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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", "Resolved via #754" ]

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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", "Letting finders-of-this-thread know that the new link is: https://huggingface.co/docs/datasets/process#data-augmentation\r\n" ]

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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", "It happened when try to change the old project which use 'nlp' to new project which use 'datasets'. You should check you old 'my_squad.py' file, change the inherit class from `nlp.xxx` to `datasets.xxx`. Otherwise datasets - load.py - import_main_class() `if inspect.isclass(obj) and issubclass(obj, main_cls_type):` can not find the main_cls." ]

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

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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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[ "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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[ "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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[ "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`", "Hi, so I took this example from the HF implementation. What I can see is that the precision of `Hello there` being summarized to `general kenobi` is 1. I don't understand how this calculation is correct.\r\nIs the comparison just counting the words?\r\nand if Yes, then how does this translates to summarization evaluation?\r\n```\r\n >>> rouge = datasets.load_metric('rouge')\r\n >>> predictions = [\"hello there\", \"general kenobi\"]\r\n >>> references = [\"hello there\", \"general kenobi\"]\r\n >>> results = rouge.compute(predictions=predictions, references=references)\r\n >>> print(list(results.keys()))\r\n ['rouge1', 'rouge2', 'rougeL', 'rougeLsum']\r\n >>> print(results[\"rouge1\"])\r\n AggregateScore(low=Score(precision=1.0, recall=1.0, fmeasure=1.0), mid=Score(precision=1.0, recall=1.0, fmeasure=1.0), high=Score(precision=1.0, recall=1.0, fmeasure=1.0))\r\n >>> print(results[\"rouge1\"].mid.fmeasure)\r\n 1.0\r\n\"\"\", stored examples: 0)\r\n```\r\n\r\n\r\n" ]

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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?", "This specific issue has been fixed in https://github.com/huggingface/datasets/pull/645\r\n\r\nIf you still have this error, could you open a new issue and explain how to reproduce the error ?", "same error here in version 2.1.0", "Facing the same issue. \r\nSteps to reproduce: (dataset is a few GB big so try in colab maybe)\r\nDatasets version - 2.11.0\r\n```\r\nimport datasets\r\nimport re\r\n\r\nds = datasets.load_dataset('nishanthc/dnd_map_dataset_v0.1', split = 'train')\r\n\r\ndef get_text_caption(example):\r\n regex_pattern = r'\\s\\d+x\\d+|,\\sLQ|,\\sgrid|\\.\\w+$'\r\n example['text_caption'] = re.sub(regex_pattern, '', example['picture_text'])\r\n return example\r\n\r\nds = ds.map(get_text_caption)\r\n```\r\n\r\nI am trying to apply a regex to remove certain patterns from a text column. Not sure why this error is showing up." ]

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[ "Can you give us stats/information on your pandas DataFrame?", "```\r\n<class 'pandas.core.frame.DataFrame'>\r\nInt64Index: 17136104 entries, 0 to 17136103\r\nData columns (total 6 columns):\r\n # Column Dtype \r\n--- ------ ----- \r\n 0 item_id int64 \r\n 1 item_titl object \r\n 2 start_price float64\r\n 3 shipping_fee float64\r\n 4 picture_url object \r\n 5 embeddings object \r\ndtypes: float64(2), int64(1), object(3)\r\nmemory usage: 915.2+ MB\r\n```", "Thanks and some more on the `embeddings` and `picture_url` would be nice as well (type and max lengths of the elements)", "`embedding` is `np.array` of shape `(128,)`. `picture_url` is url, such as 'https://i.ebayimg.com/00/s/MTE5OVgxNjAw/z/ZOsAAOSwAG9fHQq5/$_12.JPG?set_id=880000500F;https://i.ebayimg.com/00/s/MTE5OVgxNjAw/z/OSgAAOSwokBfHQq8/$_12.JPG?set_id=880000500F'", "It looks like a Pyarrow limitation.\r\nI was able to reproduce the error with \r\n\r\n```python\r\nimport pandas as pd\r\nimport numpy as np\r\nimport pyarrow as pa\r\n\r\n n = 1713614\r\ndf = pd.DataFrame.from_dict({\"a\": list(np.zeros((n, 128))), \"b\": range(n)})\r\npa.Table.from_pandas(df)\r\n```\r\n\r\nI also tried with 50% of the dataframe and it actually works.\r\nI created an issue on Apache Arrow's JIRA [here](https://issues.apache.org/jira/browse/ARROW-9976)\r\n\r\nOne way to fix that would be to chunk the dataframe and concatenate arrow tables.", "It looks like it's going to be fixed in pyarrow 2.0.0 :)\r\n\r\nIn the meantime I suggest to chunk big dataframes to create several small datasets, and then concatenate them using [concatenate_datasets](https://huggingface.co/docs/datasets/package_reference/main_classes.html?highlight=concatenate#datasets.concatenate_datasets)" ]

