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[ "Nice ! :)\r\nIt's indeed the first time we have such contributions so we'll have to figure out the appropriate way to integrate them.\r\nCould you add details on what they could be used for ?\r\n", "They can be used as training data for a smaller model.", "Sounds just like a regular dataset to me then, no?", "A new configuration for those datasets should do the job then.\r\nNote that until now datasets like xsum only had one configuration. It means that users didn't have to specify the configuration name when loading the dataset. If we add new configs, users that update the lib will have to update their code to specify the default/standard configuration name (not the one with pseudo labels).", "Could also be a `user-namespace` dataset maybe?", "Oh yes why not. I'm more in favor of this actually since pseudo labels are things that users (not dataset authors in general) can compute by themselves and share with the community", "\r\n\r\nI assume I should (for example) rename the xsum dir, change the URL, and put the modified dir somewhere in S3?", "You can use the `datasets-cli` to upload the folder with your version of xsum with the pseudo labels.\r\n\r\n```\r\ndatasets-cli upload_dataset path/to/xsum\r\n```" ]

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[ "This might be interesting to @kayoyin the author of https://github.com/kayoyin/transformer-slt – pinging you just in case :)", "Thanks Amit, this is a great idea! I'm thinking of porting the SLT models from my paper here as well, having this dataset would be perfect for that :)" ]

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[ "We only support http by default for downloading.\r\nIf you really need to use ftp, then feel free to use a library that allows to download through ftp in your dataset script (I see that you've started working on #722 , that's awesome !). The users will get a message to install the extra library when they load the dataset.\r\n\r\nTo make the download_manager work with a custom downloader, you can call `download_manager.download_custom` instead of `download_manager.download_and_extract`. The expected arguments are the following:\r\n```\r\nurl_or_urls: url or `list`/`dict` of urls to download and extract. Each\r\n url is a `str`.\r\ncustom_download: Callable with signature (src_url: str, dst_path: str) -> Any\r\n as for example `tf.io.gfile.copy`, that lets you download from google storage\r\n```\r\n", "Also maybe it coud be interesting to have a direct support of ftp inside the `datasets` library. Do you know any good libraries that we might consider adding as a (optional ?) dependency ?", "Downloading an `ftp` file is as simple as:\r\n```python\r\nimport urllib \r\nurllib.urlretrieve('ftp://server/path/to/file', 'file')\r\n```\r\n\r\nI believe this should be supported by the library, as its not using any dependency and is trivial amount of code.", "I know its unorthodox, but I added `ftp` download support to `file_utils` in the same PR https://github.com/huggingface/datasets/pull/722\r\nSo its possible to understand the interaction of the download component with the ftp download ability", "Awesome ! I'll take a look :)", "@AmitMY Can you now download the Phoenix2014 Dataset?", "@hoanganhpham1006 yes.\r\nSee pull request https://github.com/huggingface/datasets/pull/722 , it has a loader for this dataset, mostly ready.\r\nThere's one issue that delays it being merged - https://github.com/huggingface/datasets/issues/741 - regarding memory consumption.", "The problem which I have now is that this dataset seems does not allow to download? Can you share it with me pls", "The dataset loader is not yet ready, because of that issue.\r\nIf you want to just download the dataset the old-fashioned way, just go to: https://www-i6.informatik.rwth-aachen.de/ftp/pub/rwth-phoenix/2016/phoenix-2014-T.v3.tar.gz (the ftp link is now broken, and its available over https)", "Got it, thank you so much!" ]

