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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..!!!", "Sorry to write on a closed issue but, has there been any progress on parallelizing the `load_dataset` function?", "Hi ! No but this is in our plans (probably a few weeks)", "I'm literally crying waiting for the trainer to restart from checkpoint. It's getting stuck at `get_train_dataloader` and I think this is to do with the same issue... has there been any progress on this?", "> I'm literally crying waiting for the trainer to restart from checkpoint. It's getting stuck at get_train_dataloader and I think this is to do with the same issue...\r\n\r\nOnce the dataset is cached once, it's not regenerated again. Your issue seems different", "hmmm, yes. I'll come back with details on this, fairly easy to reproduce. Takes about 30 minutes to get from checkpoint loading to starting training..." ]

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

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[ "I'll take a look!" ]

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[ "Docs that explain how to train a tokenizer with `datasets` are available here: https://huggingface.co/docs/tokenizers/training_from_memory#using-the-datasets-library" ]

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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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[ "I am facing probably facing similar issues with \r\n\r\n`wiki40b_en_100_0`", "Could you try to run `dataset = load_dataset(\"text\", data_files=file_path, split=\"train\")` once before calling the script ?\r\n\r\nIt looks like several processes try to create the dataset in arrow format at the same time. If the dataset is already created it should be fine", "Thanks! I tested on 328MB text data on `n1-standard-8 (8 vCPUs, 30 GB memory)`. The main script ran without any issue, but it seems to require a huge space in the drive.\r\n\r\nAs suggested, I ran the following script before running the pre-training command with `xla_spawn.py`.\r\n\r\n```python\r\nfrom nlp import load_dataset\r\n\r\nfile_path=\"your_file_name\"\r\nload_dataset(\"text\", data_files=file_path, split=\"train\")\r\n```\r\nThis will create `text-train.arrow` under the default cache directory. Then, I run the script with `xla_spawn.py`. It will load data from the cached file. My understanding is that there's no other way but to do this two-step process with the current version (0.4) of `nlp`.\r\n\r\nDuring another caching process that happens in the main script:\r\n\r\n```\r\n08/26/2020 09:19:51 - INFO - nlp.utils.info_utils - All the checksums matched successfully for post processing resources\r\n08/26/2020 09:19:53 - INFO - nlp.arrow_dataset - Caching processed dataset at /home/*****/.cache/huggingface/datasets/text/default-b0932b2bdbb63283/0.0.0/447f2bcfa2a721a37bc8fdf23800eade1523cf07f7eada6fe661fe4d070d380d/cache-f90f341e5308a7469\r\n8d872bcc88f9c0e.arrow\r\n```\r\n\r\n`nlp` generates a temporary file per core, each of which is three times larger than the original text data. If each process is actually writing on the disk, you will need a huge amount of space in your drive. (Maybe I'm missing something.)\r\n\r\n```\r\n-rw-r--r-- 1 ***** ***** 674 Aug 26 09:19 dataset_info.json\r\n-rw-r--r-- 1 ***** ***** 0 Aug 26 09:19 LICENSE\r\n-rw-r--r-- 1 ***** ***** 332M Aug 26 09:10 text-train.arrow\r\n-rw------- 1 ***** ***** 940M Aug 26 09:31 tmp0k43sazw\r\n-rw------- 1 ***** ***** 940M Aug 26 09:31 tmp7sxs9mj5\r\n-rw------- 1 ***** ***** 939M Aug 26 09:31 tmpbbiqw2vp\r\n-rw------- 1 ***** ***** 937M Aug 26 09:31 tmpjxb5ptyu\r\n-rw------- 1 ***** ***** 933M Aug 26 09:31 tmpk3hkdh0e\r\n-rw------- 1 ***** ***** 944M Aug 26 09:31 tmpnoalwftz\r\n-rw------- 1 ***** ***** 931M Aug 26 09:31 tmpuxdr_dz3\r\n-rw------- 1 ***** ***** 945M Aug 26 09:31 tmpxjyuy6dk\r\n```\r\nAfter the caching process, they seem to be merged into one file.\r\n\r\n```\r\n-rw------- 1 ***** ***** 989M Aug 26 09:32 cache-f90f341e5308a74698d872bcc88f9c0e.arrow\r\n-rw-r--r-- 1 ***** ***** 674 Aug 26 09:19 dataset_info.json\r\n-rw-r--r-- 1 ***** ***** 0 Aug 26 09:19 LICENSE\r\n-rw-r--r-- 1 ***** ***** 332M Aug 26 09:10 text-train.arrow\r\n```", "Again it looks like every process tries to tokenize the full dataset at the same time.\r\nIf you do the tokenization before calling `xla_spawn.py` once, then each process will then use the tokenized cached file `cache-f90f341e5308a74698d872bcc88f9c0e.arrow` and not recompute it.\r\n\r\nNot sure if there's a better way to do that cc @julien-c @thomwolf ", "I wrote a separate script just for preparing a cached file, including tokenization. Each process did use the tokenized cached file.\r\n\r\nCurrently I'm testing the pipeline on 24GB text data. It took about 1.5 hour to create a cached file on `n1-highmem-16 (16 vCPUs, 104 GB memory)`. I assume loading this cached file in the main script with `xla_spawn.py` won't be an issue (even if there are 8 processes).\r\n\r\n```\r\ntotal 98G\r\ndrwxr-xr-x 2 ***** ***** 4.0K Aug 26 13:38 .\r\ndrwxr-xr-x 3 ***** ***** 4.0K Aug 26 12:24 ..\r\n-rw------- 1 ***** ***** 74G Aug 26 13:38 cache-a7aa04134ba7b1aff5d9710f14a4e334.arrow\r\n-rw-r--r-- 1 ***** ***** 681 Aug 26 12:24 dataset_info.json\r\n-rw-r--r-- 1 ***** ***** 0 Aug 26 12:24 LICENSE\r\n-rw-r--r-- 1 ***** ***** 25G Aug 26 12:24 text-train.arrow\r\n```", "Yes loading the cached file should be fine from different processes", "Sorry, I thought it was working, but actually the second call doesn't use the cached file that was generated separately, and it will generate another cache-****.arrorw file with a different name. If I run the training script again (with `xla_spawn.py`), it will use the second cached file, which was generated by the training script itself in the previous run.\r\n\r\n```\r\ndrwxr-xr-x 2 ***** ***** 4.0K Aug 26 15:35 .\r\ndrwxr-xr-x 3 ***** ***** 4.0K Aug 26 15:29 ..