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[ "Could you try\r\n```python\r\nload_dataset('text', data_files='test.txt',cache_dir=\"./\", split=\"train\")\r\n```\r\n?\r\n\r\n`load_dataset` returns a dictionary by default, like {\"train\": your_dataset}", "Hi @lhoestq\r\nThanks for your suggestion.\r\n\r\nI tried \r\n```\r\ndataset = load_dataset('text', data_files='test.txt',cache_dir=\"./\", split=\"train\")\r\nprint(dataset)\r\ndataset.set_format(type='torch',columns=[\"text\"])\r\ndataloader = torch.utils.data.DataLoader(dataset, batch_size=8)\r\nnext(iter(dataloader))\r\n```\r\n\r\nBut it still doesn't work and got error:\r\n```\r\n---------------------------------------------------------------------------\r\nTypeError Traceback (most recent call last)\r\n<ipython-input-7-388aca337e2f> in <module>\r\n----> 1 next(iter(dataloader))\r\n\r\n/Library/Python/3.7/site-packages/torch/utils/data/dataloader.py in __next__(self)\r\n 361 \r\n 362 def __next__(self):\r\n--> 363 data = self._next_data()\r\n 364 self._num_yielded += 1\r\n 365 if self._dataset_kind == _DatasetKind.Iterable and \\\r\n\r\n/Library/Python/3.7/site-packages/torch/utils/data/dataloader.py in _next_data(self)\r\n 401 def _next_data(self):\r\n 402 index = self._next_index() # may raise StopIteration\r\n--> 403 data = self._dataset_fetcher.fetch(index) # may raise StopIteration\r\n 404 if self._pin_memory:\r\n 405 data = _utils.pin_memory.pin_memory(data)\r\n\r\n/Library/Python/3.7/site-packages/torch/utils/data/_utils/fetch.py in fetch(self, possibly_batched_index)\r\n 42 def fetch(self, possibly_batched_index):\r\n 43 if self.auto_collation:\r\n---> 44 data = [self.dataset[idx] for idx in possibly_batched_index]\r\n 45 else:\r\n 46 data = self.dataset[possibly_batched_index]\r\n\r\n/Library/Python/3.7/site-packages/torch/utils/data/_utils/fetch.py in <listcomp>(.0)\r\n 42 def fetch(self, possibly_batched_index):\r\n 43 if self.auto_collation:\r\n---> 44 data = [self.dataset[idx] for idx in possibly_batched_index]\r\n 45 else:\r\n 46 data = self.dataset[possibly_batched_index]\r\n\r\n/Library/Python/3.7/site-packages/datasets-0.4.0-py3.7.egg/datasets/arrow_dataset.py in __getitem__(self, key)\r\n 1069 format_columns=self._format_columns,\r\n 1070 output_all_columns=self._output_all_columns,\r\n-> 1071 format_kwargs=self._format_kwargs,\r\n 1072 )\r\n 1073 \r\n\r\n/Library/Python/3.7/site-packages/datasets-0.4.0-py3.7.egg/datasets/arrow_dataset.py in _getitem(self, key, format_type, format_columns, output_all_columns, format_kwargs)\r\n 1056 format_columns=format_columns,\r\n 1057 output_all_columns=output_all_columns,\r\n-> 1058 format_kwargs=format_kwargs,\r\n 1059 )\r\n 1060 return outputs\r\n\r\n/Library/Python/3.7/site-packages/datasets-0.4.0-py3.7.egg/datasets/arrow_dataset.py in _convert_outputs(self, outputs, format_type, format_columns, output_all_columns, format_kwargs)\r\n 872 continue\r\n 873 if format_columns is None or k in format_columns:\r\n--> 874 v = map_nested(command, v, **map_nested_kwargs)\r\n 875 output_dict[k] = v\r\n 876 return output_dict\r\n\r\n/Library/Python/3.7/site-packages/datasets-0.4.0-py3.7.egg/datasets/utils/py_utils.py in map_nested(function, data_struct, dict_only, map_list, map_tuple, map_numpy, num_proc, types)\r\n 214 # Singleton\r\n 215 if not isinstance(data_struct, dict) and not isinstance(data_struct, types):\r\n--> 216 return function(data_struct)\r\n 217 \r\n 218 disable_tqdm = bool(logger.getEffectiveLevel() > INFO)\r\n\r\n/Library/Python/3.7/site-packages/datasets-0.4.0-py3.7.egg/datasets/arrow_dataset.py in command(x)\r\n 833 if x.dtype == np.object: # pytorch tensors cannot be instantied from an array of objects\r\n 834 return [map_nested(command, i, **map_nested_kwargs) for i in x]\r\n--> 835 return torch.tensor(x, **format_kwargs)\r\n 836 \r\n 837 elif format_type == \"tensorflow\":\r\n\r\nTypeError: new(): invalid data type 'str'\r\n```\r\n\r\nI found type can be ['numpy', 'torch', 'tensorflow', 'pandas'] only, how can I deal with the string type?", "You need to tokenize the string inputs to convert them in integers before you can feed them to a pytorch dataloader.\r\n\r\nYou can read the quicktour of the datasets or the transformers libraries to know more about that:\r\n- transformers: https://huggingface.co/transformers/quicktour.html\r\n- dataset: https://huggingface.co/docs/datasets/quicktour.html", "Hey @chiyuzhang94, I was also having trouble in loading a large text file (11GB).\r\nBut finally got it working. This is what I did after looking into the documentation.\r\n\r\n1. split the whole dataset file into smaller files\r\n```bash\r\nmkdir ./shards\r\nsplit -a 4 -l 256000 -d full_raw_corpus.txt ./shards/shard_\r\n````\r\n2. Pass paths of small data files to `load_dataset`\r\n```python\r\nfiles = glob.glob('shards/*')\r\nfrom datasets import load_dataset\r\ndataset = load_dataset('text', data_files=files, split='train')\r\n```\r\n(On passing the whole dataset file (11GB) directly to `load_dataset` was resulting into RAM issue)\r\n\r\n3. Tokenization\r\n```python\r\ndef encode(examples):\r\n return tokenizer(examples['text'], truncation=True, padding='max_length')\r\ndataset = dataset.map(encode, batched=True)\r\ndataset.set_format(type='torch', columns=['input_ids', 'attention_mask'])\r\n```\r\n Now you can pass `dataset` to `Trainer` or `pytorch DataLoader`\r\n```python\r\ndataloader = torch.utils.data.DataLoader(dataset, batch_size=4)\r\nnext(iter(dataloader))\r\n```\r\nHope this helps\r\n", "Thanks, @thomwolf and @sipah00 ,\r\n\r\nI tried to implement your suggestions in my scripts. \r\nNow, I am facing some connection time-out error. I am using my local file, I have no idea why the module request s3 database.\r\n\r\nThe log is:\r\n```\r\nTraceback (most recent call last):\r\n File \"/home/.local/lib/python3.6/site-packages/requests/adapters.py\", line 449, in send\r\n raise err\r\n File \"/home/.local/lib/python3.6/site-packages/urllib3/util/connection.py\", line 74, in create_connection\r\n timeout=timeout\r\n File \"/home/.local/lib/python3.6/site-packages/urllib3/connectionpool.py\", line 720, in urlopen\r\n sock.connect(sa)\r\nTimeoutError: [Errno 110] Connection timed out\r\n\r\nTraceback (most recent call last):\r\n File \"/home/.local/lib/python3.6/site-packages/urllib3/connectionpool.py\", line 672, in urlopen\r\n method, url, error=e, _pool=self, _stacktrace=sys.exc_info()[2]\r\n File \"/home/.local/lib/python3.6/site-packages/urllib3/util/retry.py\", line 436, in increment\r\n chunked=chunked,\r\n File \"/home/.local/lib/python3.6/site-packages/urllib3/connectionpool.py\", line 376, in _make_request\r\n raise MaxRetryError(_pool, url, error or ResponseError(cause))\r\nurllib3.exceptions.MaxRetryError: HTTPSConnectionPool(host='s3.amazonaws.com', port=443): Max retries exceeded with url: /datasets.huggingface.co/datasets/datasets/text/text.py (Caused by NewConnectionError('<urllib3.connection.VerifiedHTTPSConnection obj\r\nect at 0x7fff401e0e48>: Failed to establish a new connection: [Errno 110] Connection timed out',))\r\n\r\nTraceback (most recent call last):\r\n File \"/scratch/roberta_emohash/run_language_modeling.py\", line 1019, in <module>\r\n main()\r\n File \"/scratch/roberta_emohash/run_language_modeling.py\", line 962, in main\r\n train_dataset = load_and_cache_examples(args, tokenizer, evaluate=False)\r\n File \"/scratch/roberta_emohash/run_language_modeling.py\", line 177, in load_and_cache_examples\r\n return HG_Datasets(tokenizer, file_path, args)\r\n File \"/scratch/roberta_emohash/run_language_modeling.py\", line 117, in HG_Datasets\r\n dataset = load_dataset('text', data_files=files, cache_dir = args.data_cache_dir, split=\"train\")\r\n File \"/arc/project/evn_py36/datasets/datasets/src/datasets/load.py\", line 590, in load_dataset\r\n self._validate_conn(conn)\r\n File \"/home/.local/lib/python3.6/site-packages/urllib3/connectionpool.py\", line 994, in _validate_conn\r\n conn.connect()\r\n File \"/home/.local/lib/python3.6/site-packages/urllib3/connection.py\", line 300, in connect\r\n conn = self._new_conn()\r\n File \"/home/.local/lib/python3.6/site-packages/urllib3/connection.py\", line 169, in _new_conn\r\n self, \"Failed to establish a new connection: %s\" % e\r\nurllib3.exceptions.NewConnectionError: <urllib3.connection.VerifiedHTTPSConnection object at 0x7fff401e0da0>: Failed to establish a new connection: [Errno 110] Connection timed out\r\n\r\n``` \r\n\r\nDo you have any experience on this issue?", "No, I didn't encounter this problem, it seems to me a network problem", "I noticed this is because I use a cloud server where does not provide for connections from our standard compute nodes to outside resources. \r\n\r\nFor the `datasets` package, it seems that if the loading script is not already cached in the library it will attempt to connect to an AWS resource to download the dataset loading script. \r\n\r\nI am wondering why the package works in this way. Do you have any suggestions to solve this issue? ", "I solved the above issue by downloading text.py manually and passing the path to the `load_dataset` function. \r\n\r\nNow, I have a new issue with the Read-only file system.\r\n\r\nThe error is: \r\n```\r\nI0916 22:14:38.453380 140737353971520 filelock.py:274] Lock 140734268996072 acquired on /scratch/chiyuzh/roberta/text.py.lock\r\nFound main folder for dataset /scratch/chiyuzh/roberta/text.py at /home/chiyuzh/.cache/huggingface/modules/datasets_modules/datasets/text\r\nCreating specific version folder for dataset /scratch/chiyuzh/roberta/text.py at /home/chiyuzh/.cache/huggingface/modules/datasets_modules/datasets/text/512f465342e4f4cd07a8791428a629c043bb89d55ad7817cbf7fcc649178b014\r\nI0916 22:14:38.530371 140737353971520 filelock.py:318] Lock 140734268996072 released on /scratch/chiyuzh/roberta/text.py.lock\r\nTraceback (most recent call last):\r\n File \"/scratch/chiyuzh/roberta/run_language_modeling_hg.py\", line 1019, in <module>\r\n main()\r\n File \"/scratch/chiyuzh/roberta/run_language_modeling_hg.py\", line 962, in main\r\n train_dataset = load_and_cache_examples(args, tokenizer, evaluate=False)\r\n File \"/scratch/chiyuzh/roberta/run_language_modeling_hg.py\", line 177, in load_and_cache_examples\r\n return HG_Datasets(tokenizer, file_path, args)\r\n File \"/scratch/chiyuzh/roberta/run_language_modeling_hg.py\", line 117, in HG_Datasets\r\n dataset = load_dataset('/scratch/chiyuzh/roberta/text.py', data_files=files, cache_dir = args.data_cache_dir, split=\"train\")\r\n File \"/arc/project/chiyuzh/evn_py36/datasets/src/datasets/load.py\", line 590, in load_dataset\r\n path, script_version=script_version, download_config=download_config, download_mode=download_mode, dataset=True\r\n File \"/arc/project/chiyuzh/evn_py36/datasets/src/datasets/load.py\", line 385, in prepare_module\r\n os.makedirs(hash_folder_path)\r\n File \"/project/chiyuzh/evn_py36/lib/python3.6/os.py\", line 220, in makedirs\r\n mkdir(name, mode)\r\nOSError: [Errno 30] Read-only file system: '/home/chiyuzh/.cache/huggingface/modules/datasets_modules/datasets/text/512f465342e4f4cd07a8791428a629c043bb89d55ad7817cbf7fcc649178b014'\r\n\r\n```\r\n\r\nI installed datasets at /project/chiyuzh/evn_py36/datasets/src where is a writable directory.\r\nI also tried change the environment variables to the writable directory:\r\n`export HF_MODULES_PATH=/project/chiyuzh/evn_py36/datasets/cache_dir/`\r\n`export HF_DATASETS_CACHE=/project/chiyuzh/evn_py36/datasets/cache_dir/`\r\n \r\nIn my scripts, I also changed to:\r\n`dataset = load_dataset('/scratch/chiyuzh/roberta/text.py', data_files=files, cache_dir = args.data_cache_dir, split=\"train\")`\r\n`data_cache_dir = $TMPDIR/data/` that also a writable directory.\r\n \r\nBut it still try to make directory at /home/chiyuzh/.cache/huggingface/modules/.\r\nDo you have any idea about this issue? @thomwolf \r\n", "> Hey @chiyuzhang94, I was also having trouble in loading a large text file (11GB).\r\n> But finally got it working. This is what I did after looking into the documentation.\r\n> \r\n> 1. split the whole dataset file into smaller files\r\n> \r\n> ```shell\r\n> mkdir ./shards\r\n> split -a 4 -l 256000 -d full_raw_corpus.txt ./shards/shard_\r\n> ```\r\n> \r\n> 1. Pass paths of small data files to `load_dataset`\r\n> \r\n> ```python\r\n> files = glob.glob('shards/*')\r\n> from datasets import load_dataset\r\n> dataset = load_dataset('text', data_files=files, split='train')\r\n> ```\r\n> \r\n> (On passing the whole dataset file (11GB) directly to `load_dataset` was resulting into RAM issue)\r\n> \r\n> 1. Tokenization\r\n> \r\n> ```python\r\n> def encode(examples):\r\n> return tokenizer(examples['text'], truncation=True, padding='max_length')\r\n> dataset = dataset.map(encode, batched=True)\r\n> dataset.set_format(type='torch', columns=['input_ids', 'attention_mask'])\r\n> ```\r\n> \r\n> Now you can pass `dataset` to `Trainer` or `pytorch DataLoader`\r\n> \r\n> ```python\r\n> dataloader = torch.utils.data.DataLoader(dataset, batch_size=4)\r\n> next(iter(dataloader))\r\n> ```\r\n> \r\n> Hope this helps\r\n\r\nWhen I run 'dataset = dataset.map(encode, batched=True)',\r\nI encountered a problem like this:\r\n\r\n> Testing the mapped function outputs\r\nTraceback (most recent call last):\r\n File \"<stdin>\", line 1, in <module>\r\n File \"/anaconda3/envs/torch-xla-1.6/lib/python3.6/site-packages/datasets/dataset_dict.py\", line 300, in map\r\n for k, dataset in self.items()\r\n File \"/anaconda3/envs/torch-xla-1.6/lib/python3.6/site-packages/datasets/dataset_dict.py\", line 300, in <dictcomp>\r\n for k, dataset in self.items()\r\n File \"/anaconda3/envs/torch-xla-1.6/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 \"/anaconda3/envs/torch-xla-1.6/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 \"<stdin>\", line 3, in encode\r\nTypeError: __init__() takes 1 positional argument but 2 were given", "> > Hey @chiyuzhang94, I was also having trouble in loading a large text file (11GB).\r\n> > But finally got it working. This is what I did after looking into the documentation.\r\n> > \r\n> > 1. split the whole dataset file into smaller files\r\n> > \r\n> > ```shell\r\n> > mkdir ./shards\r\n> > split -a 4 -l 256000 -d full_raw_corpus.txt ./shards/shard_\r\n> > ```\r\n> > \r\n> > \r\n> > \r\n> > 1. Pass paths of small data files to `load_dataset`\r\n> > \r\n> > ```python\r\n> > files = glob.glob('shards/*')\r\n> > from datasets import load_dataset\r\n> > dataset = load_dataset('text', data_files=files, split='train')\r\n> > ```\r\n> > \r\n> > \r\n> > (On passing the whole dataset file (11GB) directly to `load_dataset` was resulting into RAM issue)\r\n> > \r\n> > 1. Tokenization\r\n> > \r\n> > ```python\r\n> > def encode(examples):\r\n> > return tokenizer(examples['text'], truncation=True, padding='max_length')\r\n> > dataset = dataset.map(encode, batched=True)\r\n> > dataset.set_format(type='torch', columns=['input_ids', 'attention_mask'])\r\n> > ```\r\n> > \r\n> > \r\n> > Now you can pass `dataset` to `Trainer` or `pytorch DataLoader`\r\n> > ```python\r\n> > dataloader = torch.utils.data.DataLoader(dataset, batch_size=4)\r\n> > next(iter(dataloader))\r\n> > ```\r\n> > \r\n> > \r\n> > Hope this helps\r\n> \r\n> When I run 'dataset = dataset.map(encode, batched=True)',\r\n> I encountered a problem like this:\r\n> \r\n> > Testing the mapped function outputs\r\n> > Traceback (most recent call last):\r\n> > File \"\", line 1, in \r\n> > File \"/anaconda3/envs/torch-xla-1.6/lib/python3.6/site-packages/datasets/dataset_dict.py\", line 300, in map\r\n> > for k, dataset in self.items()\r\n> > File \"/anaconda3/envs/torch-xla-1.6/lib/python3.6/site-packages/datasets/dataset_dict.py\", line 300, in \r\n> > for k, dataset in self.items()\r\n> > File \"/anaconda3/envs/torch-xla-1.6/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 \"/anaconda3/envs/torch-xla-1.6/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 \"\", line 3, in encode\r\n> > TypeError: **init**() takes 1 positional argument but 2 were given\r\n\r\nWhat is your encoder function?", "> ```python\r\n> def encode(examples):\r\n> return tokenizer(examples['text'], truncation=True, padding='max_length')\r\n> ```\r\n\r\nIt is the same as suggested:\r\n\r\n> def encode(examples):\r\n return tokenizer(examples['text'], truncation=True, padding='max_length')", "> > ```python\r\n> > def encode(examples):\r\n> > return tokenizer(examples['text'], truncation=True, padding='max_length')\r\n> > ```\r\n> \r\n> It is the same as suggested:\r\n> \r\n> > def encode(examples):\r\n> > return tokenizer(examples['text'], truncation=True, padding='max_length')\r\n\r\nDo you use this function in a `class` object? \r\n\r\ninit() takes 1 positional argument but 2 were given. I guess the additional argument is self?", "> > > ```python\r\n> > > def encode(examples):\r\n> > > return tokenizer(examples['text'], truncation=True, padding='max_length')\r\n> > > ```\r\n> > \r\n> > \r\n> > It is the same as suggested:\r\n> > > def encode(examples):\r\n> > > return tokenizer(examples['text'], truncation=True, padding='max_length')\r\n> \r\n> Do you use this function in a `class` object?\r\n> \r\n> init() takes 1 positional argument but 2 were given. I guess the additional argument is self?\r\n\r\nThanks for your reply.\r\nCould you provide some simple example here?\r\nCurrently, I do not use this function in a class object. \r\nI think you are right and I was wondering how to construct this class.\r\nI try to modify it based on transformers' LineByLineTextDataset. Am I correct?\r\n\r\n> class LineByLineTextDataset(Dataset):\r\n \"\"\"\r\n This will be superseded by a framework-agnostic approach\r\n soon.\r\n \"\"\"\r\n\r\n def __init__(self, tokenizer: PreTrainedTokenizer, file_path: str, block_size: int):\r\n assert os.path.isfile(file_path), f\"Input file path {file_path} not found\"\r\n # Here, we do not cache the features, operating under the assumption\r\n # that we will soon use fast multithreaded tokenizers from the\r\n # `tokenizers` repo everywhere =)\r\n #logger.info(\"Creating features from dataset file at %s\", file_path)\r\n #with open(file_path, encoding=\"utf-8\") as f:\r\n # lines = [line for line in f.read().splitlines() if (len(line) > 0 and not line.isspace())]\r\n #batch_encoding = tokenizer(lines, add_special_tokens=True, truncation=True, max_length=block_size)\r\n\r\n\timport glob\r\n\tfiles = glob.glob('/home/mtzhang111/fairseq/cs_doc/shards/shard_003*')\r\n\tfrom datasets import load_dataset\r\n\tdataset = load_dataset('text', data_files=files)\r\n batch_encoding= dataset.map(encode, batched=True)\r\n self.examples = batch_encoding[\"input_ids\"]\r\n\t\r\n\r\n def encode(examples):\r\n return tokenizer(examples['text'], truncation=True, padding='max_length')\r\n\r\n def __len__(self):\r\n return len(self.examples)\r\n\r\n def __getitem__(self, i) -> torch.Tensor:\r\n return torch.tensor(self.examples[i], dtype=torch.long)\r\n", "> > > > ```python\r\n> > > > def encode(examples):\r\n> > > > return tokenizer(examples['text'], truncation=True, padding='max_length')\r\n> > > > ```\r\n> > > \r\n> > > \r\n> > > It is the same as suggested:\r\n> > > > def encode(examples):\r\n> > > > return tokenizer(examples['text'], truncation=True, padding='max_length')\r\n> > \r\n> > \r\n> > Do you use this function in a `class` object?\r\n> > init() takes 1 positional argument but 2 were given. I guess the additional argument is self?\r\n> \r\n> Thanks for your reply.\r\n> Could you provide some simple example here?\r\n> Currently, I do not use this function in a class object.\r\n> I think you are right and I was wondering how to construct this class.\r\n> I try to modify it based on transformers' LineByLineTextDataset. Am I correct?\r\n> \r\n> > class LineByLineTextDataset(Dataset):\r\n> > \"\"\"\r\n> > This will be superseded by a framework-agnostic approach\r\n> > soon.\r\n> > \"\"\"\r\n> \r\n> ```\r\n> def __init__(self, tokenizer: PreTrainedTokenizer, file_path: str, block_size: int):\r\n> assert os.path.isfile(file_path), f\"Input file path {file_path} not found\"\r\n> # Here, we do not cache the features, operating under the assumption\r\n> # that we will soon use fast multithreaded tokenizers from the\r\n> # `tokenizers` repo everywhere =)\r\n> #logger.info(\"Creating features from dataset file at %s\", file_path)\r\n> #with open(file_path, encoding=\"utf-8\") as f:\r\n> # lines = [line for line in f.read().splitlines() if (len(line) > 0 and not line.isspace())]\r\n> #batch_encoding = tokenizer(lines, add_special_tokens=True, truncation=True, max_length=block_size)\r\n> \r\n> import glob\r\n> files = glob.glob('/home/mtzhang111/fairseq/cs_doc/shards/shard_003*')\r\n> from datasets import load_dataset\r\n> dataset = load_dataset('text', data_files=files)\r\n> batch_encoding= dataset.map(encode, batched=True)\r\n> self.examples = batch_encoding[\"input_ids\"]\r\n> \r\n> \r\n> def encode(examples):\r\n> return tokenizer(examples['text'], truncation=True, padding='max_length')\r\n> \r\n> def __len__(self):\r\n> return len(self.examples)\r\n> \r\n> def __getitem__(self, i) -> torch.Tensor:\r\n> return torch.tensor(self.examples[i], dtype=torch.long)\r\n> ```\r\n\r\nI am also struggling with this adaptation. \r\nI am not sure whether I am right.\r\n\r\nI think you don't need to construct `class LazyLineByLineTextDataset(Dataset)` at all. \r\ntorch.utils.data.Dataset is a generator. \r\n\r\nNow, we use `dataset = dataset.map(encode, batched=True)` as a generator. So we just pass dataset to torch.utils.data.DataLoader. ", "@chiyuzhang94 Thanks for your reply. After some changes, currently, I managed to make the data loading process running.\r\nI published it in case you might want to take a look. Thanks for your help!\r\nhttps://github.com/shizhediao/Transformers_TPU", "Hi @shizhediao ,\r\n\r\nThanks! It looks great!\r\n\r\nBut my problem still is the cache directory is a read-only file system. \r\n[As I mentioned](https://github.com/huggingface/datasets/issues/610#issuecomment-693912285), I tried to change the cache directory but it didn't work. \r\n\r\nDo you have any suggestions?\r\n\r\n", "> I installed datasets at /project/chiyuzh/evn_py36/datasets/src where is a writable directory.\r\n> I also tried change the environment variables to the writable directory:\r\n> `export HF_MODULES_PATH=/project/chiyuzh/evn_py36/datasets/cache_dir/`\r\n\r\nI think it is `HF_MODULES_CACHE` and not `HF_MODULES_PATH` @chiyuzhang94 .\r\nCould you try again and let me know if it fixes your issue ?\r\n", "We should probably add a section in the doc on the caching system with the env variables in particular.", "Hi @thomwolf , @lhoestq ,\r\n\r\nThanks for your suggestions. With the latest version of this package, I can load text data without Internet. \r\n\r\nBut I found the speed of dataset loading is very slow. \r\n\r\nMy scrips like this: \r\n```\r\n def token_encode(examples):\r\n tokenizer_out = tokenizer(examples['text'], truncation=True, padding=\"max_length\", add_special_tokens=True, max_length=args.block_size)\r\n return tokenizer_out\r\n \r\n path = Path(file_path)\r\n files = sorted(path.glob('*'))\r\n dataset = load_dataset('./text.py', data_files=files, cache_dir = args.data_cache_dir, split=\"train\")\r\n dataset = dataset.map(token_encode, batched=True)\r\n\r\n dataset.set_format(type='torch', columns=['input_ids', 'attention_mask'])\r\n```\r\n\r\nI have 1,123,870,657 lines in my input directory. \r\nI can find the processing speed as following. It is very slow. \r\n```\r\n| 13/1123871 [00:02<62:37:39, 4.98ba/s]^M 0%| \r\n| 14/1123871 [00:03<61:27:31, 5.08ba/s]^M 0%| \r\n| 15/1123871 [00:03<66:34:19, 4.69ba/s]^M 0%| \r\n| 16/1123871 [00:03<68:25:01, 4.56ba/s]^M 0%| \r\n| 17/1123871 [00:03<72:00:03, 4.34ba/s]^M 0%| \r\n```\r\nDo you have any suggestions to accelerate this loading process?", "You can use multiprocessing by specifying `num_proc=` in `.map()`\r\n\r\nAlso it looks like you have `1123871` batches of 1000 elements (default batch size), i.e. 1,123,871,000 lines in total.\r\nAm I right ?", "> You can use multiprocessing by specifying `num_proc=` in `.map()`\r\n> \r\n> Also it looks like you have `1123871` batches of 1000 elements (default batch size), i.e. 1,123,871,000 lines in total.\r\n> Am I right ?\r\n\r\nHi @lhoestq ,\r\n\r\nThanks. I will try it.\r\n\r\nYou are right. I have 1,123,870,657 lines totally in the path. I split the large file into 440 small files. Each file has 2,560,000 lines.\r\n\r\nI have another question. Because I am using a cloud server where only allows running a job up to 7 days. Hence, I need to resume my model every week. If the script needs to load and process the dataset every time. It is very low efficient based on the current processing speed. Is it possible that I process the dataset once and use the process cache to in the future work? \r\n", "Hi @lhoestq ,\r\n\r\nI tried to use multi-processor, but I got errors as follow: \r\nBecause I am using python distributed training, it seems some conflicts with the distributed job. \r\n\r\nDo you have any suggestions?\r\n```\r\nI0925 10:19:35.603023 140737353971520 filelock.py:318] Lock 140737229443368 released on /tmp/pbs.1120510.pbsha.ib.sockeye/cache/_tmp_pbs.1120510.pbsha.ib.sockeye_cache_text_default-7fb934ed6fac5d01_0.0.0_512f465342e4f4cd07a8791428a629c043bb89d55ad7817cbf7\r\nfcc649178b014.lock\r\nTraceback (most recent call last):\r\n File \"/scratch/chiyuzh/roberta/run_language_modeling.py\", line 1024, in <module>\r\n main()\r\n File \"/scratch/chiyuzh/roberta/run_language_modeling.py\", line 967, in main\r\n train_dataset = load_and_cache_examples(args, tokenizer, evaluate=False)\r\n File \"/scratch/chiyuzh/roberta/run_language_modeling.py\", line 180, in load_and_cache_examples\r\n return HG_Datasets(tokenizer, file_path, args)\r\n File \"/scratch/chiyuzh/roberta/run_language_modeling.py\", line 119, in HG_Datasets\r\n dataset = dataset.map(token_encode, batched=True, batch_size = 10000, num_proc = 16)\r\n File \"/project/chiyuzh/evn_py36/lib/python3.6/site-packages/datasets/arrow_dataset.py\", line 1287, in map\r\n transformed_shards = [r.get() for r in results]\r\n File \"/project/chiyuzh/evn_py36/lib/python3.6/site-packages/datasets/arrow_dataset.py\", line 1287, in <listcomp>\r\n transformed_shards = [r.get() for r in results]\r\n File \"/project/chiyuzh/evn_py36/lib/python3.6/multiprocessing/pool.py\", line 644, in get\r\n raise self._value\r\n File \"/project/chiyuzh/evn_py36/lib/python3.6/multiprocessing/pool.py\", line 424, in _handle_tasks\r\n put(task)\r\n File \"/project/chiyuzh/evn_py36/lib/python3.6/multiprocessing/connection.py\", line 206, in send\r\n self._send_bytes(_ForkingPickler.dumps(obj))\r\n File \"/project/chiyuzh/evn_py36/lib/python3.6/multiprocessing/reduction.py\", line 51, in dumps\r\n cls(buf, protocol).dump(obj)\r\nAttributeError: Can't pickle local object 'HG_Datasets.<locals>.token_encode'\r\n```", "For multiprocessing, the function given to `map` must be picklable.\r\nMaybe you could try to define `token_encode` outside `HG_Datasets` ?\r\n\r\nAlso maybe #656 could make functions defined locally picklable for multiprocessing, once it's merged.", "> I have another question. Because I am using a cloud server where only allows running a job up to 7 days. Hence, I need to resume my model every week. If the script needs to load and process the dataset every time. It is very low efficient based on the current processing speed. Is it possible that I process the dataset once and use the process cache to in the future work?\r\n\r\nFeel free to save your processed dataset using `dataset.save_to_disk(\"path/to/save/directory\")`.\r\n\r\nThen you'll be able to reload it again using\r\n```python\r\nfrom datasets import load_from_disk\r\n\r\ndataset = load_from_disk(\"path/to/save/directory\")\r\n```", "Hi @lhoestq ,\r\n\r\nThanks for your suggestion. \r\nI tried to process the dataset and save it to disk. \r\nI have 1.12B samples in the raw dataset. I used 16 processors.\r\nI run this process job for 7 days. But it didn't finish. I don't why the processing is such slow. \r\n\r\nThe log shows that some processors (\\#12, \\#14, \\#15) are very slow. The different processor has a different speed. These slow processors look like a bottleneck. \r\n\r\nCould you please give me any suggestion to improve the processing speed? \r\n\r\nThanks. \r\nChiyu\r\n\r\nHere is my code:\r\n```\r\ndef token_encode(examples):\r\n tokenizer_out = tokenizer(examples['text'], truncation=True, padding=\"max_length\", add_special_tokens=True, max_length=args.block_size)\r\n return tokenizer_out\r\n\r\npath = Path(file_path)\r\nfiles = sorted(path.glob('*'))\r\ndataset = load_dataset('./text.py', data_files=files, cache_dir = args.data_cache_dir, split=\"train\")\r\ndataset = dataset.map(token_encode, batched=True, batch_size = 16384, num_proc = 16)\r\ndataset.set_format(type='torch', columns=['input_ids', 'attention_mask'])\r\ndataset.save_to_disk(output_dir)\r\n```\r\nHere is the log. \r\n```\r\n^M#6: 1%|▏ | 59/4288 [55:10<66:11:58, 56.35s/ba]\r\n^M#1: 8%|▊ | 356/4288 [55:39<10:40:02, 9.77s/ba]\r\n^M#2: 5%|▍ | 210/4288 [55:33<17:47:19, 15.70s/ba]\r\n^M#0: 19%|█▉ | 836/4288 [55:53<4:08:56, 4.33s/ba]\r\n^M#0: 20%|█▉ | 837/4288 [55:57<4:01:52, 4.21s/ba]\r\n^M#1: 8%|▊ | 357/4288 [55:48<10:38:09, 9.74s/ba]\r\n^M#0: 20%|█▉ | 838/4288 [56:01<4:02:56, 4.23s/ba]\r\n^M#3: 4%|▎ | 155/4288 [55:43<24:41:20, 21.51s/ba]\r\n^M#0: 20%|█▉ | 839/4288 [56:05<4:04:48, 4.26s/ba]\r\n^M#12: 1%| | 29/4288 [54:50<133:20:53, 112.72s/ba]\r\n^M#2: 5%|▍ | 211/4288 [55:48<17:40:33, 15.61s/ba]\r\n^M#14: 0%| | 2/4288 [04:24<157:17:50, 132.12s/ba]\r\n^M#15: 0%| | 1/4288 [02:24<172:11:37, 144.60s/ba]\r\n```", "Hi !\r\n\r\nAs far as I can tell, there could be several reasons for your processes to have different speeds:\r\n- some parts of your dataset have short passages while some have longer passages, that take more time to be processed\r\n- OR there are other processes running that prevent some of them to run at full speed\r\n- OR the value of `num_proc` is higher than the number of actual processes that you can run in parallel at full speed.\r\n\r\nSo I'd suggest you to check that you have nothing else running in parallel to your processing job, and also maybe take a look at the slow parts of the datasets.\r\nWhen doing multiprocessing, the dataset is sharded in `num_proc` contiguous parts that are processed individually in each process. If you want to take a look at the dataset processed in the 12th shard of 16 for example, you can do:\r\n\r\n```python\r\nmy_shard = dataset.shard(num_shards=16, index=12, contiguous=True)\r\n```\r\n\r\nHope this helps, let me know if you find what is causing this slow down.", "Do you use a fast or a slow tokenizer from the `transformers` library @chiyuzhang94?", "> Do you use a fast or a slow tokenizer from the `transformers` library @chiyuzhang94?\r\n\r\nHi @thomwolf ,\r\n I use this: \r\n```\r\nfrom transformers import\r\nAutoTokenizer.from_pretrained(args.model_name_or_path, cache_dir=args.cache_dir)\r\n```\r\n\r\nI guess this is a slow one, let me explore the fast tokenizer. ", "> Hi !\r\n> \r\n> As far as I can tell, there could be several reasons for your processes to have different speeds:\r\n> \r\n> * some parts of your dataset have short passages while some have longer passages, that take more time to be processed\r\n> * OR there are other processes running that prevent some of them to run at full speed\r\n> * OR the value of `num_proc` is higher than the number of actual processes that you can run in parallel at full speed.\r\n> \r\n> So I'd suggest you to check that you have nothing else running in parallel to your processing job, and also maybe take a look at the slow parts of the datasets.\r\n> When doing multiprocessing, the dataset is sharded in `num_proc` contiguous parts that are processed individually in each process. If you want to take a look at the dataset processed in the 12th shard of 16 for example, you can do:\r\n> \r\n> ```python\r\n> my_shard = dataset.shard(num_shards=16, index=12, contiguous=True)\r\n> ```\r\n> \r\n> Hope this helps, let me know if you find what is causing this slow down.\r\n\r\nHi @lhoestq ,\r\n\r\nThanks for your suggestions. \r\nI don't think my problem is due to any one of these seasons. \r\n\r\n1. I have 1,123,870,657 lines totally in the path. I split the large file into 440 small files. Each file has 2,560,000 lines. The last file is smaller a little bit. But they are similar. I randomly shuffled all the 1,123,870,657 lines. Hence, the sequences should also be similar across all the files. \r\n\r\n2. I run this script on the entire node. I requested all the resources on the nodes (40 CPUs, 384GB memory). Hence, these were not any other processes. \r\n\r\n 3. As I say, the node has 40 CPUs, but I set num_proc = 16. This should not be a problem.", "Hi @thomwolf \r\nI am using `RobertaTokenizerFast` now. \r\n\r\nBut the speed is still imbalanced, some processors are still slow. \r\nHere is the part of the log. #0 is always much fast than lower rank processors. \r\n\r\n```\r\n#15: 3%|▎ | 115/3513 [3:18:36<98:01:33, 103.85s/ba]\r\n#2: 24%|██▍ | 847/3513 [3:20:43<11:06:49, 15.01s/ba]\r\n#1: 37%|███▋ | 1287/3513 [3:20:52<6:19:02, 10.22s/ba]\r\n#0: 72%|███████▏ | 2546/3513 [3:20:52<1:51:03, 6.89s/ba]\r\n#3: 18%|█▊ | 617/3513 [3:20:36<15:50:30, 19.69s/ba]\r\n#0: 73%|███████▎ | 2547/3513 [3:20:59<1:50:25, 6.86s/ba]\r\n#1: 37%|███▋ | 1288/3513 [3:21:02<6:21:13, 10.28s/ba]\r\n#7: 7%|▋ | 252/3513 [3:20:09<44:09:03, 48.74s/ba]\r\n#12: 4%|▍ | 144/3513 [3:19:19<78:00:54, 83.36s/ba]\r\n#4: 14%|█▍ | 494/3513 [3:20:37<20:46:06, 24.77s/ba]\r\n#0: 73%|███████▎ | 2548/3513 [3:21:06<1:49:26, 6.80s/ba]\r\n#2: 24%|██▍ | 848/3513 [3:20:58<11:06:17, 15.00s/ba]\r\n```\r\nHere is my script related to the datasets processing, \r\n\r\n```\r\ntokenizer = RobertaTokenizerFast.from_pretrained(args.model_name_or_path, cache_dir=args.cache_dir)\r\n\r\ndef token_encode(examples):\r\n tokenizer_out = tokenizer(examples['text'], truncation=True, padding=\"max_length\", add_special_tokens=True, max_length=128)\r\n return tokenizer_out\r\n\r\ndef HG_Datasets(tokenizer, file_path, args):\r\n \r\n path = Path(file_path)\r\n files = sorted(path.glob('*'))\r\n dataset = load_dataset('./text.py', data_files=files, cache_dir = \"\"./, split=\"train\")\r\n dataset = dataset.map(token_encode, batched=True, batch_size = 20000, num_proc = 16)\r\n\r\n dataset.set_format(type='torch', columns=['input_ids', 'attention_mask'])\r\n return dataset\r\n\r\n```\r\nI have 1,123,870,657 lines totally in the path. I split the large file into 440 small files. Each file has 2,560,000 lines.\r\n\r\nCould you please give any suggestion? Thanks very much!!" ]

