gb without collapsing the RAM memory. However, today I got the csv error message mentioned in this issue. After installing the new (datasets) library from source and specifying the script_verson = 'master' I'm still having this same error message. Furthermore, I cannot use the dictionary \"trick\" to load the dataset since the system kills the process due to a RAM out of memory problem. Is there any other solution to this error? Thank you in advance. ", "Hi @raruidol \r\nTo fix the RAM issue you'll need to shard your text files into smaller files (see https://github.com/huggingface/datasets/issues/610#issuecomment-691672919 for example)\r\n\r\nI'm not sure why you're having the csv error on linux.\r\nDo you think you could to to reproduce it on google colab for example ?\r\nOr send me a dummy .txt file that reproduces the issue ?", "@lhoestq \r\n\r\nThe crash message shows up when loading the dataset:\r\n```\r\nprint('Loading corpus...') \r\nfiles = glob.glob('corpora/shards/*') \r\n-> dataset = load_dataset('text', script_version='master', data_files=files) \r\nprint('Corpus loaded.')\r\n```\r\nAnd this is the exact message:\r\n```\r\nTraceback (most recent call last):\r\n File \"run_language_modeling.py\", line 27, in <module>\r\n dataset = load_dataset('text', script_version='master', data_files=files)\r\n File \"/home/jupyter-raruidol/DebatAnalyser/env/lib/python3.7/site-packages/datasets/load.py\", line 611, in load_dataset\r\n ignore_verifications=ignore_verifications,\r\n File \"/home/jupyter-raruidol/DebatAnalyser/env/lib/python3.7/site-packages/datasets/builder.py\", line 471, in download_and_prepare\r\n dl_manager=dl_manager, verify_infos=verify_infos, **download_and_prepare_kwargs\r\n File \"/home/jupyter-raruidol/DebatAnalyser/env/lib/python3.7/site-packages/datasets/builder.py\", line 548, in _download_and_prepare\r\n self._prepare_split(split_generator, **prepare_split_kwargs)\r\n File \"/home/jupyter-raruidol/DebatAnalyser/env/lib/python3.7/site-packages/datasets/builder.py\", line 892, in _prepare_split\r\n for key, table in utils.tqdm(generator, unit=\" tables\", leave=False, disable=not_verbose):\r\n File \"/home/jupyter-raruidol/DebatAnalyser/env/lib/python3.7/site-packages/tqdm/std.py\", line 1130, in __iter__\r\n for obj in iterable:\r\n File \"/home/jupyter-raruidol/.cache/huggingface/modules/datasets_modules/datasets/text/512f465342e4f4cd07a8791428a629c043bb89d55ad7817cbf7fcc649178b014/text.py\", line 107, in _generate_tables\r\n convert_options=self.config.convert_options,\r\n File \"pyarrow/_csv.pyx\", line 714, in pyarrow._csv.read_csv\r\n File \"pyarrow/error.pxi\", line 122, in pyarrow.lib.pyarrow_internal_check_status\r\n File \"pyarrow/error.pxi\", line 84, in pyarrow.lib.check_status\r\npyarrow.lib.ArrowInvalid: CSV parse error: Expected 1 columns, got 2\r\n```\r\n\r\nAnd these are the pip packages I have atm and their versions:\r\n\r\n```\r\nPackage Version Location \r\n--------------- --------- -------------------------------------------------------------\r\ncertifi 2020.6.20 \r\nchardet 3.0.4 \r\nclick 7.1.2 \r\ndatasets 1.0.2 \r\ndill 0.3.2 \r\nfilelock 3.0.12 \r\nfuture 0.18.2 \r\nidna 2.10 \r\njoblib 0.16.0 \r\nnumpy 1.19.1 \r\npackaging 20.4 \r\npandas 1.1.1 \r\npip 19.0.3 \r\npyarrow 1.0.1 \r\npyparsing 2.4.7 \r\npython-dateutil 2.8.1 \r\npytz 2020.1 \r\nregex 2020.7.14 \r\nrequests 2.24.0 \r\nsacremoses 0.0.43 \r\nsentencepiece 0.1.91 \r\nsetuptools 40.8.0 \r\nsix 1.15.0 \r\ntokenizers 0.8.1rc2 \r\ntorch 1.6.0 \r\ntqdm 4.48.2 \r\ntransformers 3.0.2 /home/jupyter-raruidol/DebatAnalyser/env/src/transformers/src\r\n```\r\n\r\n\r\n", "I tested on google colab which is also linux using this code:\r\n\r\n- first download an arbitrary text file\r\n```bash\r\nwget https://raw.githubusercontent.com/abisee/cnn-dailymail/master/url_lists/all_train.txt\r\n```\r\n- then run\r\n```python\r\nfrom datasets import load_dataset\r\n\r\nd = load_dataset(\"text\", data_files=\"all_train.txt\", script_version='master')\r\n```\r\nAnd I don't get this issue.\r\n\r\n\\> Could you test on your side if these lines work @raruidol ?\r\n\r\nalso cc @Skyy93 as it seems you have the same issue\r\n\r\nIf it works:\r\nIt could mean that the issue could come from unexpected patterns in the files you want to use.\r\nIn that case we should find a way to handle them.\r\n\r\nAnd if it doesn't work:\r\nIt could mean that it comes from the way pyarrow reads text files on linux.\r\nIn that case we should report it to pyarrow and find a workaround in the meantime\r\n\r\nEither way it should help to find where this bug comes from and fix it :)\r\n\r\nThank you in advance !", "Update: also tested the above code in a docker container from [jupyter/minimal-notebook](https://hub.docker.com/r/jupyter/minimal-notebook/) (based on ubuntu) and still not able to reproduce", "It looks like with your text input file works without any problem. I have been doing some experiments this morning with my input files and I'm almost certain that the crash is caused by some unexpected pattern in the files. However, I've not been able to spot the main cause of it. What I find strange is that this same corpus was being loaded by the nlp 0.4.0 library without any problem... Where can I find the code where you structure the input text data in order to use it with pyarrow?", "Under the hood it does\r\n```python\r\nimport pyarrow as pa\r\nimport pyarrow.csv\r\n\r\n# Use csv reader from Pyarrow with one column for text files\r\n\r\n# To force the one-column setting, we set an arbitrary character\r\n# that is not in text files as delimiter, such as \\b or \\v.\r\n# The bell character, \\b, was used to make beeps back in the days\r\nparse_options = pa.csv.ParseOptions( \r\n delimiter=\"\\b\", \r\n quote_char=False, \r\n double_quote=False, \r\n escape_char=False, \r\n newlines_in_values=False, \r\n ignore_empty_lines=False, \r\n)\r\n\r\nread_options= pa.csv.ReadOptions(use_threads=True, column_names=[\"text\"])\r\n\r\npa_table = pa.csv.read_csv(\"all_train.txt\", read_options=read_options, parse_options=parse_options)\r\n```\r\n\r\nNote that we changed the parse options with datasets 1.0\r\nIn particular the delimiter used to be `\\r` but this delimiter doesn't work on windows.", "Could you try with `\\a` instead of `\\b` ? It looks like the bell character is \\a in python and not \\b", "I was just exploring if the crash was happening in every shard or not, and which shards were generating the error message. With \\b I got the following list of shards crashing:\r\n\r\n```\r\nErrors on files: ['corpora/shards/shard_0069', 'corpora/shards/shard_0043', 'corpora/shards/shard_0014', 'corpora/shards/shard_0032', 'corpora/shards/shard_0088', 'corpora/shards/shard_0018', 'corpora/shards/shard_0073', 'corpora/shards/shard_0079', 'corpora/shards/shard_0038', 'corpora/shards/shard_0041', 