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[ "Same issue here. I will be digging further, but it looks like the [script](https://github.com/huggingface/datasets/blob/master/datasets/wiki_dpr/wiki_dpr.py#L132) is attempting to open a file that is not downloaded yet. \r\n\r\n```\r\n99dcbca09109e58502e6b9271d4d3f3791b43f61f3161a76b25d2775ab1a4498.lock\r\n```\r\n\r\n```\r\n---------------------------------------------------------------------------\r\nUnpicklingError Traceback (most recent call last)\r\n~/anaconda3/envs/eqa/lib/python3.7/site-packages/numpy/lib/npyio.py in load(file, mmap_mode, allow_pickle, fix_imports, encoding)\r\n 446 try:\r\n--> 447 return pickle.load(fid, **pickle_kwargs)\r\n 448 except Exception:\r\n\r\nUnpicklingError: pickle data was truncated\r\n\r\nDuring handling of the above exception, another exception occurred:\r\n\r\nOSError Traceback (most recent call last)\r\n~/src/datasets/src/datasets/builder.py in _download_and_prepare(self, dl_manager, verify_infos, **prepare_split_kwargs)\r\n 559 \r\n--> 560 if verify_infos:\r\n 561 verify_splits(self.info.splits, split_dict)\r\n\r\n~/src/datasets/src/datasets/builder.py in _prepare_split(self, split_generator)\r\n 847 writer.write(example)\r\n--> 848 finally:\r\n 849 num_examples, num_bytes = writer.finalize()\r\n\r\n~/anaconda3/envs/eqa/lib/python3.7/site-packages/tqdm/notebook.py in __iter__(self, *args, **kwargs)\r\n 227 try:\r\n--> 228 for obj in super(tqdm_notebook, self).__iter__(*args, **kwargs):\r\n 229 # return super(tqdm...) will not catch exception\r\n\r\n~/anaconda3/envs/eqa/lib/python3.7/site-packages/tqdm/std.py in __iter__(self)\r\n 1132 try:\r\n-> 1133 for obj in iterable:\r\n 1134 yield obj\r\n\r\n/hdd/rag/cache/huggingface/modules/datasets_modules/datasets/wiki_dpr/14b973bf2a456087ff69c0fd34526684eed22e48e0dfce4338f9a22b965ce7c2/wiki_dpr.py in _generate_examples(self, data_file, vectors_files)\r\n 131 break\r\n--> 132 vecs = np.load(open(vectors_files.pop(0), \"rb\"), allow_pickle=True)\r\n 133 vec_idx = 0\r\n\r\n~/anaconda3/envs/eqa/lib/python3.7/site-packages/numpy/lib/npyio.py in load(file, mmap_mode, allow_pickle, fix_imports, encoding)\r\n 449 raise IOError(\r\n--> 450 \"Failed to interpret file %s as a pickle\" % repr(file))\r\n 451 \r\n\r\nOSError: Failed to interpret file <_io.BufferedReader name='/hdd/rag/downloads/99dcbca09109e58502e6b9271d4d3f3791b43f61f3161a76b25d2775ab1a4498'> as a pickle\r\n\r\nDuring handling of the above exception, another exception occurred:\r\n\r\nOSError Traceback (most recent call last)\r\n<ipython-input-8-24351ff8ce44> in <module>\r\n 4 retriever = RagRetriever.from_pretrained(\"facebook/rag-sequence-nq\", \r\n 5 index_name=\"exact\",\r\n----> 6 use_dummy_dataset=False)\r\n\r\n~/src/transformers/src/transformers/retrieval_rag.py in from_pretrained(cls, retriever_name_or_path, **kwargs)\r\n 321 generator_tokenizer = rag_tokenizer.generator\r\n 322 return cls(\r\n--> 323 config, question_encoder_tokenizer=question_encoder_tokenizer, generator_tokenizer=generator_tokenizer\r\n 324 )\r\n 325 \r\n\r\n~/src/transformers/src/transformers/retrieval_rag.py in __init__(self, config, question_encoder_tokenizer, generator_tokenizer)\r\n 310 self.config = config\r\n 311 if self._init_retrieval:\r\n--> 312 self.init_retrieval()\r\n 313 \r\n 314 @classmethod\r\n\r\n~/src/transformers/src/transformers/retrieval_rag.py in init_retrieval(self)\r\n 338 \r\n 339 logger.info(\"initializing retrieval\")\r\n--> 340 self.index.init_index()\r\n 341 \r\n 342 def postprocess_docs(self, docs, input_strings, prefix, n_docs, return_tensors=None):\r\n\r\n~/src/transformers/src/transformers/retrieval_rag.py in init_index(self)\r\n 248 split=self.dataset_split,\r\n 249 index_name=self.index_name,\r\n--> 250 dummy=self.use_dummy_dataset,\r\n 251 )\r\n 252 self.dataset.set_format(\"numpy\", columns=[\"embeddings\"], output_all_columns=True)\r\n\r\n~/src/datasets/src/datasets/load.py in load_dataset(path, name, data_dir, data_files, split, cache_dir, features, download_config, download_mode, ignore_verifications, save_infos, script_version, **config_kwargs)\r\n 615 builder_instance.download_and_prepare(\r\n 616 download_config=download_config,\r\n--> 617 download_mode=download_mode,\r\n 618 ignore_verifications=ignore_verifications,\r\n 619 )\r\n\r\n~/src/datasets/src/datasets/builder.py in download_and_prepare(self, download_config, download_mode, ignore_verifications, try_from_hf_gcs, dl_manager, **download_and_prepare_kwargs)\r\n 481 # Sync info\r\n 482 self.info.dataset_size = sum(split.num_bytes for split in self.info.splits.values())\r\n--> 483 self.info.download_checksums = dl_manager.get_recorded_sizes_checksums()\r\n 484 self.info.size_in_bytes = self.info.dataset_size + self.info.download_size\r\n 485 # Save info\r\n\r\n~/src/datasets/src/datasets/builder.py in _download_and_prepare(self, dl_manager, verify_infos, **prepare_split_kwargs)\r\n 560 if verify_infos:\r\n 561 verify_splits(self.info.splits, split_dict)\r\n--> 562 \r\n 563 # Update the info object with the splits.\r\n 564 self.info.splits = split_dict\r\n\r\nOSError: Cannot find data file.\r\n```\r\n\r\nThank you.", "An update on my end. This seems like a transient issue. Reran the script from scratch overnight with no errors. ", "Closing this one. Feel free to re-open if you have other questions about this issue" ]

