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[ "Oh, really cool @sshleifer!" ]

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[ "@lhoestq I took a shot at addressing your comments but the build scripts seem to be complaining about not being able to open dummy files. How do I resolve those errors without copying the full dataset into the dummy dir?", "Could you check that the names of the dummy data files are right ?\r\nYou can use \r\n```\r\ndatasets-cli dummy_data ./datasets/xum\r\n```\r\nto print the expected file names", "Ok @lhoestq looks like I got the tests to pass :)" ]

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[ "Thanks for the report, can reproduce. Will fix", "Fixed now @ogabrielluiz " ]

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[ "There was a similar issue in #294 \r\nClearing the cache and download again the dataset did the job. Could you try to clear your cache and download the dataset again ?", "I was able to load the dataset successfully, I'm pretty sure it's just a cache issue that you have.\r\nLet me know if clearing your cache fixes the problem", "Yes, it indeed was a cache issue!\r\nThanks for reaching out!!" ]

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[ "Amazing! ", "Small poll @thomwolf @yjernite @lhoestq @JetRunner @qiweizhen .\r\n\r\nAs stated in the XGLUE paper: https://arxiv.org/pdf/2004.01401.pdf , for each of the 11 down-stream tasks training data is only available in English, whereas development and test data is available in multiple different language *cf.* here: \r\n\r\n\r\n\r\nSo, I'd suggest to have exactly 11 \"language-independent\" configs: \"ner\", \"pos\", ... and give the sample in each dataset in the config a \"language\" label being one of \"ar\", \"bg\", .... => To me this makes more sense than making languaga specific config, *e.g.* \"ner-de\", ...especially because training data is only available in English. Do you guys agree? ", "In this case we should have named splits, so config `ner` has splits `train`, `validation`, `test-en`, `test-ar`, `test-bg`, etc...\r\n\r\nThis is more in the spirit of the task afaiu, and will avoid making users do the filtering step themselves when testing different models or different configurations of the same model.", "I see your point! \r\n\r\nI think this would be quite feasible to do and makes sense to me as well! In the paper results are reported per language, so it seems more natural to do it this way. \r\n\r\nGood for me @yjernite ! What do the others think? @lhoestq \r\n", "I agree with Yacine on this!", "Okey actually not that easy to add things like `test-de` to `datasets` => this would be the first dataset to have this.\r\nSee: https://github.com/huggingface/datasets/pull/802", "IMO we should have one config per language. That's what we're doing for xnli, xtreme etc.\r\nHaving split names that depend on the language seems wrong. We should try to avoid split names that are not train/val/test.\r\nSorry for late response on this one", "@lhoestq agreed on having one config per language, but we also need to be able to have different split names and people are going to want to use hyphens, so we should at the very least warn them why it's failing :) E.g. for ANLI with different stages of data (currently using underscores) or https://www.tau-nlp.org/commonsenseqa with their train-sanity or dev-sanity splits", "Yes sure ! Could you open a separate issue for that ?", "Really cool dataset 👍 btw. does Transformers support all 11 tasks 🤔 would be awesome to have a xglue script (like the \"normal\" glue one)", "Just to make sure this is what we want here. If we add one config per language, \r\n\r\nthis means that this dataset ends up with well over 100 different configs most of which will have the same `train` split. The train split is always in English. Also, I'm not sure whether it's better for the user to be honest. \r\n\r\nI think it could be quite confusing for the user to have\r\n\r\n```python\r\ntrain_dataset = load_dataset(\"xglue\", \"ner-de\", split=\"train\")\r\n```\r\n\r\nin English even though it's `ner-de`.\r\n\r\nTo be honest, I'd prefer:\r\n\r\n```python\r\ntrain_dataset = load_dataset(\"xglue\", \"ner\", split=\"train\")\r\ntest_dataset_de = load_dataset(\"xglue\", \"ner\", split=\"test-de\")\r\ntest_dataset_fr = load_dataset(\"xglue\", \"ner\", split=\"test-fr\")\r\n```\r\n\r\nhere", "Oh yes right I didn't notice the train set was always in english sorry.\r\nMoreover it seems that the way this dataset is used is to pick a pretrained multilingual model, fine-tune it on the english train set and then evaluate on each test set (one per language).\r\nSo to better fit the usual usage of this dataset, I agree that it's better to have one test split per language. \r\n\r\nSomething like your latest example patrick is fine imo :\r\n```python\r\ntrain_dataset = load_dataset(\"xglue\", \"ner\", split=\"train\")\r\ntest_dataset_de = load_dataset(\"xglue\", \"ner\", split=\"test.de\")\r\n```\r\n\r\nI just replace test-de with test.de since `-` is not allowed for split names (it has to follow the `\\w+` regex), and usually we specify the language after a point. ", "Closing since XGLUE has been added in #802 , thanks patrick :) " ]

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[ "No worries. Always happy to help and thanks for your support in fixing the issue :)" ]

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[ "Hello, I probably have a silly question but the labels of the emotion dataset are in the form of numbers and not string, so I can not use the function classification_report because it mixes numbers and string (prediction). How can I access the label in the form of a string and not a number? \r\nThank you in advance." ]

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[ "Oups wrong click.\r\nThis one is for you @srush" ]