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[ "Feel free to open the PR ;)\r\nThanks for updating the dataset_info.json file !" ]

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[ "When I change from python3.6 to python3.8, it works! ", "Does it work when you install `nlp` from source on python 3.6?", "No, still the pickling error.", "I wasn't able to reproduce on google colab (python 3.6.9 as well) with \r\n\r\npickle==4.0\r\ndill=0.3.2\r\ntransformers==3.1.0\r\ndatasets=1.0.1 (also tried nlp 0.4.0)\r\n\r\nIf I try\r\n\r\n```python\r\nfrom datasets import load_dataset # or from nlp\r\nfrom transformers import BertTokenizer\r\n\r\ntokenizer = BertTokenizer.from_pretrained(\"bert-base-uncased\")\r\ndataset = load_dataset(\"text\", data_files=file_path, split=\"train\")\r\ndataset = dataset.map(lambda ex: tokenizer(ex[\"text\"], add_special_tokens=True,\r\n truncation=True, max_length=512), batched=True)\r\ndataset.set_format(type='torch', columns=['input_ids'])\r\n```\r\nIt runs without error", "Closing since it looks like it's working on >= 3.6.9\r\nFeel free to re-open if you have other questions :)" ]

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[ "Thanks for reporting !\r\nWhich version of transformers are you using ?\r\nIt looks like it doesn't have the PreTrainedTokenizerBase class", "I was using transformer 2.9. And I switch to the latest transformer package. Everything works just fine!!\r\n\r\nThanks for helping! I should look more carefully next time. Didn't realize loading the data part requires using tokenizer.\r\n", "Yes it shouldn’t fail with older version of transformers since this is only a special feature to make caching more efficient when using transformers for tokenization.\r\nWe’ll update this." ]

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[ "I fixed a bug that could cause this issue earlier today. Could you pull the latest version and try again ?", "Still the case on master.\r\nI guess we should have an offset in the multi-procs indeed (hopefully it's enough).\r\n\r\nAlso, side note is that we should add some logging before the \"test\" to say we are testing the function otherwise its confusing for the user to see two outputs I think. Proposal (see the \"Testing the mapped function outputs:\" lines):\r\n```\r\n>>> d.select(range(10)).map(fn, with_indices=True, batched=True, num_proc=2)\r\nDone writing 10 indices in 80 bytes .\r\nDone writing 5 indices in 41 bytes .\r\nDone writing 5 indices in 41 bytes .\r\nSpawning 2 processes\r\nTesting the mapped function outputs:\r\ninds: [0, 1]\r\ninds: [0, 1]\r\nTesting finished, running the mapped function on the dataset:\r\n#0: 0%| | 0/1 [00:00<?, ?ba/s]\r\ninds: [0, 1, 2, 3, 4] inds: [0, 1, 2, 3, 4] | 0/1 [00:00<?, ?ba/s]\r\n#0: 100%|██████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████| 1/1 [00:00<00:00, 1321.04ba/s]\r\n#1: 100%|██████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████| 1/1 [00:00<00:00, 1841.22ba/s]\r\nConcatenating 2 shards from multiprocessing\r\nDataset(features: {'text': Value(dtype='string', id=None), 'label': ClassLabel(num_classes=2, names=['neg', 'pos'], names_file=None, id=None)}, num_rows: 10)\r\n```" ]

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[ "`pip install git+https://github.com/huggingface/nlp.git` should have done the job.\r\n\r\nDid you uninstall `nlp` before installing from github ?", "> Did you uninstall `nlp` before installing from github ?\r\n\r\nI did not. I created a new environment and installed `nlp` directly from `github` and it worked!\r\n\r\nThanks.\r\n" ]

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[ "Hi, which version of `nlp` are you using?\r\n\r\nBy the way we'll be releasing today a significant update fixing many issues (but also comprising a few breaking changes).\r\nYou can see more informations here #545 and try it by installing from source from the master branch.", "I'm using version 0.4.0.\r\n\r\n", "Ok, it's probably fixed on master. Otherwise if you can give me a fully self-contained exemple to reproduce the error, I can try to investigate.", "I get the same behavior, on Windows, when `map`ping a function to a loaded dataset. \r\nThe error doesn't occur if I re-run the cell a second time though! \r\nI'm on version 1.0.1.", "This is going to be fixed by #644 ", "@saareliad I got the same issue that troubled me quite a while. Unfortunately, there are no good answers to this issue online, I tried it on Linux and that's absolutely fine. After hacking the source code, I solved this problem as follows.\r\n\r\nIn the source code file: arrow_dataset.py -> _map_single(...)\r\n\r\nchange\r\n```python\r\nif update_data and tmp_file is not None:\r\n shutil.move(tmp_file.name, cache_file_name)\r\n```\r\nto\r\n```python\r\ntmp_file.close()\r\nif update_data and tmp_file is not None:\r\n shutil.move(tmp_file.name, cache_file_name)\r\n```\r\n\r\nThen it works without needing multiple times runs to avoid the permission error.\r\nI know this solution is unusual since it changes the source code. Hopefully, the lib's contributors can have better solutions in the future.\r\n", "@wangcongcong123 thanks for sharing.\n(BTW I also solved it locally on windows by putting the problematic line under try except and not using cache... On windows I just needed 1% of the dataset anyway)" ]

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[ "Couln't reproduce on my side :/ \r\nlet me know if you manage to reproduce on another env (colab for example)", "Fixed with a clean re-install!" ]

