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vkhangpham

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[ "Sure, let's include this in today's release.", "Cool ! The windows CI should be fixed on master now, feel free to merge :)" ]

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[ "Works smoothly with the `transformers.Trainer` class now, thank you!" ]

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[ "Closed and moved to transformers." ]

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[ "This issue was original reported at https://github.com/huggingface/transformers/issues/14931 and It seems like this issue also occur with other AutoClass like AutoFeatureExtractor.", "Thanks for moving the issue here !\r\n\r\nI wasn't able to reproduce the issue on my env (the hashes stay the same):\r\n```\r\n- `transformers` version: 1.15.0\r\n- `tokenizers` version: 0.10.3\r\n- `datasets` version: 1.18.1\r\n- `dill` version: 0.3.4\r\n- Platform: Linux-4.19.0-18-cloud-amd64-x86_64-with-debian-10.11\r\n- Python version: 3.7.10\r\n- PyArrow version: 6.0.1\r\n```\r\nHowever I was able to reproduce it on Google Colab (the hashes end up different):\r\n```\r\n- `transformers` version: 1.15.0\r\n- `tokenizers` version: 0.10.3\r\n- `datasets` version: 1.18.1\r\n- `dill` version: 0.3.4\r\n- Platform: Linux-5.4.144+-x86_64-with-Ubuntu-18.04-bionic\r\n- Python version: 3.7.12\r\n- PyArrow version: 3.0.0\r\n```\r\nI'll investigate why it doesn't work properly on Google Colab :)", "I found the issue: the tokenizer has something inside it that changes.\r\n\r\nBefore the call, `tokenizer._tokenizer.truncation` is None, and after the call it changes to this for some reason:\r\n```\r\n{'max_length': 512, 'strategy': 'longest_first', 'stride': 0}\r\n```\r\n\r\nDoes anybody know why calling the tokenizer would change its state this way ? cc @Narsil @SaulLu maybe ?", "`tokenizer.encode(..)` does not accept argument like max_length, strategy or stride.\r\n\r\nIn `tokenizers` you have to modify the tokenizer state by setting various `TruncationParams` (and/or `PaddingParams`).\r\nHowever, since this is modifying the state, you need to mutably borrow the tokenizer (a rust concept). The key principle is that there can ever be only 1 mutable borrow at a time during the span of the tokenizer lifecycle.\r\n\r\nBecause of this, if `transformers` blindly set `TruncationParams` and `PaddingParams` on every call, it would cause the tokenizer to crash (or make the various threads accessing it hang, which is not necessarily better).\r\n\r\nIn order to avoid that, we decided to handle it this way : https://github.com/huggingface/transformers/pull/12550 . \r\n\r\nWhich should explain the state of the tokenizer being modified (hence its hash).\r\n\r\nNow for a temporary solution, simply encoding once with the tokenizer should give it it's proper hash (since by default the tokenizer doesn't have this state, looks at the first encoding call, and creates it).\r\n\r\nWe could try and set these 2 dicts at initialization time, but it wouldn't work if a user modified the tokenizer state later\r\n```python\r\ntokenizer = AutoTokenizer.from_pretrained(..)\r\ntokenizer.truncation_side = \"left\"\r\n# Now we have a difference between `tokenizer._tokenizer.truncation` and `tokenizer.truncation_side`\r\n```\r\nIf we wanted to fix it correctly it would mean mapping every assignation to it's proper location on `tokenizer.{padding/truncation}`\r\n\r\nI think it's important to note that we cannot guarantee a tokenizer' hash remains the same if *any* of those parameters are modified through the `.map` function.\r\n\r\nEdit: Another option would be to override the default __hash__ function, but I don't know if there's a sound implementation that could fit.", "Thanks a lot for the explanation !\r\nI think if we set these 2 dicts at initialization time it would be amazing already\r\n\r\nShall we open an issue in `transformers` to ask for these dictionaries to be set when the tokenizer is instantiated ?\r\n\r\n> Edit: Another option would be to override the default hash function, but I don't know if there's a sound implementation that could fit.\r\n\r\nIn `datasets` we can easily have custom hashing for objects of the other HF libraries if we want. For example we ignore the cache some tokenizers have. However in this specific case it touches parameters that may change the behavior of the tokenizer itself. I'm not sure the logic that determines how a tokenizer behaves should be in `datasets`", "A hack we could have in the `datasets` lib would be to call the tokenizer before hashing it in order to set all its parameters correctly - but it sounds a lot like a hack and I'm not sure this can work in the long run", "Fully agree with everything you said. \r\n\r\nI think the best course of action is creating an issue in `transformers`. I can start the work on this.\r\nI think the code changes are fairly simple. Making a sound test + not breaking other stuff might be different :D", "It should be noted that this problem also occurs in other AutoClasses, such as AutoFeatureExtractor, so I don't think handling it in Datasets is a long-term practice either.", "> I think the best course of action is creating an issue in `transformers`. I can start the work on this.\r\n\r\n@Narsil Hi, I reopen this issue in `transformers` https://github.com/huggingface/transformers/issues/14931" ]