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[ "Thank you !\r\nCould you provide a csv file that reproduces the error ?\r\nIt doesn't have to be one of your dataset. As long as it reproduces the error\r\nThat would help a lot !", "I think another good example is the following:\r\n`\r\nfrom datasets import load_dataset\r\n`\r\n`\r\ndataset = load_dataset(\"csv\", data_files=[\"./sts-dev.csv\"], delimiter=\"\\t\", column_names=[\"one\", \"two\", \"three\", \"four\", \"score\", \"sentence1\", \"sentence2\"], script_version=\"master\")`\r\n`\r\n\r\nDisplayed error `CSV parse error: Expected 7 columns, got 6` even tough I put 7 columns. First four columns from the csv don't have a name, so I've named them by default. The csv file is the .dev file from STSb benchmark dataset.\r\n\r\n", "Hi, seems I also can't read csv file. I was trying with a dummy csv with only three rows.\r\n\r\n```\r\ntext,label\r\nI hate google,negative\r\nI love Microsoft,positive\r\nI don't like you,negative\r\n```\r\nI was using the HuggingFace image in Paperspace Gradient (datasets==1.1.3). The following code doesn't work:\r\n\r\n```\r\nfrom datasets import load_dataset\r\ndataset = load_dataset('csv', script_version=\"master\", data_files=['test_data.csv'], delimiter=\",\")\r\n```\r\nIt outputs the following:\r\n```\r\nUsing custom data configuration default\r\nDownloading and preparing dataset csv/default-3b6254ff4dd403e5 (download: Unknown size, generated: Unknown size, post-processed: Unknown size, total: Unknown size) to /root/.cache/huggingface/datasets/csv/default-3b6254ff4dd403e5/0.0.0/2960f95a26e85d40ca41a230ac88787f715ee3003edaacb8b1f0891e9f04dda2...\r\nDataset csv downloaded and prepared to /root/.cache/huggingface/datasets/csv/default-3b6254ff4dd403e5/0.0.0/2960f95a26e85d40ca41a230ac88787f715ee3003edaacb8b1f0891e9f04dda2. Subsequent calls will reuse this data.\r\n```\r\nBut `len(dataset)` gives `1` and I can't access rows with indexing `dataset[0]` (it gives `KeyError: 0`).\r\n\r\nHowever, loading from pandas dataframe is working.\r\n```\r\nfrom datasets import Dataset\r\nimport pandas as pd\r\ndf = pd.read_csv('test_data.csv')\r\ndataset = Dataset.from_pandas(df)\r\n```\r\n\r\n", "This is because load_dataset without `split=` returns a dictionary of split names (train/validation/test) to dataset.\r\nYou can do\r\n```python\r\nfrom datasets import load_dataset\r\ndataset = load_dataset('csv', script_version=\"master\", data_files=['test_data.csv'], delimiter=\",\")\r\nprint(dataset[\"train\"][0])\r\n```\r\n\r\nOr if you want to directly get the train split:\r\n\r\n```python\r\nfrom datasets import load_dataset\r\ndataset = load_dataset('csv', script_version=\"master\", data_files=['test_data.csv'], delimiter=\",\", split=\"train\")\r\nprint(dataset[0])\r\n```\r\n", "Good point\r\n\r\nDesign question for us, though: should `load_dataset` when no split is specified and only one split is present in the dataset (common use case with CSV/text/JSON datasets) return a `Dataset` instead of a `DatsetDict`? I feel like it's often what the user is expecting. I break a bit the paradigm of a unique return type but since this library is designed for widespread DS people more than CS people usage I would tend to think that UX should take precedence over CS reasons. What do you think?", "In this case the user expects to get only one dataset object instead of the dictionary of datasets since only one csv file was specified without any split specifications.\r\nI'm ok with returning the dataset object if no split specifications are given for text/json/csv/pandas.\r\n\r\nFor the other datasets ton the other hand the user doesn't know in advance the splits so I would keep the dictionary by default. What do you think ?", "Thanks for your quick response! I'm fine with specifying the split as @lhoestq suggested. My only concern is when I'm loading from python dict or pandas, the library returns a dataset instead of a dictionary of datasets when no split is specified. I know that they use a different function `Dataset.from_dict` or `Dataset.from_pandas` but the text/csv files use `load_dataset()`. However, to the user, they do the same task and we probably expect them to have the same behavior.", "```\r\nfrom datasets import load_dataset\r\ndataset = load_dataset('csv', data_files='./amazon_data/Video_Games_5.csv', delimiter=\",\", split=['train', 'test'])\r\n```\r\nI was running the above line, but got this error.\r\n\r\n```ValueError: Unknown split \"test\". Should be one of ['train'].```\r\n\r\nThe data is amazon product data. I load the Video_Games_5.json.gz data into pandas and save it as csv file. and then load the csv file using the above code. I thought, ```split=['train', 'test']``` would split the data into train and test. did I misunderstood?\r\n\r\nThank you!\r\n\r\n", "Hi ! the `split` argument in `load_dataset` is used to select the splits you want among the available splits.\r\nHowever when loading a csv with a single file as you did, only a `train` split is available by default.\r\n\r\nIndeed since `data_files='./amazon_data/Video_Games_5.csv'` is equivalent to `data_files={\"train\": './amazon_data/Video_Games_5.csv'}`, you can get a dataset with \r\n```python\r\nfrom datasets import load_dataset\r\ndataset = load_dataset('csv', data_files='./amazon_data/Video_Games_5.csv', delimiter=\",\", split=\"train\")\r\n```\r\n\r\nAnd then to get both a train and test split you can do\r\n```python\r\ndataset = dataset.train_test_split()\r\nprint(dataset.keys())\r\n# ['train', 'test']\r\n```\r\n\r\n\r\nAlso note that a csv dataset may have several available splits if it is defined this way:\r\n```python\r\nfrom datasets import load_dataset\r\ndataset = load_dataset('csv', data_files={\r\n \"train\": './amazon_data/Video_Games_5_train.csv',\r\n \"test\": './amazon_data/Video_Games_5_test.csv'\r\n})\r\nprint(dataset.keys())\r\n# ['train', 'test']\r\n```\r\n", "> In this case the user expects to get only one dataset object instead of the dictionary of datasets since only one csv file was specified without any split specifications.\r\n> I'm ok with returning the dataset object if no split specifications are given for text/json/csv/pandas.\r\n> \r\n> For the other datasets ton the other hand the user doesn't know in advance the splits so I would keep the dictionary by default. What do you think ?\r\n\r\nYes maybe this would be good. I think having to select 'train' from the resulting object why the user gave no split information is a confusing and not intuitive behavior.", "> Similar to #622, I've noticed there is a problem when trying to load a CSV file with datasets.