\r\n-rw------- 1 ***** ***** 99M Aug 26 15:35 cache-0d77dfce704493dbe63f071eed6a5431.arrow\r\n-rw------- 1 ***** ***** 99M Aug 26 15:29 cache-69633651476e943b93c89ace715f9487.arrow\r\n-rw-r--r-- 1 ***** ***** 670 Aug 26 15:33 dataset_info.json\r\n-rw-r--r-- 1 ***** ***** 0 Aug 26 15:33 LICENSE\r\n-rw-r--r-- 1 ***** ***** 33M Aug 26 15:29 text-train.arrow\r\n```", "So if I understand correctly it means that the cached file generated by your separated script is different by the one used by the training script ?", "Yes.\r\n\r\n1. `cache-69633651476e943b93c89ace715f9487.arrow` was generated with a separate script. \r\n2. I ran the entire script with `xla_spawn.py`.\r\n3. `cache-69633651476e943b93c89ace715f9487.arrow` is not used.\r\n4. `cache-0d77dfce704493dbe63f071eed6a5431.arrow` is created.\r\n5. training starts...\r\n\r\nNow, if I kill the process at step 5, and do the step 2 again, it will use `cache-0d77dfce704493dbe63f071eed6a5431.arrow` (cached file created at step 4) without any issue.\r\n\r\nI used the following to generate the first cached file.\r\n```python\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)\r\ndataset.set_format(type='torch', columns=['input_ids'])\r\n```", "1. Here's the log from the first step.\r\n```\r\nDownloading and preparing dataset text/default-e84dd29acc4ad9ef (download: Unknown size, generated: Unknown size, post-processed: Unknown size, total: Unknown size) to /home/*****/.cache/huggingface/datasets/text/default-e84dd29acc4ad9ef/0.0.0/\r\n447f2bcfa2a721a37bc8fdf23800eade1523cf07f7eada6fe661fe4d070d380d...\r\nDataset text downloaded and prepared to /home/*****/.cache/huggingface/datasets/text/default-e84dd29acc4ad9ef/0.0.0/447f2bcfa2a721a37bc8fdf23800eade1523cf07f7eada6fe661fe4d070d380d. Subsequent calls will reuse this data.\r\n```\r\nThere's a file named `cache-7b1440ba7077af0f0d9035b5a55d01fc.arrow`, so it did create a cached file.\r\n```\r\ndrwxr-xr-x 2 ***** ***** 4.0K Aug 26 15:59 .\r\ndrwxr-xr-x 3 ***** ***** 4.0K Aug 26 15:58 ..\r\n-rw------- 1 ***** ***** 99M Aug 26 15:59 cache-7b1440ba7077af0f0d9035b5a55d01fc.arrow\r\n-rw-r--r-- 1 ***** ***** 670 Aug 26 15:58 dataset_info.json\r\n-rw-r--r-- 1 ***** ***** 0 Aug 26 15:58 LICENSE\r\n-rw-r--r-- 1 ***** ***** 33M Aug 26 15:58 text-train.arrow\r\n```\r\n2. Ideally, `cache-7b1440ba7077af0f0d9035b5a55d01fc.arrow` should be used in `run_language_modeling.py` (modified version using `nlp`) with `xla_spawn.py`. But it looks like it's creating a new cached file.\r\n\r\n```\r\n08/26/2020 16:13:03 - INFO - filelock - Lock 139635836351096 released on /home/*****/.cache/huggingface/datasets/3e34209a2741375a1db1ff03bf1abba1a9bd0e6016912d3ead0114b9d1ca2685.202fa4f84f552bff1f5400ae012663839c61efb3de068c6c8722d34ac0ea6192\r\n.py.lock\r\n08/26/2020 16:13:03 - WARNING - nlp.builder - Using custom data configuration default\r\n08/26/2020 16:13:03 - INFO - nlp.builder - Overwrite dataset info from restored data version.\r\n08/26/2020 16:13:03 - INFO - nlp.info - Loading Dataset info from /home/*****/.cache/huggingface/datasets/text/default-e84dd29acc4ad9ef/0.0.0/447f2bcfa2a721a37bc8fdf23800eade1523cf07f7eada6fe661fe4d070d380d\r\n08/26/2020 16:13:03 - INFO - nlp.builder - Reusing dataset text (/home/*****/.cache/huggingface/datasets/text/default-e84dd29acc4ad9ef/0.0.0/447f2bcfa2a721a37bc8fdf23800eade1523cf07f7eada6fe661fe4d070d380d)\r\n08/26/2020 16:13:03 - INFO - nlp.builder - Constructing Dataset for split train, from /home/*****/.cache/huggingface/datasets/text/default-e84dd29acc4ad9ef/0.0.0/447f2bcfa2a721a37bc8fdf23800eade1523cf07f7eada6fe661fe4d070d380d\r\n08/26/2020 16:13:03 - INFO - nlp.utils.info_utils - All the checksums matched successfully for post processing resources\r\n08/26/2020 16:13:03 - INFO - nlp.builder - Overwrite dataset info from restored data version.\r\n08/26/2020 16:13:03 - INFO - nlp.info - Loading Dataset info from /home/*****/.cache/huggingface/datasets/text/default-e84dd29acc4ad9ef/0.0.0/447f2bcfa2a721a37bc8fdf23800eade1523cf07f7eada6fe661fe4d070d380d\r\n08/26/2020 16:13:03 - INFO - nlp.builder - Reusing dataset text (/home/*****/.cache/huggingface/datasets/text/default-e84dd29acc4ad9ef/0.0.0/447f2bcfa2a721a37bc8fdf23800eade1523cf07f7eada6fe661fe4d070d380d)\r\n08/26/2020 16:13:03 - INFO - nlp.builder - Constructing Dataset for split train, from /home/*****/.cache/huggingface/datasets/text/default-e84dd29acc4ad9ef/0.0.0/447f2bcfa2a721a37bc8fdf23800eade1523cf07f7eada6fe661fe4d070d380d\r\n08/26/2020 16:13:03 - INFO - nlp.utils.info_utils - All the checksums matched successfully for post processing resources\r\n08/26/2020 16:13:05 - INFO - nlp.arrow_dataset - Caching processed dataset at /home/*****/.cache/huggingface/datasets/text/default-e84dd29acc4ad9ef/0.0.0/447f2bcfa2a721a37bc8fdf23800eade1523cf07f7eada6fe661fe4d070d380d/cache-0d77dfce704493dbe\r\n63f071eed6a5431.arrow\r\n^M 0%| | 0/100 [00:00<?, ?it/s]08/26/2020 16:13:05 - INFO - nlp.arrow_dataset - Caching processed dataset at /home/*****/.cache/huggingface/datasets/text/default-e84dd29acc4ad9ef/0.0.0/447f2bcfa2a721a37bc8fdf23800eade1523cf07f7eada6\r\nfe661fe4d070d380d/cache-0d77dfce704493dbe63f071eed6a5431.arrow\r\n```\r\n\r\nThere are two cached files in the directory:\r\n\r\n```\r\ndrwxr-xr-x 2 ***** ***** 4.0K Aug 26 16:14 .\r\ndrwxr-xr-x 3 ***** ***** 4.0K Aug 26 15:58 ..\r\n-rw------- 1 ***** ***** 99M Aug 26 16:14 cache-0d77dfce704493dbe63f071eed6a5431.arrow\r\n-rw------- 1 ***** ***** 99M Aug 26 15:59 cache-7b1440ba7077af0f0d9035b5a55d01fc.arrow\r\n-rw-r--r-- 1 ***** ***** 670 Aug 26 16:13 dataset_info.json\r\n-rw-r--r-- 1 ***** ***** 0 Aug 26 16:13 LICENSE\r\n-rw-r--r-- 1 ***** ***** 33M Aug 26 15:58 text-train.arrow\r\n```\r\n\r\nIf I kill the process, and run it again, it will use the second cached file.\r\n\r\n```\r\n08/26/2020 16:19:52 - WARNING - nlp.builder - Using custom data configuration default\r\n08/26/2020 16:19:52 - INFO - nlp.builder - Overwrite dataset info from restored data version.