Datasets:

abhishek-shrm

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[ "I took your comments into account.\r\nAlso now after compressing the dummy_data.zip file it runs a dummy data test (=make sure each split has at least 1 example using the dummy data)", "I just tested the tool with some datasets and found out that it's not working for datasets that download files using `download_and_extract(file_url)` (where file_url is a `str`). That's because in that case the dummy_data.zip is not a folder but a single zipped file.\r\n\r\nI think we have to fix that or we can have unexpected behavior when a scripts calls `download_and_extract(file_url)` several times, since it would always point to the same dummy data file.\r\n\r\nSo I decided to change that to have a folder containing the dummy files instead but it breaks around 90 tests so I need to update 90 dummy data files to follow this scheme. I'll probably fix them tomorrow morning.\r\n\r\nWhat do you guys think ? Also cc @patrickvonplaten to make sure I understand things correctly", "Ok I changed to use the dummy_data.zip content to be a folder even for single url calls to `dl_manager.download_and_extract`. Therefore the automatic dummy data generation tool works for most datasets now.\r\n\r\nTo avoid having to change all the old dummy_data.zip files I added backward compatiblity. \r\n\r\nThe only test failing is `tests/test_dataset_common.py::RemoteDatasetTest::test_load_dataset_xcopa`\r\nIt is expected to fail since I had modify its dummy data structure that was wrong. It was causing issue with backward compatibility. It will be fixed as soon as this PR is merged" ]

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[ "Not at the moment but we could likely support this feature.", "?", "I think it would be a very helpful feature, because sometimes one only wants to evaluate models on the dev set, and the whole training data may be many times bigger.\r\nThis makes the task impossible with limited memory resources." ]

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[ "I’ll take this one to test the workflow for the sprint next week cc @yjernite @lhoestq ", "@thomwolf here's a slightly adapted version of the code from the [official Tapas repository](https://github.com/google-research/tapas/blob/master/tapas/utils/interaction_utils.py) that is used to turn the `answer_coordinates` and `answer_texts` columns into true Python lists of tuples/strings:\r\n\r\n```\r\nimport pandas as pd\r\nimport ast\r\n\r\ndata = pd.read_csv(\"/content/sqa_data/random-split-1-dev.tsv\", sep='\\t')\r\n\r\ndef _parse_answer_coordinates(answer_coordinate_str):\r\n \"\"\"Parses the answer_coordinates of a question.\r\n Args:\r\n answer_coordinate_str: A string representation of a Python list of tuple\r\n strings.\r\n For example: \"['(1, 4)','(1, 3)', ...]\"\r\n \"\"\"\r\n\r\n try:\r\n answer_coordinates = []\r\n # make a list of strings\r\n coords = ast.literal_eval(answer_coordinate_str)\r\n # parse each string as a tuple\r\n for row_index, column_index in sorted(\r\n ast.literal_eval(coord) for coord in coords):\r\n answer_coordinates.append((row_index, column_index))\r\n except SyntaxError:\r\n raise ValueError('Unable to evaluate %s' % answer_coordinate_str)\r\n \r\n return answer_coordinates\r\n\r\n\r\ndef _parse_answer_text(answer_text):\r\n \"\"\"Populates the answer_texts field of `answer` by parsing `answer_text`.\r\n Args:\r\n answer_text: A string representation of a Python list of strings.\r\n For example: \"[u'test', u'hello', ...]\"\r\n \"\"\"\r\n try:\r\n answer = []\r\n for value in ast.literal_eval(answer_text):\r\n answer.append(value)\r\n except SyntaxError:\r\n raise ValueError('Unable to evaluate %s' % answer_text)\r\n\r\n return answer\r\n\r\ndata['answer_coordinates'] = data['answer_coordinates'].apply(lambda coords_str: _parse_answer_coordinates(coords_str))\r\ndata['answer_text'] = data['answer_text'].apply(lambda txt: _parse_answer_text(txt))\r\n```\r\n\r\nHere I'm using Pandas to read in one of the TSV files (the dev set). \r\n\r\n", "Closing since SQA was added in #1566 " ]

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[ "Hi ! It runs on my side without issues. I tried\r\n```python\r\nfrom datasets import load_dataset\r\nload_dataset(\"boolq\")\r\n```\r\n\r\nWhat version of datasets and tensorflow are your runnning ?\r\nAlso if you manage to get a minimal reproducible script (on google colab for example) that would be useful.", "hey\ni do the exact same commands. for me it fails i guess might be issues with\ncaching maybe?\nthanks\nbest\nrabeeh\n\nOn Tue, Nov 24, 2020, 10:24 AM Quentin Lhoest <notifications@github.com>\nwrote:\n\n> Hi ! It runs on my side without issues. I tried\n>\n> from datasets import load_datasetload_dataset(\"boolq\")\n>\n> What version of datasets and tensorflow are your runnning ?\n> Also if you manage to get a minimal reproducible script (on google colab\n> for example) that would be useful.\n>\n> —\n> You are receiving this because you authored the thread.\n> Reply to this email directly, view it on GitHub\n> <https://github.com/huggingface/datasets/issues/879#issuecomment-732769114>,\n> or unsubscribe\n> <https://github.com/notifications/unsubscribe-auth/ABP4ZCGGDR2FUMRKZTIY5CTSRN3VXANCNFSM4T7R3U6A>\n> .\n>\n", "Could you check if it works on the master branch ?\r\nYou can use `load_dataset(\"boolq\", script_version=\"master\")` to do so.\r\nWe did some changes recently in boolq to remove the TF dependency and we changed the way the data files are downloaded in https://github.com/huggingface/datasets/pull/881" ]

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[ "This would be a neat feature", "> neat feature\r\n\r\nI dint get these clearly, can you please elaborate like how to work on these ", "It could maybe work almost out of the box just by using `cached_path` in the text/csv/json scripts, no?", "Thanks thomwolf and julien-c\r\n\r\nI'm still confusion on what you guys said, \r\n\r\nI have solved the problem as follows:\r\n\r\n1. read the csv file using pandas from s3 \r\n2. Convert to dictionary key as column name and values as list column data\r\n3. convert it to Dataset using \r\n`from datasets import Dataset`\r\n`train_dataset = Dataset.from_dict(train_dict)`", "We were brainstorming around your use-case.\r\n\r\nLet's keep the issue open for now, I think this is an interesting question to think about.", "> We were brainstorming around your use-case.\r\n> \r\n> Let's keep the issue open for now, I think this is an interesting question to think about.\r\n\r\nSure thomwolf, Thanks for your concern ", "I agree it would be cool to have that feature. Also that's good to know that pandas supports this.\r\nFor the moment I'd suggest to first download the files locally as thom suggested and then load the dataset by providing paths to the local files", "Don't get\n", "Any updates on this issue?\r\nI face a similar issue. I have many parquet files in S3 and I would like to train on them. \r\nTo be honest I even face issues with only getting the last layer embedding out of them.", "Hi dorlavie, \r\nYou can find one solution that i have mentioned above, that can help you. \r\nAnd there is one more solution also which is downloading files locally\r\n", "> Hi dorlavie,\r\n> You can find one solution that i have mentioned above, that can help you.\r\n> And there is one more solution also which is downloading files locally\r\n\r\nmahesh1amour, thanks for the fast reply\r\n\r\nUnfortunately, in my case I can not read with pandas. The dataset is too big (50GB). \r\nIn addition, due to security concerns I am not allowed to save the data locally", "@dorlavie could use `boto3` to download the data to your local machine and then load it with `dataset`\r\n\r\nboto3 example [documentation](https://boto3.amazonaws.com/v1/documentation/api/latest/guide/s3-example-download-file.html)\r\n```python\r\nimport boto3\r\n\r\ns3 = boto3.client('s3')\r\ns3.download_file('BUCKET_NAME', 'OBJECT_NAME', 'FILE_NAME')\r\n```\r\n\r\ndatasets example [documentation](https://huggingface.co/docs/datasets/loading_datasets.html)\r\n\r\n```python\r\nfrom datasets import load_dataset\r\ndataset = load_dataset('csv', data_files=['my_file_1.csv', 'my_file_2.csv', 'my_file_3.csv'])\r\n```\r\n", "Thanks @philschmid for the suggestion.\r\nAs I mentioned in the previous comment, due to security issues I can not save the data locally.\r\nI need to read it from S3 and process it directly.\r\n\r\nI guess that many other people try to train / fit those models on huge datasets (e.g entire Wiki), what is the best practice in those cases?", "If I understand correctly you're not allowed to write data on disk that you downloaded from S3 for example ?\r\nOr is it the use of the `boto3` library that is not allowed in your case ?", "@lhoestq yes you are correct.\r\nI am not allowed to save the \"raw text\" locally - The \"raw text\" must be saved only on S3.\r\nI am allowed to save the output of any model locally. \r\nIt doesn't matter how I do it boto3/pandas/pyarrow, it is forbidden", "@dorlavie are you using sagemaker for training too? Then you could use S3 URI, for example `s3://my-bucket/my-training-data` and pass it within the `.fit()` function when you start the sagemaker training job. Sagemaker would then download the data from s3 into the training runtime and you could load it from disk\r\n\r\n**sagemaker start training job**\r\n```python\r\npytorch_estimator.fit({'train':'s3://my-bucket/my-training-data','eval':'s3://my-bucket/my-evaluation-data'})\r\n```\r\n\r\n**in the train.py script**\r\n```python\r\nfrom datasets import load_from_disk\r\n\r\ntrain_dataset = load_from_disk(os.environ['SM_CHANNEL_TRAIN'])\r\n```\r\n\r\nI have created an example of how to use transformers and datasets with sagemaker. \r\nhttps://github.com/philschmid/huggingface-sagemaker-example/tree/main/03_huggingface_sagemaker_trainer_with_data_from_s3\r\n\r\nThe example contains a jupyter notebook `sagemaker-example.ipynb` and an `src/` folder. The sagemaker-example is a jupyter notebook that is used to create the training job on AWS Sagemaker. The `src/` folder contains the `train.py`, our training script, and `requirements.txt` for additional dependencies.\r\n\r\n" ]