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[ "Thanks for adding this @mariosasko! It worked for me when running it with a local data file, however, when using the file on Google Drive I get the following error:\r\n```Python\r\nds = load_dataset(\"./ted_talks_iwslt\",\"eu_ca_2014\", streaming=True, split=\"train\", use_auth_token=True)\r\nnext(iter(ds))\r\n```\r\n```\r\n---------------------------------------------------------------------------\r\nClientResponseError Traceback (most recent call last)\r\nFile ~/git/bigscience-datasets/env/lib/python3.9/site-packages/fsspec/implementations/http.py:383, in HTTPFileSystem._info(self, url, **kwargs)\r\n 381 try:\r\n 382 info.update(\r\n--> 383 await _file_info(\r\n 384 url,\r\n 385 size_policy=policy,\r\n 386 session=session,\r\n 387 **self.kwargs,\r\n 388 **kwargs,\r\n 389 )\r\n 390 )\r\n 391 if info.get(\"size\") is not None:\r\n\r\nFile ~/git/bigscience-datasets/env/lib/python3.9/site-packages/fsspec/implementations/http.py:734, in _file_info(url, session, size_policy, **kwargs)\r\n 733 async with r:\r\n--> 734 r.raise_for_status()\r\n 736 # TODO:\r\n 737 # recognise lack of 'Accept-Ranges',\r\n 738 # or 'Accept-Ranges': 'none' (not 'bytes')\r\n 739 # to mean streaming only, no random access => return None\r\n\r\nFile ~/git/bigscience-datasets/env/lib/python3.9/site-packages/aiohttp/client_reqrep.py:1004, in ClientResponse.raise_for_status(self)\r\n 1003 self.release()\r\n-> 1004 raise ClientResponseError(\r\n 1005 self.request_info,\r\n 1006 self.history,\r\n 1007 status=self.status,\r\n 1008 message=self.reason,\r\n 1009 headers=self.headers,\r\n 1010 )\r\n\r\nClientResponseError: 403, message='Forbidden', url=URL('https://drive.google.com/u/0/uc?id=1Cz1Un9p8Xn9IpEMMrg2kXSDt0dnjxc4z&export=download&confirm=1RJz')\r\n\r\nThe above exception was the direct cause of the following exception:\r\n\r\nFileNotFoundError Traceback (most recent call last)\r\nInput In [9], in <module>\r\n 1 iterable = iter(ds)\r\n 2 for i in range(10):\r\n----> 3 item = next(iterable)\r\n 4 print(item['text'][:10], item['meta'])\r\n\r\nFile ~/git/bigscience-datasets/env/lib/python3.9/site-packages/datasets/iterable_dataset.py:341, in IterableDataset.__iter__(self)\r\n 340 def __iter__(self):\r\n--> 341 for key, example in self._iter():\r\n 342 if self.features:\r\n 343 # we encode the example for ClassLabel feature types for example\r\n 344 encoded_example = self.features.encode_example(example)\r\n\r\nFile ~/git/bigscience-datasets/env/lib/python3.9/site-packages/datasets/iterable_dataset.py:338, in IterableDataset._iter(self)\r\n 336 else:\r\n 337 ex_iterable = self._ex_iterable\r\n--> 338 yield from ex_iterable\r\n\r\nFile ~/git/bigscience-datasets/env/lib/python3.9/site-packages/datasets/iterable_dataset.py:78, in ExamplesIterable.__iter__(self)\r\n 77 def __iter__(self):\r\n---> 78 for key, example in self.generate_examples_fn(**self.kwargs):\r\n 79 yield key, example\r\n\r\nFile ~/.cache/huggingface/modules/datasets_modules/datasets/lm_en_ted_talks_iwslt/756148758e86e64a350f9b320744a2bd5ed5cff74f7df620763a2b5e1a45e6c6/lm_en_ted_talks_iwslt.py:118, in TedTalksIWSLT._generate_examples(self, files)\r\n 116 for _LANG in _LANG_CODES:\r\n 117 source_file_path = _YEAR_FOLDER[year] + \"/ted_\" + _LANG + _YEAR[year] + \".zip\"\r\n--> 118 for path, file in files:\r\n 119 if path.endswith(source_file_path):\r\n 120 source_talks, _ = parse_zip_file(path, file.read())\r\n\r\nFile ~/git/bigscience-datasets/env/lib/python3.9/site-packages/datasets/utils/streaming_download_manager.py:596, in StreamingDownloadManager.iter_archive(self, urlpath_or_buf)\r\n 594 yield from _iter_archive(urlpath_or_buf)\r\n 595 else:\r\n--> 596 with xopen(urlpath_or_buf, \"rb\", use_auth_token=self.download_config.use_auth_token) as f:\r\n 597 yield from _iter_archive(f)\r\n\r\nFile ~/git/bigscience-datasets/env/lib/python3.9/site-packages/datasets/utils/streaming_download_manager.py:296, in xopen(file, mode, use_auth_token, *args, **kwargs)\r\n 294 new_kwargs = {}\r\n 295 kwargs = {**kwargs, **new_kwargs}\r\n--> 296 file_obj = fsspec.open(file, mode=mode, *args, **kwargs).open()\r\n 297 _add_retries_to_file_obj_read_method(file_obj)\r\n 298 return file_obj\r\n\r\nFile ~/git/bigscience-datasets/env/lib/python3.9/site-packages/fsspec/core.py:140, in OpenFile.open(self)\r\n 132 def open(self):\r\n 133 \"\"\"Materialise this as a real open file without context\r\n 134 \r\n 135 The file should be explicitly closed to avoid enclosed file\r\n (...)\r\n 138 been deleted; but a with-context is better style.\r\n 139 \"\"\"\r\n--> 140 out = self.__enter__()\r\n 141 closer = out.close\r\n 142 fobjects = self.fobjects.copy()[:-1]\r\n\r\nFile ~/git/bigscience-datasets/env/lib/python3.9/site-packages/fsspec/core.py:103, in OpenFile.__enter__(self)\r\n 100 def __enter__(self):\r\n 101 mode = self.mode.replace(\"t\", \"\").replace(\"b\", \"\") + \"b\"\r\n--> 103 f = self.fs.open(self.path, mode=mode)\r\n 105 self.fobjects = [f]\r\n 107 if self.compression is not None:\r\n\r\nFile ~/git/bigscience-datasets/env/lib/python3.9/site-packages/fsspec/spec.py:1009, in AbstractFileSystem.open(self, path, mode, block_size, cache_options, compression, **kwargs)\r\n 1007 else:\r\n 1008 ac = kwargs.pop(\"autocommit\", not self._intrans)\r\n-> 1009 f = self._open(\r\n 1010 path,\r\n 1011 mode=mode,\r\n 1012 block_size=block_size,\r\n 1013 autocommit=ac,\r\n 1014 cache_options=cache_options,\r\n 1015 **kwargs,\r\n 1016 )\r\n 1017 if compression is not None:\r\n 1018 from fsspec.compression import compr\r\n\r\nFile ~/git/bigscience-datasets/env/lib/python3.9/site-packages/fsspec/implementations/http.py:343, in HTTPFileSystem._open(self, path, mode, block_size, autocommit, cache_type, cache_options, size, **kwargs)\r\n 341 kw[\"asynchronous\"] = self.asynchronous\r\n 342 kw.update(kwargs)\r\n--> 343 size = size or self.info(path, **kwargs)[\"size\"]\r\n 344 session = sync(self.loop, self.set_session)\r\n 345 if block_size and size:\r\n\r\nFile ~/git/bigscience-datasets/env/lib/python3.9/site-packages/fsspec/asyn.py:91, in sync_wrapper.<locals>.wrapper(*args, **kwargs)\r\n 88 @functools.wraps(func)\r\n 89 def wrapper(*args, **kwargs):\r\n 90 self = obj or args[0]\r\n---> 91 return sync(self.loop, func, *args, **kwargs)\r\n\r\nFile ~/git/bigscience-datasets/env/lib/python3.9/site-packages/fsspec/asyn.py:71, in sync(loop, func, timeout, *args, **kwargs)\r\n 69 raise FSTimeoutError from return_result\r\n 70 elif isinstance(return_result, BaseException):\r\n---> 71 raise return_result\r\n 72 else:\r\n 73 return return_result\r\n\r\nFile ~/git/bigscience-datasets/env/lib/python3.9/site-packages/fsspec/asyn.py:25, in _runner(event, coro, result, timeout)\r\n 23 coro = asyncio.wait_for(coro, timeout=timeout)\r\n 24 try:\r\n---> 25 result[0] = await coro\r\n 26 except Exception as ex:\r\n 27 result[0] = ex\r\n\r\nFile ~/git/bigscience-datasets/env/lib/python3.9/site-packages/fsspec/implementations/http.py:396, in HTTPFileSystem._info(self, url, **kwargs)\r\n 393 except Exception as exc:\r\n 394 if policy == \"get\":\r\n 395 # If get failed, then raise a FileNotFoundError\r\n--> 396 raise FileNotFoundError(url) from exc\r\n 397 logger.debug(str(exc))\r\n 399 return {\"name\": url, \"size\": None, **info, \"type\": \"file\"}\r\n\r\nFileNotFoundError: https://drive.google.com/u/0/uc?id=1Cz1Un9p8Xn9IpEMMrg2kXSDt0dnjxc4z&export=download&confirm=1RJz\r\n```" ]