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[ "Some wikipedia languages have already been processed by us and are hosted on our google storage. This is the case for \"fr\" and \"en\" for example.\r\n\r\nFor other smaller languages (in terms of bytes), they are directly downloaded and parsed from the wikipedia dump site.\r\nParsing can take some time for languages with hundreds of MB of xml.\r\n\r\nLet me know if you encounter an error or if you feel that is is taking too long for you.\r\nWe could process those that really take too much time", "Ok, thanks for clarifying, that makes sense. I will time those examples later today and post back here.\r\n\r\nAlso, it seems that not all dumps should use the same date. For instance, I was checking the Spanish dump doing the following:\r\n```\r\ndata = nlp.load_dataset('wikipedia', '20200501.es', beam_runner='DirectRunner', split='train')\r\n```\r\n\r\nI got the error below because this URL does not exist: https://dumps.wikimedia.org/eswiki/20200501/dumpstatus.json. So I checked the actual available dates here https://dumps.wikimedia.org/eswiki/ and there is no 20200501. If one tries for a date available in the link, then the nlp library does not allow such a request because is not in the list of expected datasets.\r\n\r\n```\r\nDownloading and preparing dataset wikipedia/20200501.es (download: Unknown size, generated: Unknown size, post-processed: Unknown sizetotal: Unknown size) to /home/gaguilar/.cache/huggingface/datasets/wikipedia/20200501.es/1.0.0/7be7f4324255faf70687be8692de57cf79197afdc33ff08d6a04ed602df32d50...\r\nTraceback (most recent call last):\r\n File \"<stdin>\", line 1, in <module>\r\n File \"/home/gaguilar/.conda/envs/pytorch/lib/python3.8/site-packages/nlp/load.py\", line 548, in load_dataset\r\n builder_instance.download_and_prepare(\r\n File \"/home/gaguilar/.conda/envs/pytorch/lib/python3.8/site-packages/nlp/builder.py\", line 462, in download_and_prepare\r\n self._download_and_prepare(\r\n File \"/home/gaguilar/.conda/envs/pytorch/lib/python3.8/site-packages/nlp/builder.py\", line 965, in _download_and_prepare\r\n super(BeamBasedBuilder, self)._download_and_prepare(\r\n File \"/home/gaguilar/.conda/envs/pytorch/lib/python3.8/site-packages/nlp/builder.py\", line 518, in _download_and_prepare\r\n split_generators = self._split_generators(dl_manager, **split_generators_kwargs)\r\n File \"/home/gaguilar/.conda/envs/pytorch/lib/python3.8/site-packages/nlp/datasets/wikipedia/7be7f4324255faf70687be8692de57cf79197afdc33ff08d6a04ed602df32d50/wikipedia.py\", line 422, in _split_generators\r\n downloaded_files = dl_manager.download_and_extract({\"info\": info_url})\r\n File \"/home/gaguilar/.conda/envs/pytorch/lib/python3.8/site-packages/nlp/utils/download_manager.py\", line 220, in download_and_extract\r\n return self.extract(self.download(url_or_urls))\r\n File \"/home/gaguilar/.conda/envs/pytorch/lib/python3.8/site-packages/nlp/utils/download_manager.py\", line 155, in download\r\n downloaded_path_or_paths = map_nested(\r\n File \"/home/gaguilar/.conda/envs/pytorch/lib/python3.8/site-packages/nlp/utils/py_utils.py\", line 163, in map_nested\r\n return {\r\n File \"/home/gaguilar/.conda/envs/pytorch/lib/python3.8/site-packages/nlp/utils/py_utils.py\", line 164, in <dictcomp>\r\n k: map_nested(\r\n File \"/home/gaguilar/.conda/envs/pytorch/lib/python3.8/site-packages/nlp/utils/py_utils.py\", line 191, in map_nested\r\n return function(data_struct)\r\n File \"/home/gaguilar/.conda/envs/pytorch/lib/python3.8/site-packages/nlp/utils/download_manager.py\", line 156, in <lambda>\r\n lambda url: cached_path(url, download_config=self._download_config,), url_or_urls,\r\n File \"/home/gaguilar/.conda/envs/pytorch/lib/python3.8/site-packages/nlp/utils/file_utils.py\", line 191, in cached_path\r\n output_path = get_from_cache(\r\n File \"/home/gaguilar/.conda/envs/pytorch/lib/python3.8/site-packages/nlp/utils/file_utils.py\", line 356, in get_from_cache\r\n raise ConnectionError(\"Couldn't reach {}\".format(url))\r\nConnectionError: Couldn't reach https://dumps.wikimedia.org/eswiki/20200501/dumpstatus.json\r\n```", "Thanks ! This will be very helpful.\r\n\r\nAbout the date issue, I think it's possible to use another date with\r\n\r\n```python\r\nload_dataset(\"wikipedia\", language=\"es\", date=\"...\", beam_runner=\"...\")\r\n```\r\n\r\nHowever we've not processed wikipedia dumps for other dates than 20200501 (yet ?)\r\n\r\nOne more thing that is specific to 20200501.es: it was available once but the `mwparserfromhell` was not able to parse it for some reason, so we didn't manage to get a processed version of 20200501.es (see #321 )", "Cool! Thanks for the trick regarding different dates!\r\n\r\nI checked the download/processing time for retrieving the Arabic Wikipedia dump, and it took about 3.2 hours. I think that this may be a bit impractical when it comes to working with multiple languages (although I understand that storing those datasets in your Google storage may not be very appealing either). \r\n\r\nFor the record, here's what I did:\r\n```python\r\nimport nlp\r\nimport time\r\n\r\ndef timeit(filename):\r\n elapsed = time.time()\r\n data = nlp.load_dataset('wikipedia', filename, beam_runner='DirectRunner', split='train')\r\n elapsed = time.time() - elapsed\r\n print(f\"Loading the '{filename}' data took {elapsed:,.1f} seconds...\")\r\n return data\r\n\r\ndata = timeit('20200501.ar')\r\n```\r\n\r\nHere's the output:\r\n```\r\nDownloading: 100%|█████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████| 13.0k/13.0k [00:00<00:00, 8.34MB/s]\r\nDownloading: 100%|██████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████| 28.7k/28.7k [00:00<00:00, 954kB/s]\r\nDownloading and preparing dataset wikipedia/20200501.ar (download: Unknown size, generated: Unknown size, post-processed: Unknown sizetotal: Unknown size) to /home/gaguil20/.cache/huggingface/datasets/wikipedia/20200501.ar/1.0.0/7be7f4324255faf70687be8692de57cf79197afdc33ff08d6a04ed602df32d50...\r\nDownloading: 100%|█████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████| 47.4k/47.4k [00:00<00:00, 1.40MB/s]\r\nDownloading: 100%|█████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████| 79.8M/79.8M [00:15<00:00, 5.13MB/s]\r\nDownloading: 100%|███████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████| 171M/171M [00:33<00:00, 5.13MB/s]\r\nDownloading: 100%|███████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████| 103M/103M [00:20<00:00, 5.14MB/s]\r\nDownloading: 100%|███████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████| 227M/227M [00:44<00:00, 5.06MB/s]\r\nDownloading: 100%|███████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████| 140M/140M [00:28<00:00, 4.96MB/s]\r\nDownloading: 100%|███████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████| 160M/160M [00:30<00:00, 5.20MB/s]\r\nDownloading: 100%|█████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████| 97.5M/97.5M [00:19<00:00, 5.06MB/s]\r\nDownloading: 100%|███████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████| 222M/222M [00:42<00:00, 5.21MB/s]\r\n100%|███████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████| 1/1 [03:16<00:00, 196.39s/sources]\r\nDataset wikipedia downloaded and prepared to /home/gaguil20/.cache/huggingface/datasets/wikipedia/20200501.ar/1.0.0/7be7f4324255faf70687be8692de57cf79197afdc33ff08d6a04ed602df32d50. Subsequent calls will reuse this data.\r\nLoading the '20200501.ar' data took 11,582.7 seconds...\r\n````", "> About the date issue, I think it's possible to use another date with\r\n> ```python\r\n> load_dataset(\"wikipedia\", language=\"es\", date=\"...\", beam_runner=\"...\")\r\n> ```\r\n\r\nI tried your suggestion about the date and the function does not accept the language and date keywords. I tried both on `nlp` v0.4 and the new `datasets` library (v1.0.2):\r\n```\r\nload_dataset(\"wikipedia\", language=\"es\", date=\"20200601\", beam_runner='DirectRunner', split='train')\r\n```\r\nFor now, my quick workaround to keep things moving was to simply change the date inside the library at this line: [https://github.com/huggingface/datasets/blob/master/datasets/wikipedia/wikipedia.py#L403](https://github.com/huggingface/datasets/blob/master/datasets/wikipedia/wikipedia.py#L403)\r\n\r\nNote that the date and languages are valid: [https://dumps.wikimedia.org/eswiki/20200601/dumpstatus.json](https://dumps.wikimedia.org/eswiki/20200601/dumpstatus.json)\r\n\r\nAny suggestion is welcome :) @lhoestq \r\n\r\n\r\n## **[UPDATE]**\r\n\r\nThe workaround I mentioned fetched the data, but then I faced another issue (even the log says to report this as bug):\r\n```\r\nERROR:root:mwparserfromhell ParseError: This is a bug and should be reported. Info: C tokenizer exited with non-empty token stack.\r\n```\r\n\r\nHere's the full stack (which says that there is a key error caused by this key: `KeyError: '000nbsp'`):\r\n\r\n```Downloading and preparing dataset wikipedia/20200601.es (download: Unknown size, generated: Unknown size, post-processed: Unknown sizetotal: Unknown size) to /home/gustavoag/.cache/huggingface/datasets/wikipedia/20200601.es/1.0.0/7be7f4324255faf70687be8692de57cf79197afdc33ff08d6a04ed602df32d50...\r\nDownloading: 100%|██████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████| 74.7k/74.7k [00:00<00:00, 1.53MB/s]\r\nDownloading: 100%|████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████| 232M/232M [00:48<00:00, 4.75MB/s]\r\nDownloading: 100%|████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████| 442M/442M [01:39<00:00, 4.44MB/s]\r\nDownloading: 100%|████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████| 173M/173M [00:33<00:00, 5.12MB/s]\r\nDownloading: 100%|████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████| 344M/344M [01:14<00:00, 4.59MB/s]\r\nDownloading: 100%|████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████| 541M/541M [01:59<00:00, 4.52MB/s]\r\nDownloading: 100%|████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████| 476M/476M [01:31<00:00, 5.18MB/s]\r\nDownloading: 100%|████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████| 545M/545M [02:02<00:00, 4.46MB/s]\r\nDownloading: 100%|████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████| 299M/299M [01:01<00:00, 4.89MB/s]\r\nDownloading: 100%|██████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████| 9.60M/9.60M [00:01<00:00, 4.84MB/s]\r\nDownloading: 100%|████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████| 423M/423M [01:36<00:00, 4.38MB/s]\r\nWARNING:apache_beam.options.pipeline_options:Discarding unparseable args: ['--lang', 'es', '--date', '20200601', '--tokenizer', 'bert-base-multilingual-cased', '--cache', 'train', 'valid', '--max_dataset_length', '200000', '10000']\r\n\r\nERROR:root:mwparserfromhell ParseError: This is a bug and should be reported. Info: C tokenizer exited with non-empty token stack.\r\nERROR:root:mwparserfromhell ParseError: This is a bug and should be reported. Info: C tokenizer exited with non-empty token stack.\r\nERROR:root:mwparserfromhell ParseError: This is a bug and should be reported. Info: C tokenizer exited with non-empty token stack.\r\nERROR:root:mwparserfromhell ParseError: This is a bug and should be reported. Info: C tokenizer exited with non-empty token stack.\r\nTraceback (most recent call last):\r\n File \"apache_beam/runners/common.py\", line 961, in apache_beam.runners.common.DoFnRunner.process\r\n File \"apache_beam/runners/common.py\", line 553, in apache_beam.runners.common.SimpleInvoker.invoke_process\r\n File \"apache_beam/runners/common.py\", line 1095, in apache_beam.runners.common._OutputProcessor.process_outputs\r\n File \"/home/gustavoag/anaconda3/envs/pytorch/lib/python3.8/site-packages/nlp/datasets/wikipedia/7be7f4324255faf70687be8692de57cf79197afdc33ff08d6a04ed602df32d50/wikipedia.py\", line 500, in _clean_content\r\n text = _parse_and_clean_wikicode(raw_content, parser=mwparserfromhell)\r\n File \"/home/gustavoag/anaconda3/envs/pytorch/lib/python3.8/site-packages/nlp/datasets/wikipedia/7be7f4324255faf70687be8692de57cf79197afdc33ff08d6a04ed602df32d50/wikipedia.py\", line 556, in _parse_and_clean_wikicode\r\n section_text.append(section.strip_code().strip())\r\n File \"/home/gustavoag/anaconda3/envs/pytorch/lib/python3.8/site-packages/mwparserfromhell/wikicode.py\", line 643, in strip_code\r\n stripped = node.__strip__(**kwargs)\r\n File \"/home/gustavoag/anaconda3/envs/pytorch/lib/python3.8/site-packages/mwparserfromhell/nodes/html_entity.py\", line 63, in __strip__\r\n return self.normalize()\r\n File \"/home/gustavoag/anaconda3/envs/pytorch/lib/python3.8/site-packages/mwparserfromhell/nodes/html_entity.py\", line 178, in normalize\r\n return chrfunc(htmlentities.name2codepoint[self.value])\r\nKeyError: '000nbsp'\r\n\r\nDuring handling of the above exception, another exception occurred:\r\n\r\nTraceback (most recent call last):\r\n File \"/home/gustavoag/anaconda3/envs/pytorch/lib/python3.8/runpy.py\", line 194, in _run_module_as_main\r\n return _run_code(code, main_globals, None,\r\n File \"/home/gustavoag/anaconda3/envs/pytorch/lib/python3.8/runpy.py\", line 87, in _run_code\r\n exec(code, run_globals)\r\n File \"/raid/data/gustavoag/projects/char2subword/research/preprocessing/split_wiki.py\", line 96, in <module>\r\n main()\r\n File \"/raid/data/gustavoag/projects/char2subword/research/preprocessing/split_wiki.py\", line 65, in main\r\n data = nlp.load_dataset('wikipedia', f'{args.date}.{args.lang}', beam_runner='DirectRunner', split='train')\r\n File \"/home/gustavoag/anaconda3/envs/pytorch/lib/python3.8/site-packages/nlp/load.py\", line 548, in load_dataset\r\n builder_instance.download_and_prepare(\r\n File \"/home/gustavoag/anaconda3/envs/pytorch/lib/python3.8/site-packages/nlp/builder.py\", line 462, in download_and_prepare\r\n self._download_and_prepare(\r\n File \"/home/gustavoag/anaconda3/envs/pytorch/lib/python3.8/site-packages/nlp/builder.py\", line 969, in _download_and_prepare\r\n pipeline_results = pipeline.run()\r\n File \"/home/gustavoag/anaconda3/envs/pytorch/lib/python3.8/site-packages/apache_beam/pipeline.py\", line 534, in run\r\n return self.runner.run_pipeline(self, self._options)\r\n File \"/home/gustavoag/anaconda3/envs/pytorch/lib/python3.8/site-packages/apache_beam/runners/direct/direct_runner.py\", line 119, in run_pipeline\r\n return runner.run_pipeline(pipeline, options)\r\n File \"/home/gustavoag/anaconda3/envs/pytorch/lib/python3.8/site-packages/apache_beam/runners/portability/fn_api_runner/fn_runner.py\", line 172, in run_pipeline\r\n self._latest_run_result = self.run_via_runner_api(\r\n File \"/home/gustavoag/anaconda3/envs/pytorch/lib/python3.8/site-packages/apache_beam/runners/portability/fn_api_runner/fn_runner.py\", line 183, in run_via_runner_api\r\n return self.run_stages(stage_context, stages)\r\n File \"/home/gustavoag/anaconda3/envs/pytorch/lib/python3.8/site-packages/apache_beam/runners/portability/fn_api_runner/fn_runner.py\", line 338, in run_stages\r\n stage_results = self._run_stage(\r\n File \"/home/gustavoag/anaconda3/envs/pytorch/lib/python3.8/site-packages/apache_beam/runners/portability/fn_api_runner/fn_runner.py\", line 512, in _run_stage\r\n last_result, deferred_inputs, fired_timers = self._run_bundle(\r\n File \"/home/gustavoag/anaconda3/envs/pytorch/lib/python3.8/site-packages/apache_beam/runners/portability/fn_api_runner/fn_runner.py\", line 556, in _run_bundle\r\n result, splits = bundle_manager.process_bundle(\r\n File \"/home/gustavoag/anaconda3/envs/pytorch/lib/python3.8/site-packages/apache_beam/runners/portability/fn_api_runner/fn_runner.py\", line 940, in process_bundle\r\n for result, split_result in executor.map(execute, zip(part_inputs, # pylint: disable=zip-builtin-not-iterating\r\n File \"/home/gustavoag/anaconda3/envs/pytorch/lib/python3.8/concurrent/futures/_base.py\", line 611, in result_iterator\r\n yield fs.pop().result()\r\n File \"/home/gustavoag/anaconda3/envs/pytorch/lib/python3.8/concurrent/futures/_base.py\", line 439, in result\r\n return self.__get_result()\r\n File \"/home/gustavoag/anaconda3/envs/pytorch/lib/python3.8/concurrent/futures/_base.py\", line 388, in __get_result\r\n raise self._exception\r\n File \"/home/gustavoag/anaconda3/envs/pytorch/lib/python3.8/site-packages/apache_beam/utils/thread_pool_executor.py\", line 44, in run\r\n self._future.set_result(self._fn(*self._fn_args, **self._fn_kwargs))\r\n File \"/home/gustavoag/anaconda3/envs/pytorch/lib/python3.8/site-packages/apache_beam/runners/portability/fn_api_runner/fn_runner.py\", line 932, in execute\r\n return bundle_manager.process_bundle(\r\n File \"/home/gustavoag/anaconda3/envs/pytorch/lib/python3.8/site-packages/apache_beam/runners/portability/fn_api_runner/fn_runner.py\", line 837, in process_bundle\r\n result_future = self._worker_handler.control_conn.push(process_bundle_req)\r\n File \"/home/gustavoag/anaconda3/envs/pytorch/lib/python3.8/site-packages/apache_beam/runners/portability/fn_api_runner/worker_handlers.py\", line 352, in push\r\n response = self.worker.do_instruction(request)\r\n File \"/home/gustavoag/anaconda3/envs/pytorch/lib/python3.8/site-packages/apache_beam/runners/worker/sdk_worker.py\", line 479, in do_instruction\r\n return getattr(self, request_type)(\r\n File \"/home/gustavoag/anaconda3/envs/pytorch/lib/python3.8/site-packages/apache_beam/runners/worker/sdk_worker.py\", line 515, in process_bundle\r\n bundle_processor.process_bundle(instruction_id))\r\n File \"/home/gustavoag/anaconda3/envs/pytorch/lib/python3.8/site-packages/apache_beam/runners/worker/bundle_processor.py\", line 977, in process_bundle\r\n input_op_by_transform_id[element.transform_id].process_encoded(\r\n File \"/home/gustavoag/anaconda3/envs/pytorch/lib/python3.8/site-packages/apache_beam/runners/worker/bundle_processor.py\", line 218, in process_encoded\r\n self.output(decoded_value)\r\n File \"apache_beam/runners/worker/operations.py\", line 330, in apache_beam.runners.worker.operations.Operation.output\r\n File \"apache_beam/runners/worker/operations.py\", line 332, in apache_beam.runners.worker.operations.Operation.output\r\n File \"apache_beam/runners/worker/operations.py\", line 195, in apache_beam.runners.worker.operations.SingletonConsumerSet.receive\r\n File \"apache_beam/runners/worker/operations.py\", line 670, in apache_beam.runners.worker.operations.DoOperation.process\r\n File \"apache_beam/runners/worker/operations.py\", line 671, in apache_beam.runners.worker.operations.DoOperation.process\r\n File \"apache_beam/runners/common.py\", line 963, in apache_beam.runners.common.DoFnRunner.process\r\n File \"apache_beam/runners/common.py\", line 1030, in apache_beam.runners.common.DoFnRunner._reraise_augmented\r\n File \"apache_beam/runners/common.py\", line 961, in apache_beam.runners.common.DoFnRunner.process\r\n File \"apache_beam/runners/common.py\", line 553, in apache_beam.runners.common.SimpleInvoker.invoke_process\r\n File \"apache_beam/runners/common.py\", line 1122, in apache_beam.runners.common._OutputProcessor.process_outputs\r\n File \"apache_beam/runners/worker/operations.py\", line 195, in apache_beam.runners.worker.operations.SingletonConsumerSet.receive\r\n File \"apache_beam/runners/worker/operations.py\", line 670, in apache_beam.runners.worker.operations.DoOperation.process\r\n File \"apache_beam/runners/worker/operations.py\", line 671, in apache_beam.runners.worker.operations.DoOperation.process\r\n File \"apache_beam/runners/common.py\", line 963, in apache_beam.runners.common.DoFnRunner.process\r\n File \"apache_beam/runners/common.py\", line 1030, in apache_beam.runners.common.DoFnRunner._reraise_augmented\r\n File \"apache_beam/runners/common.py\", line 961, in apache_beam.runners.common.DoFnRunner.process\r\n File \"apache_beam/runners/common.py\", line 553, in apache_beam.runners.common.SimpleInvoker.invoke_process\r\n File \"apache_beam/runners/common.py\", line 1122, in apache_beam.runners.common._OutputProcessor.process_outputs\r\n File \"apache_beam/runners/worker/operations.py\", line 195, in apache_beam.runners.worker.operations.SingletonConsumerSet.receive\r\n File \"apache_beam/runners/worker/operations.py\", line 670, in apache_beam.runners.worker.operations.DoOperation.process\r\n File \"apache_beam/runners/worker/operations.py\", line 671, in apache_beam.runners.worker.operations.DoOperation.process\r\n File \"apache_beam/runners/common.py\", line 963, in apache_beam.runners.common.DoFnRunner.process\r\n File \"apache_beam/runners/common.py\", line 1045, in apache_beam.runners.common.DoFnRunner._reraise_augmented\r\n File \"/home/gustavoag/anaconda3/envs/pytorch/lib/python3.8/site-packages/future/utils/__init__.py\", line 446, in raise_with_traceback\r\n raise exc.with_traceback(traceback)\r\n File \"apache_beam/runners/common.py\", line 961, in apache_beam.runners.common.DoFnRunner.process\r\n File \"apache_beam/runners/common.py\", line 553, in apache_beam.runners.common.SimpleInvoker.invoke_process\r\n File \"apache_beam/runners/common.py\", line 1095, in apache_beam.runners.common._OutputProcessor.process_outputs\r\n File \"/home/gustavoag/anaconda3/envs/pytorch/lib/python3.8/site-packages/nlp/datasets/wikipedia/7be7f4324255faf70687be8692de57cf79197afdc33ff08d6a04ed602df32d50/wikipedia.py\", line 500, in _clean_content\r\n text = _parse_and_clean_wikicode(raw_content, parser=mwparserfromhell)\r\n File \"/home/gustavoag/anaconda3/envs/pytorch/lib/python3.8/site-packages/nlp/datasets/wikipedia/7be7f4324255faf70687be8692de57cf79197afdc33ff08d6a04ed602df32d50/wikipedia.py\", line 556, in _parse_and_clean_wikicode\r\n section_text.append(section.strip_code().strip())\r\n File \"/home/gustavoag/anaconda3/envs/pytorch/lib/python3.8/site-packages/mwparserfromhell/wikicode.py\", line 643, in strip_code\r\n stripped = node.__strip__(**kwargs)\r\n File \"/home/gustavoag/anaconda3/envs/pytorch/lib/python3.8/site-packages/mwparserfromhell/nodes/html_entity.py\", line 63, in __strip__\r\n return self.normalize()\r\n File \"/home/gustavoag/anaconda3/envs/pytorch/lib/python3.8/site-packages/mwparserfromhell/nodes/html_entity.py\", line 178, in normalize\r\n return chrfunc(htmlentities.name2codepoint[self.value])\r\nKeyError: \"000nbsp [while running 'train/Clean content']\"```", "@lhoestq Any updates on this? I have similar issues with the Romanian dump, tnx.", "Hey @gaguilar ,\r\n\r\nI just found the [\"char2subword\" paper](https://arxiv.org/pdf/2010.12730.pdf) and I'm really interested in trying it out on own vocabs/datasets like for historical texts (I've already [trained some lms](https://github.com/stefan-it/europeana-bert) on newspaper articles with OCR errors).\r\n\r\nDo you plan to release the code for your paper or is it possible to get the implementation 🤔 Many thanks :hugs: ", "Hi @stefan-it! Thanks for your interest in our work! We do plan to release the code, but we will make it available once the paper has been published at a conference. Sorry for the inconvenience!\r\n\r\nHi @lhoestq, do you have any insights for this issue by any chance? Thanks!", "This is an issue on the `mwparserfromhell` side. You could try to update `mwparserfromhell` and see if it fixes the issue. If it doesn't we'll have to create an issue on their repo for them to fix it.\r\nBut first let's see if the latest version of `mwparserfromhell` does the job.", "I think the work around as suggested in the issue [#886] is not working for several languages, such as `id`. For example, I tried all the dates to download dataset for `id` langauge from the following link: (https://github.com/huggingface/datasets/pull/886) [https://dumps.wikimedia.org/idwiki/](https://dumps.wikimedia.org/idwiki/ )\r\n\r\n> >>> dataset = load_dataset('wikipedia', language='id', date=\"20210501\", beam_runner='DirectRunner')\r\nWARNING:datasets.builder:Using custom data configuration 20210501.id-date=20210501,language=id\r\nDownloading and preparing dataset wikipedia/20210501.id (download: Unknown size, generated: Unknown size, post-processed: Unknown size, total: Unknown size) to /Users/.cache/huggingface/datasets/wikipedia/20210501.id-date=20210501,language=id/0.0.0/2fe8db1405aef67dff9fcc51e133e1f9c5b0106f9d9e9638188176d278fd5ff1...\r\nTraceback (most recent call last):\r\n File \"<stdin>\", line 1, in <module>\r\n File \"/Users/opt/anaconda3/envs/proj/lib/python3.9/site-packages/datasets/load.py\", line 745, in load_dataset\r\n builder_instance.download_and_prepare(\r\n File \"/Users/opt/anaconda3/envs/proj/lib/python3.9/site-packages/datasets/builder.py\", line 574, in download_and_prepare\r\n self._download_and_prepare(\r\n File \"/Users/opt/anaconda3/envs/proj/lib/python3.9/site-packages/datasets/builder.py\", line 1139, in _download_and_prepare\r\n super(BeamBasedBuilder, self)._download_and_prepare(\r\n File \"/Users/opt/anaconda3/envs/proj/lib/python3.9/site-packages/datasets/builder.py\", line 630, in _download_and_prepare\r\n split_generators = self._split_generators(dl_manager, **split_generators_kwargs)\r\n File \"/Users/.cache/huggingface/modules/datasets_modules/datasets/wikipedia/2fe8db1405aef67dff9fcc51e133e1f9c5b0106f9d9e9638188176d278fd5ff1/wikipedia.py\", line 420, in _split_generators\r\n downloaded_files = dl_manager.download_and_extract({\"info\": info_url})\r\n File \"/Users/opt/anaconda3/envs/proj/lib/python3.9/site-packages/datasets/utils/download_manager.py\", line 287, in download_and_extract\r\n return self.extract(self.download(url_or_urls))\r\n File \"/Users/opt/anaconda3/envs/proj/lib/python3.9/site-packages/datasets/utils/download_manager.py\", line 195, in download\r\n downloaded_path_or_paths = map_nested(\r\n File \"/Users/opt/anaconda3/envs/proj/lib/python3.9/site-packages/datasets/utils/py_utils.py\", line 203, in map_nested\r\n mapped = [\r\n File \"/Users/opt/anaconda3/envs/proj/lib/python3.9/site-packages/datasets/utils/py_utils.py\", line 204, in <listcomp>\r\n _single_map_nested((function, obj, types, None, True)) for obj in tqdm(iterable, disable=disable_tqdm)\r\n File \"/Users/opt/anaconda3/envs/proj/lib/python3.9/site-packages/datasets/utils/py_utils.py\", line 142, in _single_map_nested\r\n return function(data_struct)\r\n File \"/Users/opt/anaconda3/envs/proj/lib/python3.9/site-packages/datasets/utils/download_manager.py\", line 218, in _download\r\n return cached_path(url_or_filename, download_config=download_config)\r\n File \"/Users/opt/anaconda3/envs/proj/lib/python3.9/site-packages/datasets/utils/file_utils.py\", line 281, in cached_path\r\n output_path = get_from_cache(\r\n File \"/Users/opt/anaconda3/envs/proj/lib/python3.9/site-packages/datasets/utils/file_utils.py\", line 623, in get_from_cache\r\n raise ConnectionError(\"Couldn't reach {}\".format(url))\r\nConnectionError: Couldn't reach https://dumps.wikimedia.org/idwiki/20210501/dumpstatus.json\r\n\r\nMoreover the downloading speed for `non-en` language is very very slow. And interestingly the download stopped after approx a couple minutes due to the read time-out. I tried numerous times and the results is same. Is there any feasible way to download non-en language using huggingface?\r\n\r\n> File \"/Users/miislamg/opt/anaconda3/envs/proj-semlm/lib/python3.9/site-packages/requests/models.py\", line 760, in generate\r\n raise ConnectionError(e)\r\nrequests.exceptions.ConnectionError: HTTPSConnectionPool(host='dumps.wikimedia.org', port=443): Read timed out.\r\nDownloading: 7%|████████▎ | 10.2M/153M [03:35<50:07, 47.4kB/s]", "Hi ! The link https://dumps.wikimedia.org/idwiki/20210501/dumpstatus.json seems to be working fine for me.\r\n\r\nRegarding the time outs, it must come either from an issue on the wikimedia host side, or from your internet connection.\r\nFeel free to try again several times.", "I was trying to download dataset for `es` language, however I am getting the following error:\r\n```\r\ndataset = load_dataset('wikipedia', language='es', date=\"20210320\", beam_runner='DirectRunner') \r\n```\r\n\r\n```\r\nDownloading and preparing dataset wikipedia/20210320.es (download: Unknown size, generated: Unknown size, post-processed: Unknown size, total: Unknown size) to /scratch/user_name/datasets/wikipedia/20210320.es-date=20210320,language=es/0.0.0/2fe8db1405aef67dff9fcc51e133e1f9c5b0106f9d9e9638188176d278fd5ff1...\r\nTraceback (most recent call last):\r\n File \"apache_beam/runners/common.py\", line 1233, in apache_beam.runners.common.DoFnRunner.process\r\n File \"apache_beam/runners/common.py\", line 581, in apache_beam.runners.common.SimpleInvoker.invoke_process\r\n File \"apache_beam/runners/common.py\", line 1368, in apache_beam.runners.common._OutputProcessor.process_outputs\r\n File \"/scratch/user_name/modules/datasets_modules/datasets/wikipedia/2fe8db1405aef67dff9fcc51e133e1f9c5b0106f9d9e9638188176d278fd5ff1/wikipedia.py\", line 492, in _clean_content\r\n text = _parse_and_clean_wikicode(raw_content, parser=mwparserfromhell)\r\n File \"/scratch/user_name/modules/datasets_modules/datasets/wikipedia/2fe8db1405aef67dff9fcc51e133e1f9c5b0106f9d9e9638188176d278fd5ff1/wikipedia.py\", line 548, in _parse_and_clean_wikicode\r\n section_text.append(section.strip_code().strip())\r\n File \"/opt/conda/lib/python3.7/site-packages/mwparserfromhell/wikicode.py\", line 639, in strip_code\r\n stripped = node.__strip__(**kwargs)\r\n File \"/opt/conda/lib/python3.7/site-packages/mwparserfromhell/nodes/html_entity.py\", line 60, in __strip__\r\n return self.normalize()\r\n File \"/opt/conda/lib/python3.7/site-packages/mwparserfromhell/nodes/html_entity.py\", line 150, in normalize\r\n return chr(htmlentities.name2codepoint[self.value])\r\nKeyError: '000nbsp'\r\n\r\nDuring handling of the above exception, another exception occurred:\r\n\r\nTraceback (most recent call last):\r\n File \"download_dataset_all.py\", line 8, in <module>\r\n dataset = load_dataset('wikipedia', language=language, date=\"20210320\", beam_runner='DirectRunner') \r\n File \"/opt/conda/lib/python3.7/site-packages/datasets/load.py\", line 748, in load_dataset\r\n use_auth_token=use_auth_token,\r\n File \"/opt/conda/lib/python3.7/site-packages/datasets/builder.py\", line 575, in download_and_prepare\r\n dl_manager=dl_manager, verify_infos=verify_infos, **download_and_prepare_kwargs\r\n File \"/opt/conda/lib/python3.7/site-packages/datasets/builder.py\", line 1152, in _download_and_prepare\r\n pipeline_results = pipeline.run()\r\n File \"/opt/conda/lib/python3.7/site-packages/apache_beam/pipeline.py\", line 564, in run\r\n return self.runner.run_pipeline(self, self._options)\r\n File \"/opt/conda/lib/python3.7/site-packages/apache_beam/runners/direct/direct_runner.py\", line 131, in run_pipeline\r\n return runner.run_pipeline(pipeline, options)\r\n File \"/opt/conda/lib/python3.7/site-packages/apache_beam/runners/portability/fn_api_runner/fn_runner.py\", line 190, in run_pipeline\r\n pipeline.to_runner_api(default_environment=self._default_environment))\r\n File \"/opt/conda/lib/python3.7/site-packages/apache_beam/runners/portability/fn_api_runner/fn_runner.py\", line 200, in run_via_runner_api\r\n return self.run_stages(stage_context, stages)\r\n File \"/opt/conda/lib/python3.7/site-packages/apache_beam/runners/portability/fn_api_runner/fn_runner.py\", line 366, in run_stages\r\n bundle_context_manager,\r\n File \"/opt/conda/lib/python3.7/site-packages/apache_beam/runners/portability/fn_api_runner/fn_runner.py\", line 562, in _run_stage\r\n bundle_manager)\r\n File \"/opt/conda/lib/python3.7/site-packages/apache_beam/runners/portability/fn_api_runner/fn_runner.py\", line 602, in _run_bundle\r\n data_input, data_output, input_timers, expected_timer_output)\r\n File \"/opt/conda/lib/python3.7/site-packages/apache_beam/runners/portability/fn_api_runner/fn_runner.py\", line 903, in process_bundle\r\n result_future = self._worker_handler.control_conn.push(process_bundle_req)\r\n File \"/opt/conda/lib/python3.7/site-packages/apache_beam/runners/portability/fn_api_runner/worker_handlers.py\", line 378, in push\r\n response = self.worker.do_instruction(request)\r\n File \"/opt/conda/lib/python3.7/site-packages/apache_beam/runners/worker/sdk_worker.py\", line 610, in do_instruction\r\n getattr(request, request_type), request.instruction_id)\r\n File \"/opt/conda/lib/python3.7/site-packages/apache_beam/runners/worker/sdk_worker.py\", line 647, in process_bundle\r\n bundle_processor.process_bundle(instruction_id))\r\n File \"/opt/conda/lib/python3.7/site-packages/apache_beam/runners/worker/bundle_processor.py\", line 1001, in process_bundle\r\n element.data)\r\n File \"/opt/conda/lib/python3.7/site-packages/apache_beam/runners/worker/bundle_processor.py\", line 229, in process_encoded\r\n self.output(decoded_value)\r\n File \"apache_beam/runners/worker/operations.py\", line 356, in apache_beam.runners.worker.operations.Operation.output\r\n File \"apache_beam/runners/worker/operations.py\", line 358, in apache_beam.runners.worker.operations.Operation.output\r\n File \"apache_beam/runners/worker/operations.py\", line 220, in apache_beam.runners.worker.operations.SingletonConsumerSet.receive\r\n File \"apache_beam/runners/worker/operations.py\", line 717, in apache_beam.runners.worker.operations.DoOperation.process\r\n File \"apache_beam/runners/worker/operations.py\", line 718, in apache_beam.runners.worker.operations.DoOperation.process\r\n File \"apache_beam/runners/common.py\", line 1235, in apache_beam.runners.common.DoFnRunner.process\r\n File \"apache_beam/runners/common.py\", line 1300, in apache_beam.runners.common.DoFnRunner._reraise_augmented\r\n File \"apache_beam/runners/common.py\", line 1233, in apache_beam.runners.common.DoFnRunner.process\r\n File \"apache_beam/runners/common.py\", line 581, in apache_beam.runners.common.SimpleInvoker.invoke_process\r\n File \"apache_beam/runners/common.py\", line 1395, in apache_beam.runners.common._OutputProcessor.process_outputs\r\n File \"apache_beam/runners/worker/operations.py\", line 220, in apache_beam.runners.worker.operations.SingletonConsumerSet.receive\r\n File \"apache_beam/runners/worker/operations.py\", line 717, in apache_beam.runners.worker.operations.DoOperation.process\r\n File \"apache_beam/runners/worker/operations.py\", line 718, in apache_beam.runners.worker.operations.DoOperation.process\r\n File \"apache_beam/runners/common.py\", line 1235, in apache_beam.runners.common.DoFnRunner.process\r\n File \"apache_beam/runners/common.py\", line 1300, in apache_beam.runners.common.DoFnRunner._reraise_augmented\r\n File \"apache_beam/runners/common.py\", line 1233, in apache_beam.runners.common.DoFnRunner.process\r\n File \"apache_beam/runners/common.py\", line 581, in apache_beam.runners.common.SimpleInvoker.invoke_process\r\n File \"apache_beam/runners/common.py\", line 1395, in apache_beam.runners.common._OutputProcessor.process_outputs\r\n File \"apache_beam/runners/worker/operations.py\", line 220, in apache_beam.runners.worker.operations.SingletonConsumerSet.receive\r\n File \"apache_beam/runners/worker/operations.py\", line 717, in apache_beam.runners.worker.operations.DoOperation.process\r\n File \"apache_beam/runners/worker/operations.py\", line 718, in apache_beam.runners.worker.operations.DoOperation.process\r\n File \"apache_beam/runners/common.py\", line 1235, in apache_beam.runners.common.DoFnRunner.process\r\n File \"apache_beam/runners/common.py\", line 1315, in apache_beam.runners.common.DoFnRunner._reraise_augmented\r\n File \"/opt/conda/lib/python3.7/site-packages/future/utils/__init__.py\", line 446, in raise_with_traceback\r\n raise exc.with_traceback(traceback)\r\n File \"apache_beam/runners/common.py\", line 1233, in apache_beam.runners.common.DoFnRunner.process\r\n File \"apache_beam/runners/common.py\", line 581, in apache_beam.runners.common.SimpleInvoker.invoke_process\r\n File \"apache_beam/runners/common.py\", line 1368, in apache_beam.runners.common._OutputProcessor.process_outputs\r\n File \"/scratch/user_name/modules/datasets_modules/datasets/wikipedia/2fe8db1405aef67dff9fcc51e133e1f9c5b0106f9d9e9638188176d278fd5ff1/wikipedia.py\", line 492, in _clean_content\r\n text = _parse_and_clean_wikicode(raw_content, parser=mwparserfromhell)\r\n File \"/scratch/user_name/modules/datasets_modules/datasets/wikipedia/2fe8db1405aef67dff9fcc51e133e1f9c5b0106f9d9e9638188176d278fd5ff1/wikipedia.py\", line 548, in _parse_and_clean_wikicode\r\n section_text.append(section.strip_code().strip())\r\n File \"/opt/conda/lib/python3.7/site-packages/mwparserfromhell/wikicode.py\", line 639, in strip_code\r\n stripped = node.__strip__(**kwargs)\r\n File \"/opt/conda/lib/python3.7/site-packages/mwparserfromhell/nodes/html_entity.py\", line 60, in __strip__\r\n return self.normalize()\r\n File \"/opt/conda/lib/python3.7/site-packages/mwparserfromhell/nodes/html_entity.py\", line 150, in normalize\r\n return chr(htmlentities.name2codepoint[self.value])\r\nKeyError: \"000nbsp [while running 'train/Clean content']\"\r\n```", "Hi ! This looks related to this issue: https://github.com/huggingface/datasets/issues/1994\r\nBasically the parser that is used (mwparserfromhell) has some issues for some pages in `es`.\r\nWe already reported some issues for `es` on their repo at https://github.com/earwig/mwparserfromhell/issues/247 but it looks like there are still a few issues. Might be a good idea to open a new issue on the mwparserfromhell repo", "Any updates on this so far?", "The issue:\r\n```\r\nKeyError: \"000nbsp [while running 'train/Clean content']\"\r\n```\r\nreported in comments:\r\n- https://github.com/huggingface/datasets/issues/577#issuecomment-701890059 (by @gaguilar)\r\n- https://github.com/huggingface/datasets/issues/577#issuecomment-879513227 (by @mmiakashs)\r\n\r\nwas normally fixed in the `mwparserfromhell` library and will be accessible in their next release version `0.7`:\r\n- https://github.com/earwig/mwparserfromhell/issues/288", "mwparserfromhell 0.7 has still not been released, but you might have luck with the dev version:\r\n`pip install git+https://github.com/earwig/mwparserfromhell.git@0f89f44`" ]