\r\n> \r\n> `from datasets import load_dataset`\r\n> `dataset = load_dataset(\"csv\", data_files=[\"./sample_data.csv\"], delimiter=\"\\t\", column_names=[\"title\", \"text\"], script_version=\"master\")`\r\n> \r\n> Displayed error:\r\n> `... ArrowInvalid: CSV parse error: Expected 2 columns, got 1`\r\n\r\nI'm also facing the same issue when trying to load from a csv file locally:\r\n\r\n```python\r\nfrom nlp import load_dataset\r\ndataset = load_dataset('csv', data_files='sample_data.csv')\r\n```\r\n\r\nError when executed from Google Colab:\r\n```python\r\nArrowInvalid Traceback (most recent call last)\r\n<ipython-input-34-79a8d4f65ed6> in <module>()\r\n 1 from nlp import load_dataset\r\n----> 2 dataset = load_dataset('csv', data_files='sample_data.csv')\r\n\r\n9 frames\r\n/usr/local/lib/python3.7/dist-packages/nlp/load.py in load_dataset(path, name, version, data_dir, data_files, split, cache_dir, features, download_config, download_mode, ignore_verifications, save_infos, **config_kwargs)\r\n 547 # Download and prepare data\r\n 548 builder_instance.download_and_prepare(\r\n--> 549 download_config=download_config, download_mode=download_mode, ignore_verifications=ignore_verifications,\r\n 550 )\r\n 551 \r\n\r\n/usr/local/lib/python3.7/dist-packages/nlp/builder.py in download_and_prepare(self, download_config, download_mode, ignore_verifications, try_from_hf_gcs, dl_manager, **download_and_prepare_kwargs)\r\n 461 if not downloaded_from_gcs:\r\n 462 self._download_and_prepare(\r\n--> 463 dl_manager=dl_manager, verify_infos=verify_infos, **download_and_prepare_kwargs\r\n 464 )\r\n 465 # Sync info\r\n\r\n/usr/local/lib/python3.7/dist-packages/nlp/builder.py in _download_and_prepare(self, dl_manager, verify_infos, **prepare_split_kwargs)\r\n 535 try:\r\n 536 # Prepare split will record examples associated to the split\r\n--> 537 self._prepare_split(split_generator, **prepare_split_kwargs)\r\n 538 except OSError:\r\n 539 raise OSError(\"Cannot find data file. \" + (self.manual_download_instructions or \"\"))\r\n\r\n/usr/local/lib/python3.7/dist-packages/nlp/builder.py in _prepare_split(self, split_generator)\r\n 863 \r\n 864 generator = self._generate_tables(**split_generator.gen_kwargs)\r\n--> 865 for key, table in utils.tqdm(generator, unit=\" tables\", leave=False):\r\n 866 writer.write_table(table)\r\n 867 num_examples, num_bytes = writer.finalize()\r\n\r\n/usr/local/lib/python3.7/dist-packages/tqdm/notebook.py in __iter__(self, *args, **kwargs)\r\n 213 def __iter__(self, *args, **kwargs):\r\n 214 try:\r\n--> 215 for obj in super(tqdm_notebook, self).__iter__(*args, **kwargs):\r\n 216 # return super(tqdm...) will not catch exception\r\n 217 yield obj\r\n\r\n/usr/local/lib/python3.7/dist-packages/tqdm/std.py in __iter__(self)\r\n 1102 fp_write=getattr(self.fp, 'write', sys.stderr.write))\r\n 1103 \r\n-> 1104 for obj in iterable:\r\n 1105 yield obj\r\n 1106 # Update and possibly print the progressbar.\r\n\r\n/usr/local/lib/python3.7/dist-packages/nlp/datasets/csv/ede98314803c971fef04bcee45d660c62f3332e8a74491e0b876106f3d99bd9b/csv.py in _generate_tables(self, files)\r\n 78 read_options=self.config.pa_read_options,\r\n 79 parse_options=self.config.pa_parse_options,\r\n---> 80 convert_options=self.config.convert_options,\r\n 81 )\r\n 82 yield i, pa_table\r\n\r\n/usr/local/lib/python3.7/dist-packages/pyarrow/_csv.pyx in pyarrow._csv.read_csv()\r\n\r\n/usr/local/lib/python3.7/dist-packages/pyarrow/error.pxi in pyarrow.lib.pyarrow_internal_check_status()\r\n\r\n/usr/local/lib/python3.7/dist-packages/pyarrow/error.pxi in pyarrow.lib.check_status()\r\n\r\nArrowInvalid: CSV parse error: Expected 1 columns, got 8\r\n```\r\n\r\nVersion:\r\n```\r\nnlp==0.4.0\r\n```", "Hi @kauvinlucas\r\n\r\nYou can use the latest versions of `datasets` to do this.\r\nTo do so, just `pip install datasets` instead of `nlp` (the library was renamed) and then\r\n```python\r\nfrom datasets import load_dataset\r\ndataset = load_dataset('csv', data_files='sample_data.csv')", "Hi \r\nI'm having a different problem with loading local csv. \r\n```Python\r\nfrom datasets import load_dataset \r\ndataset = load_dataset('csv', data_files='sample.csv') \r\n``` \r\n\r\ngives `ValueError: Specified named and prefix; you can only specify one.` error \r\n\r\nversions: \r\n- datasets: 1.1.3 \r\n- python: 3.9.6 \r\n- pyarrow: 2.0.0 ", "Oh.. I figured it out. According to issue #[42387](https://github.com/pandas-dev/pandas/issues/42387) from pandas, this new version does not accept None for both parameters (which was being done by the repo I'm testing). Dowgrading Pandas==1.0.4 and Python==3.8 worked", "Hi, \r\nI got an `OSError: Cannot find data file. ` when I tried to use load_dataset with tsv files. I have checked the paths, and they are correct. \r\n\r\nversions\r\n- python: 3.7.9\r\n- datasets: 1.1.3\r\n- pyarrow: 2.0.0\r\n- transformers: 4.2.2\r\n\r\n~~~\r\ndata_files = {\"train\": \"train.tsv\", \"test\",: \"test.tsv\"}\r\ndatasets = load_dataset(\"csv\", data_files=data_files, delimiter=\"\\t\")\r\n~~~\r\n\r\nThe entire Error message is on below:\r\n\r\n```08/14/2021 16:55:44 - INFO - __main__ - load a local file for train: /project/media-framing/transformer4/data/0/val/label1.tsv\r\n08/14/2021 16:55:44 - INFO - __main__ - load a local file for test: /project/media-framing/transformer4/data/unlabel/test.tsv\r\nUsing custom data configuration default\r\nDownloading and preparing dataset csv/default-00a4200ae8507533 (download: Unknown size, generated: Unknown size, post-processed: Unknown size, total: Unknown size) to /usr4/cs542sp/hey1/.cache/huggingface/datasets/csv/default-00a4200ae8507533/0.0.0/2960f95a26e85d40ca41a230ac88787f715ee3003edaacb8b1f0891e9f04dda2...\r\nTraceback (most recent call last):\r\n File \"/projectnb2/media-framing/env-trans4/lib/python3.7/site-packages/datasets/builder.py\", line 592, in _download_and_prepare\r\n self._prepare_split(split_generator, **prepare_split_kwargs)\r\n File \"/projectnb2/media-framing/env-trans4/lib/python3.7/site-packages/datasets/builder.py\", line 944, in _prepare_split\r\n num_examples, num_bytes = writer.finalize()\r\n File \"/projectnb2/media-framing/env-trans4/lib/python3.7/site-packages/datasets/arrow_writer.py\", line 307, in finalize\r\n self.stream.close()\r\n File \"pyarrow/io.pxi\", line 132, in pyarrow.lib.NativeFile.close\r\n File \"pyarrow/error.pxi\", line 99, in pyarrow.lib.check_status\r\nOSError: error closing file\r\n\r\nDuring handling of the above exception, another exception occurred:\r\n\r\nTraceback (most recent call last):\r\n File \"run_glue.py\", line 484, in <module>\r\n main()\r\n File \"run_glue.py\", line 243, in main\r\n datasets = load_dataset(\"csv\", data_files=data_files, delimiter=\"\\t\")\r\n File \"/projectnb2/media-framing/env-trans4/lib/python3.7/site-packages/datasets/load.py\", line 610, in load_dataset\r\n ignore_verifications=ignore_verifications,\r\n File \"/projectnb2/media-framing/env-trans4/lib/python3.7/site-packages/datasets/builder.py\", line 515, in download_and_prepare\r\n dl_manager=dl_manager, verify_infos=verify_infos, **download_and_prepare_kwargs\r\n File \"/projectnb2/media-framing/env-trans4/lib/python3.7/site-packages/datasets/builder.py\", line 594, in _download_and_prepare\r\n raise OSError(\"Cannot find data file. \" + (self.manual_download_instructions or \"\"))\r\nOSError: Cannot find data file. ```", "Hi ! It looks like the error stacktrace doesn't match with your code snippet.\r\n\r\nWhat error do you get when running this ?\r\n```\r\ndata_files = {\"train\": \"train.tsv\", \"test\",: \"test.tsv\"}\r\ndatasets = load_dataset(\"csv\", data_files=data_files, delimiter=\"\\t\")\r\n```\r\ncan you check that both tsv files are in the same folder as the current working directory of your shell ?", "Hi @lhoestq, Below is the entire error message after I move both tsv files to the same directory. It's the same with I got before.