\r\n08/26/2020 16:19:52 - INFO - nlp.info - Loading Dataset info from /home/*****/.cache/huggingface/datasets/text/default-e84dd29acc4ad9ef/0.0.0/447f2bcfa2a721a37bc8fdf23800eade1523cf07f7eada6fe661fe4d070d380d\r\n08/26/2020 16:19:52 - INFO - nlp.builder - Reusing dataset text (/home/*****/.cache/huggingface/datasets/text/default-e84dd29acc4ad9ef/0.0.0/447f2bcfa2a721a37bc8fdf23800eade1523cf07f7eada6fe661fe4d070d380d)\r\n08/26/2020 16:19:52 - INFO - nlp.builder - Constructing Dataset for split train, from /home/*****/.cache/huggingface/datasets/text/default-e84dd29acc4ad9ef/0.0.0/447f2bcfa2a721a37bc8fdf23800eade1523cf07f7eada6fe661fe4d070d380d\r\n08/26/2020 16:19:52 - INFO - nlp.utils.info_utils - All the checksums matched successfully for post processing resources\r\n08/26/2020 16:19:53 - INFO - nlp.arrow_dataset - Loading cached processed dataset at /home/*****/.cache/huggingface/datasets/text/default-e84dd29acc4ad9ef/0.0.0/447f2bcfa2a721a37bc8fdf23800eade1523cf07f7eada6fe661fe4d070d380d/cache-0d77dfce70\r\n4493dbe63f071eed6a5431.arrow\r\n08/26/2020 16:19:53 - INFO - nlp.arrow_dataset - Set __getitem__(key) output type to torch for ['input_ids'] columns (when key is int or slice) and don't output other (un-formatted) columns.\r\n```", "Thanks for all the details.\r\nThe two cached files are supposed to be the same. I suspect that the caching has a problem with the tokenizer.\r\nWhich tokenizer did you use ?", "I trained a byte-level BPE tokenizer on my data with `tokenziers` library following this [example](https://github.com/huggingface/tokenizers/blob/master/bindings/python/examples/train_bytelevel_bpe.py).\r\n\r\nAnd I put these model files in a directory named `\"model_name\"`. I also put config.json, which is the original RoBERTa config file.\r\n\r\n```bash\r\n%ls model_name\r\nconfig.json merges.txt vocab.json\r\n```\r\n\r\n[This](https://github.com/huggingface/transformers/blob/4bd7be9a4268221d2a0000c7e8033aaeb365c03b/examples/language-modeling/run_language_modeling.py#L196) is the line where `run_language_modeling.py` loads the tokenier.\r\n\r\n```python\r\ntokenizer = AutoTokenizer.from_pretrained(model_args.tokenizer_name, cache_dir=model_args.cache_dir)\r\n```\r\n\r\nI use `\"model_name\"` for `model_args.tokenizer_name`. I don't specify `model_args.cache_dir`. It is 'None' by default.", "In my separated script for caching, I'm using `use_fast=True` when initializing a tokenizer.\r\n\r\n```python\r\ntokenizer = AutoTokenizer.from_pretrained(args.config_name, use_fast=True)\r\n```\r\nI wasn't using that option in the main script. That could be the reason...", "Yea it could definitely explain why you have two different cache files.\r\nLet me know if using the same tokenizers on both sides fixes the issue", "It still creates a new file even if I remove `use_fast=True`... \r\n\r\nHere's the script used to create a cached file.\r\n```python \r\n#!/usr/bin/env python3\r\n\r\nimport argparse\r\n\r\nfrom transformers import AutoTokenizer\r\n\r\nfrom nlp import load_dataset\r\n\r\n\r\ndef main():\r\n parser = argparse.ArgumentParser(description='description')\r\n parser.add_argument('--config_name', type=str, help='Pretrained config name or path if not the same as model_name')\r\n parser.add_argument('--data_file', type=str, help='The input data file (a text file).')\r\n parser.add_argument('--block_size', type=int, default=-1, help='The training dataset will be truncated in block of this size for training')\r\n args = parser.parse_args()\r\n\r\n tokenizer = AutoTokenizer.from_pretrained(args.config_name)\r\n\r\n dataset = load_dataset(\"text\", data_files=args.data_file, split=\"train\")\r\n dataset = dataset.map(lambda ex: tokenizer(ex[\"text\"], add_special_tokens=True,\r\n truncation=True, max_length=args.block_size), batched=True)\r\n dataset.set_format(type='torch', columns=['input_ids'])\r\n\r\n\r\nif __name__ == \"__main__\":\r\n main()\r\n```\r\n\r\nHere's how the data is loaded in the modified `run_language_modeling.py`. [[original function](https://github.com/huggingface/transformers/blob/971d1802d009d9996b36a34a34477cee849ef39f/examples/language-modeling/run_language_modeling.py#L128-L135)]\r\n\r\n```python\r\ndef get_dataset(args: DataTrainingArguments, tokenizer: PreTrainedTokenizer, evaluate=False):\r\n file_path = args.eval_data_file if evaluate else args.train_data_file\r\n split = \"validation\" if evaluate else \"train\"\r\n if args.line_by_line:\r\n # return LineByLineTextDataset(tokenizer=tokenizer, file_path=file_path, block_size=args.block_size)\r\n dataset = load_dataset(\"text\", data_files=file_path, split=\"train\")\r\n dataset = dataset.map(lambda ex: tokenizer(ex[\"text\"], add_special_tokens=True,\r\n truncation=True, max_length=args.block_size), batched=True)\r\n dataset.set_format(type='torch', columns=['input_ids'])\r\n return dataset\r\n\r\n else:\r\n return TextDataset(\r\n tokenizer=tokenizer, file_path=file_path, block_size=args.block_size, overwrite_cache=args.overwrite_cache\r\n )\r\n```\r\n\r\nProbably I don't need this part in the main script,\r\n\r\n```python\r\ndataset = dataset.map(lambda ex: tokenizer(ex[\"text\"], add_special_tokens=True,\r\n truncation=True, max_length=args.block_size), batched=True)\r\n dataset.set_format(type='torch', columns=['input_ids'])\r\n```\r\nand simply do this?\r\n```python\r\ndataset = load_dataset(\"text\", data_files=file_path, split=\"train\")\r\nreturn dataset\r\n```", "You need this part in the main script or it will use the dataset that is not tokenized\r\n\r\n", "I can see that the tokenizer in `run_language_modeling.py` is not instantiated the same way as in your separated script.\r\nIndeed we can see L196:\r\n```python\r\ntokenizer = AutoTokenizer.from_pretrained(model_args.tokenizer_name, cache_dir=model_args.cache_dir)\r\n```\r\nCould you try to make it so they are instantiated the exact same way please ?", "I updated my separated script, but it's creating a cached file again. If I don't use the `model_args.cache_dir`, both will get `None`, so they should be the same.\r\n\r\n```python\r\n#!/usr/bin/env python3\r\nimport argparse\r\n\r\nfrom transformers import AutoTokenizer\r\nfrom nlp import load_dataset\r\n\r\ndef main():\r\n parser = argparse.ArgumentParser(description='description')\r\n parser.add_argument('--tokenizer_name', type=str, help='Pretrained tokenizer name or path if not the same as model_name')\r\n parser.add_argument('--data_file', type=str, help='The input data file (a text file).')