'corpora/shards/shard_0007', 'corpora/shards/shard_0004', 'corpora/shards/shard_0102', 'corpora/shards/shard_0096', 'corpora/shards/shard_0030', 'corpora/shards/shard_0076', 'corpora/shards/shard_0067', 'corpora/shards/shard_0052', 'corpora/shards/shard_0026', 'corpora/shards/shard_0024', 'corpora/shards/shard_0064', 'corpora/shards/shard_0044', 'corpora/shards/shard_0013', 'corpora/shards/shard_0062', 'corpora/shards/shard_0057', 'corpora/shards/shard_0097', 'corpora/shards/shard_0094', 'corpora/shards/shard_0078', 'corpora/shards/shard_0075', 'corpora/shards/shard_0039', 'corpora/shards/shard_0077', 'corpora/shards/shard_0021', 'corpora/shards/shard_0040', 'corpora/shards/shard_0009', 'corpora/shards/shard_0023', 'corpora/shards/shard_0095', 'corpora/shards/shard_0107', 'corpora/shards/shard_0063', 'corpora/shards/shard_0086', 'corpora/shards/shard_0047', 'corpora/shards/shard_0089', 'corpora/shards/shard_0037', 'corpora/shards/shard_0101', 'corpora/shards/shard_0093', 'corpora/shards/shard_0082', 'corpora/shards/shard_0091', 'corpora/shards/shard_0065', 'corpora/shards/shard_0020', 'corpora/shards/shard_0070', 'corpora/shards/shard_0008', 'corpora/shards/shard_0058', 'corpora/shards/shard_0060', 'corpora/shards/shard_0022', 'corpora/shards/shard_0059', 'corpora/shards/shard_0100', 'corpora/shards/shard_0027', 'corpora/shards/shard_0072', 'corpora/shards/shard_0098', 'corpora/shards/shard_0019', 'corpora/shards/shard_0066', 'corpora/shards/shard_0042', 'corpora/shards/shard_0053']\r\n```\r\n\r\nI also tried with \\a and the list decreased but there were still several crashes:\r\n\r\n```\r\nErrors on files: ['corpora/shards/shard_0069', 'corpora/shards/shard_0055', 'corpora/shards/shard_0043', 'corpora/shards/shard_0014', 'corpora/shards/shard_0073', 'corpora/shards/shard_0025', 'corpora/shards/shard_0068', 'corpora/shards/shard_0102', 'corpora/shards/shard_0096', 'corpora/shards/shard_0076', 'corpora/shards/shard_0067', 'corpora/shards/shard_0026', 'corpora/shards/shard_0024', 'corpora/shards/shard_0044', 'corpora/shards/shard_0087', 'corpora/shards/shard_0092', 'corpora/shards/shard_0074', 'corpora/shards/shard_0094', 'corpora/shards/shard_0078', 'corpora/shards/shard_0039', 'corpora/shards/shard_0077', 'corpora/shards/shard_0040', 'corpora/shards/shard_0009', 'corpora/shards/shard_0107', 'corpora/shards/shard_0063', 'corpora/shards/shard_0103', 'corpora/shards/shard_0047', 'corpora/shards/shard_0033', 'corpora/shards/shard_0089', 'corpora/shards/shard_0037', 'corpora/shards/shard_0082', 'corpora/shards/shard_0071', 'corpora/shards/shard_0091', 'corpora/shards/shard_0065', 'corpora/shards/shard_0070', 'corpora/shards/shard_0058', 'corpora/shards/shard_0081', 'corpora/shards/shard_0060', 'corpora/shards/shard_0002', 'corpora/shards/shard_0059', 'corpora/shards/shard_0027', 'corpora/shards/shard_0072', 'corpora/shards/shard_0098', 'corpora/shards/shard_0019', 'corpora/shards/shard_0045', 'corpora/shards/shard_0036', 'corpora/shards/shard_0066', 'corpora/shards/shard_0053']\r\n```\r\n\r\nWhich means that it is quite possible that the assumption of that some unexpected pattern in the files is causing the crashes is true. If I am able to reach any conclusion I will post It here asap.", "Hmmm I was expecting it to work with \\a, not sure why they appear in your text files though", "Hi @lhoestq, is there any input length restriction which was not before the update of the nlp library?", "No we never set any input length restriction on our side (maybe arrow but I don't think so)", "@lhoestq Can you ever be certain that a delimiter character is not present in a plain text file? In other formats (e.g. CSV) , rules are set of what is allowed and what isn't so that it actually constitutes a CSV file. In a text file you basically have \"anything goes\", so I don't think you can ever be entirely sure that the chosen delimiter does not exist in the text file, or am I wrong? \r\n\r\nIf I understand correctly you choose a delimiter that we hope does not exist in the file, so that when the CSV parser starts splitting into columns, it will only ever create one column? Why can't we use a newline character though?", "Okay, I have splitted the crashing shards into individual sentences and some examples of the inputs that are causing the crashes are the following ones:\r\n\r\n\r\n_4. DE L’ORGANITZACIÓ ESTAMENTAL A L’ORGANITZACIÓ EN CLASSES A mesura que es desenvolupava un sistema econòmic capitalista i naixia una classe burgesa cada vegada més preparada per a substituir els dirigents de les velles monarquies absolutistes, es qüestionava l’abundància de béns amortitzats, que com s’ha dit estaven fora del mercat i no pagaven tributs, pels perjudicis que ocasionaven a les finances públiques i a l’economia en general. Aquest estat d’opinió revolucionari va desembocar en un conjunt de mesures pràctiques de caràcter liberal. D’una banda, les que intentaven desposseir les mans mortes del domini de béns acumulats, procés que acostumem a denominar desamortització, i que no és més que la nacionalització i venda d’aquests béns eclesiàstics o civils en subhasta pública al millor postor. D’altra banda, les que redimien o reduïen els censos i delmes o aixecaven les prohibicions de venda, és a dir, les vinculacions. La desamortització, que va afectar béns dels ordes religiosos, dels pobles i d’algunes corporacions civils, no va ser un camí fàcil, perquè costava i costa trobar algú que sigui indiferent a la pèrdua de béns, drets i privilegis. I té una gran transcendència, va privar els antics estaments de les Espanyes, clero i pobles —la noblesa en queda al marge—, de la força econòmica que els donaven bona part de les seves terres i, en última instància, va preparar el terreny per a la substitució de la vella societat estamental per la nova societat classista. En aquesta societat, en teoria, les agrupacions socials són obertes, no tenen cap estatut jurídic privilegiat i estan definides per la possessió o no d’uns béns econòmics que són lliurement alienables. A les Espanyes la transformació va afectar poc l’aristocràcia latifundista, allà on n’hi havia. Aquesta situació va afavorir, en part, la persistència de la vella cultura de la societat estamental en determinats ambients, i això ha influït decisivament en la manca de democràcia que caracteritza la majoria de règims polítics que s’han anat succeint. Una manera de pensar que sempre sura en un moment o altre, i que de fet no acaba de desaparèixer del tot. 5. INICI DE LA DESAMORTITZACIÓ A LES ESPANYES Durant el segle xviii, dins d’aquesta visió lliberal, va agafar força en alguns cercles de les Espanyes el corrent d’opinió contrari a les mans mortes. Durant el regnat de Carles III, s’arbitraren les primeres mesures desamortitzadores proposades per alguns ministres il·lustrats. Aquestes disposicions foren modestes i poc eficaces, no van aturar l’acumulació de terres per part dels estaments que constituïen les mans mortes i varen afectar principalment béns dels pobles. L’Església no va ser tocada, excepte en el cas de 110_\r\n\r\n_la revolució liberal, perquè, encara que havia perdut els seus drets jurisdiccionals, havia conservat la majoria de terres i fins i tot les havia incrementat amb d’altres que procedien de la desamortització. En la nova situació, les mans mortes del bosc públic eren l’Estat, que no cerca mai l’autofinançament de les despeses de gestió; els diners que manquin ja els posarà l’Estat. 