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[ "Update:\r\n\r\nThe imdb download completed after a long time (about 45 mins). Ofcourse once download loading was instantaneous. Also, the loaded object was of type `arrow_dataset`. \r\n\r\nThe urls for glue still doesn't work though.", "Thanks for the report, I'll give a look!", "I am also seeing a similar error when running the following:\r\n\r\n```\r\nimport nlp\r\ndataset = load_dataset('cola')\r\n```\r\nError:\r\n```\r\nTraceback (most recent call last):\r\n File \"<stdin>\", line 1, in <module>\r\n File \"/home/js11133/.conda/envs/jiant/lib/python3.8/site-packages/nlp/load.py\", line 509, in load_dataset\r\n module_path = prepare_module(path, download_config=download_config, dataset=True)\r\n File \"/home/js11133/.conda/envs/jiant/lib/python3.8/site-packages/nlp/load.py\", line 248, in prepare_module\r\n local_path = cached_path(file_path, download_config=download_config)\r\n File \"/home/js11133/.conda/envs/jiant/lib/python3.8/site-packages/nlp/utils/file_utils.py\", line 191, in cached_path\r\n output_path = get_from_cache(\r\n File \"/home/js11133/.conda/envs/jiant/lib/python3.8/site-packages/nlp/utils/file_utils.py\", line 356, in get_from_cache\r\n raise ConnectionError(\"Couldn't reach {}\".format(url))\r\nConnectionError: Couldn't reach https://s3.amazonaws.com/datasets.huggingface.co/nlp/datasets/cola/cola.py\r\n```", "@jeswan `\"cola\"` is not a valid dataset identifier (you can check the up-to-date list on https://huggingface.co/datasets) but you can find cola inside glue.", "Ah right. Thanks!", "Hi. Closing this one since #626 updated the glue urls.\r\n\r\n> 1. Why is it still blocking? Is it still downloading?\r\n\r\nAfter downloading it generates the arrow file by iterating through the examples.\r\nThe number of examples processed by second is shown during the processing (not sure why it was not the case for you)\r\n\r\n> 2. I specified split as train, so why is the test folder being populated?\r\n\r\nIt downloads every split\r\n\r\n\r\n\r\n" ]

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[ "Hmm might be related to what we are solving in #564", "Could you try to update to `datasets>=1.0.0` (we changed the name of the library) and try again ?\r\nIf is was related to the distributed setup settings it must be fixed.\r\nIf it was related to empty metric inputs it's going to be fixed in #654 ", "Closing this one as it was fixed in #654 \r\nFeel free to re-open if you have other questions" ]

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[ "Thanks for reporting.\r\n\r\nApparently this is a versioning issue: the lib downloaded the `bleurt` script from the master branch where we did this change recently. We'll fix that in a new release this week or early next week. Cc @thomwolf \r\n\r\nUntil that, I'd suggest you to download the right bleurt folder from github ([this one](https://github.com/huggingface/nlp/tree/0.4.0/metrics/bleurt)) and do\r\n\r\n```python\r\nfrom nlp import load_metric\r\n\r\nbleurt = load_metric(\"path/to/bleurt/folder\")\r\n```\r\n\r\nTo download it you can either clone the repo or download the `bleurt.py` file and place it in a folder named `bleurt` ", "Actually we can fix this on our side, this script didn't had to be updated. I'll do it in a few minutes" ]