\r\n parser.add_argument('--cache_dir', type=str, default=None, help='Where do you want to store the pretrained models downloaded from s3')\r\n parser.add_argument('--block_size', type=int, default=-1, help='The training dataset will be truncated in block of this size for training')\r\n\r\n model_args = parser.parse_args()\r\n\r\n tokenizer = AutoTokenizer.from_pretrained(model_args.tokenizer_name, cache_dir=model_args.cache_dir)\r\n\r\n dataset = load_dataset(\"text\", data_files=model_args.data_file, split=\"train\")\r\n dataset = dataset.map(lambda ex: tokenizer(ex[\"text\"], add_special_tokens=True,\r\n truncation=True, max_length=model_args.block_size), batched=True)\r\n dataset.set_format(type='torch', columns=['input_ids'])\r\n\r\nif __name__ == \"__main__\":\r\n main()\r\n```\r\n\r\nIs there a way to specify the cache file to load, and skip the re-computation?", "Could you also check that the `args.block_size` used in the lambda function is the same as well ?", "Here's a minimal working example to reproduce this issue.\r\n\r\nAssumption:\r\n- You have access to TPU.\r\n- You have installed `transformers` and `nlp`.\r\n- You have tokenizer files (`config.json`, `merges.txt`, `vocab.json`) under the directory named `model_name`.\r\n- You have `xla_spawn.py` (Download from https://github.com/huggingface/transformers/blob/master/examples/xla_spawn.py).\r\n- You have saved the following script as `prepare_cached_dataset.py`.\r\n\r\n```python\r\n#!/usr/bin/env python3\r\nimport argparse\r\nfrom transformers import AutoTokenizer\r\nfrom nlp import load_dataset\r\n\r\ndef main():\r\n parser = argparse.ArgumentParser(description='description')\r\n parser.add_argument('--tokenizer_name', type=str, help='Pretrained tokenizer name or path if not the same as model_name')\r\n parser.add_argument('--data_file', type=str, help='The input data file (a text file).')\r\n parser.add_argument('--cache_dir', type=str, default=None, help='Where do you want to store the pretrained models downloaded from s3')\r\n parser.add_argument('--block_size', type=int, default=-1, help='The training dataset will be truncated in block of this size for training')\r\n parser.add_argument('--tpu_num_cores', type=int, default=1, help='Number of TPU cores to use (1 or 8). For xla_apwan.py')\r\n model_args = parser.parse_args()\r\n \r\n tokenizer = AutoTokenizer.from_pretrained(model_args.tokenizer_name, cache_dir=model_args.cache_dir, use_fast=True)\r\n \r\n dataset = load_dataset(\"text\", data_files=model_args.data_file, split=\"train\")\r\n dataset = dataset.map(lambda ex: tokenizer(ex[\"text\"], add_special_tokens=True,\r\n truncation=True, max_length=model_args.block_size), batched=True)\r\n dataset.set_format(type='torch', columns=['input_ids'])\r\n\r\ndef _mp_fn(index):\r\n # For xla_spawn (TPUs)\r\n main()\r\n\r\nif __name__ == \"__main__\":\r\n main()\r\n```\r\n\r\n- Run the following command. Replace `your_training_data` with some text file.\r\n\r\n```bash\r\nexport TRAIN_DATA=your_training_data\r\n\r\npython prepare_cached_dataset.py \\\r\n--tokenizer_name=model_name \\\r\n--block_size=512 \\\r\n--data_file=$TRAIN_DATA\r\n```\r\n- Check the cached directory.\r\n```bash\r\nls -lha /home/*****/.cache/huggingface/datasets/text/default-e84dd29acc4ad9ef/0.0.0/447f2bcfa2a721a37bc8fdf23800eade1523cf07f7eada6fe661fe4d070d380d\r\ntotal 132M\r\ndrwxr-xr-x 2 ***** ***** 4.0K Aug 28 13:08 .\r\ndrwxr-xr-x 3 ***** ***** 4.0K Aug 28 13:08 ..\r\n-rw------- 1 ***** ***** 99M Aug 28 13:08 cache-bfc7cb0702426d19242db5e8c079f04b.arrow\r\n-rw-r--r-- 1 ***** ***** 670 Aug 28 13:08 dataset_info.json\r\n-rw-r--r-- 1 ***** ***** 0 Aug 28 13:08 LICENSE\r\n-rw-r--r-- 1 ***** ***** 33M Aug 28 13:08 text-train.arrow\r\n```\r\n\r\n- Run the same script again. (The output should be just `Using custom data configuration default`.)\r\n```\r\npython prepare_cached_dataset.py \\\r\n--tokenizer_name=model_name \\\r\n--block_size=512 \\\r\n--data_file=$TRAIN_DATA\r\n```\r\n- Check the cached directory.\r\n```bash\r\nls -lha /home/*****/.cache/huggingface/datasets/text/default-e84dd29acc4ad9ef/0.0.0/447f2bcfa2a721a37bc8fdf23800eade1523cf07f7eada6fe661fe4d070d380d\r\ntotal 132M\r\ndrwxr-xr-x 2 ***** ***** 4.0K Aug 28 13:08 .\r\ndrwxr-xr-x 3 ***** ***** 4.0K Aug 28 13:08 ..\r\n-rw------- 1 ***** ***** 99M Aug 28 13:08 cache-bfc7cb0702426d19242db5e8c079f04b.arrow\r\n-rw-r--r-- 1 ***** ***** 670 Aug 28 13:20 dataset_info.json\r\n-rw-r--r-- 1 ***** ***** 0 Aug 28 13:20 LICENSE\r\n-rw-r--r-- 1 ***** ***** 33M Aug 28 13:08 text-train.arrow\r\n```\r\n- The cached file (`cache-bfc7cb0702426d19242db5e8c079f04b.arrow`) is reused.\r\n- Now, run this script with `xla_spawn.py`. Ideally, it should reuse the cached file, however, you will see each process is creating a cache file again.\r\n\r\n```bash\r\npython xla_spawn.py --num_cores 8 \\\r\nprepare_cached_dataset.py \\\r\n--tokenizer_name=model_name \\\r\n--block_size=512 \\\r\n--data_file=$TRAIN_DATA\r\n```\r\n\r\n- Check the cached directory. There are two arrrow files.\r\n```bash\r\nls -lha /home/*****/.cache/huggingface/datasets/text/default-e84dd29acc4ad9ef/0.0.0/447f2bcfa2a721a37bc8fdf23800eade1523cf07f7eada6fe661fe4d070d380d\r\ntotal 230M\r\ndrwxr-xr-x 2 ***** ***** 4.0K Aug 28 13:25 .\r\ndrwxr-xr-x 3 ***** ***** 4.0K Aug 28 13:08 ..\r\n-rw------- 1 ***** ***** 99M Aug 28 13:08 cache-bfc7cb0702426d19242db5e8c079f04b.arrow\r\n-rw------- 1 ***** ***** 99M Aug 28 13:25 cache-e0e2313e49c8a110aafcc8133154c19a.arrow\r\n-rw-r--r-- 1 ***** ***** 670 Aug 28 13:24 dataset_info.json\r\n-rw-r--r-- 1 ***** ***** 0 Aug 28 13:24 LICENSE\r\n-rw-r--r-- 1 ***** ***** 33M Aug 28 13:08 text-train.arrow\r\n```\r\n", "I ended up specifying the `cache_file_name` argument when I call `map` function.\r\n\r\n```python\r\ndataset = dataset.map(lambda ex: tokenizer(ex[\"text\"], add_special_tokens=True, truncation=True, max_length=args.block_size),\r\n batched=True,\r\n cache_file_name=cache_file_name)\r\n```\r\n\r\nNote:\r\n- `text` dataset in `nlp` does not strip `\"\\n\"`. If you want the same output as in [`LineByLineTextDataset`](https://github.com/huggingface/transformers/blob/afc4ece462ad83a090af620ff4da099a0272e171/src/transformers/data/datasets/language_modeling.py#L88-L111), you would need to create your own dataset class where you replace `line` to `line.strip()` [here](https://github.com/huggingface/nlp/blob/master/datasets/text/text.py#L35).\r\n" ]