9. DEFENSA I INTENTS DE RECUPERACIÓ DELS BÉNS COMUNALS DESAMORTITZATS El procés de centralització no era senzill, perquè, d’una banda, la nova organització apartava de la gestió moltes corporacions locals i molts veïns que l’havien portada des de l’edat mitjana, i, de l’altra, era difícil de coordinar la nova silvicultura amb moltes pràctiques forestals i drets tradicionals, com la pastura, fer llenya o tallar un arbre aquí i un altre allà quan tenia el gruix suficient, les pràctiques que s’havien fet sempre. Les primeres passes de la nova organització centralitzada varen tenir moltes dificultats en aquells indrets en què els terrenys municipals i comunals tenien un paper important en l’economia local. La desobediència a determinades normes imposades varen prendre formes diferents. Algunes institucions, com, per exemple, la Diputació de Lleida, varen retardar la tramitació d’alguns expedients i varen evitar la venda de béns municipals. Molts pobles permeteren deixar que els veïns continuessin amb les seves pràctiques tradicionals, d’altres varen boicotejar les subhastes d’aprofitaments. L’Estat va reaccionar encomanant a la Guàrdia Civil el compliment de les noves directrius. Imposar el nou règim va costar a l’Administració un grapat d’anys, però de mica en mica, amb molta, molta guarderia i gens de negociació, ho va aconseguir. La nova gestió estatal dels béns municipals va deixar, com hem comentat, molta gent sense uns recursos necessaris per a la supervivència, sobre tot en àrees on predominaven les grans propietats, i on els pagesos sense terra treballaven de jornalers temporers. Això va afavorir que, a bona part de les Espanyes, les primeres lluites camperoles de la segona meitat del segle xix defensessin la recuperació dels comunals desamortitzats; per a molts aquella expropiació i venda dirigida pels governs monàrquics era la causa de molta misèria. D’altres, més radicalitzats, varen entendre que l’eliminació de la propietat col·lectiva i la gestió estatal dels boscos no desamortitzats suposava una usurpació pura i dura. En les zones més afectades per la desamortització això va donar lloc a un imaginari centrat en la defensa del comunal. La Segona República va arribar en una conjuntura econòmica de crisi, generada pel crac del 1929. Al camp, aquesta situació va produir una forta caiguda dels preus dels productes agraris i un increment important de l’atur. QUADERNS AGRARIS 42 (juny 2017), p. 105-126_\r\n\r\nI think that the main difference between the crashing samples and the rest is their length. Therefore, couldn't the length be causing the message errors? I hope with these samples you can identify what is causing the crashes considering that the 0.4.0 nlp library was loading them properly.", "So we're using the csv reader to read text files because arrow doesn't have a text reader.\r\nTo workaround the fact that text files are just csv with one column, we want to set a delimiter that doesn't appear in text files.\r\nUntil now I thought that it would do the job but unfortunately it looks like even characters like \\a appear in text files.\r\n\r\nSo we have to option:\r\n- find another delimiter that does the job (maybe `\\x1b` esc or `\\x18` cancel)\r\n- don't use the csv reader from arrow but the text reader from pandas instead (or any other reader). The only important thing is that it must be fast (arrow's reader has a nice and fast multithreaded for csv that we're using now but hopefully we can find an alternative)\r\n\r\n\r\n\r\n> @lhoestq Can you ever be certain that a delimiter character is not present in a plain text file? In other formats (e.g. CSV) , rules are set of what is allowed and what isn't so that it actually constitutes a CSV file. In a text file you basically have \"anything goes\", so I don't think you can ever be entirely sure that the chosen delimiter does not exist in the text file, or am I wrong?\r\n\r\nAs long as the text file follows some encoding it wouldn't make sense to have characters such as the bell character. However I agree it can happen.\r\n\r\n> If I understand correctly you choose a delimiter that we hope does not exist in the file, so that when the CSV parser starts splitting into columns, it will only ever create one column? Why can't we use a newline character though?\r\n\r\nExactly. Arrow doesn't allow the newline character unfortunately.", "> Okay, I have splitted the crashing shards into individual sentences and some examples of the inputs that are causing the crashes are the following ones\r\n\r\nThanks for digging into it !\r\n\r\nCharacters like \\a or \\b are not shown when printing the text, so as it is I can't tell if it contains unexpected characters.\r\nMaybe could could open the file in python and check if `\"\\b\" in open(\"path/to/file\", \"r\").read()` ?\r\n\r\n> I think that the main difference between the crashing samples and the rest is their length. Therefore, couldn't the length be causing the message errors? I hope with these samples you can identify what is causing the crashes considering that the 0.4.0 nlp library was loading them properly.\r\n\r\nTo check that you could try to run \r\n\r\n```python\r\nimport pyarrow as pa\r\nimport pyarrow.csv\r\n\r\nopen(\"dummy.txt\", \"w\").write(((\"a\" * 10_000) + \"\\n\") * 4) # 4 lines of 10 000 'a'\r\n\r\nparse_options = pa.csv.ParseOptions( \r\n delimiter=\"\\b\", \r\n quote_char=False, \r\n double_quote=False, \r\n escape_char=False, \r\n newlines_in_values=False, \r\n ignore_empty_lines=False, \r\n)\r\n\r\nread_options= pa.csv.ReadOptions(use_threads=True, column_names=[\"text\"])\r\n\r\npa_table = pa.csv.read_csv(\"dummy.txt\", read_options=read_options, parse_options=parse_options)\r\n```\r\n\r\non my side it runs without error though", "That's true, It was my error printing the text that way. Maybe as a workaround, I can force all my input samples to have \"\\b\" at the end?", "> That's true, It was my error printing the text that way. Maybe as a workaround, I can force all my input samples to have \"\\b\" at the end?\r\n\r\nI don't think it would work since we only want one column, and \"\\b\" is set to be the delimiter between two columns, so it will raise the same issue again. Pyarrow would think that there is more than one column if the delimiter is found somewhere.\r\n\r\nAnyway, I I'll work on a new text reader if we don't find the right workaround about this delimiter issue." ]