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[ "From my limited understanding, part of the issue seems related to the `prepare_module` and `download_and_prepare` functions each handling the case where no config is passed. For example, `prepare_module` does mutate the object passed and forces the flags `extract_compressed_file` and `force_extract` to `True`.\r\n\r\nSee:\r\n* https://github.com/huggingface/nlp/blob/5fb61e1012bda724a9b6b847307d90a1380abfa5/src/nlp/load.py#L227\r\n* https://github.com/huggingface/nlp/blob/5fb61e1012bda724a9b6b847307d90a1380abfa5/src/nlp/builder.py#L388\r\n\r\nMaybe a cleaner solution would be to always instantiate a default `DownloadConfig` object at the top-level, have it as non-optional for the lower-level functions and treat it as immutable. ", "Thanks for the report, I'll take a look.\r\n\r\nWhat is your specific use-case for providing a DownloadConfig object?\r\n", "Thanks. Our use case involves running a training job behind a corporate firewall with no access to any external resources (S3, GCP or other web resources).\r\n\r\nI was thinking about a 2-steps process:\r\n1) Download the resources / artifacts using some secure corporate channel, ie run `nlp.load_dataset()` without a specific `DownloadConfig`. After that, collect the files from the `$HF_HOME` folder\r\n2) Copy the `$HF_HOME` folder in the firewalled environment. Run `nlp.load_dataset()` with a custom config `DownloadConfig(local_files_only=True)`\r\n\r\nHowever this ends up a bit clunky in practice, even when solving the `DownloadConfig` issue above. For example, the `filename` hash computed in `get_from_cache()` differs in the `local_files_only=False` vs `local_files_only=True` case (local case defaults `etag` to `None`, which results in a different hash). So effectively step 2) above doesn't work because the hash computed differs from the hash in the cache folder. Some hacks / workaround are possible but this solution becomes very convoluted.\r\nhttps://github.com/huggingface/nlp/blob/c214aa5a4430c1df1bcd0619fd94d6abdf9d2da7/src/nlp/utils/file_utils.py#L417\r\n\r\nWould you recommend a different path?\r\n", "I see.\r\n\r\nProbably the easiest way for you would be that we add simple serialization/deserialization methods to the Dataset and DatasetDict objects once the data files have been downloaded and all the dataset is processed.\r\n\r\nWhat do you think @lhoestq ?", "This use-case will be solved with #571 ", "Thank you very much @thomwolf and @lhoestq we will give it a try" ]

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[ "Indeed this is a known issue arising from the fact that we try to be compatible with cloupickle.\r\n\r\nDoes this also happen if you are installing in a virtual environment?", "> Indeed this is a know issue with the fact that we try to be compatible with cloupickle.\r\n> \r\n> Does this also happen if you are installing in a virtual environment?\r\n\r\nThen it would work, because the package is in a writable path.", "If it's fine for you then this is the recommended way to solve this issue.", "> If it's fine for you then this is the recommended way to solve this issue.\r\n\r\nI don't want to use a virtual environment, because Nix is fully reproducible, and virtual environments are not. And I am the maintainer of the `transformers` in nixpkgs, so sooner or later I will have to package `nlp`, since it is becoming a dependency of `transformers` ;).", "Ok interesting. We could have another check to see if it's possible to download and import the datasets script at another location than the module path. I think this would probably involve tweaking the python system path dynamically.\r\n\r\nI don't know anything about Nix so if you want to give this a try your self we can guide you or you can give us more information on your general project and how this works.\r\n\r\nRegarding `nlp` and `transformers`, we are not sure `nlp` will become a required dependency for `transformers`. It will probably be used a lot in the examples but I think it probably won't be a required dependency for the main package since we try to keep it as light as possible in terms of deps.\r\n\r\nHappy to help you make all these things work better for your use-case ", "@danieldk modules are now installed in a different location (by default in the cache directory of the lib, in `~/.cache/huggingface/modules`). You can also change that using the environment variable `HF_MODULES_PATH`\r\n\r\nFeel free to play with this change from the master branch for now, and let us know if it sounds good for you :)\r\nWe plan to do a release in the next coming days", "Awesome! I’ll hopefully have some time in the coming days to try this.", "> Feel free to play with this change from the master branch for now, and let us know if it sounds good for you :)\r\n> We plan to do a release in the next coming days\r\n\r\nThanks for making this change! I just packaged the latest commit on master and it works like a charm now! :partying_face: " ]

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[ "When you load a text file for the first time with `nlp`, the file is converted into Apache Arrow format. Arrow allows to use memory-mapping, which means that you can load an arbitrary large dataset.\r\n\r\nNote that as soon as the conversion has been done once, the next time you'll load the dataset it will be much faster.\r\n\r\nHowever for a 1TB dataset, the conversion can indeed take time. You could try to load parts of it in parallel, and then use `nlp.concatenate_datasets` to get your full dataset.", "Humm, we can give a look at these large scale datasets indeed.\r\n\r\nDo you mind sharing a few stats on your dataset so I can try to test on a similar one?\r\n\r\nIn particular some orders of magnitudes for the number of files, number of lines per files, line lengths.", "@lhoestq Yes, I understand that the first time requires more time. The concatenate_datasets seems to be a workaround, but I believe a multi-processing method should be integrated into load_dataset to make it easier and more efficient for users.\r\n\r\n@thomwolf Sure, here are the statistics:\r\nNumber of lines: 4.2 Billion\r\nNumber of files: 6K\r\nNumber of tokens: 800 Billion\r\nThe number of lines is distributed equally across these 6k files.\r\nThe line length varies between 100 tokens to 40k tokens.\r\n", "@agemagician you can give a try at a multithreaded version if you want (currently on the #548).\r\n\r\nTo test it, you just need to copy the new `text` processing script which is [here](https://github.com/huggingface/nlp/blob/07d92a82b7594498ff702f3cca55c074e2052257/datasets/text/text.py) somewhere on your drive and give it's local path instead of `text` to `load_dataset`. E.g. in your example:\r\n```python\r\ntrain_files = glob.glob(\"xxx/*.txt\",recursive=True)\r\nrandom.shuffle(train_files)\r\n\r\nprint(train_files)\r\n\r\ndataset = nlp.load_dataset('./datasets/text.py', # path to where you've dowloaded the multi-threaded text loading script\r\n data_files=train_files,\r\n name=\"customDataset\",\r\n version=\"1.0.0\",\r\n cache_dir=\"xxx/nlp\")\r\n```", "I have already generated the dataset, but now I tried to reload it and it is still very slow.\r\n\r\nI also have installed your commit and it is slow, even after the dataset was already generated.\r\n`pip install git+https://github.com/huggingface/nlp.git@07d92a82b7594498ff702f3cca55c074e2052257`\r\n\r\nIt uses only a single thread.\r\n\r\nDid I miss something ?", "As mentioned in #548 , each time you call `load_dataset` with `data_files=`, they are hashed to get the cache directory name. Hashing can be too slow with 1TB of data. I feel like we should have a faster way of getting a hash that identifies the input data files", "I believe this is really a very important feature, otherwise, we will still have the issue of too slow loading problems even if the data cache generation is fast.", "Hmm ok then maybe it's the hashing step indeed.\r\n\r\nLet's see if we can improve this as well.\r\n\r\n(you will very likely have to regenerate your dataset if we change this part of the lib though since I expect modifications on this part of the lib to results in new hashes)", "Also, @agemagician you have to follow the step I indicate in my previous message [here](https://github.com/huggingface/nlp/issues/546#issuecomment-684648927) to use the new text loading script.\r\n\r\nJust doing `pip install git+https://github.com/huggingface/nlp.git@07d92a82b7594498ff702f3cca55c074e2052257` like you did won't use the new script (they are not inside the library but hosted on our hub).", "No problem, I will regenerate it. This will make us see if we solved both issues and now both the data generation step, as well as the hashing step, is fast.", "Any news for the hashing ?", "I'm working on it today :)", "Ok so now the text files won't be hashed.\r\n\r\nI also updated #548 to include this change.\r\nLet us know if it helps @agemagician :)", "Perfect thanks for your amazing work.", "Right now, for caching 18Gb data, it is taking 1 hour 10 minute. Is that proper expected time? @lhoestq @agemagician \r\nIn this rate (assuming large file will caching at the same rate) caching full mC4 (27TB) requires a month (~26 days). \r\n", "Hi ! Currently it is that slow because we haven't implemented parallelism for the dataset generation yet.\r\nThough we will definitely work on this :)\r\n\r\nFor now I'd recommend loading the dataset shard by shard in parallel, and then concatenate them:\r\n```python\r\n# in one process, load first 100 files for english\r\nshard1 = load_dataset(\"allenai/c4\", data_files=\"multilingual/c4-en.tfrecord-000**.json.gz\")\r\n# in another process load next 100 files for english\r\nshard2 = load_dataset(\"allenai/c4\", data_files=\"multilingual/c4-en.tfrecord-001**.json.gz\")\r\n\r\n# finally\r\nconcatenate_datasets([shard1, shard2, ...])", "Thanks for the help..!!!", "Sorry to write on a closed issue but, has there been any progress on parallelizing the `load_dataset` function?", "Hi ! No but this is in our plans (probably a few weeks)", "I'm literally crying waiting for the trainer to restart from checkpoint. It's getting stuck at `get_train_dataloader` and I think this is to do with the same issue... has there been any progress on this?", "> I'm literally crying waiting for the trainer to restart from checkpoint. It's getting stuck at get_train_dataloader and I think this is to do with the same issue...\r\n\r\nOnce the dataset is cached once, it's not regenerated again. Your issue seems different", "hmmm, yes. I'll come back with details on this, fairly easy to reproduce. Takes about 30 minutes to get from checkpoint loading to starting training..." ]

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[ "Update: release is planed mid-next week." ]

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[ "I'll take a look!" ]

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[ "Docs that explain how to train a tokenizer with `datasets` are available here: https://huggingface.co/docs/tokenizers/training_from_memory#using-the-datasets-library" ]

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[ "Hi @gaguilar \r\n\r\nIf you want to take care of this, it very simple, you just need to regenerate the `dataset_infos.json` file as indicated [in the doc](https://huggingface.co/nlp/share_dataset.html#adding-metadata) by [installing from source](https://huggingface.co/nlp/installation.html#installing-from-source) and running the following command from the root of the repo:\r\n```bash\r\npython nlp-cli test ./datasets/lince --save_infos --all_configs\r\n```\r\nAnd then you can open a pull-request with the updated json file.\r\n\r\nOtherwise we'll do it sometime this week.", "Hi @thomwolf \r\n\r\nThanks for the details! I just created a PR with the updated `dataset_infos.json` file (#550).", "Thanks for updating the json file. Closing this one" ]

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[ "`NonMatchingChecksumError` means that the checksum of the downloaded file is not the expected one.\r\nEither the file you downloaded was corrupted along the way, or the host updated the file.\r\nCould you try to clear your cache and run `load_dataset` again ? If the error is still there, it means that there was an update in the data, and we may have to update the expected checksum value.", "I just cleared the cache an run it again. The error persists ):\r\n\r\n```\r\n nlp (master) $ rm -rf /Users/abarbosa/.cache/huggingface/\r\n nlp (master) $ python\r\nPython 3.8.5 (default, Aug 5 2020, 03:39:04)\r\n[Clang 10.0.0 ] :: Anaconda, Inc. on darwin\r\nType \"help\", \"copyright\", \"credits\" or \"license\" for more information.\r\n>>> import nlp\r\n>>> dataset = nlp.load_dataset(\"race\")\r\nDownloading: 100%|██████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████| 4.39k/4.39k [00:00<00:00, 661kB/s]\r\nDownloading: 100%|██████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████| 1.81k/1.81k [00:00<00:00, 644kB/s]\r\nUsing custom data configuration default\r\nDownloading and preparing dataset race/default (download: 84.52 MiB, generated: 132.61 MiB, post-processed: Unknown size, total: 217.13 MiB) to /Users/abarbosa/.cache/huggingface/datasets/race/default/0.1.0/5461327f1a83549ca0d845a3159c806d2baf4f8d0d8f7d657157ce7cdf3899c2...\r\nDownloading: 100%|██████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████| 25.4M/25.4M [01:03<00:00, 401kB/s]\r\nTraceback (most recent call last):\r\n File \"<stdin>\", line 1, in <module>\r\n File \"/Users/abarbosa/Documents/nlp/src/nlp/load.py\", line 550, in load_dataset\r\n builder_instance.download_and_prepare(\r\n File \"/Users/abarbosa/Documents/nlp/src/nlp/builder.py\", line 471, in download_and_prepare\r\n self._download_and_prepare(\r\n File \"/Users/abarbosa/Documents/nlp/src/nlp/builder.py\", line 530, in _download_and_prepare\r\n verify_checksums(\r\n File \"/Users/abarbosa/Documents/nlp/src/nlp/utils/info_utils.py\", line 38, in verify_checksums\r\n raise NonMatchingChecksumError(error_msg + str(bad_urls))\r\nnlp.utils.info_utils.NonMatchingChecksumError: Checksums didn't match for dataset source files:\r\n['http://www.cs.cmu.edu/~glai1/data/race/RACE.tar.gz']\r\n>>>\r\n```", "Dealing with the same issue please update the checksum on nlp library end. The data seems to have changed on their end.", "We have a discussion on this datasets here: https://github.com/huggingface/nlp/pull/540\r\n\r\nFeel free to participate if you have some opinion on the scope of data which should be included in this dataset.", "At least for me, the file that was downloaded from CMU isn't the complete dataset, but a small subset of it (~25MB vs ~85MB). I've previously downloaded the dataset directly, so for my personal needs I could just swap out the corrupted file with the correct one. Perhaps you could host it like you do for the Wikipedia and BookCorpus datasets.\r\n\r\n", "> At least for me, the file that was downloaded from CMU isn't the complete dataset, but a small subset of it (~25MB vs ~85MB). I've previously downloaded the dataset directly, so for my personal needs I could just swap out the corrupted file with the correct one. Perhaps you could host it like you do for the Wikipedia and BookCorpus datasets.\r\n\r\nCould you upload this please?", "> > At least for me, the file that was downloaded from CMU isn't the complete dataset, but a small subset of it (~25MB vs ~85MB). I've previously downloaded the dataset directly, so for my personal needs I could just swap out the corrupted file with the correct one. Perhaps you could host it like you do for the Wikipedia and BookCorpus datasets.\r\n> \r\n> Could you upload this please?\r\n\r\nNot sure if I can upload it according to their license (\"You agree not to reproduce, duplicate, copy, sell, trade, resell or exploit for any commercial purpose, any portion of the contexts and any portion of derived data.\").", "I managed to fix it in #540 :)", "Closing since @540 is merged\r\n\r\nThanks again @abarbosa94 " ]

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[ "Thanks for reporting !\r\nThis has been fixed in #475 and the fix will be available in the next release", "What you can do instead to get the list of the datasets is call\r\n\r\n```python\r\nprint([dataset.id for dataset in nlp.list_datasets()])\r\n```", "Thanks @lhoestq . " ]