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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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[ "Thanks for creating the issue :)\r\nThe download link for wmt-en-de raw looks like a mirror. We should use that instead of the current url.\r\nIs this mirror official ?\r\n\r\nAlso it looks like for `ro-en` it tried to download other languages. If we manage to only download the one that is asked it'd be cool\r\n\r\nAlso cc @patrickvonplaten ", "Mirror is not official.", "Shall we host the files ourselves or it is fine to use this mirror in your opinion ?", "Should we add an argument in `load_dataset` to override some URL with a custom URL (e.g. mirror) or a local path?\r\n\r\nThis could also be used to provide local files instead of the original files as requested by some users (e.g. when you made a dataset with the same format than SQuAD and what to use it instead of the official dataset files).", "@lhoestq I think we should host it ourselves. I'll put the subset of wmt (without preprocessed files) that we need on s3 and post a link over the weekend.", "Is there a solution yet? The download speed is still too slow. 60-70kbps download for wmt16 and around 100kbps for wmt19. @sshleifer ", "I'm working on mirror links which will provide high download speed :)\r\nSee https://github.com/huggingface/datasets/issues/1892", "Resolved via https://github.com/huggingface/datasets/pull/1912" ]

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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 huggingface/evaluate#105 " ]

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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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[ "Any updates on this?", "This request is still an open issue waiting to be addressed by any community member, @GuillemGSubies." ]

