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[ "I noticed that datasets like `cnn_dailymail` need the `version` parameter to be passed to its `config_kwargs`.\r\nShall we rename the `version` paramater in `load_dataset` ? Maybe `repo_version` or `script_version` ?" ]

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

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[ "thanks :)" ]

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

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[ "All the tests pass on my side. Not sure if it is a cache issue or a pytest issue or a circleci issue.\r\nEDIT: I have the same error on google colab. Trying to fix that", "I think I fixed it (sorry didn't notice you were on it as well)" ]

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[ "Thanks, @lhoestq! I've addressed the comments. \r\nAlso, I have tried to use `ClassLabel` [when possible](https://github.com/huggingface/nlp/pull/572/files#diff-1026ac7d7b78bf029cb0ebe63162c77dR297). Is there still somewhere else we can use `ClassLabel`? ", "I believe CI failure is unrelated.", "Great job! " ]

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[ "I've added save/load for dataset dicts.\r\n\r\nI agree that in the future we should also have a way to save indexes too, and also the in-place history of transforms.\r\n\r\nAlso I understand that it would be cool to have the load function directly at the root of the library, but I'm not sure this should be inside `load_dataset` that loads dataset scripts and data from the dataset repository. Maybe something like `load_from_disk` ?", "Yes `load_from_disk` and `save_to_disk` could work as well.", "I renamed save/load to save_to_dick/load_from_disk, and I added `nlp.load_from_disk`\r\n\r\n`nlp.load_from_disk` can load either a Dataset or a DatasetDict.", "Awesome! Let's add them to the doc and we're good to go!" ]

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

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

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[ "I agree this fix the problem for the CI where the files are always created in a new and clean temporary directory.\r\n\r\nHowever, in a general setting of a succession of fast distributed operation, the files could already exist from previous metrics runs but one process may still finish before another has even started in which case it would mix results from separate operations.\r\n\r\nI feel like the most robust way to solve this is to setup a rendez-vous on the first time we write on files and where each process will test and only finish its operation when it cannot acquire a lock on all the other processes (meaning they all have started).\r\n\r\nWhat do you think?", "What do you think of this @thomwolf ? I check all the locks before finalizing", "Ok on my side @lhoestq (cannot add you as a reviewer)", "The test doesn't pass if I add:\r\n```python\r\n import time\r\n if self.process_id == 1:\r\n time.sleep(0.5)\r\n```\r\nright before `self.add_batch` in `Metric.compute`.\r\n\r\nI'm investigating why it doesn't work in that case", "It looks like the process 1 runs `_check_all_processes_locks` correctly and then finishes and releases its lock before process 0 even managed to to run `_check_all_processes_locks` correctly.", "Strange!", "I changed the way the rendez-vous is done @thomwolf , let me know what you think.\r\nThe idea is that the master process has an additional lock `rendez_vous_lock` to tell every other process to wait for everyone to be ready before starting to write" ]

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[ "Looks all good :)\r\nI rebased from master and added a test for parallel `map_nested`", "you're da best" ]

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[ "Closing this one in favor of #584 " ]

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[ "Feel free to merge when you are happy with it @yjernite :-)" ]