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[ "Referencing the wrong issue # in the commit message. Closing this to fix it again." ]

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[ "One thing though, could we try to read the files in parallel?", "We could but I'm not sure this would help a lot since the bottleneck is the drive IO if the files are big enough.\r\nIt could make sense for very small files.", "Looks like windows is not a big fan of this approach\r\nI'm working on a fix", "I remember issue https://github.com/huggingface/datasets/issues/546 where this was kinda requested (but maybe IO would bottleneck). What do you think?", "I think it's worth testing multiprocessing. It could also be something we add to our speed benchmarks", "> I remember issue #546 where this was kinda requested (but maybe IO would bottleneck). What do you think?\r\n\r\nIt still would be interesting I think, especially in scenarios where IO is less of an issue (SSDs particularly) and where there are many smaller files. Wrapping this function in a `pool.map` is perhaps an easy thing to try. ", "Merging this one for now for the patch release" ]

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[ "Discussed in #622, fixed in #715. Closing the issue. Thanks @lhoestq, it works now! 👍 " ]

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[ "Do this:\r\n``` python\r\nsquad_dataset = list_datasets(with_details=True)[datasets.index('squad')]\r\npprint(squad_dataset.__dict__) # It's a simple python dataclass\r\n```", "Thanks! This worked. I have created a PR to fix this in the notebook. " ]

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[ "Datasets does not use elasticsearch API to define custom similarity. If you want to use a custom similarity, the best would be to run a curl request directly to your elasticsearch instance (see sample hereafter, directly from ES documentation), then you should be able to use `my_similarity` in your configuration passed to datasets\r\n\r\n```\r\ncurl -X PUT \"localhost:9200/index?pretty\" -H 'Content-Type: application/json' -d'\r\n{\r\n \"settings\": {\r\n \"index\": {\r\n \"similarity\": {\r\n \"my_similarity\": {\r\n \"type\": \"DFR\",\r\n \"basic_model\": \"g\",\r\n \"after_effect\": \"l\",\r\n \"normalization\": \"h2\",\r\n \"normalization.h2.c\": \"3.0\"\r\n }\r\n }\r\n }\r\n }\r\n}\r\n'\r\n\r\n```" ]