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[ "This seems to be fixed in #513 for the filter function, replacing `cache_file_name` with `indices_cache_file_name` in the assert. Although not for the `map()` function @thomwolf ", "Maybe I'm a bit tired but I fail to see the issue here.\r\n\r\nSince `cache_file_name` is `None` by default, if you set `keep_in_memory` to `True`, the assert should pass, no?", "I failed to realise that this only applies to `shuffle()`. Whenever `keep_in_memory` is set to True, this is passed on to the `select()` function. However, if `cache_file_name` is None, it will be defined in the `shuffle()` function before it is passed on to `select()`. \r\n\r\nThus, `select()` is called with `keep_in_memory=True` and a not None value for `cache_file_name`. \r\nThis is essentially fixed in #513 \r\n\r\nEasily reproducible:\r\n```python\r\n>>> import nlp\r\n>>> data = nlp.load_dataset(\"cosmos_qa\", split=\"train\")\r\nUsing custom data configuration default\r\n>>> data.shuffle(keep_in_memory=True)\r\nTraceback (most recent call last):\r\n File \"<stdin>\", line 1, in <module>\r\n File \"/home/vegarab/.conda/envs/torch/lib/python3.7/site-packages/nlp/arrow_dataset.py\", line 1398, in shuffle\r\n verbose=verbose,\r\n File \"/home/vegarab/.conda/envs/torch/lib/python3.7/site-packages/nlp/arrow_dataset.py\", line 1178, in select\r\n ), \"Please use either `keep_in_memory` or `cache_file_name` but not both.\"\r\nAssertionError: Please use either `keep_in_memory` or `cache_file_name` but not both.\r\n>>>data.select([0], keep_in_memory=True)\r\n# No error\r\n```", "Oh yes ok got it thanks. Should be fixed if we are happy with #513 indeed.", "My bad. This is actually not fixed in #513. Sorry about that...\r\nThe new `indices_cache_file_name` is set to a non-None value in the new `shuffle()` as well. \r\n\r\nThe buffer and caching mechanisms used in the `select()` function are too intricate for me to understand why the check is there at all. I've removed it in my local build and it seems to be working fine for my project, without really considering other implications of the change. \r\n\r\n", "Ok I'll investigate and add a series of tests on the `keep_in_memory=True` settings which is under-tested atm", "Hey, still seeing this issue with the latest version.", "The same :(", "These are the steps needed to fix this issue:\r\n1. add the following check to `Dataset.shuffle`:\r\n```python\r\nif keep_in_memory and indices_cache_file_name is not None:\r\n raise ValueError(\"Please use either `keep_in_memory` or `indices_cache_file_name` but not both.\")\r\n```\r\n2. set `indices_cache_file_name` to `None` if `keep_in_memory` is True in the call to `select`\r\n3. add a test with `shuffle(keep_in_memory=True)`", "Hi @mariosasko , I have opened this PR #5082 " ]