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

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[ "Logging looks like\r\n\r\n```\r\nDone writing 21900 indices in 3854400 bytes .\r\nProcess #0 will write at playground/tmp_00000_of_00004.arrow\r\nDone writing 21900 indices in 3854400 bytes .\r\nProcess #1 will write at playground/tmp_00001_of_00004.arrow\r\nDone writing 21900 indices in 3854400 bytes .\r\nProcess #2 will write at playground/tmp_00002_of_00004.arrow\r\nDone writing 21899 indices in 3854224 bytes .\r\nProcess #3 will write at playground/tmp_00003_of_00004.arrow\r\nSpawning 4 processes\r\n#3: 100%|████████████████████████████████████████████████| 21899/21899 [00:02<00:00, 8027.41ex/s]\r\n#0: 100%|████████████████████████████████████████████████| 21900/21900 [00:02<00:00, 7982.87ex/s]\r\n#1: 100%|████████████████████████████████████████████████| 21900/21900 [00:02<00:00, 7923.89ex/s]\r\n#2: 100%|████████████████████████████████████████████████| 21900/21900 [00:02<00:00, 7920.04ex/s]\r\nConcatenating 4 shards from multiprocessing\r\n```", "I added tests and improved logging.\r\nBoth `map` and `filter` support multiprocessing", "A bit strange that the benchmarks on map/filter are worth than `master`.\r\n(maybe because they are not done on the same machine)", "The benchmark also got worse in other PRs (see [here](https://github.com/huggingface/nlp/pull/550#commitcomment-41931609) for example, where we have 16sec for `map fast-tokenizer batched` and 18 sec for `map identity`)", "Hi,\r\n\r\nwhen I use the multiprocessing in ```.map```:\r\n```\r\ndataset = load_dataset(\"text\", data_files=file_path, split=\"train\")\r\ndataset = dataset.map(lambda ex: tokenizer(ex[\"text\"], add_special_tokens=True,\r\n truncation=True, max_length=args.block_size), batched=True, num_proc=16)\r\ndataset.set_format(type='torch', columns=['input_ids'])\r\n```\r\nI get the following error:\r\n```\r\nTraceback (most recent call last):\r\n File \"src/run.py\", line 373, in <module>\r\n main()\r\n File \"src/run.py\", line 295, in main\r\n get_dataset(data_args, tokenizer=tokenizer, cache_dir=model_args.cache_dir) if training_args.do_train else None\r\n File \"src/run.py\", line 153, in get_dataset\r\n dataset = dataset.map(lambda ex: tokenizer(ex[\"text\"], add_special_tokens=True,\r\n File \"/root/miniconda3/envs/py3.8/lib/python3.8/site-packages/datasets/arrow_dataset.py\", line 1287, in map\r\n transformed_shards = [r.get() for r in results]\r\n File \"/root/miniconda3/envs/py3.8/lib/python3.8/site-packages/datasets/arrow_dataset.py\", line 1287, in <listcomp>\r\n transformed_shards = [r.get() for r in results]\r\n File \"/root/miniconda3/envs/py3.8/lib/python3.8/multiprocessing/pool.py\", line 771, in get\r\n raise self._value\r\n put(task)\r\n File \"/root/miniconda3/envs/py3.8/lib/python3.8/multiprocessing/connection.py\", line 206, in send\r\n self._send_bytes(_ForkingPickler.dumps(obj))\r\n File \"/root/miniconda3/envs/py3.8/lib/python3.8/multiprocessing/reduction.py\", line 51, in dumps\r\n cls(buf, protocol).dump(obj)\r\nAttributeError: Can't pickle local object 'get_dataset.<locals>.<lambda>'\r\n```\r\nI think you should use [pathos](https://github.com/uqfoundation/pathos) to pickle the lambda function and some others!\r\nI change the 30 line of src/datasets/arrow_dataset.py as following:\r\n```\r\n# 30 line: from multiprocessing import Pool, RLock\r\nimport pathos\r\nfrom pathos.multiprocessing import Pool\r\nfrom multiprocessing import RLock\r\n```\r\nand it works!", "That's very cool indeed !\r\nShall we condiser adding this dependency @thomwolf ?", "We already use `dill` so that's definitely a very interesting option indeed!", "it gets stuck on debian 9 when num_proc > 1\r\n", "Are you using a tokenizer ?\r\nDid you try to set `TOKENIZERS_PARALLELISM=false` ?\r\n\r\nFeel free to discuss it in #620 , we're discussing this issue", "I set `TOKENIZERS_PARALLELISM=false`. Just the warning went away. The program was still stuck\r\n" ]

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[ "Thanks a lot for that!\r\nThe line you are mentioning is a bug indeed, do you mind fixing it at the same time?", "No worries! \r\n\r\nI pushed right away the fix, but then I realized that the master branch already had it, so I ended up merging the master branch with lince locally and then overwriting the previous commit in origin/lince. Hopefully, this is not too messy :)\r\n" ]

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[ "That’s a good point that we started to discuss internally as well. We should pin the dataset en metrics code by default indeed.\r\nLet’s update this in the coming release.", "Ok closed this with #567 and we are working on a more general solution to pin dataset version in #562 (should be in the coming release)." ]

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[ "Awesome !\r\nAlso I was wondering if we should try to make the hashing of the `data_files` faster (it is used to build the cache directory of datasets like `text` or `json`). Right now it reads each file and hashes all of its data. We could simply hash the path and some metadata including the `time last modified` tag no ? Apparently we can get this tag with `os.path.getmtime(path)`", "I just rebased from master to include the hashing changes from #573 ", "I think this is ready to merge, no?", "Indeed it's ready to merge :)", "Ok added the breaking change info and we can merge indeed.\r\n" ]