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[ "Facing a similar issue here. My model using SQuAD dataset takes about 1h to process with in memory data and more than 2h with datasets directly.", "And if you use in-memory-data with datasets with `load_dataset(..., keep_in_memory=True)`?", "Thanks for the tip @thomwolf ! I did not see that flag in the docs. I'll try with that.", "We should add it indeed and also maybe a specific section with all the tips for maximal speed. What do you think @lhoestq @SBrandeis @yjernite ?", "By default the datasets loaded with `load_dataset` live on disk.\r\nIt's possible to load them in memory by using some transforms like `.map(..., keep_in_memory=True)`.\r\n\r\nSmall correction to @thomwolf 's comment above: currently we don't have the `keep_in_memory` parameter for `load_dataset` AFAIK but it would be nice to add it indeed :)", "Yes indeed we should add it!", "Great! Thanks a lot.\r\n\r\nI did a test using `map(..., keep_in_memory=True)` and also a test using in-memory only data.\r\n\r\n```python\r\nfeatures = dataset.map(tokenize, batched=True, remove_columns=dataset['train'].column_names)\r\nfeatures.set_format(type='torch', columns=['input_ids', 'token_type_ids', 'attention_mask'])\r\n\r\nfeatures_in_memory = dataset.map(tokenize, batched=True, keep_in_memory=True, remove_columns=dataset['train'].column_names)\r\nfeatures_in_memory.set_format(type='torch', columns=['input_ids', 'token_type_ids', 'attention_mask'])\r\n\r\nin_memory = [features['train'][i] for i in range(len(features['train']))]\r\n```\r\n\r\nFor using the features without any tweak, I got **1min17s** for copying the entire DataLoader to CUDA:\r\n\r\n```\r\n%%time\r\n\r\nfor i, batch in enumerate(DataLoader(features['train'], batch_size=16, num_workers=4)):\r\n batch['input_ids'].to(device)\r\n```\r\n\r\nFor using the features mapped with `keep_in_memory=True`, I also got **1min17s** for copying the entire DataLoader to CUDA:\r\n\r\n```\r\n%%time\r\n\r\nfor i, batch in enumerate(DataLoader(features_in_memory['train'], batch_size=16, num_workers=4)):\r\n batch['input_ids'].to(device)\r\n```\r\n\r\nAnd for the case using every element in memory, converted from the original dataset, I got **12.5s**:\r\n\r\n```\r\n%%time\r\n\r\nfor i, batch in enumerate(DataLoader(in_memory, batch_size=16, num_workers=4)):\r\n batch['input_ids'].to(device)\r\n```\r\n\r\nTaking a closer look in my SQuAD code, using a profiler, I see a lot of calls to `posix read` api. It seems that it is really reliying on disk, which results in a very high train time.", "I am having the same issue here. When loading from memory I can get the GPU up to 70% util but when loading after mapping I can only get 40%.\r\n\r\nIn disk:\r\n```\r\nbook_corpus = load_dataset('bookcorpus', 'plain_text', cache_dir='/home/ad/Desktop/bookcorpus', split='train[:20%]')\r\nbook_corpus = book_corpus.map(encode, batched=True, num_proc=20, load_from_cache_file=True, batch_size=2500)\r\nbook_corpus.set_format(type='torch', columns=['text', \"input_ids\", \"attention_mask\", \"token_type_ids\"])\r\n\r\ntraining_args = TrainingArguments(\r\n output_dir=\"./mobile_bert_big\",\r\n overwrite_output_dir=True,\r\n num_train_epochs=1,\r\n per_device_train_batch_size=32,\r\n per_device_eval_batch_size=16,\r\n save_steps=50,\r\n save_total_limit=2,\r\n logging_first_step=True,\r\n warmup_steps=100,\r\n logging_steps=50,\r\n eval_steps=100,\r\n no_cuda=False,\r\n gradient_accumulation_steps=16,\r\n fp16=True)\r\n\r\ntrainer = Trainer(\r\n model=model,\r\n args=training_args,\r\n data_collator=data_collator,\r\n train_dataset=book_corpus,\r\n tokenizer=tokenizer)\r\n```\r\n\r\nIn disk I can only get 0,17 it/s:\r\n`[ 13/28907 01:03 < 46:03:27, 0.17 it/s, Epoch 0.00/1] `\r\n\r\nIf I load it with torch.utils.data.Dataset()\r\n```\r\nclass BCorpusDataset(torch.utils.data.Dataset):\r\n def __init__(self, encodings):\r\n self.encodings = encodings\r\n\r\n def __getitem__(self, idx):\r\n item = [torch.tensor(val[idx]) for key, val in self.encodings.items()][0]\r\n return item\r\n\r\n def __len__(self):\r\n length = [len(val) for key, val in self.encodings.items()][0]\r\n return length\r\n\r\n**book_corpus = book_corpus.select([i for i in range(16*2000)])** # filtering to not have 20% of BC in memory...\r\nbook_corpus = book_corpus(book_corpus)\r\n```\r\nI can get:\r\n` [ 5/62 00:09 < 03:03, 0.31 it/s, Epoch 0.06/1]`\r\n\r\nBut obviously I can not get BookCorpus in memory xD\r\n\r\nEDIT: it is something weird. If i load in disk 1% of bookcorpus:\r\n```\r\nbook_corpus = load_dataset('bookcorpus', 'plain_text', cache_dir='/home/ad/Desktop/bookcorpus', split='train[:1%]')\r\n```\r\n\r\nI can get 0.28 it/s, (the same that in memory) but if I load 20% of bookcorpus:\r\n```\r\nbook_corpus = load_dataset('bookcorpus', 'plain_text', cache_dir='/home/ad/Desktop/bookcorpus', split='train[:20%]')\r\n```\r\nI get again 0.17 it/s. \r\n\r\nI am missing something? I think it is something related to size, and not disk or in-memory.", "There is a way to increase the batches read from memory? or multiprocessed it? I think that one of two or it is reading with just 1 core o it is reading very small chunks from disk and left my GPU at 0 between batches", "My fault! I had not seen the `dataloader_num_workers` in `TrainingArguments` ! Now I can parallelize and go fast! Sorry, and thanks." ]