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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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[ "Thanks for reporting the issue ! I'm creating a new PR to fix it and add tests.\r\n(I'm doing a new PR because I know there's some other place where it needs to be fixed)", "I'm closing this one as I created the other PR." ]

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[ "Thank you @ghomasHudson for making our dataset available! This is great!", "The test passes since #527 :)" ]

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[ "I don't see any significant change in the dataset script (except the version value update), can you check that again please ?", "Hi @aleSuglia , can you check that all the changes you wanted to do are in the dataset script ?", "Hey sorry but I'm in the middle of a conference deadline. I'll let you know asap!", "Ok np :)\r\nGood luck with your work for the conference", "I finally managed to find some time to complete this. The only weird thing about this release is that I had to run the tests with the ignore checksum flag. Could it be because the Dropbox link doesn't change but the file does? Sorry didn't have the time to check the code to see what's happening behind the scenes.\r\n", "Yes if the file changed, then the checksum verification won't pass as it expects to see the checksum of the old file.\r\nThe checksum is computed by hashing the complete file.\r\nYou can update the checksum by doing \r\n\r\n```\r\nnlp-cli test ./datasets/compguesswhat --save_infos --all_configs\r\n```", "Any updates on this?", "Hi :)\r\n\r\nI think what's left to do is\r\n1- rebase from master, since we changed the name of the library\r\n2- update the metadata file of the dataset using the command \r\n```\r\ndatasets-cli test ./datasets/compguesswhat --save_infos --all_configs --ignore_verifications\r\n```\r\n\r\nThis command should update the checksum of the dropbox file", "That's perfect. I'll have a look at it later today!", "Nice thanks !", "@lhoestq not sure why the quality check doesn't pass. Unfortunately CircleCI doesn't show the actual error. If I run `black` on my machine it works just fine. Ideas?", "@lhoestq any updates? :) ", "Your version of `black` might be outdated, or you run using `black` instead of `make style` since it reformatted 100+ files.\r\nCould you try to update black, then `make style` ?", "Yes I think my versions of isort and black were outdated. Thanks @lhoestq :)\r\n", "It still doesn't look right in terms of line-length.\r\nAre you running `black` or `make style` ?", "I'm running `make style`. This is the output of the command:\r\n\r\n```\r\nblack --line-length 119 --target-version py36 tests src benchmarks datasets metrics\r\nAll done! ✨ 🍰 ✨\r\n250 files left unchanged.\r\nisort tests src benchmarks datasets metrics\r\n```", "Weird I have the same output without file changes with black `20.8b1` and isort `5.6.4` using `make style` too", "I think that's because black doesn't revert the changes you first did with the old version.\r\nCould you open a new PR with only the ComGuessWhat files updated ? Hopefully now that black is up to date it should work directly (and to avoid 100+ files changes)", "I will have a look at it tomorrow. Thanks for your help!", "I'm closing this one and I'll open a new one." ]

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[ "This should fix #501 and also the issue you sent me on slack @sgugger ." ]

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[ "Thanks for reporting !\r\n\r\nTo store the cache file, we compute a hash of the function given in `.map`, using our own hashing function.\r\nThe hash doesn't seem to stay the same over sessions for the tokenizer.\r\nApparently this is because of the regex at `tokenizer.pat` is not well supported by our hashing function.\r\n\r\nI'm working on a fix", "Thanks everyone. Works great now.", "Hi. I believe the fix was for the nlp library. Is there a solution to handle compiled regex expressions in .map() with the caching. I want to run a simple regex pattern on a big dataset, but I am running into the issue of compiled expression not being cached. \r\n\r\nInstead of opening a new issue, I thought I would put my query here. Let me know if a new issue would be more suitable. Thanks", "Hi @MaveriQ! This fix is also included in the `datasets` library. Can you provide a reproducer?" ]