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[ "Hi ! Thanks for reporting.\r\nDo you have the same slowdown when you iterate through the raw dataset object as well ? (no dataloader)\r\nIt would be nice to know whether it comes from the dataloader or not", "> Hi ! Thanks for reporting.\r\n> Do you have the same slowdown when you iterate through the raw dataset object as well ? (no dataloader)\r\n> It would be nice to know whether it comes from the dataloader or not\r\n\r\nI did not iter data from raw dataset, maybe I will test later. Now I iter all files directly from `open(file)`, around 20000it/s." ]

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[ "It looks like there was an issue while building the imdb dataset.\r\nCould you provide more information about your OS and the version of python and `datasets` ?\r\n\r\nAlso could you try again with \r\n```python\r\ndataset = datasets.load_dataset(\"imdb\", split=\"train\", download_mode=\"force_redownload\")\r\n```\r\nto make sure it's not a corrupted file issue ?", "I was using version 1.1.2 and this resolved with version 1.1.3, thanks. ", "Hello,\r\nI have the same pb with 1.8.0", "Hi ! I just tried in 1.8.0 and it worked fine. Can you try again ? Maybe the dataset host had some issues that are fixed now", "Hello,\r\nIt works fine now :) !\r\nThanks !" ]

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[ "I just opened a PR to fix this.\r\nThanks for reporting !" ]

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[ "This was fixed in #740 \r\nCould you try to update `datasets` and try again ?", "This has been fixed in datasets 1.1.3" ]

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[ "I get the same error. It was fixed some days ago, but again it appears", "Hi @mrm8488 it's working again today without any fix so I am closing this issue.", "I see the issue happening again today - \r\n\r\n[nltk_data] Downloading package stopwords to /root/nltk_data...\r\n[nltk_data] Package stopwords is already up-to-date!\r\nDownloading and preparing dataset cnn_dailymail/3.0.0 (download: 558.32 MiB, generated: 1.28 GiB, post-processed: Unknown size, total: 1.82 GiB) to /root/.cache/huggingface/datasets/cnn_dailymail/3.0.0/3.0.0/0128610a44e10f25b4af6689441c72af86205282d26399642f7db38fa7535602...\r\n\r\n---------------------------------------------------------------------------\r\n\r\nNotADirectoryError Traceback (most recent call last)\r\n\r\n<ipython-input-9-cd4bf8bea840> in <module>()\r\n 22 \r\n 23 \r\n---> 24 train = load_dataset('cnn_dailymail', '3.0.0', split='train')\r\n 25 validation = load_dataset('cnn_dailymail', '3.0.0', split='validation')\r\n 26 test = load_dataset('cnn_dailymail', '3.0.0', split='test')\r\n\r\n5 frames\r\n\r\n/root/.cache/huggingface/modules/datasets_modules/datasets/cnn_dailymail/0128610a44e10f25b4af6689441c72af86205282d26399642f7db38fa7535602/cnn_dailymail.py in _find_files(dl_paths, publisher, url_dict)\r\n 132 else:\r\n 133 logging.fatal(\"Unsupported publisher: %s\", publisher)\r\n--> 134 files = sorted(os.listdir(top_dir))\r\n 135 \r\n 136 ret_files = []\r\n\r\nNotADirectoryError: [Errno 20] Not a directory: '/root/.cache/huggingface/datasets/downloads/1bc05d24fa6dda2468e83a73cf6dc207226e01e3c48a507ea716dc0421da583b/cnn/stories'\r\n\r\nCan someone please take a look ?", "Sometimes happens. Try in a while", "It is working now, thank you. ", "Has anyone solved this ? I still get this error ", "> atal(\"Unsupported publisher: %s\", publisher) --> 134 files = sorted(os.listdir(top_dir)) 135 136 ret_files = []\r\n> \r\n> NotADirectoryError: [Errno 20] Not a directory: '/root/.cache/huggingface/datasets/downloads/1bc05d24fa6dda2468e83a73cf6dc207226e01e3c48a507ea716dc0421da583b/cnn/stories'\r\n> \r\n> Can someone please take a look ?\r\n\r\n2 short-term workarounds:\r\n\r\n1. Use this line instead `dataset = load_dataset('ccdv/cnn_dailymail', '3.0.0')`. [In a related issue](https://github.com/huggingface/datasets/issues/996#issuecomment-997343101), this person mentioned another data source copy that just works.\r\n2. Use the same data source, but edit the urls. Instead of google drive quota problems mentioned in #996, I was getting the \"can't scan this file for viruses\" problem, which results in that prompted html getting downloaded instead of the files. You can get around this by:\r\n 1. Look at the traceback and find out where `cnn_dailymail.py` is on your computer.\r\n 2. Edit the `cnn_stories` and `dm_stories` url's by adding the following to the end of them `&confirm=t`. This should be around line 67.\r\n 3. You may have to remove those confirmation html files in your download directory (`~/.cache/huggingface/datasets/downloads` for me) so that they don't get in the way of the new download attempts.\r\n\r\nEither method works for me. I would've made a PR, but not sure if they want to go with the new ccdv/cnn_dailymail source or not.", "experience the same problem, ccdv/cnn_dailymail not working either. \r\n\r\nSolve this problem by installing datasets library from the master branch:\r\npython -m pip install git+https://github.com/huggingface/datasets.git@master", "Seem to be getting this again even with 1.18.4. I believe it worked yesterday." ]

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[ "thanks ! merging now" ]

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[ "Loading the iwslt2017-en-nl config of iwslt2017 works fine on my side. \r\nMaybe you can open an issue on transformers as well ? And also add more details about your environment (OS, python version, version of transformers and datasets etc.)", "closing now, figured out this is because the max length of decoder was set smaller than the input_dimensions. thanks " ]

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[ "Hi ! Thanks for your message.\r\nIndeed it's a free feature we can add and that can be useful.\r\nIf you want to contribute, feel free to open a PR to add it to the text dataset script :)" ]

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[ ":+1: Thanks for fixing it!" ]

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[ "I keep this PR in stand-by for next week's datasets sprint. If the next release is 2.0.0 then we can include it given that it's breaking for many metrics" ]

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[ "PR is already open here : #348 \r\nThe only thing remaining is to compute the metadata of each subdataset (one per language + shuffled/unshuffled).\r\nAs soon as #863 is merged we can start computing them. This will take a bit of time though", "Grand, thanks for this!" ]

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[ "I'm sorry, this was a problem with my environment.\r\nNow that I have identified the cause of environmental dependency, I would like to fix it and try it.\r\nExcuse me for making a noise." ]

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[ "Same error here!\r\n", "Same here! My kaggle notebook stopped working like yesterday. It's strange because I have fixed version of datasets==1.1.2", "I'm looking at it right now", "I couldn't reproduce unfortunately. I tried\r\n```python\r\nfrom datasets import load_dataset\r\n\r\nload_dataset(\"cnn_dailymail\", \"3.0.0\", download_mode=\"force_redownload\")\r\n```\r\nand it worked fine on both my env (python 3.7.2) and colab (python 3.6.9)\r\n\r\nMaybe there was an issue with the google drive download link of the dataset ?\r\nAre you still having the issue ? If so could your give me more info about your python and requests version ?", "No, It's working fine now. Very strange. Here are my python and request versions\r\n\r\nrequests 2.24.0\r\nPython 3.8.2", "It's working as expected. Closing the issue \r\n\r\nThanks everybody." ]