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[ "Hello @mathcass I would want to work on this issue. May I do the same? ", "@punitaojha, certainly. Feel free to work on this. Let me know if you need any help or clarity.", "Hello @mathcass \r\n1. I did fork the repository and clone the same on my local system. \r\n\r\n2. Then learnt about how we can publish our package on pypi.org. Also, found some instructions on same in setup.py documentation.\r\n\r\n3. Then I Perplexity document link that you shared above. I created a colab link from there keep both tensorflow and pytorch means a mixed option and tried to run it in colab but I encountered no errors at a point where you mentioned. Can you help me to figure out the issue. \r\n\r\n4.Here is the link of the colab file with my saved responses. \r\nhttps://colab.research.google.com/drive/1hfYz8Ira39FnREbxgwa_goZWpOojp2NH?usp=sharing", "Also, please share some links which made you conclude that pyarrow < 1 would help. ", "Access granted for the colab link. ", "Thanks for looking at this @punitaojha and thanks for sharing the notebook. \r\n\r\nI just tried to reproduce this on my own (based on the environment where I had this issue) and I can't reproduce it somehow. If I run into this again, I'll include some steps to reproduce it. I'll close this as invalid. \r\n\r\nThanks again. ", "I am sorry for hijacking this closed issue, but I believe I was able to reproduce this very issue. Strangely enough, it also turned out that running `pip install \"pyarrow<1\" --upgrade` did indeed fix the issue (PyArrow was installed in version `0.14.1` in my case).\r\n\r\nPlease see the Colab below:\r\n\r\nhttps://colab.research.google.com/drive/15QQS3xWjlKW2aK0J74eEcRFuhXUddUST\r\n\r\nThanks!" ]