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[ "I'm not sure if this is expected behavior.\r\n\r\nAm I supposed to work with a copy of the dataset, i.e. `shuffled_dataset = data.shuffle(seed=None)`?\r\n\r\n```diff\r\nimport datasets\r\n\r\n# Some toy example\r\ndata = datasets.Dataset.from_dict(\r\n {\"feature\": [1, 2, 3, 4, 5], \"label\": [\"a\", \"b\", \"c\", \"d\", \"e\"]}\r\n)\r\n\r\n+shuffled_data = data.shuffle(seed=None)\r\n\r\n# Doesn't work as expected\r\nprint(\"Shuffle dataset\")\r\nfor _ in range(3):\r\n+ shuffled_data = shuffled_data.shuffle(seed=None)\r\n+ print(shuffled_data[:])\r\n- print(data.shuffle(seed=None)[:])\r\n\r\n# This seems to work with pandas\r\nprint(\"\\nShuffle via pandas\")\r\nfor _ in range(3):\r\n df = data.to_pandas().sample(frac=1.0)\r\n print(datasets.Dataset.from_pandas(df, preserve_index=False)[:])\r\n\r\n```\r\n\r\nor provide a `generator` instead?\r\n\r\n```diff\r\nimport datasets\r\n+from numpy.random import default_rng\r\n\r\n# Some toy example\r\ndata = datasets.Dataset.from_dict(\r\n {\"feature\": [1, 2, 3, 4, 5], \"label\": [\"a\", \"b\", \"c\", \"d\", \"e\"]}\r\n)\r\n\r\n+rng = default_rng()\r\n\r\n# Doesn't work as expected\r\nprint(\"Shuffle dataset\")\r\nfor _ in range(3):\r\n+ print(data.shuffle(generator=rng)[:])\r\n- print(data.shuffle(seed=None)[:])\r\n\r\n# This seems to work with pandas\r\nprint(\"\\nShuffle via pandas\")\r\nfor _ in range(3):\r\n df = data.to_pandas().sample(frac=1.0)\r\n print(datasets.Dataset.from_pandas(df, preserve_index=False)[:])\r\n\r\n```", "Hi! Thanks for reporting! Yes, this is not expected behavior. I've opened a PR with the fix." ]