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[ "It seems that I ran into the same problem\r\n```\r\ndef tokenize(cols, example):\r\n for in_col, out_col in cols.items():\r\n example[out_col] = hf_tokenizer.convert_tokens_to_ids(hf_tokenizer.tokenize(example[in_col]))\r\n return example\r\ncola = datasets.load_dataset('glue', 'cola')\r\ntokenized_cola = cola.map(partial(tokenize, {'sentence': 'text_idxs'}),\r\n num_proc=2,)\r\n```\r\nand it outpus (exceprts)\r\n```\r\nConcatenating 2 shards from multiprocessing\r\nSet __getitem__(key) output type to python objects for ['idx', 'label', 'sentence', 'text_idxs'] columns (when key is int or slice) and don't output other (un-formatted) columns.\r\nTesting the mapped function outputs\r\nTesting finished, running the mapping function on the dataset\r\nDone writing 532 indices in 4256 bytes .\r\nDone writing 531 indices in 4248 bytes .\r\nProcess #0 will write at /home/yisiang/.cache/huggingface/datasets/glue/cola/1.0.0/930e9d141872db65102cabb9fa8ac01c11ffc8a1b72c2e364d8cdda4610df542/tokenized_test_00000_of_00002.arrow\r\nProcess #1 will write at /home/yisiang/.cache/huggingface/datasets/glue/cola/1.0.0/930e9d141872db65102cabb9fa8ac01c11ffc8a1b72c2e364d8cdda4610df542/tokenized_test_00001_of_00002.arrow\r\nSpawning 2 processes\r\n```\r\nand then the program never stop.", "same problem.\r\n`encoded_dataset = core_data.map(lambda examples: tokenizer(examples[\"query\"], examples[\"document\"], padding=True, truncation='longest_first', return_tensors=\"pt\", max_length=384), num_proc=16, keep_in_memory=True)`\r\nit outputs:\r\n```\r\nSet __getitem__(key) output type to python objects for ['document', 'is_random', 'query'] columns (when key is int or slice) and don't output other (un-formatted) columns.\r\nDone writing 1787500 indices in 25568400000 bytes .\r\nSet __getitem__(key) output type to python objects for ['document', 'is_random', 'query'] columns (when key is int or slice) and don't output other (un-formatted) columns.\r\nDone writing 1787500 indices in 25568400000 bytes .\r\nSet __getitem__(key) output type to python objects for ['document', 'is_random', 'query'] columns (when key is int or slice) and don't output other (un-formatted) columns.\r\nDone writing 1787500 indices in 25568400000 bytes .\r\nSet __getitem__(key) output type to python objects for ['document', 'is_random', 'query'] columns (when key is int or slice) and don't output other (un-formatted) columns.\r\nDone writing 1787500 indices in 25568400000 bytes .\r\nSet __getitem__(key) output type to python objects for ['document', 'is_random', 'query'] columns (when key is int or slice) and don't output other (un-formatted) columns.\r\nDone writing 1787500 indices in 25568400000 bytes .\r\nSet __getitem__(key) output type to python objects for ['document', 'is_random', 'query'] columns (when key is int or slice) and don't output other (un-formatted) columns.\r\nDone writing 1787500 indices in 25568400000 bytes .\r\nSet __getitem__(key) output type to python objects for ['document', 'is_random', 'query'] columns (when key is int or slice) and don't output other (un-formatted) columns.\r\nDone writing 1787500 indices in 25568400000 bytes .\r\nSet __getitem__(key) output type to python objects for ['document', 'is_random', 'query'] columns (when key is int or slice) and don't output other (un-formatted) columns.\r\nDone writing 1787499 indices in 25568385696 bytes .\r\nSet __getitem__(key) output type to python objects for ['document', 'is_random', 'query'] columns (when key is int or slice) and don't output other (un-formatted) columns.\r\nSpawning 16 processes\r\n```", "Thanks for reporting.\r\n\r\n\r\nWhich tokenizers are you using ? What platform are you on ? Can you tell me which version of datasets and pyarrow you're using ? @timothyjlaurent @richarddwang @HuangLianzhe \r\n\r\nAlso if you're able to reproduce the issue on google colab that would be very helpful.\r\n\r\nI tried to run your code @richarddwang with the bert tokenizer and I wasn't able to reproduce", "Hi, Sorry that I forgot to see what my version was.\r\nBut after updating datasets to master (editable install), and latest pyarrow. \r\nIt works now ~", "Sorry, I just noticed this.\r\nI'm running this on MACOS the version of datasets I'm was 1.0.0 but I've also tried it on 1.0.2. `pyarrow==1.0.1`, Python 3.6\r\n\r\nConsider this code:\r\n```python\r\n\r\n loader_path = str(Path(__file__).parent / \"prodigy_dataset_builder.py\")\r\n ds = load_dataset(\r\n loader_path, name=\"prodigy-ds\", data_files=list(file_paths), cache_dir=cache_dir\r\n )[\"train\"]\r\n valid_relations = set(vocabulary.relation_types.keys())\r\n\r\n ds = ds.filter(filter_good_rows, fn_kwargs=dict(valid_rel_labels=valid_relations))\r\n\r\n ds = ds.map(map_bpe_encodings, batched=True, fn_kwargs=dict(tokenizer=vocabulary.tokenizer), num_proc=10)\r\n\r\n # add all feature data\r\n ner_ds: Dataset = ds.map(\r\n add_bio_tags,\r\n fn_kwargs=dict(ner_label_map=vocabulary.ner_labels, tokenizer=vocabulary.tokenizer),\r\n )\r\n rel_ds: Dataset = ner_ds.map(\r\n relation_ds_factory,\r\n batched=True,\r\n writer_batch_size=100,\r\n fn_kwargs=dict(tokenizer=vocabulary.tokenizer, vocabulary=vocabulary),\r\n )\r\n```\r\nThe loader is essentially a jsonloader with some extra error handling. The data is a jsonlines format with text field and a list of span objects and relation objects. \r\n\r\nIn the `ner_ds` a field, `ner_labels` is added, this is used in the downstream `relation_ds_factory`. It all runs fine in a single process but I get a KeyError error if run with num_proc set\r\n:\r\n\r\n```\r\n File \"/Users/timothy.laurent/src/inv-text2struct/text2struct/model/dataset.py\", line 348, in relation_ds_factory\r\n ner_labels = example[\"ner_labels\"]\r\nKeyError: 'ner_labels'\r\n``` \r\n\r\nThis is just one example of what goes wrong. I've started just saving the dataset as arrow at the end because it takes a long time to map/filter/shuffle and the caching isn't working (tracked it down to byte differences in the pickled functions). \r\n\r\n^^ Interestingly if I heed the warning from Tokenizers and set the environment variable, `TOKENIZERS_PARALLELISM=true` the map just hangs:\r\n\r\n```\r\n[I 200921 21:43:18 filelock:318] Lock 5694118768 released on /Users/timothy.laurent/.cache/huggingface/datasets/_Users_timothy.laurent_.cache_huggingface_datasets_prodigy_dataset_builder_prodigy-ds-5f34378723c4e83f_0.0.0_e67d9b43d5cd82c50b1eae8f2097daf95b601a04dc03ddd504f2b234a5fa247a.lock\r\n100%|████████████████████████████████████████████████████████████████████████████████████████████████████████| 1/1 [00:00<00:00, 1.34ba/s]\r\n#0: 0%| | 0/1 [00:00<?, ?ba/s]\r\n#1: 0%| | 0/1 [00:00<?, ?ba/s]\r\n#2: 0%| | 0/1 [00:00<?, ?ba/s]\r\n#3: 0%| | 0/1 [00:00<?, ?ba/s]\r\n#4: 0%| | 0/1 [00:00<?, ?ba/s]\r\n#5: 0%| | 0/1 [00:00<?, ?ba/s]\r\n#6: 0%| | 0/1 [00:00<?, ?ba/s]\r\n#7: 0%| | 0/1 [00:00<?, ?ba/s]\r\n#8: 0%| | 0/1 [00:00<?, ?ba/s]\r\n```", "Thank you, I was able to reproduce :)\r\nI'm on it", "#659 should fix the `KeyError` issue. It was due to the formatting not getting updated the right way", "Also maybe @n1t0 knows why setting `TOKENIZERS_PARALLELISM=true` creates deadlock issues when calling `map` with multiprocessing ?", "@lhoestq \r\n\r\nThanks for taking a look. I pulled the master but I still see the key error.