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[ "Hi !\r\nThanks for reporting :) \r\nIndeed this is an issue on the `datasets` side.\r\nI'm creating a PR", "Thanks @lhoestq !" ]

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[ "Awesome :) \r\n\r\nDon't pay attention to the RemoteDatasetTest error, I'm fixing it right now", "You can rebase from master to fix the CI test :)", "If we're lucky we can even include this dataset in today's release", "Just thinking since `type` can only be `comparison` or `bridge` and `level` can only be `easy`, `medium`, `hard` should they be `ClassLabel`?", "> Just thinking since `type` can only be `comparison` or `bridge` and `level` can only be `easy`, `medium`, `hard` should they be `ClassLabel`?\r\n\r\nI think it's more a tag than a label. I guess a string is fine\r\n" ]

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[ "Oups too late, sorry" ]

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[ "Indeed there's currently a mismatch between the description and what it rouge actually returns.\r\nThanks for proposing this fix :) \r\n\r\nI think it's better to return rouge 1-2-L.\r\nWas there a reason to only include rouge 1 and rouge L @thomwolf ? ", "rougeLsum is also missing, could you add it ?", "Adding `RougeLSum` would fix https://github.com/huggingface/datasets/issues/617", "I am opening a PR with both of them right now actually :)", "Also the format of the output isn't exactly ideal, It's usually only the F-1 score that is cared about. \r\n\r\nFormatting the output to reflect how `ROUGE-1-5-5` (the perl version thats usually used and pyrouge is a wrapper over it), would be better.\r\n\r\n", "I'll close this since you seem to have already added it in another PR. Sorry for the delay in responding to you @lhoestq.", "What do you mean by \"Formatting the output to reflect how ROUGE-1-5-5\" @Shashi456 ?", "I like the idea of returning all the scores for two reason:\r\n- Rouge's aggregator does sampling and therefore it returns \"low\" \"mid\" and \"high\" scores\r\n- It is interesting to have the precision and recall to see how the F1 score was computed\r\nBut I understand your point that returning only the F1 score makes sense since it's the one that's always used ", "@thomwolf the scores now returned look like this:\r\n```\r\n{'rouge1': AggregateScore(low=Score(precision=0.16620308156871524, recall=0.18219819615984395, fmeasure=0.16226017699359463), mid=Score(precision=0.17274338501705871, recall=0.1890957812369246, fmeasure=0.16823877588620403), high=Score(precision=0.17934569582981455, recall=0.1965626706042028, fmeasure=0.17491509794856058)), \r\n'rouge2': AggregateScore(low=Score(precision=0.12478835737689957, recall=0.1362113231755514, fmeasure=0.12055941950062395), mid=Score(precision=0.1303967602691664, recall=0.1423747229852964, fmeasure=0.1258363976151122), high=Score(precision=0.13654527560789362, recall=0.1488071465116122, fmeasure=0.13184989406704056)), \r\n'rougeL': AggregateScore(low=Score(precision=0.16568068818352072, recall=0.1811919016674486, fmeasure=0.1614784523482225), mid=Score(precision=0.17156684723552357, recall=0.1879777628247058, fmeasure=0.16720699286250762), high=Score(precision=0.17788847350584547, recall=0.1948899838530898, fmeasure=0.17316501523379826))}\r\n```\r\n\r\nWhile when computed through the perl rouge script, it looks like:\r\n```\r\nROUGE-1 Average_R: 0.34775 (95%-conf.int. 0.34546 - 0.35025)\r\nROUGE-1 Average_P: 0.19381 (95%-conf.int. 0.19246 - 0.19538)\r\nROUGE-1 Average_F: 0.24070 (95%-conf.int. 0.23925 - 0.24230)\r\n---------------------------------------------\r\nROUGE-2 Average_R: 0.07160 (95%-conf.int. 0.07010 - 0.07298)\r\nROUGE-2 Average_F: 0.04845 (95%-conf.int. 0.04741 - 0.04942)\r\n---------------------------------------------\r\nROUGE-L Average_R: 0.26404 (95%-conf.int. 0.26215 - 0.26598)\r\nROUGE-L Average_P: 0.14696 (95%-conf.int. 0.14576 - 0.14815)\r\nROUGE-L Average_F: 0.18245 (95%-conf.int. 0.18120 - 0.18367)\r\n```\r\nwhile the wrapper returns the much more readable:\r\n```\r\n[2020-07-30 18:13:38,556 INFO] Rouges at step 13000 \r\n>> ROUGE-F(1/2/3/l): 43.43/20.42/39.78 \r\nROUGE-R(1/2/3/l): 53.91/25.34/49.32\r\n```\r\n\r\nThe formatting allows for easy reading, and although \"low\", \"mid\", \"high\" make sense, this is more concise and effective. \r\n\r\nOne way of changing this might be to return a dictionary that returns values like `rouge_1_precision`, `rouge_1_F1`, `rouge_1_recall`, and maybe also having the ability to get the values you are interested in and keeping `recall` and `F1` as default.", "cc: @lhoestq ", "Ok I see.\r\nI think it's also important to follow one of the existing output format (there are already too many different formats, let's try not to add another different one)\r\nI'd still stick with the current format and not transform the output of the python implementation of rouge since it's already widely used.\r\nWhat do you think ?", "Maybe we could convert the dataclasses in dictionnaries, would that help @Shashi456 ?", "@thomwolf yeah I think that would help. I initially didn't understand the high low mid categories. Dictionaries could help in this case I guess, and if we allow the user to choose what they want i.e F1 and precision or recall." ]