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[ "We should also update the `bookcorpusopen` download url (see #3561) , no? ", "For `the_pile_openwebtext2` and `the_pile_stack_exchange` I did not regenerate the JSON files, but instead I just changed the download_checksums URL. " ]

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[ "A question to the datasets maintainers: is there a policy about how the set of allowed metadata fields is maintained and expanded?\r\n\r\nMetadata are very important, but defining the standard is always a struggle between allowing exhaustivity without being too complex. Archivists have Dublin Core, open data has https://frictionlessdata.io/, geo has ISO 19139 and INSPIRE, etc. and it's always a mess! I'm not sure we want to dig too much into it, but I'm curious to know if there has been some work on the metadata standard.", "> Metadata are very important, but defining the standard is always a struggle between allowing exhaustivity without being too complex. Archivists have Dublin Core, open data has [frictionlessdata.io](https://frictionlessdata.io/), geo has ISO 19139 and INSPIRE, etc. and it's always a mess! I'm not sure we want to dig too much into it, but I'm curious to know if there has been some work on the metadata standard.\r\n\r\n\r\nI thought this is a potential issue with adding this field since it might be hard to define what is general enough to be useful for most data vs what becomes very domain-specific. Potentially adding one extra field leads to more and more fields in the future. \r\n\r\nAnother issue is that there are some metadata standards around data i.e. [datacite](https://schema.datacite.org/meta/kernel-4.4/), but not many aimed explicitly at ML data afaik. Some of the discussions around metadata for ML are also more focused on versioning/managing data in production environments. My thinking is that here, some reference to the time of production would also often be tracked/relevant, i.e. for triggering model training, so having this information available in the hub would also help address this use case. " ]

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[ { "id": 1935892857, "node_id": "MDU6TGFiZWwxOTM1ODkyODU3", "url": "https://api.github.com/repos/huggingface/datasets/labels/bug", "name": "bug", "color": "d73a4a", "default": true, "description": "Something isn't working" } ]

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[ "Hi! We use `tqdm` to display progress bars, so I suggest you open this issue in their repo.", "It depends on how you use `tqdm`, no? \r\n\r\nDoesn't this library import via; \r\n\r\n```\r\nfrom tqdm.notebook import tqdm\r\n```" ]

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