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[ "The preprocessing tokenizes the input text. Tokenization outputs `input_ids`, `attention_mask`, `token_type_ids` and `special_tokens_mask`. All those are of length`max_seq_length` because of padding. Therefore for each sample it generate 4 *`max_seq_length` integers. Currently they're all saved as int64. This is why the tokenization takes so much space.\r\n\r\nI'm sure we can optimize that though\r\nWhat do you think @sgugger ?", "First I think we should disable padding in the dataset processing and let the data collator do it.\r\n\r\nThen I'm wondering if you need attention_mask and token_type_ids at this point ?\r\n\r\nFinally we can also specify the output feature types at this line https://github.com/huggingface/transformers/blob/master/examples/language-modeling/run_mlm.py#L280 to use more optimized integer precisions for the output. Maybe something like:\r\n- input_ids: uint16 or uint32\r\n- token_type_ids: uint8 or bool\r\n- attention_mask: bool\r\n- special_tokens_mask: bool\r\n\r\nAlso IMO these changes are all on the `transformers` side. Maybe we should discuss on the `transformers` repo", "> First I think we should disable padding in the dataset processing and let the data collator do it.\r\n\r\nNo, you can't do that on TPUs as dynamic shapes will result in a very slow training. The script can however be tweaked to use the `PaddingDataCollator` with a fixed max length instead of dynamic batching.\r\n\r\nFor the other optimizations, they can be done by changing the script directly for each user's use case. Not sure we can find something that is general enough to be in transformers or the examples script.", "Oh yes right..\r\nDo you think that a lazy map feature on the `datasets` side could help to avoid storing padded tokenized texts then ?", "I think I can do the tweak mentioned above with the data collator as short fix (but fully focused on v4 right now so that will be for later this week, beginning of next week :-) ).\r\nIf it doesn't hurt performance to tokenize on the fly, that would clearly be the long-term solution however!", "> Hey guys,\r\n> \r\n> I was trying to create a new bert model from scratch via _huggingface transformers + tokenizers + dataets_ (actually using this example script by your team: https://github.com/huggingface/transformers/blob/master/examples/language-modeling/run_mlm.py). It was supposed to be a first test with a small 5 GB raw text file but I can't even end the preprocessing handled by datasets because this tiny 5 GB text file becomes more than 1 TB when processing. My system was running out of space and crashed prematurely.\r\n> \r\n> I've done training from scratch via Google's bert repo in the past and I can remember that the resulting pretraining data can become quite big. But 5 GB becoming 1 TB was never the case. Is this considered normal or is it a bug?\r\n> \r\n> I've used the following CMD:\r\n> `python xla_spawn.py --num_cores=8 run_mlm.py --model_type bert --config_name config.json --tokenizer_name tokenizer.json --train_file dataset_full.txt --do_train --output_dir out --max_steps 500000 --save_steps 2500 --save_total_limit 2 --prediction_loss_only --line_by_line --max_seq_length 128 --pad_to_max_length --preprocessing_num_workers 16 --per_device_train_batch_size 128 --overwrite_output_dir --debug`\r\n\r\nIt's actually because of the parameter 'preprocessing_num_worker' when using TPU. \r\nI am also planning to have my model trained on the google TPU with a 11gb text corpus. With x8 cores enabled, each TPU core has its own dataset. When not using distributed training, the preprocessed file is about 77gb. On the opposite, if enable xla, the file produced will easily consume all my free space(more than 220gb, I think it will be, in the end, around 600gb ). \r\nSo I think that's maybe where the problem came from. \r\n\r\nIs there any possibility that all of the cores share the same preprocess dataset?\r\n\r\n@sgugger @RammMaschine ", "Hi @NebelAI, we have optimized Datasets' disk usage in the latest release v1.5.\r\n\r\nFeel free to update your Datasets version\r\n```shell\r\npip install -U datasets\r\n```\r\nand see if it better suits your needs." ]

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[ "Added dummy data and encoding to open(). Now everything should be fine, hopefully :)" ]

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[ "@lhoestq I fixed issues except for the dummy_data zip file. But I think I know why is it happening. So when unzipping dummy_data.zip it gets save in /tmp directory where glob doesn't pick it up. For regular downloads, the archive gets unzipped in ~/.cache/huggingface. Could that be a reason?", "Nice thanks :)\r\n\r\nWhen testing with the dummy data, the `download_manager.download_and_extract()` call returns the path to the unzipped dummy_data.zip archive. Therefore glob should be able to find your dummy .epub.txt file", "@lhoestq I understand but for some reason, it is not happening. I added logs to see where dummy_data.zip gets unzipped in /tmp but I suppose when the test process finishes that tmp is gone. I also tried to glob anything in _generate_examples from that directory using /* instead of **/*.epub.txt and nothing is being returned. Always an empty array. ", "Ok weird ! I can take a look tomorrow if you want", "Please do, I will take a fresh look as well. ", "In _generate_examples_ I wrote the following:\r\n```\r\nglob_target = os.path.join(directory, \"**/*.epub.txt\")\r\nprint(f\"Glob target {glob_target }\")\r\n```\r\n\r\nAnd here is the test failure:\r\n\r\n\r\n========================================================================================== FAILURES ===========================================================================================\r\n________________________________________________________________ LocalDatasetTest.test_load_dataset_all_configs_bookcorpusopen ________________________________________________________________\r\n\r\nself = <tests.test_dataset_common.LocalDatasetTest testMethod=test_load_dataset_all_configs_bookcorpusopen>, dataset_name = 'bookcorpusopen'\r\n\r\n @slow\r\n def test_load_dataset_all_configs(self, dataset_name):\r\n configs = self.dataset_tester.load_all_configs(dataset_name, is_local=True)\r\n> self.dataset_tester.check_load_dataset(dataset_name, configs, is_local=True)\r\n\r\ntests/test_dataset_common.py:232: \r\n_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _\r\ntests/test_dataset_common.py:193: in check_load_dataset\r\n self.parent.assertTrue(len(dataset[split]) > 0)\r\nE AssertionError: False is not true\r\n------------------------------------------------------------------------------------ Captured stdout call -------------------------------------------------------------------------------------\r\nDownloading and preparing dataset book_corpus_open/plain_text (download: 1.00 MiB, generated: 1.00 MiB, post-processed: Unknown size, total: 2.00 MiB) to /var/folders/y_/6k6zhblx0k9dsdz5nd_z9x5c0000gp/T/tmpmuu0_ln2/book_corpus_open/plain_text/1.0.0...\r\nGlob target /var/folders/y_/6k6zhblx0k9dsdz5nd_z9x5c0000gp/T/tmpm6tpvb3f/extracted/d953b414cceb4fe3985eeaf68aec2f4435f166b2edf66863d805e3825b7d336b/dummy_data/**/*.epub.txt\r\nDataset book_corpus_open downloaded and prepared to /var/folders/y_/6k6zhblx0k9dsdz5nd_z9x5c0000gp/T/tmpmuu0_ln2/book_corpus_open/plain_text/1.0.0. Subsequent calls will reuse this data.\r\n------------------------------------------------------------------------------------ Captured stderr call -------------------------------------------------------------------------------------\r\n \r\n", "And when I do os.listdir on the given directory I get:\r\n\r\n glob_target = os.path.join(directory, \"**/*.epub.txt\")\r\n print(f\"Glob target {glob_target }\")\r\n> print(os.listdir(path=directory))\r\nE FileNotFoundError: [Errno 2] No such file or directory: '/var/folders/y_/6k6zhblx0k9dsdz5nd_z9x5c0000gp/T/tmpbu_aom5q/extracted/d953b414cceb4fe3985eeaf68aec2f4435f166b2edf66863d805e3825b7d336b/dummy_data'\r\n", "Thanks for the info, I'm looking at it right now", "Ok found the issue !\r\n\r\nThe dummy_data.zip file must be an archive of a folder named dummy_data. Currently the dummy_data.zip is an archive of a folder named book1. In order to have a valid dummy_data.zip file you must first take the dummy book1 folder, place it inside a folder named dummy_data and then compress the dummy_data folder to get dummy_data.zip", "Excellent, I am on it @lhoestq ", "> Awesome thank you so much for adding it :)\r\n\r\nYou're welcome, ok all tests are green now! I needed it asap as well. Thanks for your help @lhoestq .", "I just wanted to say thank you to everyone involved in making this happen! I was certain that I would have to add bookcorpusnew myself, but then @vblagoje came along and did it, and @lhoestq gave some great support in a timely fashion.\r\n\r\nBy the way @vblagoje, are you on Twitter? I'm https://twitter.com/theshawwn if you'd like to DM and say hello. Once again, thanks for doing this!\r\n\r\nI'll mention over at https://github.com/soskek/bookcorpus/issues/27 that this was merged.", "Thank you Shawn. You did all the heavy lifting ;-)", "@vblagoje Would you be interested in adding books3 as well? https://twitter.com/theshawwn/status/1320282149329784833\r\n\r\nHuggingface is interested and asked me to add it, but I had a bit of trouble during setup (https://github.com/huggingface/datasets/issues/790) and never got around to it. At this point you have much more experience than I do with the datasets lib.\r\n\r\nIt *seems* like it might simply be a matter of copy-pasting this PR, changing books1 to books3, and possibly trimming off the leading paths -- each book is at e.g. the-eye/Books/Bibliotok/J/Jurassic Park.epub.txt, which is rather lengthy compared to just the filename -- but the full path is probably fine, so feel free to do the least amount of work that gets the job done. Otherwise I suppose I'll get around to it eventually; thanks again!", "@shawwn I'll take a look as soon as I clear my work queue. TBH, I would likely work on making sure HF datasets has all the datasets used to train https://github.com/alexa/bort/ and these are: Wikipedia, Wiktionary, OpenWebText (Gokaslan and Cohen, 2019), UrbanDictionary, Onel Billion Words (Chelba et al., 2014), the news subset of Common Crawl (Nagel, 2016)10, and Bookcorpus. cc @lhoestq " ]