\r\n\r\n```\r\nTo disable this warning, you can either:\r\n - Avoid using `tokenizers` before the fork if possible\r\n - Explicitly set the environment variable TOKENIZERS_PARALLELISM=(true | false)\r\n#0: 100%|███████████████████████████████████████████████████████████████████████████████████████████████████████| 1/1 [00:00<00:00, 21.56ba/s]\r\n#1: 100%|███████████████████████████████████████████████████████████████████████████████████████████████████████| 1/1 [00:00<00:00, 17.71ba/s]\r\n#2: 100%|███████████████████████████████████████████████████████████████████████████████████████████████████████| 1/1 [00:00<00:00, 20.45ba/s]\r\n#3: 100%|███████████████████████████████████████████████████████████████████████████████████████████████████████| 1/1 [00:00<00:00, 26.05ba/s]\r\n#4: 100%|███████████████████████████████████████████████████████████████████████████████████████████████████████| 1/1 [00:00<00:00, 26.83ba/s]\r\n#5: 100%|███████████████████████████████████████████████████████████████████████████████████████████████████████| 1/1 [00:00<00:00, 27.00ba/s]\r\n#6: 100%|███████████████████████████████████████████████████████████████████████████████████████████████████████| 1/1 [00:00<00:00, 27.40ba/s]\r\n#7: 100%|███████████████████████████████████████████████████████████████████████████████████████████████████████| 1/1 [00:00<00:00, 25.91ba/s]\r\n#8: 100%|███████████████████████████████████████████████████████████████████████████████████████████████████████| 1/1 [00:00<00:00, 22.46ba/s]\r\n#9: 100%|███████████████████████████████████████████████████████████████████████████████████████████████████████| 1/1 [00:00<00:00, 20.15ba/s]\r\n#10: 100%|██████████████████████████████████████████████████████████████████████████████████████████████████████| 1/1 [00:00<00:00, 26.81ba/s]\r\n#11: 100%|██████████████████████████████████████████████████████████████████████████████████████████████████████| 1/1 [00:00<00:00, 27.45ba/s]\r\n100%|█████████████████████████████████████████████████████████████████████████████████████████████████████| 322/322 [00:00<00:00, 1462.85ex/s]\r\nTraceback (most recent call last): | 0/1 [00:00<?, ?ba/s]\r\n File \"text2struct/run_model.py\", line 372, in <module>\r\n main()\r\n File \"text2struct/run_model.py\", line 368, in main | 0/1 [00:00<?, ?ba/s]\r\n run_model(auto_envvar_prefix=\"GFB_CIES\") # pragma: no cover\r\n File \"/Users/timothy.laurent/.virtualenvs/inv-text2struct/lib/python3.6/site-packages/click/core.py\", line 829, in __call__\r\n return self.main(*args, **kwargs) | 0/1 [00:00<?, ?ba/s]\r\n File \"/Users/timothy.laurent/.virtualenvs/inv-text2struct/lib/python3.6/site-packages/click/core.py\", line 782, in main\r\n rv = self.invoke(ctx)\r\n File \"/Users/timothy.laurent/.virtualenvs/inv-text2struct/lib/python3.6/site-packages/click/core.py\", line 1236, in invoke\r\n return Command.invoke(self, ctx)\r\n File \"/Users/timothy.laurent/.virtualenvs/inv-text2struct/lib/python3.6/site-packages/click/core.py\", line 1066, in invoke\r\n return ctx.invoke(self.callback, **ctx.params)\r\n File \"/Users/timothy.laurent/.virtualenvs/inv-text2struct/lib/python3.6/site-packages/click/core.py\", line 610, in invoke\r\n return callback(*args, **kwargs)\r\n File \"/Users/timothy.laurent/.virtualenvs/inv-text2struct/lib/python3.6/site-packages/click/decorators.py\", line 21, in new_func\r\n return f(get_current_context(), *args, **kwargs)\r\n File \"text2struct/run_model.py\", line 136, in run_model\r\n ctx.invoke(ctx.command.commands[config_dict[\"mode\"]])\r\n File \"/Users/timothy.laurent/.virtualenvs/inv-text2struct/lib/python3.6/site-packages/click/core.py\", line 610, in invoke\r\n return callback(*args, **kwargs)\r\n File \"/Users/timothy.laurent/.virtualenvs/inv-text2struct/lib/python3.6/site-packages/click/decorators.py\", line 21, in new_func\r\n return f(get_current_context(), *args, **kwargs)\r\n File \"text2struct/run_model.py\", line 187, in train\r\n run_train_model(_parse_subcommand(ctx))\r\n File \"text2struct/run_model.py\", line 241, in run_train_model\r\n checkpoint_steps=config.train.checkpoint_steps,\r\n File \"/Users/timothy.laurent/src/inv-text2struct/text2struct/model/train.py\", line 153, in alternate_training\r\n max_len=config.model.dim.max_len,\r\n File \"/Users/timothy.laurent/src/inv-text2struct/text2struct/model/dataset.py\", line 466, in load_prodigy_tf_datasets\r\n folder, file_patterns, vocabulary, cache_dir=cache_dir, test_pct=test_pct\r\n File \"/Users/timothy.laurent/src/inv-text2struct/text2struct/model/dataset.py\", line 447, in load_prodigy_arrow_datasets\r\n fn_kwargs=dict(tokenizer=vocabulary.tokenizer, vocabulary=vocabulary),\r\n File \"/Users/timothy.laurent/.virtualenvs/inv-text2struct/lib/python3.6/site-packages/datasets/arrow_dataset.py\", line 1224, in map\r\n update_data = does_function_return_dict(test_inputs, test_indices)\r\n File \"/Users/timothy.laurent/.virtualenvs/inv-text2struct/lib/python3.6/site-packages/datasets/arrow_dataset.py\", line 1195, in does_function_return_dict\r\n function(*fn_args, indices, **fn_kwargs) if with_indices else function(*fn_args, **fn_kwargs)\r\n File \"/Users/timothy.laurent/src/inv-text2struct/text2struct/model/dataset.py\", line 348, in relation_ds_factory\r\n ner_labels = example[\"ner_labels\"]\r\nKeyError: 'ner_labels'\r\n\r\n```", "The parallelism is automatically disabled on `tokenizers` when the process gets forked, while we already used the parallelism capabilities of a tokenizer. We have to do it in order to avoid having the process hang, because we cannot safely fork a multithreaded process (cf https://github.com/huggingface/tokenizers/issues/187).\r\nSo if possible, the tokenizers shouldn't be used before the fork, so that each process can then make use of the parallelism. Otherwise using `TOKENIZERS_PARALLELISM=false` is the way to go.", "> Thanks for taking a look. I pulled the master but I still see the key error.\r\n\r\nI am no longer able to get the error since #659 was merged. Not sure why you still have it @timothyjlaurent \r\nMaybe it is a cache issue ? Could you try to use `load_from_cache_file=False` in your `.map()` calls ?", "> The parallelism is automatically disabled on `tokenizers` when the process gets forked, while we already used the parallelism capabilities of a tokenizer. We have to do it in order to avoid having the process hang, because we cannot safely fork a multithreaded process (cf [huggingface/tokenizers#187](https://github.com/huggingface/tokenizers/issues/187)).