\r\n```\r\n/projectnb2/media-framing/env-trans4/lib/python3.7/site-packages/torch/cuda/__init__.py:52: UserWarning: CUDA initialization: Found no NVIDIA driver on your system. Please check that you have an NVIDIA GPU and installed a driver from http://www.nvidia.com/Download/index.aspx (Triggered internally at /pytorch/c10/cuda/CUDAFunctions.cpp:100.)\r\n return torch._C._cuda_getDeviceCount() > 0\r\n08/29/2021 22:56:43 - WARNING - __main__ - Process rank: -1, device: cpu, n_gpu: 0distributed training: False, 16-bits training: False\r\n08/29/2021 22:56:43 - INFO - __main__ - Training/evaluation parameters TrainingArguments(output_dir=/projectnb/media-framing/pred_result/label1/, overwrite_output_dir=True, do_train=True, do_eval=False, do_predict=True, evaluation_strategy=EvaluationStrategy.NO, prediction_loss_only=False, per_device_train_batch_size=8, per_device_eval_batch_size=8, gradient_accumulation_steps=1, eval_accumulation_steps=None, learning_rate=5e-05, weight_decay=0.0, adam_beta1=0.9, adam_beta2=0.999, adam_epsilon=1e-08, max_grad_norm=1.0, num_train_epochs=8.0, max_steps=-1, lr_scheduler_type=SchedulerType.LINEAR, warmup_steps=0, logging_dir=runs/Aug29_22-56-43_scc1, logging_first_step=False, logging_steps=500, save_steps=3000, save_total_limit=None, no_cuda=False, seed=42, fp16=False, fp16_opt_level=O1, fp16_backend=auto, local_rank=-1, tpu_num_cores=None, tpu_metrics_debug=False, debug=False, dataloader_drop_last=False, eval_steps=500, dataloader_num_workers=0, past_index=-1, run_name=/projectnb/media-framing/pred_result/label1/, disable_tqdm=False, remove_unused_columns=True, label_names=None, load_best_model_at_end=False, metric_for_best_model=None, greater_is_better=None, ignore_data_skip=False, sharded_ddp=False, deepspeed=None, label_smoothing_factor=0.0, adafactor=False, _n_gpu=0)\r\n08/29/2021 22:56:43 - INFO - __main__ - load a local file for train: /project/media-framing/transformer4/temp_train.tsv\r\n08/29/2021 22:56:43 - INFO - __main__ - load a local file for test: /project/media-framing/transformer4/temp_test.tsv\r\n08/29/2021 22:56:43 - WARNING - datasets.builder - Using custom data configuration default-df627c23ac0e98ec\r\nDownloading and preparing dataset csv/default (download: Unknown size, generated: Unknown size, post-processed: Unknown size, total: Unknown size) to /usr4/cs542sp/hey1/.cache/huggingface/datasets/csv/default-df627c23ac0e98ec/0.0.0/9144e0a4e8435090117cea53e6c7537173ef2304525df4a077c435d8ee7828ff...\r\nTraceback (most recent call last):\r\n File \"/projectnb2/media-framing/env-trans4/lib/python3.7/site-packages/datasets/builder.py\", line 693, in _download_and_prepare\r\n self._prepare_split(split_generator, **prepare_split_kwargs)\r\n File \"/projectnb2/media-framing/env-trans4/lib/python3.7/site-packages/datasets/builder.py\", line 1166, in _prepare_split\r\n num_examples, num_bytes = writer.finalize()\r\n File \"/projectnb2/media-framing/env-trans4/lib/python3.7/site-packages/datasets/arrow_writer.py\", line 428, in finalize\r\n self.stream.close()\r\n File \"pyarrow/io.pxi\", line 132, in pyarrow.lib.NativeFile.close\r\n File \"pyarrow/error.pxi\", line 99, in pyarrow.lib.check_status\r\nOSError: error closing file\r\n\r\nDuring handling of the above exception, another exception occurred:\r\n\r\nTraceback (most recent call last):\r\n File \"run_glue.py\", line 487, in <module>\r\n main()\r\n File \"run_glue.py\", line 244, in main\r\n datasets = load_dataset(\"csv\", data_files=data_files, delimiter=\"\\t\")\r\n File \"/projectnb2/media-framing/env-trans4/lib/python3.7/site-packages/datasets/load.py\", line 852, in load_dataset\r\n use_auth_token=use_auth_token,\r\n File \"/projectnb2/media-framing/env-trans4/lib/python3.7/site-packages/datasets/builder.py\", line 616, in download_and_prepare\r\n dl_manager=dl_manager, verify_infos=verify_infos, **download_and_prepare_kwargs\r\n File \"/projectnb2/media-framing/env-trans4/lib/python3.7/site-packages/datasets/builder.py\", line 699, in _download_and_prepare\r\n + str(e)\r\nOSError: Cannot find data file. \r\nOriginal error:\r\nerror closing file\r\n```", "Hi !\r\nCan you try running this into a python shell directly ?\r\n\r\n```python\r\nimport os\r\nfrom datasets import load_dataset\r\n\r\ndata_files = {\"train\": \"train.tsv\", \"test\": \"test.tsv\"}\r\nassert all(os.path.isfile(data_file) for data_file in data_files.values()), \"Couln't find files\"\r\n\r\ndatasets = load_dataset(\"csv\", data_files=data_files, delimiter=\"\\t\")\r\nprint(\"success !\")\r\n```\r\n\r\nThis way all the code from `run_glue.py` doesn't interfere with our tests :)", "Hi @lhoestq, \r\n\r\nBelow is what I got from terminal after I copied and run your code. I think the files themselves are good since there is no assertion error. \r\n\r\n```\r\nUsing custom data configuration default-df627c23ac0e98ec\r\nDownloading and preparing dataset csv/default (download: Unknown size, generated: Unknown size, post-processed: Unknown size, total: Unknown size) to /usr4/cs542sp/hey1/.cache/huggingface/datasets/csv/default-df627c23ac0e98ec/0.0.0/9144e0a4e8435090117cea53e6c7537173ef2304525df4a077c435d8ee7828ff...\r\nTraceback (most recent call last):\r\n File \"/projectnb2/media-framing/env-trans4/lib/python3.7/site-packages/datasets/builder.py\", line 693, in _download_and_prepare\r\n self._prepare_split(split_generator, **prepare_split_kwargs)\r\n File \"/projectnb2/media-framing/env-trans4/lib/python3.7/site-packages/datasets/builder.py\", line 1166, in _prepare_split\r\n num_examples, num_bytes = writer.finalize()\r\n File \"/projectnb2/media-framing/env-trans4/lib/python3.7/site-packages/datasets/arrow_writer.py\", line 428, in finalize\r\n self.stream.close()\r\n File \"pyarrow/io.pxi\", line 132, in pyarrow.lib.NativeFile.close\r\n File \"pyarrow/error.pxi\", line 99, in pyarrow.lib.check_status\r\nOSError: error closing file\r\n\r\nDuring handling of the above exception, another exception occurred:\r\n\r\nTraceback (most recent call last):\r\n File \"test.py\", line 7, in <module>\r\n datasets = load_dataset(\"csv\", data_files=data_files, delimiter=\"\\t\")\r\n File \"/projectnb2/media-framing/env-trans4/lib/python3.7/site-packages/datasets/load.py\", line 852, in load_dataset\r\n use_auth_token=use_auth_token,\r\n File \"/projectnb2/media-framing/env-trans4/lib/python3.7/site-packages/datasets/builder.py\", line 616, in download_and_prepare\r\n dl_manager=dl_manager, verify_infos=verify_infos, **download_and_prepare_kwargs\r\n File \"/projectnb2/media-framing/env-trans4/lib/python3.7/site-packages/datasets/builder.py\", line 699, in _download_and_prepare\r\n + str(e)\r\nOSError: Cannot find data file. \r\nOriginal error:\r\nerror closing file\r\n```", "Hi, could this be a permission error ? I think it fails to close the arrow file that contains the data from your CSVs in the cache.\r\n\r\nBy default datasets are cached in `~/.cache/huggingface/datasets`, could you check that you have the right permissions ?\r\nYou can also try to change the cache directory by passing `cache_dir=\"path/to/my/cache/dir\"` to `load_dataset`.", "Thank you!! @lhoestq\r\n\r\nFor some reason, I don't have the default path for datasets to cache, maybe because I work from a remote system. The issue solved after I pass the `cache_dir` argument to the function. Thank you very much!!" ]