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[ "Hi,I also posted it to the forum, but this is a bug, perhaps it needs to be reported here? thanks ", "It looks like the official OPUS server for WMT16 doesn't provide the data files anymore (503 error).\r\nI searched a bit and couldn't find a mirror except maybe http://nlp.ffzg.hr/resources/corpora/setimes/ (the data are a cleaned version of the original ones though)\r\nShould we consider replacing the old urls with these ones even though it's not the exact same data ?", "The data storage is down at the moment. Sorry. Hopefully, it will come back soon. Apologies for the inconvenience ...", "Dear great huggingface team, this is not working yet, I really appreciate some temporary fix on this, I need this for my project and this is time sensitive and I will be grateful for your help on this. ", "We have reached out to the OPUS team which is currently working on making the data available again. Cc @jorgtied ", "thank you @thomwolf and HuggingFace team for the help. ", "OPUS is still down - hopefully back tomorrow.", "Hi, this is still down, I would be really grateful if you could ping them one more time. thank you so much. ", "Hi\r\nI am trying with multiple setting of wmt datasets and all failed so far, I need to have at least one dataset working for testing somecodes, and this is really time sensitive, I greatly appreciate letting me know of one translation datasets currently working. thanks ", "It is still down, unfortunately. I'm sorry for that. It should come up again later today or tomorrow at the latest if no additional complications will happen.", "Hi all, \r\nI pulled a request that fix this issue by replacing urls. \r\n\r\nhttps://github.com/huggingface/datasets/pull/1901\r\n\r\nThanks!\r\n", "It's still down for the wmt." ]

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[ "Unfortunately `concatenate_datasets` only supports concatenating the rows, while what you want to achieve is concatenate the columns.\r\nCurrently to add more columns to a dataset, one must use `map`.\r\nWhat you can do is somehting like this:\r\n```python\r\n# suppose you have datasets d1, d2, d3\r\ndef add_columns(example, index):\r\n example.update(d2[index])\r\n example.update(d3[index])\r\n return example\r\n\r\nfull_dataset = d1.map(add_columns, with_indices=True)\r\n```", "Closing this one, feel free to re-open if you have other questions about this issue", "That's not really difficult to add, though, no?\r\nI think it can be done without copy.\r\nMaybe let's add it to the roadmap?", "Actually it's doable but requires to update the `Dataset._data_files` schema to support this.\r\nI'm re-opening this since we may want to add this in the future", "Hi @lhoestq, I would love to help and add this feature if still needed. My plan is to add an axis variable in the `concatenate_datasets` function in `arrow_dataset.py` and when that is set to 1 concatenate columns instead of rows. ", "Hi ! I would love to see this feature implemented as well :) Thank you for proposing your help !\r\n\r\nHere is a few things about the current implementation:\r\n- A dataset object is a wrapper of one `pyarrow.Table` that contains the data\r\n- Pyarrow offers an API that allows to transform Table objects. For example there are functions like `concat_tables`, `Table.rename_columns`, `Table.add_column` etc.\r\n\r\nTherefore adding columns from another dataset is possible thanks to the pyarrow API and in particular `Table.add_column` :) \r\n\r\nHowever this breaks some features we have regarding pickle. A dataset object can be pickled and unpickled without loading all the data in memory. It is useful for multiprocessing for example. Pickling a dataset object is possible thanks to the `Dataset._data_files` which defines the list of arrow files that will be used to form the final Table (basically all the data from each files are concatenated on axis 0).\r\n\r\nTherefore to be able to add columns to a Dataset and still be able to work with it in a multiprocessing setup, we need to extend this last aspect to be able to reconstruct a Table object from multiple arrow files that are combined in both axis 0 and 1. Currently this reconstruction mechanism only supports axis 0.\r\n\r\nI'm sure we can figure something out that enables users to add columns from another dataset while keeping the multiprocessing support.", "@lhoestq, we have two Pull Requests to implement:\r\n- Dataset.add_item: #1870\r\n- Dataset.add_column: #2145\r\nwhich add a single row or column, repectively.\r\n\r\nThe request here is to implement the concatenation of *multiple* rows/columns. Am I right?\r\n\r\nWe should agree on the API:\r\n- `concatenate_datasets` with `axis`?\r\n- other Dataset method name?", "For the API, I like `concatenate_datasets` with `axis` personally :)\r\nFrom a list of `Dataset` objects, it would concatenate them to a new `Dataset` object backed by a `ConcatenationTable`, that is the concatenation of the tables of each input dataset. The concatenation is either on axis=0 (append rows) or on axis=1 (append columns).\r\n\r\nRegarding what we need to implement:\r\nThe axis=0 is already supported and is the current behavior of `concatenate_datasets`.\r\nAlso `add_item` is not needed to implement axis=1 (though it's an awesome addition to this library).\r\n\r\nTo implement axis=1, we either need `add_column` or a `ConcatenationTable` constructor to concatenate tables horizontally.\r\nI have a preference for using a `ConcatenationTable` constructor because this way we can end up with a `ConcatenationTable` with only 1 additional block per table, while `add_column` would add 1 block per new column.\r\n\r\nMaybe we can simply have an equivalent of `ConcatenationTable.from_tables` but for axis=1 ?\r\n`axis` could also be an argument of `ConcatenationTable.from_tables`", "@lhoestq I think I guessed your suggestions in advance... 😉 #2151", "Cool ! Sorry I missed this one ^^\r\nI'm taking a look ;)" ]

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[ "@alexyalunin \r\n\r\nI did something similar for others languages.\r\n\r\n[Repo: rouge-metric](https://github.com/m3hrdadfi/rouge-metric)" ]

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[ "@lhoestq Better?" ]

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[ "Thanks for reporting !\r\nWe plan to show information about the different configs of the datasets on the website, with the corresponding `load_dataset` calls.\r\n\r\nAlso I think the bullet points formatting has been fixed" ]

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[ "As you can see in the error the test checks if `indices_mappings_in_memory` is True or not, which is different from the test you do in your script. In a dataset, both the data and the indices mapping can be either on disk or in memory.\r\n\r\nThe indices mapping correspond to a mapping on top of the data table that is used to re-order/select a sample of the original data table. For example if you do `dataset.train_test_split`, then the resulting train and test datasets will have both an indices mapping to tell which examples are in train and which ones in test.\r\n\r\nBefore saving your datasets on disk, you should call `dataset.flatten_indices()` to remove the indices mapping. It should fix your issue. Under the hood it will create a new data table using the indices mapping. The new data table is going to be a subset of the old one (for example taking only the test set examples), and since the indices mapping will be gone you'll be able to concatenate your datasets.\r\n", "> As you can see in the error the test checks if `indices_mappings_in_memory` is True or not, which is different from the test you do in your script. In a dataset, both the data and the indices mapping can be either on disk or in memory.\r\n> \r\n> The indices mapping correspond to a mapping on top of the data table that is used to re-order/select a sample of the original data table. For example if you do `dataset.train_test_split`, then the resulting train and test datasets will have both an indices mapping to tell which examples are in train and which ones in test.\r\n> \r\n> Before saving your datasets on disk, you should call `dataset.flatten_indices()` to remove the indices mapping. It should fix your issue. Under the hood it will create a new data table using the indices mapping. The new data table is going to be a subset of the old one (for example taking only the test set examples), and since the indices mapping will be gone you'll be able to concatenate your datasets.\r\n\r\n`dataset.flatten_indices()` solved my problem, thanks so much!", "@lhoestq we can add a mention of `dataset.flatten_indices()` in the error message (no rush, just put it on your TODO list or I can do it when I come at it)", "Yup I agree ! And in the docs as well" ]