\r\n> So if possible, the tokenizers shouldn't be used before the fork, so that each process can then make use of the parallelism. Otherwise using `TOKENIZERS_PARALLELISM=false` is the way to go.\r\n\r\nOk thanks :)\r\n\r\nIs there something we should do on the `datasets` side to avoid that that the program hangs ?\r\n\r\nAlso when doing `.map` with a tokenizer, the tokenizer is called once on the first examples of the dataset to check the function output before spawning the processes. Is that compatible with how tokenizers are supposed to be used with multiprocessing ?", "#659 fixes the empty dict issue\r\n#688 fixes the hang issue", "Hmmm I pulled the latest commit, `b93c5517f70a480533a44e0c42638392fd53d90`, and I'm still seeing both the hanging and the key error. ", "Hi @timothyjlaurent \r\n\r\nThe hanging fix just got merged, that why you still had it.\r\n\r\nFor the key error it's possible that the code you ran reused cached datasets from where the KeyError bug was still there.\r\nCould you try to clear your cache or make sure that it doesn't reuse cached data with `.map(..., load_from_cache=False)` ?\r\nLet me know if it it helps", "Hi @lhoestq , \r\n\r\nThanks for letting me know about the update.\r\n\r\nSo I don't think it's the caching - because hashing mechanism isn't stable for me -- but that's a different issue. In any case I `rm -rf ~/.cache/huggingface` to make a clean slate.\r\n\r\nI synced with master and I see the key error has gone away, I tried with and without the `TOKENIZERS_PARALLELISM` variable set and see the log line for setting the value false before the map.\r\n\r\nNow I'm seeing an issue with `.train_test_split()` on datasets that are the product of a multiprocess map.\r\n\r\nHere is the stack trace\r\n\r\n```\r\n File \"/Users/timothy.laurent/src/inv-text2struct/text2struct/model/dataset.py\", line 451, in load_prodigy_arrow_datasets\r\n ner_ds_dict = ner_ds.train_test_split(test_size=test_pct, shuffle=True, seed=seed)\r\n File \"/Users/timothy.laurent/.virtualenvs/inv-text2struct/src/datasets/src/datasets/arrow_dataset.py\", line 168, in wrapper\r\n dataset.set_format(**new_format)\r\n File \"/Users/timothy.laurent/.virtualenvs/inv-text2struct/src/datasets/src/datasets/fingerprint.py\", line 163, in wrapper\r\n out = func(self, *args, **kwargs)\r\n File \"/Users/timothy.laurent/.virtualenvs/inv-text2struct/src/datasets/src/datasets/arrow_dataset.py\", line 794, in set_format\r\n list(filter(lambda col: col not in self._data.column_names, columns)), self._data.column_names\r\nValueError: Columns ['train', 'test'] not in the dataset. Current columns in the dataset: ['_input_hash', '_task_hash', '_view_id', 'answer', 'encoding__ids', 'encoding__offsets', 'encoding__overflowing', 'encoding__tokens', 'encoding__words', 'ner_ids', 'ner_labels', 'relations', 'spans', 'text', 'tokens']\r\n```\r\n\r\n\r\n", "Thanks for reporting.\r\nI'm going to fix that and add a test case so that it doesn't happen again :) \r\nI'll let you know when it's done\r\n\r\nIn the meantime if you could make a google colab that reproduces the issue it would be helpful ! @timothyjlaurent ", "Sure thing, @lhoestq.\r\n\r\nhttps://colab.research.google.com/drive/1lg4fbyrUO6m8ssQ2dNdVFaUqMUfA2zZ3?usp=sharing", "Thanks @timothyjlaurent ! I just merged a fix on master. I also checked your notebook and it looks like it's working now.\r\nI added some tests to make sure it works as expected now :)", "Great, @lhoestq . I'm trying to verify in the colab:\r\nchanged\r\n```\r\n!pip install datasets\r\n```\r\nto \r\n\r\n```\r\n!pip install git+https://github.com/huggingface/datasets@master\r\n```\r\n\r\nBut I'm still seeing the error - I wonder why?", "It works on my side @timothyjlaurent on google colab.\r\nDid you try to uninstall datasets first, before updating it to master's version ?", "I didn't -- it was a new sessions --- buuut - look like it's working today -- woot! I'll close this issue. Thanks @lhoestq " ]

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[ "Indeed you're right ! Thanks for reporting that\r\n\r\nCould you open a PR to fix the features names ?" ]

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[ "Also, some downloads and dataset processing can be quite long for large datasets like wikipedia/pg19/etc. We probably don't want to user to think that the library is hanging. Happy to reorganize logging between DEBUG/INFO/WARNING to make it less verbose by default though.", "The problem is that `transformers` imports `datasets` and the latter starts logging on `import`: at least 3 info messages - apache beam/torch/tf available - so it injects noise whether one uses the library or not - i.e. no choice given to the user.\r\n\r\nWould you be open for me to changing this PR, to keep the initial level at INFO, but to keep the `DATASETS_VERBOSITY` env var it introduces, to let the user control the verbosity?\r\n\r\n", "> Also, some downloads and dataset processing can be quite long for large datasets like wikipedia/pg19/etc. We probably don't want to user to think that the library is hanging.\r\n\r\nIf you're referring to tqdm progress reports, it's not affected by changing the logging levels. It's not using logging.", "> The problem is that `transformers` imports `datasets` and the latter starts logging on `import`: at least 3 info messages - apache beam/torch/tf available - so it injects noise whether one uses the library or not - i.e. no choice given to the user.\r\n> \r\n> Would you be open for me to changing this PR, to keep the initial level at INFO, but to keep the `DATASETS_VERBOSITY` env var it introduces, to let the user control the verbosity?\r\n\r\nFor now we can do that, then I'll change some messages to warnings and set the default verbosity at warning as well at that point. Does it sound good to you ?\r\n\r\n> If you're referring to tqdm progress reports, it's not affected by changing the logging levels. It's not using logging.\r\n\r\nActually we configured some progress bars to be disabled depending on the logging level ^^'\r\n", "> For now we can do that, then I'll change some messages to warnings and set the default verbosity at warning as well at that point. Does it sound good to you ?\r\n\r\nIf it is logical then by all means. \r\n\r\n> > If you're referring to tqdm progress reports, it's not affected by changing the logging levels. It's not using logging.\r\n> \r\n> Actually we configured some progress bars to be disabled depending on the logging level ^^'\r\n\r\nThis is very smart!\r\n\r\nI reverted s/WARNINGS/INFO/.