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[ "also i tried below code to solve checksum error \r\n`datasets-cli test ./datasets/xnli --save_infos --all_configs`\r\n\r\nand it shows \r\n\r\n```\r\n2020-10-02 07:06:16.588760: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcudart.so.10.1\r\nTraceback (most recent call last):\r\n File \"/opt/conda/lib/python3.7/site-packages/datasets/load.py\", line 268, in prepare_module\r\n local_path = cached_path(file_path, download_config=download_config)\r\n File \"/opt/conda/lib/python3.7/site-packages/datasets/utils/file_utils.py\", line 308, in cached_path\r\n use_etag=download_config.use_etag,\r\n File \"/opt/conda/lib/python3.7/site-packages/datasets/utils/file_utils.py\", line 474, in get_from_cache\r\n raise FileNotFoundError(\"Couldn't find file at {}\".format(url))\r\nFileNotFoundError: Couldn't find file at https://raw.githubusercontent.com/huggingface/datasets/1.0.2/datasets/./datasets/xnli/xnli.py\r\n\r\nDuring handling of the above exception, another exception occurred:\r\n\r\nTraceback (most recent call last):\r\n File \"/opt/conda/lib/python3.7/site-packages/datasets/load.py\", line 279, in prepare_module\r\n local_path = cached_path(file_path, download_config=download_config)\r\n File \"/opt/conda/lib/python3.7/site-packages/datasets/utils/file_utils.py\", line 308, in cached_path\r\n use_etag=download_config.use_etag,\r\n File \"/opt/conda/lib/python3.7/site-packages/datasets/utils/file_utils.py\", line 474, in get_from_cache\r\n raise FileNotFoundError(\"Couldn't find file at {}\".format(url))\r\nFileNotFoundError: Couldn't find file at https://s3.amazonaws.com/datasets.huggingface.co/datasets/datasets/./datasets/xnli/xnli.py\r\n\r\nDuring handling of the above exception, another exception occurred:\r\n\r\nTraceback (most recent call last):\r\n File \"/opt/conda/bin/datasets-cli\", line 36, in <module>\r\n service.run()\r\n File \"/opt/conda/lib/python3.7/site-packages/datasets/commands/test.py\", line 76, in run\r\n module_path, hash = prepare_module(path)\r\n File \"/opt/conda/lib/python3.7/site-packages/datasets/load.py\", line 283, in prepare_module\r\n combined_path, github_file_path, file_path\r\nFileNotFoundError: Couldn't find file locally at ./datasets/xnli/xnli.py, or remotely at https://raw.githubusercontent.com/huggingface/datasets/1.0.2/datasets/./datasets/xnli/xnli.py or https://s3.amazonaws.com/datasets.huggingface.co/datasets/datasets/./datasets/xnli/xnli.py\r\n```\r\n\r\n", "Hi !\r\nYes the download url changed.\r\nIt's updated on the master branch. I'm doing a release today to fix that :)", "the issue is fixed with latest release \r\n\r\n" ]