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[ "I am facing probably facing similar issues with \r\n\r\n`wiki40b_en_100_0`", "Could you try to run `dataset = load_dataset(\"text\", data_files=file_path, split=\"train\")` once before calling the script ?\r\n\r\nIt looks like several processes try to create the dataset in arrow format at the same time. If the dataset is already created it should be fine", "Thanks! I tested on 328MB text data on `n1-standard-8 (8 vCPUs, 30 GB memory)`. The main script ran without any issue, but it seems to require a huge space in the drive.\r\n\r\nAs suggested, I ran the following script before running the pre-training command with `xla_spawn.py`.\r\n\r\n```python\r\nfrom nlp import load_dataset\r\n\r\nfile_path=\"your_file_name\"\r\nload_dataset(\"text\", data_files=file_path, split=\"train\")\r\n```\r\nThis will create `text-train.arrow` under the default cache directory. Then, I run the script with `xla_spawn.py`. It will load data from the cached file. My understanding is that there's no other way but to do this two-step process with the current version (0.4) of `nlp`.\r\n\r\nDuring another caching process that happens in the main script:\r\n\r\n```\r\n08/26/2020 09:19:51 - INFO - nlp.utils.info_utils - All the checksums matched successfully for post processing resources\r\n08/26/2020 09:19:53 - INFO - nlp.arrow_dataset - Caching processed dataset at /home/*****/.cache/huggingface/datasets/text/default-b0932b2bdbb63283/0.0.0/447f2bcfa2a721a37bc8fdf23800eade1523cf07f7eada6fe661fe4d070d380d/cache-f90f341e5308a7469\r\n8d872bcc88f9c0e.arrow\r\n```\r\n\r\n`nlp` generates a temporary file per core, each of which is three times larger than the original text data. If each process is actually writing on the disk, you will need a huge amount of space in your drive. (Maybe I'm missing something.)\r\n\r\n```\r\n-rw-r--r-- 1 ***** ***** 674 Aug 26 09:19 dataset_info.json\r\n-rw-r--r-- 1 ***** ***** 0 Aug 26 09:19 LICENSE\r\n-rw-r--r-- 1 ***** ***** 332M Aug 26 09:10 text-train.arrow\r\n-rw------- 1 ***** ***** 940M Aug 26 09:31 tmp0k43sazw\r\n-rw------- 1 ***** ***** 940M Aug 26 09:31 tmp7sxs9mj5\r\n-rw------- 1 ***** ***** 939M Aug 26 09:31 tmpbbiqw2vp\r\n-rw------- 1 ***** ***** 937M Aug 26 09:31 tmpjxb5ptyu\r\n-rw------- 1 ***** ***** 933M Aug 26 09:31 tmpk3hkdh0e\r\n-rw------- 1 ***** ***** 944M Aug 26 09:31 tmpnoalwftz\r\n-rw------- 1 ***** ***** 931M Aug 26 09:31 tmpuxdr_dz3\r\n-rw------- 1 ***** ***** 945M Aug 26 09:31 tmpxjyuy6dk\r\n```\r\nAfter the caching process, they seem to be merged into one file.\r\n\r\n```\r\n-rw------- 1 ***** ***** 989M Aug 26 09:32 cache-f90f341e5308a74698d872bcc88f9c0e.arrow\r\n-rw-r--r-- 1 ***** ***** 674 Aug 26 09:19 dataset_info.json\r\n-rw-r--r-- 1 ***** ***** 0 Aug 26 09:19 LICENSE\r\n-rw-r--r-- 1 ***** ***** 332M Aug 26 09:10 text-train.arrow\r\n```", "Again it looks like every process tries to tokenize the full dataset at the same time.\r\nIf you do the tokenization before calling `xla_spawn.py` once, then each process will then use the tokenized cached file `cache-f90f341e5308a74698d872bcc88f9c0e.arrow` and not recompute it.\r\n\r\nNot sure if there's a better way to do that cc @julien-c @thomwolf ", "I wrote a separate script just for preparing a cached file, including tokenization. Each process did use the tokenized cached file.\r\n\r\nCurrently I'm testing the pipeline on 24GB text data. It took about 1.5 hour to create a cached file on `n1-highmem-16 (16 vCPUs, 104 GB memory)`. I assume loading this cached file in the main script with `xla_spawn.py` won't be an issue (even if there are 8 processes).\r\n\r\n```\r\ntotal 98G\r\ndrwxr-xr-x 2 ***** ***** 4.0K Aug 26 13:38 .\r\ndrwxr-xr-x 3 ***** ***** 4.0K Aug 26 12:24 ..\r\n-rw------- 1 ***** ***** 74G Aug 26 13:38 cache-a7aa04134ba7b1aff5d9710f14a4e334.arrow\r\n-rw-r--r-- 1 ***** ***** 681 Aug 26 12:24 dataset_info.json\r\n-rw-r--r-- 1 ***** ***** 0 Aug 26 12:24 LICENSE\r\n-rw-r--r-- 1 ***** ***** 25G Aug 26 12:24 text-train.arrow\r\n```", "Yes loading the cached file should be fine from different processes", "Sorry, I thought it was working, but actually the second call doesn't use the cached file that was generated separately, and it will generate another cache-****.arrorw file with a different name. If I run the training script again (with `xla_spawn.py`), it will use the second cached file, which was generated by the training script itself in the previous run.\r\n\r\n```\r\ndrwxr-xr-x 2 ***** ***** 4.0K Aug 26 15:35 .\r\ndrwxr-xr-x 3 ***** ***** 4.0K Aug 26 15:29 ..\r\n-rw------- 1 ***** ***** 99M Aug 26 15:35 cache-0d77dfce704493dbe63f071eed6a5431.arrow\r\n-rw------- 1 ***** ***** 99M Aug 26 15:29 cache-69633651476e943b93c89ace715f9487.arrow\r\n-rw-r--r-- 1 ***** ***** 670 Aug 26 15:33 dataset_info.json\r\n-rw-r--r-- 1 ***** ***** 0 Aug 26 15:33 LICENSE\r\n-rw-r--r-- 1 ***** ***** 33M Aug 26 15:29 text-train.arrow\r\n```", "So if I understand correctly it means that the cached file generated by your separated script is different by the one used by the training script ?", "Yes.\r\n\r\n1. `cache-69633651476e943b93c89ace715f9487.arrow` was generated with a separate script. \r\n2. I ran the entire script with `xla_spawn.py`.\r\n3. `cache-69633651476e943b93c89ace715f9487.arrow` is not used.\r\n4. `cache-0d77dfce704493dbe63f071eed6a5431.arrow` is created.\r\n5. training starts...\r\n\r\nNow, if I kill the process at step 5, and do the step 2 again, it will use `cache-0d77dfce704493dbe63f071eed6a5431.arrow` (cached file created at step 4) without any issue.\r\n\r\nI used the following to generate the first cached file.\r\n```python\r\ndataset = load_dataset(\"text\", data_files=file_path, split=\"train\")\r\ndataset = dataset.map(lambda ex: tokenizer(ex[\"text\"], add_special_tokens=True,\r\n truncation=True, max_length=args.block_size), batched=True)\r\ndataset.set_format(type='torch', columns=['input_ids'])\r\n```", "1. Here's the log from the first step.\r\n```\r\nDownloading and preparing dataset text/default-e84dd29acc4ad9ef (download: Unknown size, generated: Unknown size, post-processed: Unknown size, total: Unknown size) to /home/*****/.cache/huggingface/datasets/text/default-e84dd29acc4ad9ef/0.0.0/\r\n447f2bcfa2a721a37bc8fdf23800eade1523cf07f7eada6fe661fe4d070d380d...\r\nDataset text downloaded and prepared to /home/*****/.cache/huggingface/datasets/text/default-e84dd29acc4ad9ef/0.0.0/447f2bcfa2a721a37bc8fdf23800eade1523cf07f7eada6fe661fe4d070d380d. Subsequent calls will reuse this data.\r\n```\r\nThere's a file named `cache-7b1440ba7077af0f0d9035b5a55d01fc.arrow`, so it did create a cached file.\r\n```\r\ndrwxr-xr-x 2 ***** ***** 4.0K Aug 26 15:59 .\r\ndrwxr-xr-x 3 ***** ***** 4.0K Aug 26 15:58 ..\r\n-rw------- 1 ***** ***** 99M Aug 26 15:59 cache-7b1440ba7077af0f0d9035b5a55d01fc.arrow\r\n-rw-r--r-- 1 ***** ***** 670 Aug 26 15:58 dataset_info.json\r\n-rw-r--r-- 1 ***** ***** 0 Aug 26 15:58 LICENSE\r\n-rw-r--r-- 1 ***** ***** 33M Aug 26 15:58 text-train.arrow\r\n```\r\n2. Ideally, `cache-7b1440ba7077af0f0d9035b5a55d01fc.arrow` should be used in `run_language_modeling.py` (modified version using `nlp`) with `xla_spawn.py`. But it looks like it's creating a new cached file.\r\n\r\n```\r\n08/26/2020 16:13:03 - INFO - filelock - Lock 139635836351096 released on /home/*****/.cache/huggingface/datasets/3e34209a2741375a1db1ff03bf1abba1a9bd0e6016912d3ead0114b9d1ca2685.202fa4f84f552bff1f5400ae012663839c61efb3de068c6c8722d34ac0ea6192\r\n.py.lock\r\n08/26/2020 16:13:03 - WARNING - nlp.builder - Using custom data configuration default\r\n08/26/2020 16:13:03 - INFO - nlp.builder - Overwrite dataset info from restored data version.\r\n08/26/2020 16:13:03 - INFO - nlp.info - Loading Dataset info from /home/*****/.cache/huggingface/datasets/text/default-e84dd29acc4ad9ef/0.0.0/447f2bcfa2a721a37bc8fdf23800eade1523cf07f7eada6fe661fe4d070d380d\r\n08/26/2020 16:13:03 - INFO - nlp.builder - Reusing dataset text (/home/*****/.cache/huggingface/datasets/text/default-e84dd29acc4ad9ef/0.0.0/447f2bcfa2a721a37bc8fdf23800eade1523cf07f7eada6fe661fe4d070d380d)\r\n08/26/2020 16:13:03 - INFO - nlp.builder - Constructing Dataset for split train, from /home/*****/.cache/huggingface/datasets/text/default-e84dd29acc4ad9ef/0.0.0/447f2bcfa2a721a37bc8fdf23800eade1523cf07f7eada6fe661fe4d070d380d\r\n08/26/2020 16:13:03 - INFO - nlp.utils.info_utils - All the checksums matched successfully for post processing resources\r\n08/26/2020 16:13:03 - INFO - nlp.builder - Overwrite dataset info from restored data version.\r\n08/26/2020 16:13:03 - INFO - nlp.info - Loading Dataset info from /home/*****/.cache/huggingface/datasets/text/default-e84dd29acc4ad9ef/0.0.0/447f2bcfa2a721a37bc8fdf23800eade1523cf07f7eada6fe661fe4d070d380d\r\n08/26/2020 16:13:03 - INFO - nlp.builder - Reusing dataset text (/home/*****/.cache/huggingface/datasets/text/default-e84dd29acc4ad9ef/0.0.0/447f2bcfa2a721a37bc8fdf23800eade1523cf07f7eada6fe661fe4d070d380d)\r\n08/26/2020 16:13:03 - INFO - nlp.builder - Constructing Dataset for split train, from /home/*****/.cache/huggingface/datasets/text/default-e84dd29acc4ad9ef/0.0.0/447f2bcfa2a721a37bc8fdf23800eade1523cf07f7eada6fe661fe4d070d380d\r\n08/26/2020 16:13:03 - INFO - nlp.utils.info_utils - All the checksums matched successfully for post processing resources\r\n08/26/2020 16:13:05 - INFO - nlp.arrow_dataset - Caching processed dataset at /home/*****/.cache/huggingface/datasets/text/default-e84dd29acc4ad9ef/0.0.0/447f2bcfa2a721a37bc8fdf23800eade1523cf07f7eada6fe661fe4d070d380d/cache-0d77dfce704493dbe\r\n63f071eed6a5431.arrow\r\n^M 0%| | 0/100 [00:00<?, ?it/s]08/26/2020 16:13:05 - INFO - nlp.arrow_dataset - Caching processed dataset at /home/*****/.cache/huggingface/datasets/text/default-e84dd29acc4ad9ef/0.0.0/447f2bcfa2a721a37bc8fdf23800eade1523cf07f7eada6\r\nfe661fe4d070d380d/cache-0d77dfce704493dbe63f071eed6a5431.arrow\r\n```\r\n\r\nThere are two cached files in the directory:\r\n\r\n```\r\ndrwxr-xr-x 2 ***** ***** 4.0K Aug 26 16:14 .\r\ndrwxr-xr-x 3 ***** ***** 4.0K Aug 26 15:58 ..\r\n-rw------- 1 ***** ***** 99M Aug 26 16:14 cache-0d77dfce704493dbe63f071eed6a5431.arrow\r\n-rw------- 1 ***** ***** 99M Aug 26 15:59 cache-7b1440ba7077af0f0d9035b5a55d01fc.arrow\r\n-rw-r--r-- 1 ***** ***** 670 Aug 26 16:13 dataset_info.json\r\n-rw-r--r-- 1 ***** ***** 0 Aug 26 16:13 LICENSE\r\n-rw-r--r-- 1 ***** ***** 33M Aug 26 15:58 text-train.arrow\r\n```\r\n\r\nIf I kill the process, and run it again, it will use the second cached file.\r\n\r\n```\r\n08/26/2020 16:19:52 - WARNING - nlp.builder - Using custom data configuration default\r\n08/26/2020 16:19:52 - INFO - nlp.builder - Overwrite dataset info from restored data version.\r\n08/26/2020 16:19:52 - INFO - nlp.info - Loading Dataset info from /home/*****/.cache/huggingface/datasets/text/default-e84dd29acc4ad9ef/0.0.0/447f2bcfa2a721a37bc8fdf23800eade1523cf07f7eada6fe661fe4d070d380d\r\n08/26/2020 16:19:52 - INFO - nlp.builder - Reusing dataset text (/home/*****/.cache/huggingface/datasets/text/default-e84dd29acc4ad9ef/0.0.0/447f2bcfa2a721a37bc8fdf23800eade1523cf07f7eada6fe661fe4d070d380d)\r\n08/26/2020 16:19:52 - INFO - nlp.builder - Constructing Dataset for split train, from /home/*****/.cache/huggingface/datasets/text/default-e84dd29acc4ad9ef/0.0.0/447f2bcfa2a721a37bc8fdf23800eade1523cf07f7eada6fe661fe4d070d380d\r\n08/26/2020 16:19:52 - INFO - nlp.utils.info_utils - All the checksums matched successfully for post processing resources\r\n08/26/2020 16:19:53 - INFO - nlp.arrow_dataset - Loading cached processed dataset at /home/*****/.cache/huggingface/datasets/text/default-e84dd29acc4ad9ef/0.0.0/447f2bcfa2a721a37bc8fdf23800eade1523cf07f7eada6fe661fe4d070d380d/cache-0d77dfce70\r\n4493dbe63f071eed6a5431.arrow\r\n08/26/2020 16:19:53 - INFO - nlp.arrow_dataset - Set __getitem__(key) output type to torch for ['input_ids'] columns (when key is int or slice) and don't output other (un-formatted) columns.\r\n```", "Thanks for all the details.\r\nThe two cached files are supposed to be the same. I suspect that the caching has a problem with the tokenizer.\r\nWhich tokenizer did you use ?", "I trained a byte-level BPE tokenizer on my data with `tokenziers` library following this [example](https://github.com/huggingface/tokenizers/blob/master/bindings/python/examples/train_bytelevel_bpe.py).\r\n\r\nAnd I put these model files in a directory named `\"model_name\"`. I also put config.json, which is the original RoBERTa config file.\r\n\r\n```bash\r\n%ls model_name\r\nconfig.json merges.txt vocab.json\r\n```\r\n\r\n[This](https://github.com/huggingface/transformers/blob/4bd7be9a4268221d2a0000c7e8033aaeb365c03b/examples/language-modeling/run_language_modeling.py#L196) is the line where `run_language_modeling.py` loads the tokenier.\r\n\r\n```python\r\ntokenizer = AutoTokenizer.from_pretrained(model_args.tokenizer_name, cache_dir=model_args.cache_dir)\r\n```\r\n\r\nI use `\"model_name\"` for `model_args.tokenizer_name`. I don't specify `model_args.cache_dir`. It is 'None' by default.", "In my separated script for caching, I'm using `use_fast=True` when initializing a tokenizer.\r\n\r\n```python\r\ntokenizer = AutoTokenizer.from_pretrained(args.config_name, use_fast=True)\r\n```\r\nI wasn't using that option in the main script. That could be the reason...", "Yea it could definitely explain why you have two different cache files.\r\nLet me know if using the same tokenizers on both sides fixes the issue", "It still creates a new file even if I remove `use_fast=True`... \r\n\r\nHere's the script used to create a cached file.\r\n```python \r\n#!/usr/bin/env python3\r\n\r\nimport argparse\r\n\r\nfrom transformers import AutoTokenizer\r\n\r\nfrom nlp import load_dataset\r\n\r\n\r\ndef main():\r\n parser = argparse.ArgumentParser(description='description')\r\n parser.add_argument('--config_name', type=str, help='Pretrained config name or path if not the same as model_name')\r\n parser.add_argument('--data_file', type=str, help='The input data file (a text file).')\r\n parser.add_argument('--block_size', type=int, default=-1, help='The training dataset will be truncated in block of this size for training')\r\n args = parser.parse_args()\r\n\r\n tokenizer = AutoTokenizer.from_pretrained(args.config_name)\r\n\r\n dataset = load_dataset(\"text\", data_files=args.data_file, split=\"train\")\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\r\n\r\nif __name__ == \"__main__\":\r\n main()\r\n```\r\n\r\nHere's how the data is loaded in the modified `run_language_modeling.py`. [[original function](https://github.com/huggingface/transformers/blob/971d1802d009d9996b36a34a34477cee849ef39f/examples/language-modeling/run_language_modeling.py#L128-L135)]\r\n\r\n```python\r\ndef get_dataset(args: DataTrainingArguments, tokenizer: PreTrainedTokenizer, evaluate=False):\r\n file_path = args.eval_data_file if evaluate else args.train_data_file\r\n split = \"validation\" if evaluate else \"train\"\r\n if args.line_by_line:\r\n # return LineByLineTextDataset(tokenizer=tokenizer, file_path=file_path, block_size=args.block_size)\r\n dataset = load_dataset(\"text\", data_files=file_path, split=\"train\")\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\r\n else:\r\n return TextDataset(\r\n tokenizer=tokenizer, file_path=file_path, block_size=args.block_size, overwrite_cache=args.overwrite_cache\r\n )\r\n```\r\n\r\nProbably I don't need this part in the main script,\r\n\r\n```python\r\ndataset = 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```\r\nand simply do this?\r\n```python\r\ndataset = load_dataset(\"text\", data_files=file_path, split=\"train\")\r\nreturn dataset\r\n```", "You need this part in the main script or it will use the dataset that is not tokenized\r\n\r\n", "I can see that the tokenizer in `run_language_modeling.py` is not instantiated the same way as in your separated script.\r\nIndeed we can see L196:\r\n```python\r\ntokenizer = AutoTokenizer.from_pretrained(model_args.tokenizer_name, cache_dir=model_args.cache_dir)\r\n```\r\nCould you try to make it so they are instantiated the exact same way please ?", "I updated my separated script, but it's creating a cached file again. If I don't use the `model_args.cache_dir`, both will get `None`, so they should be the same.\r\n\r\n```python\r\n#!/usr/bin/env python3\r\nimport argparse\r\n\r\nfrom transformers import AutoTokenizer\r\nfrom nlp import load_dataset\r\n\r\ndef main():\r\n parser = argparse.ArgumentParser(description='description')\r\n parser.add_argument('--tokenizer_name', type=str, help='Pretrained tokenizer name or path if not the same as model_name')\r\n parser.add_argument('--data_file', type=str, help='The input data file (a text file).')\r\n parser.add_argument('--cache_dir', type=str, default=None, help='Where do you want to store the pretrained models downloaded from s3')\r\n parser.add_argument('--block_size', type=int, default=-1, help='The training dataset will be truncated in block of this size for training')\r\n\r\n model_args = parser.parse_args()\r\n\r\n tokenizer = AutoTokenizer.from_pretrained(model_args.tokenizer_name, cache_dir=model_args.cache_dir)\r\n\r\n dataset = load_dataset(\"text\", data_files=model_args.data_file, split=\"train\")\r\n dataset = dataset.map(lambda ex: tokenizer(ex[\"text\"], add_special_tokens=True,\r\n truncation=True, max_length=model_args.block_size), batched=True)\r\n dataset.set_format(type='torch', columns=['input_ids'])\r\n\r\nif __name__ == \"__main__\":\r\n main()\r\n```\r\n\r\nIs there a way to specify the cache file to load, and skip the re-computation?", "Could you also check that the `args.block_size` used in the lambda function is the same as well ?", "Here's a minimal working example to reproduce this issue.\r\n\r\nAssumption:\r\n- You have access to TPU.\r\n- You have installed `transformers` and `nlp`.\r\n- You have tokenizer files (`config.json`, `merges.txt`, `vocab.json`) under the directory named `model_name`.\r\n- You have `xla_spawn.py` (Download from https://github.com/huggingface/transformers/blob/master/examples/xla_spawn.py).\r\n- You have saved the following script as `prepare_cached_dataset.py`.\r\n\r\n```python\r\n#!/usr/bin/env python3\r\nimport argparse\r\nfrom transformers import AutoTokenizer\r\nfrom nlp import load_dataset\r\n\r\ndef main():\r\n parser = argparse.ArgumentParser(description='description')\r\n parser.add_argument('--tokenizer_name', type=str, help='Pretrained tokenizer name or path if not the same as model_name')\r\n parser.add_argument('--data_file', type=str, help='The input data file (a text file).')\r\n parser.add_argument('--cache_dir', type=str, default=None, help='Where do you want to store the pretrained models downloaded from s3')\r\n parser.add_argument('--block_size', type=int, default=-1, help='The training dataset will be truncated in block of this size for training')\r\n parser.add_argument('--tpu_num_cores', type=int, default=1, help='Number of TPU cores to use (1 or 8). For xla_apwan.py')\r\n model_args = parser.parse_args()\r\n \r\n tokenizer = AutoTokenizer.from_pretrained(model_args.tokenizer_name, cache_dir=model_args.cache_dir, use_fast=True)\r\n \r\n dataset = load_dataset(\"text\", data_files=model_args.data_file, split=\"train\")\r\n dataset = dataset.map(lambda ex: tokenizer(ex[\"text\"], add_special_tokens=True,\r\n truncation=True, max_length=model_args.block_size), batched=True)\r\n dataset.set_format(type='torch', columns=['input_ids'])\r\n\r\ndef _mp_fn(index):\r\n # For xla_spawn (TPUs)\r\n main()\r\n\r\nif __name__ == \"__main__\":\r\n main()\r\n```\r\n\r\n- Run the following command. Replace `your_training_data` with some text file.\r\n\r\n```bash\r\nexport TRAIN_DATA=your_training_data\r\n\r\npython prepare_cached_dataset.py \\\r\n--tokenizer_name=model_name \\\r\n--block_size=512 \\\r\n--data_file=$TRAIN_DATA\r\n```\r\n- Check the cached directory.\r\n```bash\r\nls -lha /home/*****/.cache/huggingface/datasets/text/default-e84dd29acc4ad9ef/0.0.0/447f2bcfa2a721a37bc8fdf23800eade1523cf07f7eada6fe661fe4d070d380d\r\ntotal 132M\r\ndrwxr-xr-x 2 ***** ***** 4.0K Aug 28 13:08 .\r\ndrwxr-xr-x 3 ***** ***** 4.0K Aug 28 13:08 ..\r\n-rw------- 1 ***** ***** 99M Aug 28 13:08 cache-bfc7cb0702426d19242db5e8c079f04b.arrow\r\n-rw-r--r-- 1 ***** ***** 670 Aug 28 13:08 dataset_info.json\r\n-rw-r--r-- 1 ***** ***** 0 Aug 28 13:08 LICENSE\r\n-rw-r--r-- 1 ***** ***** 33M Aug 28 13:08 text-train.arrow\r\n```\r\n\r\n- Run the same script again. (The output should be just `Using custom data configuration default`.)\r\n```\r\npython prepare_cached_dataset.py \\\r\n--tokenizer_name=model_name \\\r\n--block_size=512 \\\r\n--data_file=$TRAIN_DATA\r\n```\r\n- Check the cached directory.\r\n```bash\r\nls -lha /home/*****/.cache/huggingface/datasets/text/default-e84dd29acc4ad9ef/0.0.0/447f2bcfa2a721a37bc8fdf23800eade1523cf07f7eada6fe661fe4d070d380d\r\ntotal 132M\r\ndrwxr-xr-x 2 ***** ***** 4.0K Aug 28 13:08 .\r\ndrwxr-xr-x 3 ***** ***** 4.0K Aug 28 13:08 ..\r\n-rw------- 1 ***** ***** 99M Aug 28 13:08 cache-bfc7cb0702426d19242db5e8c079f04b.arrow\r\n-rw-r--r-- 1 ***** ***** 670 Aug 28 13:20 dataset_info.json\r\n-rw-r--r-- 1 ***** ***** 0 Aug 28 13:20 LICENSE\r\n-rw-r--r-- 1 ***** ***** 33M Aug 28 13:08 text-train.arrow\r\n```\r\n- The cached file (`cache-bfc7cb0702426d19242db5e8c079f04b.arrow`) is reused.\r\n- Now, run this script with `xla_spawn.py`. Ideally, it should reuse the cached file, however, you will see each process is creating a cache file again.\r\n\r\n```bash\r\npython xla_spawn.py --num_cores 8 \\\r\nprepare_cached_dataset.py \\\r\n--tokenizer_name=model_name \\\r\n--block_size=512 \\\r\n--data_file=$TRAIN_DATA\r\n```\r\n\r\n- Check the cached directory. There are two arrrow files.\r\n```bash\r\nls -lha /home/*****/.cache/huggingface/datasets/text/default-e84dd29acc4ad9ef/0.0.0/447f2bcfa2a721a37bc8fdf23800eade1523cf07f7eada6fe661fe4d070d380d\r\ntotal 230M\r\ndrwxr-xr-x 2 ***** ***** 4.0K Aug 28 13:25 .\r\ndrwxr-xr-x 3 ***** ***** 4.0K Aug 28 13:08 ..\r\n-rw------- 1 ***** ***** 99M Aug 28 13:08 cache-bfc7cb0702426d19242db5e8c079f04b.arrow\r\n-rw------- 1 ***** ***** 99M Aug 28 13:25 cache-e0e2313e49c8a110aafcc8133154c19a.arrow\r\n-rw-r--r-- 1 ***** ***** 670 Aug 28 13:24 dataset_info.json\r\n-rw-r--r-- 1 ***** ***** 0 Aug 28 13:24 LICENSE\r\n-rw-r--r-- 1 ***** ***** 33M Aug 28 13:08 text-train.arrow\r\n```\r\n", "I ended up specifying the `cache_file_name` argument when I call `map` function.\r\n\r\n```python\r\ndataset = dataset.map(lambda ex: tokenizer(ex[\"text\"], add_special_tokens=True, truncation=True, max_length=args.block_size),\r\n batched=True,\r\n cache_file_name=cache_file_name)\r\n```\r\n\r\nNote:\r\n- `text` dataset in `nlp` does not strip `\"\\n\"`. If you want the same output as in [`LineByLineTextDataset`](https://github.com/huggingface/transformers/blob/afc4ece462ad83a090af620ff4da099a0272e171/src/transformers/data/datasets/language_modeling.py#L88-L111), you would need to create your own dataset class where you replace `line` to `line.strip()` [here](https://github.com/huggingface/nlp/blob/master/datasets/text/text.py#L35).\r\n" ]