\r\n\r\nThank you!", "Note that it’s the same in `transformers` @stas00, tdqm are also controlled by the logging level there.", "> Note that it’s the same in `transformers` @stas00, tdqm are also controlled by the logging level there.\r\n\r\nThat's good to know, @thomwolf - thank you!\r\n\r\nI see that it's controlled in `trainer.py`, but in `examples` it's not - since that's where I usually see the progressbars (and they are great!). But I suppose they aren't API, so `examples` can behave differently.", "BTW, this is what I'm talking about:\r\n```\r\npython -c \"import transformers\"\r\n2020-09-14 21:00:58.032658: I tensorflow/stream_executor/platform/default/dso_loader.cc:48] Successfully opened dynamic library libcudart.so.10.1\r\nPyTorch version 1.7.0.dev20200910 available.\r\nTensorFlow version 2.3.0 available.\r\nApache Beam available.\r\n```\r\nwhy does the user need to see this? Especially, if they aren't even using `datasets` directly?", "Yes you are right, we should re-think the logging level of various elements.\r\nI also think that the `set_format` messages are confusing when they are the results of our internal operations (as mentioned [here](https://discuss.huggingface.co/t/pipeline-with-custom-dataset-tokenizer-when-to-save-load-manually/1084/7?u=thomwolf))", "Actually I continued this PR in #635 to set the level to warning and update the logging level of some messages.\r\n\r\nLet me know if it sounds good to you", "Closing this one sice #635 got merged", "Awesome! Thank you!\r\n\r\nAny ideas how to eliminate this remaining log line from tensorflow (I know it's not `datasets` related, but perhaps you know).\r\n```\r\npython -c \"import transformers\"\r\n2020-09-17 08:38:34.718410: I tensorflow/stream_executor/platform/default/dso_loader.cc:48] Successfully opened dynamic library libcudart.so.10.1\r\n```" ]

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[ "Updates - the differences between the following three\r\n(1) https://github.com/bheinzerling/pyrouge (previously popular. The one I trust the most)\r\n(2) https://github.com/google-research/google-research/tree/master/rouge\r\n(3) https://github.com/pltrdy/files2rouge (used in fairseq)\r\ncan be explained by two things, stemming and handling multiple sentences.\r\n\r\nStemming: \r\n(1), (2): default is no stemming. (3): default is with stemming ==> No stemming is the correct default as you did [here](https://github.com/huggingface/datasets/blob/master/metrics/rouge/rouge.py#L84)\r\n\r\nMultiple sentences:\r\n(1) `rougeL` splits text using `\\n`\r\n(2) `rougeL` ignores `\\n`. \r\n(2) `rougeLsum` splits text using `\\n`\r\n(3) `rougeL` splits text using `.`\r\n\r\nFor (2), `rougeL` and `rougeLsum` are identical if the sequence doesn't contain `\\n`. With `\\n`, it is `rougeLsum` that matches (1) not `rougeL`. \r\n\r\nOverall, and as far as I understand, for your implementation here https://github.com/huggingface/datasets/blob/master/metrics/rouge/rouge.py#L65 to match the default, you only need to change `rougeL` [here](https://github.com/huggingface/datasets/blob/master/metrics/rouge/rouge.py#L86) to `rougeLsum` to correctly compute metrics for text with newlines.\r\n\r\nTagging @sshleifer who might be interested.", "Thanks for the clarification !\r\nWe're adding Rouge Lsum in #701 ", "This is a real issue, sorry for missing the mention @ibeltagy\r\n\r\nWe implemented a more involved [solution](https://github.com/huggingface/transformers/blob/99cb924bfb6c4092bed9232bea3c242e27c6911f/examples/seq2seq/utils.py#L481) that enforces that sentences are split with `\\n` so that rougeLsum scores match papers even if models don't generate newlines. \r\n\r\nUnfortunately, the best/laziest way I found to do this introduced an `nltk` dependency (For sentence splitting, all sentences don't end in `.`!!!), but this might be avoidable with some effort.\r\n\r\n#### Sidebar: Wouldn't Deterministic Be Better?\r\n\r\n`rouge_scorer.scoring.BootstrapAggregator` is well named but is not deterministic which I would like to change for my mental health, unless there is some really good reason to sample 500 observations before computing f-scores.\r\n\r\nI have a fix on a branch, but I wanted to get some context before introducting a 4th way to compute rouge. Scores are generally within .03 Rouge2 of boostrap after multiplying by 100, e.g 22.05 vs 22.08 Rouge2.\r\n\r\n", "> This is a real issue, sorry for missing the mention @ibeltagy\r\n> \r\n> We implemented a more involved [solution](https://github.com/huggingface/transformers/blob/99cb924bfb6c4092bed9232bea3c242e27c6911f/examples/seq2seq/utils.py#L481) that enforces that sentences are split with `\\n` so that rougeLsum scores match papers even if models don't generate newlines.\r\n> \r\n> Unfortunately, the best/laziest way I found to do this introduced an `nltk` dependency (For sentence splitting, all sentences don't end in `.`!!!), but this might be avoidable with some effort.\r\n\r\nThanks for the details, I didn't know about that. Maybe we should consider adding this processing step or at least mention it somewhere in the library or the documentation\r\n\r\n> #### Sidebar: Wouldn't Deterministic Be Better?\r\n> `rouge_scorer.scoring.BootstrapAggregator` is well named but is not deterministic which I would like to change for my mental health, unless there is some really good reason to sample 500 observations before computing f-scores.\r\n> \r\n> I have a fix on a branch, but I wanted to get some context before introducting a 4th way to compute rouge. Scores are generally within .03 Rouge2 of boostrap after multiplying by 100, e.g 22.05 vs 22.08 Rouge2.\r\n\r\nI think the default `n_samples` of the aggregator is 1000. We could increase it or at least allow users to change it if they want more precise results.", "Hi, thanks for the solution. \r\n\r\nI am not sure if this is a bug, but on line [510](https://github.com/huggingface/transformers/blob/99cb924bfb6c4092bed9232bea3c242e27c6911f/examples/seq2seq/utils.py#L510), are pred, tgt supposed to be swapped?", "This looks like a bug in an old version of the examples in `transformers`" ]