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

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

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[ "I'll take a look!" ]

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[ "I'm not sure this would fix #537 .\r\nHowever your point about the missing `middle` data is right and we probably want to include these data as well.\r\nDo you think it would we worth having different configurations for this dataset for users who want to only load part of it (`high school` or `middle` or `all`) ?", "This has fixed #537 at least on my machine hahaha.\r\n\r\nNice point! I think it would totally worth it :) What the best implementation approach would you suggest?\r\n\r\nWould it be possible to have `high school`, `middle` and `all` inside each portion of `train`, `validation` and `test`? Would this make sense?", "I think we could have one dataset configuration for `high school`, one for `middle` and one for `all`.\r\nYou just need to add\r\n```python\r\n BUILDER_CONFIGS = [\r\n nlp.BuilderConfig(\r\n name=\"high school\",\r\n description=\"insert description here\",\r\n ),\r\n nlp.BuilderConfig(\r\n name=\"middle\",\r\n description=\"insert description here\",\r\n ),\r\n nlp.BuilderConfig(\r\n name=\"all\",\r\n description=\"insert description here\",\r\n ),\r\n ]\r\n```\r\nas a class attribute for the `Race` class.\r\n\r\nThen in `generate_examples` you can check the value of `self.config.name` and choose which files to include when generating examples.\r\n\r\nYou can check [mlsum](https://github.com/huggingface/nlp/blob/master/datasets/mlsum/mlsum.py) for example if you want to see how it done in general, it's a dataset that has five configurations, and each config has train/val/test splits.", "Hi @lhoestq sorry for the delay in addressing your comments. Thanks for your assistance :)\r\n\r\nYou were correct as well, as I was using the script without the `datasets/race/dataset_infos.json` file, it did not verify the checksum. I already fix it as well :)\r\n\r\nI managed to get everything running smoothly by now. Please let me know if you think that I could improve my solution" ]

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

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

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[ "I changed the way I implemented fingerprint updates to use decorator functions.\r\n\r\nI also added a new attribute called `_inplace_history` that stores the in-place history of transforms (like cast_, rename_columns, etc.). This history is useful to replay the changes that were done in-place when unpickling a dataset that is memory mapped from a file.\r\n\r\nLet me know what you think @thomwolf " ]

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

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[ "Yes I agree. Maybe something that lets specify different format depending on the column ? Especially to better control dtype and shape (and ragged for tf)\r\n\r\nOh and I forgot: this one should also fix the second issue found in #477 for the next release", "I am running into the same issue with the error message on my local windows machine -\r\nAttributeError: 'tensorflow.python.framework.ops.EagerTensor' object has no attribute 'to_tensor'. Tensorflow version is 2.6. Anything that I can do to fix it?\r\ntrain_features = {x: tf_train_dataset[x].to_tensor() for x in tokenizer.model_input_names}\r\ntrain_tf_dataset = tf.data.Dataset.from_tensor_slices((train_features, tf_train_dataset[\"label\"]))\r\ntrain_tf_dataset = train_tf_dataset.shuffle(len(tf_train_dataset)).batch(8)\r\n\r\neval_features = {x: tf_eval_dataset[x].to_tensor() for x in tokenizer.model_input_names}\r\neval_tf_dataset = tf.data.Dataset.from_tensor_slices((eval_features, tf_eval_dataset[\"label\"]))\r\neval_tf_dataset = eval_tf_dataset.batch(8)\r\n\r\nttributeError Traceback (most recent call last)\r\n<ipython-input-59-f50e45c2c0dc> in <module>\r\n----> 1 train_features = {x: tf_train_dataset[x].convert_to_tensor() for x in tokenizer.model_input_names}\r\n 2 train_tf_dataset = tf.data.Dataset.from_tensor_slices((train_features, tf_train_dataset[\"label\"]))\r\n 3 train_tf_dataset = train_tf_dataset.shuffle(len(tf_train_dataset)).batch(8)\r\n 4 \r\n 5 eval_features = {x: tf_eval_dataset[x].to_tensor() for x in tokenizer.model_input_names}\r\n\r\n<ipython-input-59-f50e45c2c0dc> in <dictcomp>(.0)\r\n----> 1 train_features = {x: tf_train_dataset[x].convert_to_tensor() for x in tokenizer.model_input_names}\r\n 2 train_tf_dataset = tf.data.Dataset.from_tensor_slices((train_features, tf_train_dataset[\"label\"]))\r\n 3 train_tf_dataset = train_tf_dataset.shuffle(len(tf_train_dataset)).batch(8)\r\n 4 \r\n 5 eval_features = {x: tf_eval_dataset[x].to_tensor() for x in tokenizer.model_input_names}\r\n\r\n~\\AppData\\Roaming\\Python\\Python38\\site-packages\\tensorflow\\python\\framework\\ops.py in __getattr__(self, name)\r\n 399 from tensorflow.python.ops.numpy_ops import np_config\r\n 400 np_config.enable_numpy_behavior()\"\"\".format(type(self).__name__, name))\r\n--> 401 self.__getattribute__(name)\r\n 402 \r\n 403 @staticmethod\r\n\r\nAttributeError: 'tensorflow.python.framework.ops.EagerTensor' object has no attribute 'convert_to_tensor'\r\n\r\n", "Hi ! Before calling `to_tensor`, make sure that your object is a RaggedTensor, because it may already be a regular Tensor if the shapes of your examples are all the same", "Okay. i am not familiar with how to check the difference between the two. I will research on this." ]