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[ "It looks like an outdated PR (we've already added mlsum). Closing it" ]

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[ "Hacktoberfest spam", "To enhance its readability.....not Hacktoberfest spam", "How is adding a punctuation to the end of a sentence justified as \"To enhance its readability\". \r\nConsidering that this is not your first \"README enhancement '' please don't spam the open source community with useless PR to get a free T-Shirt it just hurts the maintainers.\r\n\r\n//Joey", "closed as spam" ]

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[ "Thanks for reporting.\r\nThe data file must have been updated by the host.\r\nI'll update the checksum with the new one.", "Well actually it looks like the link isn't working anymore :(", "The new link is https://cims.nyu.edu/~sbowman/xnli/XNLI-1.0.zip\r\nI'll update the dataset script", "I'll do a release in the next few days to make the fix available for everyone.\r\nIn the meantime you can load `xnli` with\r\n```\r\nxnli = load_dataset('xnli', script_version=\"master\")\r\n```\r\nThis will use the latest version of the xnli script (available on master branch), instead of the old one.", "That's awesome! Thanks a lot!" ]

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[ "If the windows tests in the CI pass, today will be a happy day" ]

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[ "Hi !\r\n\r\nThis is because `encode` expects one single text as input (str), or one tokenized text (List[str]).\r\nI believe that you actually wanted to use `encode_batch` which expects a batch of texts.\r\nHowever this method is only available for our \"fast\" tokenizers (ex: BertTokenizerFast).\r\nBertJapanese is not one of them unfortunately and I don't think it will be added for now (see https://github.com/huggingface/transformers/pull/7141)...\r\ncc @thomwolf for confirmation.\r\n\r\nTherefore what I'd suggest for now is disable batching and process one text at a time using `encode`.\r\nNote that you can make it faster by using multiprocessing:\r\n\r\n```python\r\nnum_proc = None # Specify here the number of processes if you want to use multiprocessing. ex: num_proc = 4\r\nencoded = train_ds.map(\r\n lambda example: {'tokens': t.encode(example['title'], max_length=1000)}, num_proc=num_proc\r\n)\r\n```\r\n", "Thank you very much for the kind and precise suggestion!\r\nI'm looking forward to seeing BertJapaneseTokenizer built into the \"fast\" tokenizers.\r\n\r\nI tried `map` with multiprocessing as follows, and it worked!\r\n\r\n```python\r\n# There was a Pickle problem if I use `lambda` for multiprocessing\r\ndef encode(examples):\r\n return {'tokens': t.encode(examples['title'], max_length=1000)}\r\n\r\nnum_proc = 8\r\nencoded = train_ds.map(encode, num_proc=num_proc)\r\n```\r\n\r\nThank you again!" ]

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[ "Yes! might do it with @srush one of these days. Hopefully it won't break too many links (we can always redirect from old url to new)", "This was fixed but forgot to close the issue. cc @lhoestq @yjernite \r\n\r\nThanks @jarednielsen!" ]

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

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

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

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

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

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

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

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

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

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

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

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

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

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[ "Resubmitting on a new fork" ]

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

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

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

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

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

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

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

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

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