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[ "Thanks :) merging it" ]

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[ "Thanks for reporting.\r\nIn theory since the dataset script is just made to yield examples to write them into an arrow file, it's not supposed to create memory issues.\r\n\r\nCould you please try to run this exact same loop in a separate script to see if it's not an issue with `PIL` ?\r\nYou can just copy paste what's inside `_generate_examples` and remove all the code for `datasets` (remove yield).\r\n\r\nIf the RAM usage stays low after 600 examples it means that it comes from some sort of memory leak in the library, or with pyarrow.", "Here's an equivalent loading code:\r\n```python\r\nimages_path = \"PHOENIX-2014-T-release-v3/PHOENIX-2014-T/features/fullFrame-210x260px/train\"\r\n\r\nfor dir_path in tqdm(os.listdir(images_path)):\r\n frames_path = os.path.join(images_path, dir_path)\r\n np_frames = []\r\n for frame_name in os.listdir(frames_path):\r\n frame_path = os.path.join(frames_path, frame_name)\r\n im = Image.open(frame_path)\r\n np_frames.append(np.asarray(im))\r\n im.close()\r\n```\r\n\r\nThe process takes 0.3% of memory, even after 1000 examples on the small machine with 120GB RAM.\r\n\r\nI guess something in the datasets library doesn't release the reference to the objects I'm yielding, but no idea how to test for this", "I've had similar issues with Arrow once. I'll investigate...\r\n\r\nFor now maybe we can simply use the images paths in the dataset you want to add. I don't expect to fix this memory issue until 1-2 weeks unfortunately. Then we can just update the dataset with the images. What do you think ?", "If it's just 1-2 weeks, I think it's best if we wait. I don't think it is very urgent to add it, and it will be much more useful with the images loaded rather than not (the images are low resolution, and thus papers using this dataset actually fit the entire video into memory anyway)\r\n\r\nI'll keep working on other datasets in the meanwhile :) ", "Ok found the issue. This is because the batch size used by the writer is set to 10 000 elements by default so it would load your full dataset in memory (the writer has a buffer that flushes only after each batch). Moreover to write in Apache Arrow we have to use python objects so what's stored inside the ArrowWriter's buffer is actually python integers (32 bits).\r\n\r\nLowering the batch size to 10 should do the job.\r\n\r\nI will add a flag to the DatasetBuilder class of dataset scripts, so that we can customize the batch size.", "Thanks, that's awesome you managed to find the problem.\r\n\r\nAbout the 32 bits - really? there isn't a way to serialize the numpy array somehow? 32 bits would take 4 times the memory / disk space needed to store these videos.\r\n\r\nPlease let me know when the batch size is customizable and I'll try again!", "The 32 bit integrers are only used in the writer's buffer because Arrow doesn't take numpy arrays correctly as input. On disk it's stored as uint8 in arrow format ;)", "> I don't expect to fix this memory issue until 1-2 weeks unfortunately.\r\n\r\nHi @lhoestq \r\nnot to rush of course, but I was wondering if you have a new timeline so I know how to plan my work around this :) ", "Hi ! Next week for sure :) ", "Alright it should be good now.\r\nYou just have to specify `_writer_batch_size = 10` for example as a class attribute of the dataset builder class.", "I added it, but still it consumes as much memory\r\n\r\nhttps://github.com/huggingface/datasets/pull/722/files#diff-2e0d865dd4a60dedd1861d6f8c5ed281ded71508467908e1e0b1dbe7d2d420b1R66\r\n\r\nDid I not do it correctly?", "Yes you did it right.\r\nDid you rebase to include the changes of #828 ?\r\n\r\nEDIT: looks like you merged from master in the PR. Not sure why you still have an issue then, I will investigate", "Hi @lhoestq, any update on this?\r\nPerhaps even a direction I could try myself?", "Sorry for the delay, I was busy with the dataset sprint and the incredible amount of contributions to the library ^^'\r\n\r\nWhat you can try to do to find what's wrong is check at which frequency the arrow writer writes all the examples from its in-memory buffer on disk. This happens [here](https://github.com/huggingface/datasets/blob/master/src/datasets/arrow_writer.py#L257-L258) in the code.\r\n\r\nThe idea is that `write_on_file` writes the examples every `writer_batch_size` examples and clear the buffer `self. current_rows`. As soon as `writer_batch_size` is small enough you shouldn't have memory issues in theory.\r\n\r\nLet me know if you have questions or if I can help.\r\n\r\nSince the dataset sprint is over and I will also be done with all the PRs soon I will be able to go back at it and take a look.", "Thanks. I gave it a try and no success. I'm not sure what's happening there", "I had the same issue. It works for me by setting `DEFAULT_WRITER_BATCH_SIZE = 10` of my dataset builder class. (And not `_writer_batch_size` as previously mentioned). I guess this is because `_writer_batch_size` is overwritten in `__init__` (see [here](https://github.com/huggingface/datasets/blob/0e2563e5d5c2fc193ea27d7c24607bb35607f2d5/src/datasets/builder.py#L934))", "Yes the class attribute you can change is `DEFAULT_WRITER_BATCH_SIZE`.\r\nOtherwise in `load_dataset` you can specify `writer_batch_size=`", "Ok thanks for the tips. Maybe the documentation should be updated accordingly https://huggingface.co/docs/datasets/add_dataset.html.", "Thanks for reporting this mistake in the docs.\r\nI just fixed it at https://github.com/huggingface/datasets/commit/85cf7ff920c90ca2e12bedca12b36d2a043c3da2" ]