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[ "It looks like an issue with wandb/tqdm here.\r\nWe're using the `multiprocess` library instead of the `multiprocessing` builtin python package to support various types of mapping functions. Maybe there's some sort of incompatibility.\r\n\r\nCould you make a minimal script to reproduce or a google colab ?", "hi facing the same issue here - \r\n\r\n`AssertionError: Caught AssertionError in DataLoader worker process 0.\r\nOriginal Traceback (most recent call last):\r\n File \"/usr/lib/python3.6/logging/__init__.py\", line 996, in emit\r\n stream.write(msg)\r\n File \"/usr/local/lib/python3.6/dist-packages/wandb/sdk/lib/redirect.py\", line 100, in new_write\r\n cb(name, data)\r\n File \"/usr/local/lib/python3.6/dist-packages/wandb/sdk/wandb_run.py\", line 723, in _console_callback\r\n self._backend.interface.publish_output(name, data)\r\n File \"/usr/local/lib/python3.6/dist-packages/wandb/sdk/interface/interface.py\", line 153, in publish_output\r\n self._publish_output(o)\r\n File \"/usr/local/lib/python3.6/dist-packages/wandb/sdk/interface/interface.py\", line 158, in _publish_output\r\n self._publish(rec)\r\n File \"/usr/local/lib/python3.6/dist-packages/wandb/sdk/interface/interface.py\", line 456, in _publish\r\n if self._process and not self._process.is_alive():\r\n File \"/usr/lib/python3.6/multiprocessing/process.py\", line 134, in is_alive\r\n assert self._parent_pid == os.getpid(), 'can only test a child process'\r\nAssertionError: can only test a child process\r\n\r\nDuring handling of the above exception, another exception occurred:\r\n\r\nTraceback (most recent call last):\r\n File \"/usr/local/lib/python3.6/dist-packages/torch/utils/data/_utils/worker.py\", line 198, in _worker_loop\r\n data = fetcher.fetch(index)\r\n File \"/usr/local/lib/python3.6/dist-packages/torch/utils/data/_utils/fetch.py\", line 44, in fetch\r\n data = [self.dataset[idx] for idx in possibly_batched_index]\r\n File \"/usr/local/lib/python3.6/dist-packages/torch/utils/data/_utils/fetch.py\", line 44, in <listcomp>\r\n data = [self.dataset[idx] for idx in possibly_batched_index]\r\n File \"<ipython-input-8-a4d9a08d114e>\", line 20, in __getitem__\r\n return_token_type_ids=True\r\n File \"/usr/local/lib/python3.6/dist-packages/transformers/tokenization_utils_base.py\", line 2405, in encode_plus\r\n **kwargs,\r\n File \"/usr/local/lib/python3.6/dist-packages/transformers/tokenization_utils_base.py\", line 2125, in _get_padding_truncation_strategies\r\n \"Truncation was not explicitly activated but `max_length` is provided a specific value, \"\r\n File \"/usr/lib/python3.6/logging/__init__.py\", line 1320, in warning\r\n self._log(WARNING, msg, args, **kwargs)\r\n File \"/usr/lib/python3.6/logging/__init__.py\", line 1444, in _log\r\n self.handle(record)\r\n File \"/usr/lib/python3.6/logging/__init__.py\", line 1454, in handle\r\n self.callHandlers(record)\r\n File \"/usr/lib/python3.6/logging/__init__.py\", line 1516, in callHandlers\r\n hdlr.handle(record)\r\n File \"/usr/lib/python3.6/logging/__init__.py\", line 865, in handle\r\n self.emit(record)\r\n File \"/usr/lib/python3.6/logging/__init__.py\", line 1000, in emit\r\n self.handleError(record)\r\n File \"/usr/lib/python3.6/logging/__init__.py\", line 917, in handleError\r\n sys.stderr.write('--- Logging error ---\\n')\r\n File \"/usr/local/lib/python3.6/dist-packages/wandb/sdk/lib/redirect.py\", line 100, in new_write\r\n cb(name, data)\r\n File \"/usr/local/lib/python3.6/dist-packages/wandb/sdk/wandb_run.py\", line 723, in _console_callback\r\n self._backend.interface.publish_output(name, data)\r\n File \"/usr/local/lib/python3.6/dist-packages/wandb/sdk/interface/interface.py\", line 153, in publish_output\r\n self._publish_output(o)\r\n File \"/usr/local/lib/python3.6/dist-packages/wandb/sdk/interface/interface.py\", line 158, in _publish_output\r\n self._publish(rec)\r\n File \"/usr/local/lib/python3.6/dist-packages/wandb/sdk/interface/interface.py\", line 456, in _publish\r\n if self._process and not self._process.is_alive():\r\n File \"/usr/lib/python3.6/multiprocessing/process.py\", line 134, in is_alive\r\n assert self._parent_pid == os.getpid(), 'can only test a child process'\r\nAssertionError: can only test a child process`\r\n", "It looks like this warning : \r\n\"Truncation was not explicitly activated but max_length is provided a specific value, \"\r\nis not handled well by wandb.\r\n\r\nThe error occurs when calling the tokenizer.\r\nMaybe you can try to specify `truncation=True` when calling the tokenizer to remove the warning ?\r\nOtherwise I don't know why wandb would fail on a warning. Maybe one of its logging handlers have some issues with the logging of tokenizers. Maybe @n1t0 knows more about this ?", "I'm having a similar issue but when I try to do multiprocessing with the `DataLoader`\r\n\r\nCode to reproduce:\r\n\r\n```\r\nfrom datasets import load_dataset\r\n\r\nbook_corpus = load_dataset('bookcorpus', 'plain_text', cache_dir='/home/ad/Desktop/bookcorpus', split='train[:1%]')\r\nbook_corpus = book_corpus.map(encode, batched=True, num_proc=20, load_from_cache_file=True, batch_size=5000)\r\nbook_corpus.set_format(type='torch', columns=['text', \"input_ids\", \"attention_mask\", \"token_type_ids\"])\r\n\r\nfrom transformers import DataCollatorForWholeWordMask\r\nfrom transformers import Trainer, TrainingArguments\r\n\r\ndata_collator = DataCollatorForWholeWordMask(\r\n tokenizer=tokenizer, mlm=True, mlm_probability=0.15)\r\n\r\ntraining_args = TrainingArguments(\r\n output_dir=\"./mobile_linear_att_8L_128_128_03layerdrop_shared\",\r\n overwrite_output_dir=True,\r\n num_train_epochs=1,\r\n per_device_train_batch_size=64,\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 gradient_accumulation_steps=1,\r\n fp16=True,\r\n **dataloader_num_workers=10**,\r\n)\r\n\r\ntrainer = Trainer(\r\n model=model,\r\n args=training_args,\r\n data_collator=data_collator,\r\n train_dataset=book_corpus,\r\n tokenizer=tokenizer)\r\n\r\ntrainer.train()\r\n```\r\n\r\n```\r\n---------------------------------------------------------------------------\r\nAssertionError Traceback (most recent call last)\r\n<timed eval> in <module>\r\n\r\n~/anaconda3/envs/tfm/lib/python3.6/site-packages/transformers/trainer.py in train(self, model_path, trial)\r\n 869 self.control = self.callback_handler.on_epoch_begin(self.args, self.state, self.control)\r\n 870 \r\n--> 871 for step, inputs in enumerate(epoch_iterator):\r\n 872 \r\n 873 # Skip past any already trained steps if resuming training\r\n\r\n~/anaconda3/envs/tfm/lib/python3.6/site-packages/torch/utils/data/dataloader.py in __next__(self)\r\n 433 if self._sampler_iter is None:\r\n 434 self._reset()\r\n--> 435 data = self._next_data()\r\n 436 self._num_yielded += 1\r\n 437 if self._dataset_kind == _DatasetKind.Iterable and \\\r\n\r\n~/anaconda3/envs/tfm/lib/python3.6/site-packages/torch/utils/data/dataloader.py in _next_data(self)\r\n 1083 else:\r\n 1084 del self._task_info[idx]\r\n-> 1085 return self._process_data(data)\r\n 1086 \r\n 1087 def _try_put_index(self):\r\n\r\n~/anaconda3/envs/tfm/lib/python3.6/site-packages/torch/utils/data/dataloader.py in _process_data(self, data)\r\n 1109 self._try_put_index()\r\n 1110 if isinstance(data, ExceptionWrapper):\r\n-> 1111 data.reraise()\r\n 1112 return data\r\n 1113 \r\n\r\n~/anaconda3/envs/tfm/lib/python3.6/site-packages/torch/_utils.py in reraise(self)\r\n 426 # have message field\r\n 427 raise self.exc_type(message=msg)\r\n--> 428 raise self.exc_type(msg)\r\n 429 \r\n 430 \r\n\r\nAssertionError: Caught AssertionError in DataLoader worker process 0.\r\nOriginal Traceback (most recent call last):\r\n File \"/home/ad/anaconda3/envs/tfm/lib/python3.6/site-packages/torch/utils/data/_utils/worker.py\", line 198, in _worker_loop\r\n data = fetcher.fetch(index)\r\n File \"/home/ad/anaconda3/envs/tfm/lib/python3.6/site-packages/torch/utils/data/_utils/fetch.py\", line 44, in fetch\r\n data = [self.dataset[idx] for idx in possibly_batched_index]\r\n File \"/home/ad/anaconda3/envs/tfm/lib/python3.6/site-packages/torch/utils/data/_utils/fetch.py\", line 44, in <listcomp>\r\n data = [self.dataset[idx] for idx in possibly_batched_index]\r\n File \"/home/ad/anaconda3/envs/tfm/lib/python3.6/site-packages/datasets/arrow_dataset.py\", line 1087, in __getitem__\r\n format_kwargs=self._format_kwargs,\r\n File \"/home/ad/anaconda3/envs/tfm/lib/python3.6/site-packages/datasets/arrow_dataset.py\", line 1074, in _getitem\r\n format_kwargs=format_kwargs,\r\n File \"/home/ad/anaconda3/envs/tfm/lib/python3.6/site-packages/datasets/arrow_dataset.py\", line 890, in _convert_outputs\r\n v = map_nested(command, v, **map_nested_kwargs)\r\n File \"/home/ad/anaconda3/envs/tfm/lib/python3.6/site-packages/datasets/utils/py_utils.py\", line 225, in map_nested\r\n return function(data_struct)\r\n File \"/home/ad/anaconda3/envs/tfm/lib/python3.6/site-packages/datasets/arrow_dataset.py\", line 851, in command\r\n return torch.tensor(x, **format_kwargs)\r\n File \"/home/ad/anaconda3/envs/tfm/lib/python3.6/warnings.py\", line 101, in _showwarnmsg\r\n _showwarnmsg_impl(msg)\r\n File \"/home/ad/anaconda3/envs/tfm/lib/python3.6/warnings.py\", line 30, in _showwarnmsg_impl\r\n file.write(text)\r\n File \"/home/ad/anaconda3/envs/tfm/lib/python3.6/site-packages/wandb/sdk/lib/redirect.py\", line 100, in new_write\r\n cb(name, data)\r\n File \"/home/ad/anaconda3/envs/tfm/lib/python3.6/site-packages/wandb/sdk/wandb_run.py\", line 723, in _console_callback\r\n self._backend.interface.publish_output(name, data)\r\n File \"/home/ad/anaconda3/envs/tfm/lib/python3.6/site-packages/wandb/sdk/interface/interface.py\", line 153, in publish_output\r\n self._publish_output(o)\r\n File \"/home/ad/anaconda3/envs/tfm/lib/python3.6/site-packages/wandb/sdk/interface/interface.py\", line 158, in _publish_output\r\n self._publish(rec)\r\n File \"/home/ad/anaconda3/envs/tfm/lib/python3.6/site-packages/wandb/sdk/interface/interface.py\", line 456, in _publish\r\n if self._process and not self._process.is_alive():\r\n File \"/home/ad/anaconda3/envs/tfm/lib/python3.6/multiprocessing/process.py\", line 134, in is_alive\r\n assert self._parent_pid == os.getpid(), 'can only test a child process'\r\nAssertionError: can only test a child process\r\n```\r\n\r\nAs a workaround I have commented line 456 and 457 in `/home/ad/anaconda3/envs/tfm/lib/python3.6/site-packages/wandb/sdk/interface/interface.py`", "Isn't it more the pytorch warning on the use of non-writable memory for tensor that trigger this here @lhoestq? (since it seems to be a warning triggered in `torch.tensor()`", "Yep this time this is a warning from pytorch that causes wandb to not work properly.\r\nCould this by a wandb issue ?", "Hi @timothyjlaurent @gaceladri \r\nIf you're running `transformers` from `master` you can try setting the env var `WAND_DISABLE=true` (from https://github.com/huggingface/transformers/pull/9896) and try again ?\r\nThis issue might be related to https://github.com/huggingface/transformers/issues/9623 ", "I have commented the lines that cause my code break. I'm now seeing my reports on Wandb and my code does not break. I am training now, so I will check probably in 6 hours. I suppose that setting wandb disable will work as well." ]