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[ "Could you test to run the test using the changes in #527 and let me know if it fixes the issue ? If so I'll merge it and we'll be good to go :)", "Hello, it does work on the fixing real dataset branch. Merci Quentin :)", "Nice, glad to hear that :)\r\nde rien !" ]

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[ "The problem came from `default: ` that is rendered differently and hides the parameter names. I changed `default: ...` to `defaults to ...`" ]

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[ "We have to change the tests to expect `None` instead of `python` then", "Merging!" ]

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[ "Indeed, good catch!" ]

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[ "I took your comments into account and added tests for `cast_to_python_objects`" ]

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[ "Thanks!" ]

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[ "Hahah thanks Yonatan. It was not on purpose, we are just not very good at spelling :)" ]

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[ "I think I agree @lhoestq so I pushed a change.\r\nThanks for your work on the library!" ]

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[ "Update - maybe this is only failing on bleu because I was not tokenizing inputs to the metric", "Closing - seems to be just forgetting to tokenize. And found the helpful discussion in #137 " ]

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[ "(test failure is unrelated)", "As discussed with @thomwolf merging since the hyperparameter-search has been merged in transformers." ]

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[ "Ok I fixed `concatenate_datasets` and added tests\r\nFeel free to merge if it's good for you @thomwolf ", "Ok, adding some benchmarks for map/filters and then I'll merge", "Warning from pytorch that we should maybe consider at some point @lhoestq:\r\n```\r\n/__w/nlp/nlp/src/nlp/arrow_dataset.py:648: UserWarning: The given NumPy array is not writeable,\r\nand PyTorch does not support non-writeable tensors. This means you can write to the underlying\r\n(supposedly non-writeable) NumPy array using the tensor. You may want to copy the array to\r\nprotect its data or make it writeable before converting it to a tensor. This type of warning will be\r\nsuppressed for the rest of this program.\r\n(Triggered internally at /pytorch/torch/csrc/utils/tensor_numpy.cpp:141.)\r\n532\r\n return torch.tensor(x, **format_kwargs)\r\n```", "> Warning from pytorch that we should maybe consider at some point @lhoestq:\r\n> \r\n> ```\r\n> /__w/nlp/nlp/src/nlp/arrow_dataset.py:648: UserWarning: The given NumPy array is not writeable,\r\n> and PyTorch does not support non-writeable tensors. This means you can write to the underlying\r\n> (supposedly non-writeable) NumPy array using the tensor. You may want to copy the array to\r\n> protect its data or make it writeable before converting it to a tensor. This type of warning will be\r\n> suppressed for the rest of this program.\r\n> (Triggered internally at /pytorch/torch/csrc/utils/tensor_numpy.cpp:141.)\r\n> 532\r\n> return torch.tensor(x, **format_kwargs)\r\n> ```\r\n\r\nNot sure why we have that, it's probably linked to zero copy from arrow to numpy" ]

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[ "😱", "Yeah, this is spammy behavior. I've reported the user handle." ]

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

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

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

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

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

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