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[ "I still have to compute the dataset_infos, and build + host the indexes", "update: I'm computing the metadata, will update the PR soon", "Finally all green and ready to merge :)" ]

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[ "Hello,\r\n\r\nI ran https://github.com/huggingface/transformers/blob/master/examples/token-classification/run.sh\r\n\r\nAnd received this error:\r\n```\r\n100%|██████████| 407/407 [21:37<00:00, 3.44s/it]Traceback (most recent call last):\r\n File \"run_ner.py\", line 445, in <module>\r\n main()\r\n File \"run_ner.py\", line 398, in main\r\n results = trainer.evaluate()\r\n File \"/data/2021/transformers/src/transformers/trainer.py\", line 1470, in evaluate\r\n metric_key_prefix=metric_key_prefix,\r\n File \"/data/2021/transformers/src/transformers/trainer.py\", line 1622, in prediction_loop\r\n metrics = self.compute_metrics(EvalPrediction(predictions=preds, label_ids=label_ids))\r\n File \"run_ner.py\", line 345, in compute_metrics\r\n results = metric.compute(predictions=true_predictions, references=true_labels)\r\n File \"/usr/local/lib/python3.6/dist-packages/datasets/metric.py\", line 398, in compute\r\n output = self._compute(predictions=predictions, references=references, **kwargs)\r\n File \"/root/.cache/huggingface/modules/datasets_modules/metrics/seqeval/81eda1ff004361d4fa48754a446ec69bb7aa9cf4d14c7215f407d1475941c5ff/seqeval.py\", line 97, in _compute\r\n report = classification_report(y_true=references, y_pred=predictions, suffix=suffix, output_dict=True)\r\nTypeError: classification_report() got an unexpected keyword argument 'output_dict'\r\n```\r\n\r\nI'm still trying multiple things to see if I can work around this, but I thought it might be useful to mention it here.\r\n\r\n```\r\nName: transformers\r\nVersion: 4.3.0.dev0\r\n\r\nName: datasets\r\nVersion: 1.2.1\r\n```", "Hi, can you try to update your local installation of `seqeval` ?\r\n\r\n```\r\npip install --upgrade seqeval\r\n```", "@lhoestq thanks for the reply. Indeed it was some issue with my setup. I removed the \"transformers\" and \"datasets\" (that I had previously installed from the source code), cleared the cache and installed everything again. It works great now!" ]