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[ "Hi @yjernite I'm new but wanted to contribute. Has anyone already taken this problem and do you think it is suitable for newbies?", "Hi @tenjjin! This dataset is still up for grabs! Here's the link with the guide to add it. You should play around with the library first (download and look at a few datasets), then follow the steps here:\r\n\r\nhttps://github.com/huggingface/datasets/blob/master/ADD_NEW_DATASET.md", "Closed by #1399 " ]

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[ "Thanks for reporting ! That's a bug indeed\r\nIf you want to contribute, feel free to fix this issue and open a PR :)", "This error is because of a mismatch between `datasets` and `bert_score`. With `datasets=1.1.2` and `bert_score>=0.3.6` it works ok. So `pip install -U bert_score` should fix the problem. ", "Thanks for the heads up @pvl and for the PR as well :)", "Hello everyone,\r\n\r\nI think the problem is not solved: \r\n\r\n```\r\nfrom datasets import load_metric\r\nmetric=load_metric('bertscore')\r\nmetric.compute(\r\n predictions=predictions,\r\n references=references,\r\n lang='fr',\r\n rescale_with_baseline=True\r\n)\r\nTypeError: get_hash() missing 2 required positional arguments: 'use_custom_baseline' and 'use_fast_tokenizer'\r\n```\r\nThis code is produced using `Python 3.6.9 datasets==1.1.2 and bert_score==0.3.10`", "Hi ! This has been fixed by https://github.com/huggingface/datasets/pull/2770, we'll do a new release soon to make the fix available :)\r\n\r\nIn the meantime please use an older version of `bert_score`" ]

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[ "Right now multiprocessing only runs on single node.\r\n\r\nHowever it's probably possible to extend it to support multi nodes. Indeed we're using the `multiprocess` library from the `pathos` project to do multiprocessing in `datasets`, and `pathos` is made to support parallelism on several nodes. More info about pathos [on the pathos repo](https://github.com/uqfoundation/pathos).\r\n\r\nIf you're familiar with pathos or if you want to give it a try, it could be a nice addition to the library :)" ]

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[ "It seems the process needs '/datasets.huggingface.co/datasets/datasets/wikipedia/wikipedia.py'\r\nWhere and how to assign this ```wikipedia.py``` after I manually download it ?", "\r\ndownload the ```wikipedia.py``` at the working directory and go with ```dataset = load_dataset('wikipedia.py', '20200501.en')``` works." ]

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[ "With this change all the checks are passed.", "Good" ]

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[ "Which version of pyarrow do you have ? Could you try to update pyarrow and try again ?", "Thanks for the fast response. I have the latest version '2.0.0' (I tried to update)\r\nI am working with Python 3.8.5", "I think that the issue is similar to this one:https://issues.apache.org/jira/browse/ARROW-9612\r\nThe problem is in arrow when the column data contains long strings.\r\nAny ideas on how to bypass this?", "We should expose the [`block_size` argument](https://arrow.apache.org/docs/python/generated/pyarrow.csv.ReadOptions.html#pyarrow.csv.ReadOptions) of Apache Arrow csv `ReadOptions` in the [script](https://github.com/huggingface/datasets/blob/master/datasets/csv/csv.py).\r\n\r\n\r\nIn the meantime you can specify yourself the `ReadOptions` config like this:\r\n```python\r\nimport pyarrow.csv as pac # PyArrow is installed with `datasets`\r\n\r\nread_options = pac.ReadOptions(block_size=1e9) # try to find the right value for your use-case\r\ndataset = load_dataset('csv', data_files=files, read_options=read_options)\r\n```\r\n", "This did help to load the data. But the problem now is that I get:\r\nArrowInvalid: CSV parse error: Expected 5 columns, got 187\r\n\r\nIt seems that this change the parsing so I changed the table to tab-separated and tried to load it directly from pyarrow\r\nBut I got a similar error, again it loaded fine in pandas so I am not sure what to do.\r\n\r\n\r\n\r\n", "Got almost the same error loading a ~5GB TSV file, first got the same error as OP, then tried giving it my own ReadOptions and also got the same CSV parse error.", "> We should expose the [`block_size` argument](https://arrow.apache.org/docs/python/generated/pyarrow.csv.ReadOptions.html#pyarrow.csv.ReadOptions) of Apache Arrow csv `ReadOptions` in the [script](https://github.com/huggingface/datasets/blob/master/datasets/csv/csv.py).\r\n> \r\n> In the meantime you can specify yourself the `ReadOptions` config like this:\r\n> \r\n> ```python\r\n> import pyarrow.csv as pac # PyArrow is installed with `datasets`\r\n> \r\n> read_options = pac.ReadOptions(block_size=1e9) # try to find the right value for your use-case\r\n> dataset = load_dataset('csv', data_files=files, read_options=read_options)\r\n> ```\r\n\r\nThis did not work for me, I got\r\n`TypeError: __init__() got an unexpected keyword argument 'read_options'`", "Hi ! Yes because of issues with PyArrow's CSV reader we switched to using the Pandas CSV reader. In particular the `read_options` argument is not supported anymore, but you can pass any parameter of Pandas' `read_csv` function (see the list here in [Pandas documentation](https://pandas.pydata.org/docs/reference/api/pandas.read_csv.html))" ]

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[ "Hi @bminixhofer ! Parsing WikiMedia is notoriously difficult: this processing used [mwparserfromhell](https://github.com/earwig/mwparserfromhell) which is pretty good but not perfect.\r\n\r\nAs an alternative, you can also use the Wiki40b dataset which was pre-processed using an un-released Google internal tool", "Ok, thanks! I'll try the Wiki40b dataset.", "If anyone else is concerned about this, `wiki40b` does indeed seem very well cleaned." ]

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[ "Hey @yjernite. This is a very interesting dataset. Would love to work on adding it but I see that the link to the data is to a gdrive folder. Can I just confirm wether dlmanager can handle gdrive urls or would this have to be a manual dl?", "Hi @KMFODA ! A version of WikiLingua is actually already accessible in the [GEM dataset](https://huggingface.co/datasets/gem)\r\n\r\nYou can use it for example to load the French to English translation with:\r\n```python\r\nfrom datasets import load_dataset\r\nwikilingua = load_dataset(\"gem\", \"wiki_lingua_french_fr\")\r\n```\r\n\r\nClosed by https://github.com/huggingface/datasets/pull/1807" ]

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[ "closed via #1098 " ]

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[ "Hi @yjernite can I help in adding this dataset? \r\n\r\nI am excited about this because this will be my first contribution to the datasets library as well as to hugginface." ]