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[ "I have the same issue", "Same issue here when Trying to load a dataset from disk.", "I am also experiencing this issue, and don't know if it's affecting my training.", "Same here. I hope the dataset is not being modified in-place.", "I think the only way to avoid this warning would be to do a copy of the numpy array before providing it.\r\n\r\nThis would slow down a bit the iteration over the dataset but maybe it would be safer. We could disable the copy with a flag on the `set_format` command.\r\n\r\nIn most typical cases of training a NLP model, PyTorch shouldn't modify the input so it's ok to have a non-writable array but I can understand the warning is a bit scary so maybe we could choose the side of non-warning/slower by default and have an option to speedup.\r\n\r\nWhat do you think @lhoestq ? ", "@thomwolf Would it be possible to have the array look writeable, but raise an error if it is actually written to?\r\n\r\nI would like to keep my code free of warning, but I also wouldn't like to slow down the program because of unnecessary copy operations. ", "@AndreasMadsen probably not I would guess (no free lunch hahah)", "@thomwolf Why not? Writable is checked with `arr.flags.writeable`, and writing is done via magic methods.", "Well because I don't know the internal of numpy as well as you I guess hahahah, do you want to try to open a PR proposing a solution?", "@thomwolf @AndreasMadsen I think this is a terrible idea, n/o, and I am very much against it. Modifying internals of an array in such a hacky way is bound to run into other (user) issues down the line. To users it would not be clear at all what is going on e.g. when they check for write access (which will return True) but then they get a warning that the array is not writeable. That's extremely confusing. \r\n\r\nIf your only goal is to get rid of warnings in your code, then you can just use a [simplefilter](https://docs.python.org/3.8/library/warnings.html#temporarily-suppressing-warnings) for UserWarnings in your own code. Changing the code-base in such an intuitive way does not seem like a good way to go and sets a bad precedent, imo. \r\n\r\n(Feel free to disagree, of course.)\r\n\r\nIMO a warning can stay (as they can be filtered by users anyway), but it can be clarified why the warning takes place.", "> To users it would not be clear at all what is going on e.g. when they check for write access (which will return True) but then they get a warning that the array is not writeable. That's extremely confusing.\r\n\r\nConfusion can be resolved with a helpful error message. In this case, that error message can be controlled by huggingface/datasets. The right argument here is that if code depends on `.flags.writable` being truthful (not just for warnings), then it will cause unavoidable errors. Although, I can't imagine such a use-case.\r\n\r\n> If your only goal is to get rid of warnings in your code, then you can just use a simplefilter for UserWarnings in your own code. Changing the code-base in such an intuitive way does not seem like a good way to go and sets a bad precedent, imo.\r\n\r\nI don't want to ignore all `UserWarnings`, nor all warnings regarding non-writable arrays. Ignoring warnings leads to hard to debug issues.\r\n\r\n> IMO a warning can stay (as they can be filtered by users anyway), but it can be clarified why the warning takes place.\r\n\r\nPlain use cases should really not generate warnings. It teaches developers to ignore warnings which is a terrible practice.\r\n\r\n---\r\n\r\nThe best solution would be to allow non-writable arrays in `DataLoader`, but that is a PyTorch issue.", "> The right argument here is that if code depends on `.flags.writable` being truthful (not just for warnings), then it will cause unavoidable errors. Although, I can't imagine such a use-case.\r\n\r\nThat's exactly the argument in my first sentence. Too often someone \"cannot think of a use-case\", but you can not foresee the use-cases of a whole research community.\r\n \r\n> I don't want to ignore all `UserWarnings`, nor all warnings regarding non-writable arrays. Ignoring warnings leads to hard to debug issues.\r\n\r\nThat's fair.\r\n\r\n> Plain use cases should really not generate warnings. It teaches developers to ignore warnings which is a terrible practice.\r\n\r\nBut this is not a plain use-case (because Pytorch does not support these read-only tensors). Manually setting the flag to writable will solve the issue on the surface but is basically just a hack to compensate for something that is not allowed in another library. \r\n\r\nWhat about an \"ignore_warnings\" flag in `set_format` that when True wraps the offending code in a block to ignore userwarnings at that specific step in [_convert_outputs](https://github.com/huggingface/datasets/blob/880c2c76a8223a00c303eab2909371e857113063/src/datasets/arrow_dataset.py#L821)? Something like:\r\n\r\n```python\r\ndef _convert_outputs(..., ignore_warnings=True):\r\n ...\r\n with warnings.catch_warnings():\r\n if ignore_warnings:\r\n warnings.simplefilter(\"ignore\", UserWarning)\r\n return torch.tensor(...)\r\n# continues without warning filter after context manager...\r\n```", "> But this is not a plain use-case (because Pytorch does not support these read-only tensors).\r\n\r\nBy \"plain\", I mean the recommended way to use `datasets` with PyTorch according to the `datasets` documentation.", "This error is what I see when I run the first lines of the Pytorch Quickstart. It should also say that it should be ignored and/or how to fix it. BTW, this is a Pytorch error message -- not a Huggingface error message. My code runs anyway." ]

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[ "Related: https://issues.apache.org/jira/browse/ARROW-9773\r\n\r\nIt's definitely a size thing. I took a smaller dataset with 87000 rows and did:\r\n```\r\nfor i in range(10,1000,20):\r\n table = pa.concat_tables([dset._data]*i)\r\n table.take([0])\r\n```\r\nand it broke at around i=300.\r\n\r\nAlso when `_indices` is not None, this breaks indexing by slice. E.g. `dset.shuffle()[:1]` breaks.\r\n\r\nLuckily so far I haven't seen `_indices.column(0).take` break, which means it doesn't break `select` or anything like that which is where the speed really matters, it's just `_getitem`. So I'm currently working around it by just doing the arrow v0 method in `_getitem`:\r\n```\r\n#if PYARROW_V0:\r\ndata_subset = pa.concat_tables(\r\n self._data.slice(indices_array[i].as_py(), 1) for i in range(len(indices_array))\r\n)\r\n#else:\r\n #data_subset = self._data.take(indices_array)\r\n```", "Let me know if you meet other offset overflow issues @joeddav ", "Will this problem be solved in newer version?" ]

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[ "I think we should somewhere mention, that is the dataset in IOB2 tagging scheme, whereas the original dataset uses IOB1 :)", "Indeed this is something we want to mention.\r\n\r\nIf would want to add more details about the IOB1->2 change, feel free to ignore my suggestions and edit the description + update the dataset_info", "@lhoestq do you want me to update it or you'll update it. I am ok either way", "The best would be to mention this change in the description and then update the dataset_info.json file.\r\nCould you do that if you don't mind ?\r\n\r\nThen it should be ready to merge :)\r\n\r\nThanks again for adding this dataset !", "No problem @lhoestq I'll do the update", "@lhoestq please check if 847addf is exactly what we want", "Is the German task also part of this? If not, can it be accessed via the Datasets library?" ]

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