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[ "Thanks for creating the issue :)\r\nThe download link for wmt-en-de raw looks like a mirror. We should use that instead of the current url.\r\nIs this mirror official ?\r\n\r\nAlso it looks like for `ro-en` it tried to download other languages. If we manage to only download the one that is asked it'd be cool\r\n\r\nAlso cc @patrickvonplaten ", "Mirror is not official.", "Shall we host the files ourselves or it is fine to use this mirror in your opinion ?", "Should we add an argument in `load_dataset` to override some URL with a custom URL (e.g. mirror) or a local path?\r\n\r\nThis could also be used to provide local files instead of the original files as requested by some users (e.g. when you made a dataset with the same format than SQuAD and what to use it instead of the official dataset files).", "@lhoestq I think we should host it ourselves. I'll put the subset of wmt (without preprocessed files) that we need on s3 and post a link over the weekend.", "Is there a solution yet? The download speed is still too slow. 60-70kbps download for wmt16 and around 100kbps for wmt19. @sshleifer ", "I'm working on mirror links which will provide high download speed :)\r\nSee https://github.com/huggingface/datasets/issues/1892", "Resolved via https://github.com/huggingface/datasets/pull/1912" ]

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[ "Indeed, good catch!" ]

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[ "Thanks!" ]

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[ "Update - maybe this is only failing on bleu because I was not tokenizing inputs to the metric", "Closing - seems to be just forgetting to tokenize. And found the helpful discussion in huggingface/evaluate#105 " ]

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[ "Any updates on this?", "This request is still an open issue waiting to be addressed by any community member, @GuillemGSubies." ]

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[ "This seems to be fixed in #513 for the filter function, replacing `cache_file_name` with `indices_cache_file_name` in the assert. Although not for the `map()` function @thomwolf ", "Maybe I'm a bit tired but I fail to see the issue here.\r\n\r\nSince `cache_file_name` is `None` by default, if you set `keep_in_memory` to `True`, the assert should pass, no?", "I failed to realise that this only applies to `shuffle()`. Whenever `keep_in_memory` is set to True, this is passed on to the `select()` function. However, if `cache_file_name` is None, it will be defined in the `shuffle()` function before it is passed on to `select()`. \r\n\r\nThus, `select()` is called with `keep_in_memory=True` and a not None value for `cache_file_name`. \r\nThis is essentially fixed in #513 \r\n\r\nEasily reproducible:\r\n```python\r\n>>> import nlp\r\n>>> data = nlp.load_dataset(\"cosmos_qa\", split=\"train\")\r\nUsing custom data configuration default\r\n>>> data.shuffle(keep_in_memory=True)\r\nTraceback (most recent call last):\r\n File \"<stdin>\", line 1, in <module>\r\n File \"/home/vegarab/.conda/envs/torch/lib/python3.7/site-packages/nlp/arrow_dataset.py\", line 1398, in shuffle\r\n verbose=verbose,\r\n File \"/home/vegarab/.conda/envs/torch/lib/python3.7/site-packages/nlp/arrow_dataset.py\", line 1178, in select\r\n ), \"Please use either `keep_in_memory` or `cache_file_name` but not both.\"\r\nAssertionError: Please use either `keep_in_memory` or `cache_file_name` but not both.\r\n>>>data.select([0], keep_in_memory=True)\r\n# No error\r\n```", "Oh yes ok got it thanks. Should be fixed if we are happy with #513 indeed.", "My bad. This is actually not fixed in #513. Sorry about that...\r\nThe new `indices_cache_file_name` is set to a non-None value in the new `shuffle()` as well. \r\n\r\nThe buffer and caching mechanisms used in the `select()` function are too intricate for me to understand why the check is there at all. I've removed it in my local build and it seems to be working fine for my project, without really considering other implications of the change. \r\n\r\n", "Ok I'll investigate and add a series of tests on the `keep_in_memory=True` settings which is under-tested atm", "Hey, still seeing this issue with the latest version.", "The same :(", "These are the steps needed to fix this issue:\r\n1. add the following check to `Dataset.shuffle`:\r\n```python\r\nif keep_in_memory and indices_cache_file_name is not None:\r\n raise ValueError(\"Please use either `keep_in_memory` or `indices_cache_file_name` but not both.\")\r\n```\r\n2. set `indices_cache_file_name` to `None` if `keep_in_memory` is True in the call to `select`\r\n3. add a test with `shuffle(keep_in_memory=True)`", "Hi @mariosasko , I have opened this PR #5082 " ]

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[ "Hi @vegarab yes feel free to open a discussion here.\r\n\r\nThis design choice was not very much thought about.\r\n\r\nSince `dataset.select()` (like all the method without a trailing underscore) is non-destructive and returns a new dataset it has most of its properties initialized from scratch (except the table and infos).\r\n\r\nThinking about it I don't see a strong reason against transmitting the format from the parent dataset to its newly created child. It's probably what's expected by the user in most cases. What do you think @lhoestq?\r\n\r\nBy the way, I've been working today on a refactoring of all the samples re-ordering/selection methods (`select`, `sort`, `shuffle`, `shard`, `train_test_split`). The idea is to speed them up by a lot (like, really a lot) by working as much as possible with an indices mapping table instead of doing a deep copy of the full dataset as we've been doing currently. You can give it a look and try it here: https://github.com/huggingface/nlp/pull/513\r\nFeedbacks are very much welcome", "I think it's ok to keep the format.\r\nIf we want to have this behavior for `.map` too we just have to make sure it doesn't keep a column that's been removed.", "Shall we have this in the coming release by the way @lhoestq ?", "Yes sure !", "Since datasets 1.0.0 the format is not reset anymore.\r\nClosing this one, but feel free to re-open if you have other questions" ]

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[ "Seems like this method was added in 1.17. I'll add a requirement on this.", "Thank you so much. After upgrading the numpy library, it worked." ]

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[ "Do you want to convert a dataset script to the tfds format ?\r\nIf so, we currently have a comversion script nlp/commands/convert.py but it is a conversion script that goes from tfds to nlp.\r\nI think it shouldn't be too hard to do the changes in reverse (at some manual adjustments).\r\nIf you manage to make it work in reverse, feel free to open a PR to share it with the community :)", "In our docs: [Using a Dataset with PyTorch/Tensorflow](https://huggingface.co/docs/datasets/torch_tensorflow.html)." ]

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[ "Which version of Apache Beam do you have (can you copy your full environment info here)?", "apache-beam==2.23.0\r\nnlp==0.4.0\r\n\r\nFor me this was resolved by running the same python script on Linux (or really WSL). ", "Do you manage to run a dummy beam pipeline with python on windows ? \r\nYou can test a dummy pipeline with [this code](https://github.com/apache/beam/blob/master/sdks/python/apache_beam/examples/wordcount_minimal.py)\r\n\r\nIf you get the same error, it means that the issue comes from apache beam.\r\nOtherwise we'll investigate what went wrong here", "Still, same error, so I guess it is on apache beam then. \r\nThanks for the investigation.", "Thanks for trying\r\nLet us know if you find clues of what caused this issue, or if you find a fix" ]

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[ "Looks like an issue with 3.0.2 transformers version. Works fine when I use \"master\" version of transformers." ]

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[ "Thanks for reporting !\r\n\r\nTo store the cache file, we compute a hash of the function given in `.map`, using our own hashing function.\r\nThe hash doesn't seem to stay the same over sessions for the tokenizer.\r\nApparently this is because of the regex at `tokenizer.pat` is not well supported by our hashing function.\r\n\r\nI'm working on a fix", "Thanks everyone. Works great now.", "Hi. I believe the fix was for the nlp library. Is there a solution to handle compiled regex expressions in .map() with the caching. I want to run a simple regex pattern on a big dataset, but I am running into the issue of compiled expression not being cached. \r\n\r\nInstead of opening a new issue, I thought I would put my query here. Let me know if a new issue would be more suitable. Thanks", "Hi @MaveriQ! This fix is also included in the `datasets` library. Can you provide a reproducer?" ]

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[ "In 0.4.0, the assertion in `concatenate_datasets ` is on the features, and not the schema.\r\nCould you try to update `nlp` ?\r\n\r\nAlso, since 0.4.0, you can use `dset_wikipedia.cast_(dset_books.features)` to avoid the schema cast hack.", "Or maybe the assertion comes from elsewhere ?", "I'm using the master branch. The assertion failure comes from the underlying `pa.concat_tables()`, which is in the pyarrow package. That method does check schemas.\r\n\r\nSince `features.type` does not contain information about nullable vs non-nullable features, the `cast_()` method won't resolve the schema mismatch. There is information in a schema which is not stored in features.", "I'm doing a refactor of type inference in #363 . Both text fields should match after that", "By default nullable will be set to True", "It should be good now. I was able to run\r\n\r\n```python\r\n>>> from nlp import concatenate_datasets, load_dataset\r\n>>>\r\n>>> bookcorpus = load_dataset(\"bookcorpus\", split=\"train\")\r\n>>> wiki = load_dataset(\"wikipedia\", \"20200501.en\", split=\"train\")\r\n>>> wiki.remove_columns_(\"title\") # only keep the text\r\n>>>\r\n>>> assert bookcorpus.features.type == wiki.features.type\r\n>>> bert_dataset = concatenate_datasets([bookcorpus, wiki])\r\n```", "Thanks!" ]

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[ "I did the release on github, and updated the doc :)\r\nSorry for the delay", "Thanks!" ]

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[ "whoops", "please delete this" ]

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[ "I found `UNv1.0.en-ru.tar.gz` here: https://conferences.unite.un.org/uncorpus/en/downloadoverview, so it can be reconstructed with:\r\n```\r\nwget -c https://stuncorpusprod.blob.core.windows.net/corpusfiles/UNv1.0.en-ru.tar.gz.00\r\nwget -c https://stuncorpusprod.blob.core.windows.net/corpusfiles/UNv1.0.en-ru.tar.gz.01\r\nwget -c https://stuncorpusprod.blob.core.windows.net/corpusfiles/UNv1.0.en-ru.tar.gz.02\r\ncat UNv1.0.en-ru.tar.gz.0* > UNv1.0.en-ru.tar.gz\r\n```\r\nit has other languages as well, in case https://storage.googleapis.com/tfdataset-data/downloadataset/uncorpus/ is gone", "Further, `nlp.load_dataset('wmt19', 'ru-en')` has only the `train` and `val` datasets. `test` is missing.\r\n\r\nFixed locally for summarization needs, by running:\r\n```\r\npip install sacrebleu\r\nsacrebleu -t wmt19 -l ru-en --echo src > test.source\r\nsacrebleu -t wmt19 -l ru-en --echo ref > test.target\r\n```\r\nh/t @sshleifer ", "Fixed in https://github.com/huggingface/datasets/pull/1912" ]

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[ "Yes indeed it looks like some `'` and spaces are missing (for example in `dont` or `didnt`).\r\nDo you know if there exist some copies without this issue ?\r\nHow would you fix this issue on the current data exactly ? I can see that the data is raw text (not tokenized) so I'm not sure I understand how you would do it. Could you provide more details ?", "I'm afraid that I don't know how to obtain the original BookCorpus data. I believe this version came from an anonymous Google Drive link posted in another issue.\r\n\r\nGoing through the raw text in this version, it's apparent that NLTK's TreebankWordTokenizer was applied on it (I gave some examples in my original post), followed by:\r\n`' '.join(tokens)`\r\nYou can retrieve the tokenization by splitting on whitespace. You can then \"detokenize\" it with TreebankWordDetokenizer class of NLTK (though, as I suggested, use the fixed version in my repo). This will bring the text closer to its original form, but some steps of TreebankWordTokenizer are destructive, so it wouldn't be one-to-one. Something along the lines of the following should work:\r\n```\r\ntreebank_detokenizer = nltk.tokenize.treebank.TreebankWordDetokenizer()\r\ndb = nlp.load_dataset('bookcorpus', split=nlp.Split.TRAIN)\r\ndb = db.map(lambda x: treebank_detokenizer.detokenize(x['text'].split()))\r\n```\r\n\r\nRegarding other issues beyond the above, I'm afraid that I can't help with that.", "Ok I get it, that would be very cool indeed\r\n\r\nWhat kinds of patterns the detokenizer can't retrieve ?", "The TreebankTokenizer makes some assumptions about whitespace, parentheses, quotation marks, etc. For instance, while tokenizing the following text:\r\n```\r\nDwayne \"The Rock\" Johnson\r\n```\r\nwill result in:\r\n```\r\nDwayne `` The Rock '' Johnson\r\n```\r\nwhere the left and right quotation marks are turned into distinct symbols. Upon reconstruction, we can attach the left part to its token on the right, and respectively for the right part. However, the following texts would be tokenized exactly the same:\r\n```\r\nDwayne \" The Rock \" Johnson\r\nDwayne \" The Rock\" Johnson\r\nDwayne \" The Rock\" Johnson\r\n...\r\n```\r\nIn the above examples, the detokenizer would correct these inputs into the canonical text\r\n```\r\nDwayne \"The Rock\" Johnson\r\n```\r\nHowever, there are cases where there the solution cannot easily be inferred (at least without a true LM - this tokenizer is just a bunch of regexes). For instance, in cases where you have a fragment that contains the end of quote, but not its beginning, plus an accidental space:\r\n```\r\n... and it sounds fantastic, \" he said.\r\n```\r\nIn the above case, the tokenizer would assume that the quotes refer to the next token, and so upon detokenization it will result in the following mistake:\r\n```\r\n... and it sounds fantastic, \"he said.\r\n```\r\n\r\nWhile these are all odd edge cases (the basic assumptions do make sense), in noisy data they can occur, which is why I mentioned that the detokenizer cannot restore the original perfectly.\r\n", "To confirm, since this is preprocessed, this was not the exact version of the Book Corpus used to actually train the models described here (particularly Distilbert)? https://huggingface.co/datasets/bookcorpus\r\n\r\nOr does this preprocessing exactly match that of the papers?", "I believe these are just artifacts of this particular source. It might be better to crawl it again, or use another preprocessed source, as found here: https://github.com/soskek/bookcorpus ", "Yes actually the BookCorpus on hugginface is based on [this](https://github.com/soskek/bookcorpus/issues/24#issuecomment-643933352). And I kind of regret naming it as \"BookCorpus\" instead of something like \"BookCorpusLike\".\r\n\r\nBut there is a good news ! @shawwn has replicated BookCorpus in his way, and also provided a link to download the plain text files. see [here](https://github.com/soskek/bookcorpus/issues/27). There is chance we can have a \"OpenBookCorpus\" !", "Resolved via #856" ]

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