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[ "No comments ?", "I think it would be very cool. I'm currently working on a cluster from Compute Canada, and I have internet access only when I'm not in the nodes where I run the scripts. So I was expecting to be able to use the wmt14 dataset until I realized I needed internet connection even if I downloaded the data already. I'm going to try option 2 you mention for now though! Thanks ;)", "Requiring online connection is a deal breaker in some cases unfortunately so it'd be great if offline mode is added similar to how `transformers` loads models offline fine.\r\n\r\n@mandubian's second bullet point suggests that there's a workaround allowing you to use your offline (custom?) dataset with `datasets`. Could you please elaborate on how that should look like?", "here is my way to load a dataset offline, but it **requires** an online machine\r\n1. (online machine)\r\n```\r\nimport datasets\r\ndata = datasets.load_dataset(...)\r\ndata.save_to_disk(/YOUR/DATASET/DIR)\r\n```\r\n2. copy the dir from online to the offline machine\r\n3. (offline machine)\r\n```\r\nimport datasets\r\ndata = datasets.load_from_disk(/SAVED/DATA/DIR)\r\n```\r\n\r\nHTH.", "> here is my way to load a dataset offline, but it **requires** an online machine\n> \n> 1. (online machine)\n> \n> ```\n> \n> import datasets\n> \n> data = datasets.load_dataset(...)\n> \n> data.save_to_disk(/YOUR/DATASET/DIR)\n> \n> ```\n> \n> 2. copy the dir from online to the offline machine\n> \n> 3. (offline machine)\n> \n> ```\n> \n> import datasets\n> \n> data = datasets.load_from_disk(/SAVED/DATA/DIR)\n> \n> ```\n> \n> \n> \n> HTH.\n\n", "I opened a PR that allows to reload modules that have already been loaded once even if there's no internet.\r\n\r\nLet me know if you know other ways that can make the offline mode experience better. I'd be happy to add them :) \r\n\r\nI already note the \"freeze\" modules option, to prevent local modules updates. It would be a cool feature.\r\n\r\n----------\r\n\r\n> @mandubian's second bullet point suggests that there's a workaround allowing you to use your offline (custom?) dataset with `datasets`. Could you please elaborate on how that should look like?\r\n\r\nIndeed `load_dataset` allows to load remote dataset script (squad, glue, etc.) but also you own local ones.\r\nFor example if you have a dataset script at `./my_dataset/my_dataset.py` then you can do\r\n```python\r\nload_dataset(\"./my_dataset\")\r\n```\r\nand the dataset script will generate your dataset once and for all.\r\n\r\n----------\r\n\r\nAbout I'm looking into having `csv`, `json`, `text`, `pandas` dataset builders already included in the `datasets` package, so that they are available offline by default, as opposed to the other datasets that require the script to be downloaded.\r\ncf #1724 ", "The local dataset builders (csv, text , json and pandas) are now part of the `datasets` package since #1726 :)\r\nYou can now use them offline\r\n```python\r\ndatasets = load_dataset('text', data_files=data_files)\r\n```\r\n\r\nWe'll do a new release soon", "Already fixed by:\r\n- #1726" ]

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[ "Hi I don’t think this is a request for a dataset like you labeled it.\r\n\r\nI also think this would be better suited for the forum at https://discuss.huggingface.co. we try to keep the issue for the repo for bug reports and new features/dataset requests and have usage questions discussed on the forum. Thanks.", "Hi Thomas,\nwhat I do not get from documentation is that why when you set batched=True,\nthis is processed in batch, while data is not divided to batched\nbeforehand, basically this is a question on the documentation and I do not\nget the batched=True, but sure, if you think this is more appropriate in\nforum I will post it there.\nthanks\nBest\nRabeeh\n\nOn Tue, Nov 10, 2020 at 12:21 PM Thomas Wolf <notifications@github.com>\nwrote:\n\n> Hi I don’t think this is a request for a dataset like you labeled it.\n>\n> I also think this would be better suited for the forum at\n> https://discuss.huggingface.co. we try to keep the issue for the repo for\n> bug reports and new features/dataset requests and have usage questions\n> discussed on the forum. Thanks.\n>\n> —\n> You are receiving this because you authored the thread.\n> Reply to this email directly, view it on GitHub\n> <https://github.com/huggingface/datasets/issues/823#issuecomment-724639476>,\n> or unsubscribe\n> <https://github.com/notifications/unsubscribe-auth/ARPXHH4FIPFHVVUHANAE4F3SPEO2JANCNFSM4TQQVEXQ>\n> .\n>\n", "Yes the forum is perfect for that. You can post in the `datasets` section.\r\nThanks a lot!" ]

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[ "Pytorch is unable to convert strings to tensors unfortunately.\r\nYou can use `set_format(type=\"torch\")` on columns that can be converted to tensors, such as token ids.\r\n\r\nThis makes me think that we should probably raise an error or at least a warning when one tries to create pytorch tensors out of text columns" ]

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[ "Sorry, asking for help here, but the dill thread stop around 2013. Is it possible to use dill deterministically? I tried to monkeypatch the solution presented here into dill, but I suppose it requires forking their project.", "Hi ! What we did was to subclass `dill`'s Pickler to fix the non-deterministic behaviors, and it's been working fine. A fork should also do the job" ]

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[ "Done! cf #1117 and #1022" ]

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[ "To show the issue:\r\n```\r\npython -c \"from datasets.fingerprint import Hasher; a=[]; func = lambda : len(a); print(Hasher.hash(func))\"\r\n```\r\ndoesn't always return the same ouput since `globs` is a dictionary with \"a\" and \"len\" as keys but sometimes not in the same order" ]