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[ "Thanks for reporting.\r\nThat's because the download url has changed. The old url now redirects to the new one but we don't support redirection for downloads.\r\n\r\nI'm opening a PR to update the url" ]

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[ "can you also reference the `--organization` flag like in https://github.com/huggingface/transformers/blob/master/docs/source/model_sharing.rst#upload-your-model-with-the-cli ?", "done!", "Not sure if the changes in `datasets/wmt_t2t/wmt_utils.py` are intentional.\r\nIf you want to add more configs to wmt, could you do it in a serapate PR ?", "I don't think I changed wmt_utils (I think github is wrong or my setup is poorly configured).\r\n\r\nLocally git diff master --name-only says one file. Master is up to date.\r\nTried to make a new PR #755 and the same thing happened.", "Trying new fork." ]

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[ "Thanks for reporting !\r\nWe'll add support for other keys" ]

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[ "Followup: \r\nFrom the info in https://github.com/huggingface/datasets/pull/722, I probably should load the videos as array of frames directly into the database. \r\nThis will make the dataset generation time very long, but will make working with the dataset much easier.", "When I run:\r\n```\r\npython datasets-cli dummy_data datasets/wlasl\r\n```\r\n\r\nI get:\r\n```\r\nChecking datasets/wlasl/wlasl.py for additional imports. \r\nFound main folder for dataset datasets/wlasl/wlasl.py at /home/nlp/amit/.cache/huggingface/modules/datasets_modules/datasets/wlasl \r\nFound specific version folder for dataset datasets/wlasl/wlasl.py at /home/nlp/amit/.cache/huggingface/modules/datasets_modules/datasets/wlasl/f0cad785350d770804f20c471b0cff2d7e3c7b210b5c3a228c393abb95a04786 \r\nFound script file from datasets/wlasl/wlasl.py to /home/nlp/amit/.cache/huggingface/modules/datasets_modules/datasets/wlasl/f0cad785350d770804f20c471b0cff2d7e3c7b210b5c3a228c393abb95a04786/wlasl.py \r\nFound dataset infos file from datasets/wlasl/dataset_infos.json to /home/nlp/amit/.cache/huggingface/modules/datasets_modules/datasets/wlasl/f0cad785350d770804f20c471b0cff2d7e3c7b210b5c3a228c393abb95a04786/dataset_infos.json \r\nFound metadata file for dataset datasets/wlasl/wlasl.py at /home/nlp/amit/.cache/huggingface/modules/datasets_modules/datasets/wlasl/f0cad785350d770804f20c471b0cff2d7e3c7b210b5c3a228c393abb95a04786/wlasl.json \r\nUsing custom data configuration default \r\nLoading Dataset Infos from /home/nlp/amit/.cache/huggingface/modules/datasets_modules/datasets/wlasl/f0cad785350d770804f20c471b0cff2d7e3c7b210b5c3a228c393abb95a04786\r\nCreating dummy folder structure for datasets/wlasl/dummy/0.3.0... \r\nDataset datasets with config None seems to already open files in the method `_split_generators(...)`. You might consider to instead only open files in the method `_generate_examples(...)` instead. If this is not possible the dummy data has to be created with less guidance. Make sure you create the file dummy_data. \r\nTraceback (most recent call last): \r\nFile \"datasets-cli\", line 36, in \r\nservice.run() File \"/home/nlp/amit/anaconda2/envs/meta-scholar/lib/python3.7/site-packages/datasets-1.1.2-py3.7.egg/datasets/commands/dummy_data.py\", line 73, in run \r\nfor split in generator_splits: \r\nUnboundLocalError: local variable 'generator_splits' referenced before assignment\r\n```" ]