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[ "Hello !\r\nCould you give more details ?\r\n\r\nIf you mean iter through one dataset then yes, `Dataset` object does implement the `__iter__` method so you can use \r\n```python\r\nfor example in dataset:\r\n # do something\r\n```\r\n\r\nIf you want to iter through several datasets you can first concatenate them\r\n```python\r\nfrom datasets import concatenate_datasets\r\n\r\nnew_dataset = concatenate_datasets([dataset1, dataset2])\r\n```\r\nLet me know if this helps !", "Hi Huggingface/Datasets team,\nI want to use the datasets inside Seq2SeqDataset here\nhttps://github.com/huggingface/transformers/blob/master/examples/seq2seq/utils.py\nand there I need to return back each line from the datasets and I am not\nsure how to access each line and implement this?\nIt seems it also has get_item attribute? so I was not sure if this is\niterative dataset? or if this is non-iterable datasets?\nthanks.\n\n\n\nOn Mon, Nov 9, 2020 at 10:18 AM Quentin Lhoest <notifications@github.com>\nwrote:\n\n> Hello !\n> Could you give more details ?\n>\n> If you mean iter through one dataset then yes, Dataset object does\n> implement the __iter__ method so you can use\n>\n> for example in dataset:\n> # do something\n>\n> If you want to iter through several datasets you can first concatenate them\n>\n> from datasets import concatenate_datasets\n> new_dataset = concatenate_datasets([dataset1, dataset2])\n>\n> Let me know if this helps !\n>\n> —\n> You are receiving this because you authored the thread.\n> Reply to this email directly, view it on GitHub\n> <https://github.com/huggingface/datasets/issues/815#issuecomment-723881199>,\n> or unsubscribe\n> <https://github.com/notifications/unsubscribe-auth/ARPXHHYRLSSYW6NZN2HYDBTSO6XV5ANCNFSM4TPB7OWA>\n> .\n>\n", "could you tell me please if datasets also has __getitem__ any idea on how\nto integrate it with Seq2SeqDataset is appreciated thanks\n\nOn Mon, Nov 9, 2020 at 10:22 AM Rabeeh Karimi Mahabadi <rabeeh@google.com>\nwrote:\n\n> Hi Huggingface/Datasets team,\n> I want to use the datasets inside Seq2SeqDataset here\n> https://github.com/huggingface/transformers/blob/master/examples/seq2seq/utils.py\n> and there I need to return back each line from the datasets and I am not\n> sure how to access each line and implement this?\n> It seems it also has get_item attribute? so I was not sure if this is\n> iterative dataset? or if this is non-iterable datasets?\n> thanks.\n>\n>\n>\n> On Mon, Nov 9, 2020 at 10:18 AM Quentin Lhoest <notifications@github.com>\n> wrote:\n>\n>> Hello !\n>> Could you give more details ?\n>>\n>> If you mean iter through one dataset then yes, Dataset object does\n>> implement the __iter__ method so you can use\n>>\n>> for example in dataset:\n>> # do something\n>>\n>> If you want to iter through several datasets you can first concatenate\n>> them\n>>\n>> from datasets import concatenate_datasets\n>> new_dataset = concatenate_datasets([dataset1, dataset2])\n>>\n>> Let me know if this helps !\n>>\n>> —\n>> You are receiving this because you authored the thread.\n>> Reply to this email directly, view it on GitHub\n>> <https://github.com/huggingface/datasets/issues/815#issuecomment-723881199>,\n>> or unsubscribe\n>> <https://github.com/notifications/unsubscribe-auth/ARPXHHYRLSSYW6NZN2HYDBTSO6XV5ANCNFSM4TPB7OWA>\n>> .\n>>\n>\n", "`datasets.Dataset` objects implement indeed `__getitem__`. It returns a dictionary with one field per column.\r\n\r\nWe've not added the integration of the datasets library for the seq2seq utilities yet. The current seq2seq utilities are based on text files.\r\n\r\nHowever as soon as you have a `datasets.Dataset` with columns \"tgt_texts\" (str), \"src_texts\" (str), and \"id\" (int) you should be able to implement your own Seq2SeqDataset class that wraps your dataset object. Does that make sense to you ?", "Hi\nI am sorry for asking it multiple times but I am not getting the dataloader\ntype, could you confirm if the dataset library returns back an iterable\ntype dataloader or a mapping type one where one has access to __getitem__,\nin the former case, one can iterate with __iter__, and how I can configure\nit to return the data back as the iterative type? I am dealing with\nlarge-scale datasets and I do not want to bring all in memory\nthanks for your help\nBest regards\nRabeeh\n\nOn Mon, Nov 9, 2020 at 11:17 AM Quentin Lhoest <notifications@github.com>\nwrote:\n\n> datasets.Dataset objects implement indeed __getitem__. It returns a\n> dictionary with one field per column.\n>\n> We've not added the integration of the datasets library for the seq2seq\n> utilities yet. The current seq2seq utilities are based on text files.\n>\n> However as soon as you have a datasets.Dataset with columns \"tgt_texts\"\n> (str), \"src_texts\" (str), and \"id\" (int) you should be able to implement\n> your own Seq2SeqDataset class that wraps your dataset object. Does that\n> make sense ?\n>\n> —\n> You are receiving this because you authored the thread.\n> Reply to this email directly, view it on GitHub\n> <https://github.com/huggingface/datasets/issues/815#issuecomment-723915556>,\n> or unsubscribe\n> <https://github.com/notifications/unsubscribe-auth/ARPXHHYOC22EM7F666BZSOTSO66R3ANCNFSM4TPB7OWA>\n> .\n>\n", "`datasets.Dataset` objects are both iterative and mapping types: it has both `__iter__` and `__getitem__`\r\nFor example you can do\r\n```python\r\nfor example in dataset:\r\n # do something\r\n```\r\nor\r\n```python\r\nfor i in range(len(dataset)):\r\n example = dataset[i]\r\n # do something\r\n```\r\nWhen you do that, one and only one example is loaded into memory at a time.", "Hi there, \r\nHere is what I am trying, this is not working for me in map-style datasets, could you please tell me how to use datasets with being able to access ___getitem__ ? could you assist me please correcting this example? I need map-style datasets which is formed from concatenation of two datasets from your library. thanks \r\n\r\n\r\n```\r\nimport datasets\r\ndataset1 = load_dataset(\"squad\", split=\"train[:10]\")\r\ndataset1 = dataset1.map(lambda example: {\"src_texts\": \"question: {0} context: {1} \".format(\r\n example[\"question\"], example[\"context\"]),\r\n \"tgt_texts\": example[\"answers\"][\"text\"][0]}, remove_columns=dataset1.column_names)\r\ndataset2 = load_dataset(\"imdb\", split=\"train[:10]\")\r\ndataset2 = dataset2.map(lambda example: {\"src_texts\": \"imdb: \" + example[\"text\"],\r\n \"tgt_texts\": str(example[\"label\"])}, remove_columns=dataset2.column_names)\r\ntrain_dataset = datasets.concatenate_datasets([dataset1, dataset2])\r\ntrain_dataset.set_format(type='torch', columns=['src_texts', 'tgt_texts'])\r\ndataloader = torch.utils.data.DataLoader(train_dataset, batch_size=32)\r\nfor id, batch in enumerate(dataloader):\r\n print(batch)\r\n\r\n```", "closed since I found this response on the issue https://github.com/huggingface/datasets/issues/469" ]

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[ "found a solution here https://discuss.pytorch.org/t/train-simultaneously-on-two-datasets/649/35, closed for now, thanks " ]

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[ "Hi Apparently I need to shard the data and give one host a chunk, could you provide me please with examples on how to do it? I want to use it jointly with finetune_trainer.py in huggingface repo seq2seq examples. thanks. ", "Hey @rabeehkarimimahabadi I'm actually looking for the same feature. Did you manage to get somewhere?", "@rabeehkarimimahabadi need the same feature" ]

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[ "Hi ! Thanks for reporting :) \r\nI agree these one should be hidden when the logging level is warning, we'll fix that", "+1, the amount of logging is excessive.\r\n\r\nMost of it indeed comes from `filelock.py`, though there are occasionally messages from other sources too. Below is an example (all of these messages were logged after I already called `datasets.logging.set_verbosity_error()`)\r\n\r\n```\r\nI1109 21:26:01.742688 139785006901056 filelock.py:318] Lock 139778216292192 released on /home/kitaev/.cache/huggingface/datasets/9ed4f2e133395826175a892c70611f68522c7bc61a35476e8b51a31afb76e4bf.e6f3e3f3e3875a07469d1cfd32e16e1d06b149616b11eef2d081c43d515b492d.py.lock\r\nI1109 21:26:01.747898 139785006901056 filelock.py:274] Lock 139778216290176 acquired on /home/kitaev/.cache/huggingface/datasets/_home_kitaev_.cache_huggingface_datasets_glue_mnli_1.0.0_7c99657241149a24692c402a5c3f34d4c9f1df5ac2e4c3759fadea38f6cb29c4.lock\r\nI1109 21:26:01.748258 139785006901056 filelock.py:318] Lock 139778216290176 released on /home/kitaev/.cache/huggingface/datasets/_home_kitaev_.cache_huggingface_datasets_glue_mnli_1.0.0_7c99657241149a24692c402a5c3f34d4c9f1df5ac2e4c3759fadea38f6cb29c4.lock\r\nI1109 21:26:01.748412 139785006901056 filelock.py:274] Lock 139778215853024 acquired on /home/kitaev/.cache/huggingface/datasets/_home_kitaev_.cache_huggingface_datasets_glue_mnli_1.0.0_7c99657241149a24692c402a5c3f34d4c9f1df5ac2e4c3759fadea38f6cb29c4.lock\r\nI1109 21:26:01.748497 139785006901056 filelock.py:318] Lock 139778215853024 released on /home/kitaev/.cache/huggingface/datasets/_home_kitaev_.cache_huggingface_datasets_glue_mnli_1.0.0_7c99657241149a24692c402a5c3f34d4c9f1df5ac2e4c3759fadea38f6cb29c4.lock\r\nI1109 21:07:17.029001 140301730502464 filelock.py:274] Lock 140289479304360 acquired on /home/kitaev/.cache/huggingface/datasets/b16d3a04bf2cad1346896852bf120ba846ea1bebb1cd60255bb3a1a2bbcc3a67.ec871b06a00118091ec63eff0a641fddcb8d3c7cd52e855bbb2be28944df4b82.py.lock\r\nI1109 21:07:17.029341 140301730502464 filelock.py:318] Lock 140289479304360 released on /home/kitaev/.cache/huggingface/datasets/b16d3a04bf2cad1346896852bf120ba846ea1bebb1cd60255bb3a1a2bbcc3a67.ec871b06a00118091ec63eff0a641fddcb8d3c7cd52e855bbb2be28944df4b82.py.lock\r\nI1109 21:07:17.058964 140301730502464 filelock.py:274] Lock 140251889388120 acquired on /home/kitaev/.cache/huggingface/metrics/glue/mnli/default_experiment-1-0.arrow.lock\r\nI1109 21:07:17.060933 140301730502464 filelock.py:318] Lock 140251889388120 released on /home/kitaev/.cache/huggingface/metrics/glue/mnli/default_experiment-1-0.arrow.lock\r\nI1109 21:07:17.061067 140301730502464 filelock.py:274] Lock 140296072521488 acquired on /home/kitaev/.cache/huggingface/metrics/glue/mnli/default_experiment-1-0.arrow.lock\r\nI1109 21:07:17.069736 140301730502464 metric.py:400] Removing /home/kitaev/.cache/huggingface/metrics/glue/mnli/default_experiment-1-0.arrow\r\nI1109 21:07:17.069949 140301730502464 filelock.py:318] Lock 140296072521488 released on /home/kitaev/.cache/huggingface/metrics/glue/mnli/default_experiment-1-0.arrow.lock\r\n```", "So how to solve this problem?", "In the latest version of the lib the logs about locks are at the DEBUG level so you won't see them by default.\r\nAlso `set_verbosity_warning` does take into account these logs now.\r\nCan you try to update the lib ?\r\n```\r\npip install --upgrade datasets\r\n```", "Thanks. For some reason I have to use the older version. Is that possible I can fix this by some surface-level trick?\r\n\r\nI'm still using 1.13 version datasets.", "On older versions you can use\r\n```python\r\nimport logging\r\n\r\nlogging.getLogger(\"filelock\").setLevel(logging.WARNING)\r\n```", "Whoa Thank you! It works!" ]