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[ "Thanks @lhoestq \r\nAre there any guidelines for the dummy data?\r\nIn this particular case for example, the dataset fetches from two hardcoded URLs. \r\nDo I just `head -n 10` both files and zip them?\r\n\r\n", "> Thanks @lhoestq\r\n> Are there any guidelines for the dummy data?\r\n> In this particular case for example, the dataset fetches from two hardcoded URLs.\r\n> Do I just `head -n 10` both files and zip them?\r\n\r\nYes the idea is just to have a few examples to properly test the script and make sure it keeps working in the long run.\r\n\r\nAnd FYI there's a command to help you name the dummy data files correctly. More info in the documentation [here](https://huggingface.co/docs/datasets/share_dataset.html#adding-dummy-data)", "@lhoestq passes all tests" ]

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[ "Thanks for reporting. That's a bug indeed.\r\nApparently only the `data_files` parameter is taken into account right now in `DatasetBuilder._create_builder_config` but it should also be the case for `config_kwargs` (or at least the instantiated `builder_config`)", "Hi, does this bug be fixed? when I load JSON files, I get the same errors by the command \r\n`!python3 run.py --do_train --task qa --dataset squad-retrain-data/train-v2.0.json --output_dir ./re_trained_model/`\r\n\r\nchange the dateset to load json by refering to https://huggingface.co/docs/datasets/loading.html\r\n`dataset = datasets.load_dataset('json', data_files=args.dataset)`\r\n\r\nErrors:\r\n`Downloading and preparing dataset json/default (download: Unknown size, generated: Unknown size, post-processed: Unknown size, total: Unknown size) to /root/.cache/huggingface/datasets/json/default-c1e124ad488911b8/0.0.0/45636811569ec4a6630521c18235dfbbab83b7ab572e3393c5ba68ccabe98264...\r\n`" ]

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[ "Hi try, to provide more information please.\r\n\r\nExample code in a colab to reproduce the error, details on what you are trying to do and what you were expected and details on your environment (OS, PyPi packages version).", "> Hi try, to provide more information please.\r\n> \r\n> Example code in a colab to reproduce the error, details on what you are trying to do and what you were expected and details on your environment (OS, PyPi packages version).\r\n\r\nI have update the description, sorry for the incomplete issue by mistake.", "Hi, I have manually downloaded the compressed dataset `openwebtext.tar.xz' and use the following command to preprocess the examples:\r\n```\r\n>>> dataset = load_dataset('/home/admin/workspace/datasets/datasets-master/datasets-master/datasets/openwebtext', data_dir='/home/admin/workspace/datasets')\r\nUsing custom data configuration default\r\nDownloading and preparing dataset openwebtext/default (download: Unknown size, generated: Unknown size, post-processed: Unknown size, total: Unknown size) to /home/admin/.cache/huggingface/datasets/openwebtext/default/0.0.0/5c636399c7155da97c982d0d70ecdce30fbca66a4eb4fc768ad91f8331edac02...\r\nDataset openwebtext downloaded and prepared to /home/admin/.cache/huggingface/datasets/openwebtext/default/0.0.0/5c636399c7155da97c982d0d70ecdce30fbca66a4eb4fc768ad91f8331edac02. Subsequent calls will reuse this data.\r\n>>> len(dataset['train'])\r\n74571\r\n>>>\r\n```\r\nThe size of the pre-processed example file is only 354MB, however the processed bookcorpus dataset is 4.6g. Are there any problems?", "NonMatchingChecksumError: Checksums didn't match for dataset source files:\r\n\r\ni got this issue when i try to work on my own datasets kindly tell me, from where i can get checksums of train and dev file in my github repo", "Hi, I got the similar issue for xnli dataset while working on colab with python3.7. \r\n\r\n`nlp.load_dataset(path = 'xnli')`\r\n\r\nThe above command resulted in following issue : \r\n```\r\nNonMatchingChecksumError: Checksums didn't match for dataset source files:\r\n['https://www.nyu.edu/projects/bowman/xnli/XNLI-1.0.zip']\r\n```\r\n\r\nAny idea how to fix this ?", "Did anyone figure out how to fix this error?" ]

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[ "Great, as you found it useful I improved the code a bit to automate indentation in the parent class, so that the child repr doesn't need to guess the indentation level, while repr'ing nicely on its own.\r\n\r\n- do we want indent=4 or 2?\r\n- do we want `{` ... `}` or w/o?\r\n\r\ncurrently it's indent4 and w/ curly braces, so it looks:\r\n\r\n```\r\nDatasetDict({\r\n train: Dataset({\r\n features: ['text', 'headline', 'title'],\r\n num_rows: 157252\r\n })\r\n validation: Dataset({\r\n features: ['text', 'headline', 'title'],\r\n num_rows: 5599\r\n })\r\n test: Dataset({\r\n features: ['text', 'headline', 'title'],\r\n num_rows: 5577\r\n })\r\n})\r\n```\r\njust child:\r\n```\r\nDataset({\r\n features: ['text', 'headline', 'title'],\r\n num_rows: 5577\r\n})\r\n```\r\n\r\n", "Yes! A lot better indeed!" ]

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[ "Should be fixed now: \r\n\r\n\r\n\r\nNot sure I understand what you mean by the second part?\r\n", "Thank you!\r\n\r\n> Not sure I understand what you mean by the second part?\r\n\r\nCompare the 2:\r\n* https://huggingface.co/datasets/wikihow\r\n* https://huggingface.co/nlp/viewer/?dataset=wikihow&config=all\r\nCan you see the difference? 2nd has formatting, 1st doesn't.\r\n", "For context, those are two different pages (not an old vs new one), one is from the dataset viewer (you can browse data inside the datasets) while the other is just a basic reference page displayed some metadata about the dataset.\r\n\r\nFor the second one, we'll move to markdown parsing soon, so it'll be formatted better.", "I understand. I was just flagging the lack of markup issue." ]

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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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