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[ "I took a look at the dummy data creation for this dataset.\r\n\r\nMaybe it didn't work on your side might be because `master.zip` and `narrativeqa_full_text.zip` are supposed to be directories and not acutal zip files in the dummy data folder.\r\n\r\nI managed to make it work with this `dummy_data.zip` file:\r\nhttps://drive.google.com/file/d/1G9ZHAjelazNApbFI0ep2dnSAWklXgGMd/view?usp=sharing", "@lhoestq Hmmm wasn't that. Must have been something else I missed.\r\n\r\nHave committed your working version though now.", "Ok thanks.\r\nCould you rebase from master to fix the CI please ?", "Hi @ghomasHudson, did you get the chance to add the test split and regenerate the dataset_infos.json file ?", "> Hi @ghomasHudson, did you get the chance to add the test split and regenerate the dataset_infos.json file ?\r\n\r\nHave added the test set code but getting an OverflowError when trying to regen the dataset_infos.json:\r\n\r\n---\r\nOverflowError: There was an overflow in the <class 'pyarrow.lib.StructArray'>. Try to reduce writer_batch_size to have batches smaller than 2GB\r\n\r\n---\r\n", "Thanks for reporting @ghomasHudson , I'll look into it", "It looks like it's an issue with Pyarrow.\r\nBy changing the `DEFAULT_MAX_BATCH_SIZE` to 1000 instead of 10 000 in `arrow_writer.py` I was able to run the command.\r\n\r\nBasically it seems that is an Arrow StructArray has more than 1-2GB of data, then it shuffles some of its content.\r\nI can't find any issue on Apache Arrow's JIRA about this problem. It will require more investigation.\r\n\r\nMaybe we can simply automatically decrease the writer's batch size when this happens. We can just check if the arrow array is more than a certain amount of bytes. ", "@lhoestq I've finally got round to regenerating the `dataset_infos.json` for this and adding all 3 splits. I've done this and updated for the new version of datasets.\r\n\r\nThe CI tests still aren't passing though (they pass on my machine). `test_load_dataset_narrativeqa` seems to fail but I have no idea how. Would appreciate if you have any ideas - would be great to finally finish this one!", "The dummy data test fails, apparently it's because no examples are yielded for the dummy data.\r\n\r\nAlso it looks like the PR now show changes in many other files than the ones for NarrativeQA, could you create another branch and another PR please ?\r\n\r\nFeel free to ping me on the new PR so we can fi the dummy data together" ]

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[ "This PR also fixed a bug where numpy arrays were returned instead of pytorch tensors when getting with a clumn as a key." ]

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[ "this doesn't work. I can decompress the file after download locally." ]

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[ "In 0.4.0, the assertion in `concatenate_datasets ` is on the features, and not the schema.\r\nCould you try to update `nlp` ?\r\n\r\nAlso, since 0.4.0, you can use `dset_wikipedia.cast_(dset_books.features)` to avoid the schema cast hack.", "Or maybe the assertion comes from elsewhere ?", "I'm using the master branch. The assertion failure comes from the underlying `pa.concat_tables()`, which is in the pyarrow package. That method does check schemas.\r\n\r\nSince `features.type` does not contain information about nullable vs non-nullable features, the `cast_()` method won't resolve the schema mismatch. There is information in a schema which is not stored in features.", "I'm doing a refactor of type inference in #363 . Both text fields should match after that", "By default nullable will be set to True", "It should be good now. I was able to run\r\n\r\n```python\r\n>>> from nlp import concatenate_datasets, load_dataset\r\n>>>\r\n>>> bookcorpus = load_dataset(\"bookcorpus\", split=\"train\")\r\n>>> wiki = load_dataset(\"wikipedia\", \"20200501.en\", split=\"train\")\r\n>>> wiki.remove_columns_(\"title\") # only keep the text\r\n>>>\r\n>>> assert bookcorpus.features.type == wiki.features.type\r\n>>> bert_dataset = concatenate_datasets([bookcorpus, wiki])\r\n```", "Thanks!" ]

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[ "I did the release on github, and updated the doc :)\r\nSorry for the delay", "Thanks!" ]

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[ "whoops", "please delete this" ]

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[ "I found `UNv1.0.en-ru.tar.gz` here: https://conferences.unite.un.org/uncorpus/en/downloadoverview, so it can be reconstructed with:\r\n```\r\nwget -c https://stuncorpusprod.blob.core.windows.net/corpusfiles/UNv1.0.en-ru.tar.gz.00\r\nwget -c https://stuncorpusprod.blob.core.windows.net/corpusfiles/UNv1.0.en-ru.tar.gz.01\r\nwget -c https://stuncorpusprod.blob.core.windows.net/corpusfiles/UNv1.0.en-ru.tar.gz.02\r\ncat UNv1.0.en-ru.tar.gz.0* > UNv1.0.en-ru.tar.gz\r\n```\r\nit has other languages as well, in case https://storage.googleapis.com/tfdataset-data/downloadataset/uncorpus/ is gone", "Further, `nlp.load_dataset('wmt19', 'ru-en')` has only the `train` and `val` datasets. `test` is missing.\r\n\r\nFixed locally for summarization needs, by running:\r\n```\r\npip install sacrebleu\r\nsacrebleu -t wmt19 -l ru-en --echo src > test.source\r\nsacrebleu -t wmt19 -l ru-en --echo ref > test.target\r\n```\r\nh/t @sshleifer ", "Fixed in https://github.com/huggingface/datasets/pull/1912" ]

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[ "It looks like you need to rebase from master to fix the CI. Could you do that please ?" ]

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[ "Yes indeed it looks like some `'` and spaces are missing (for example in `dont` or `didnt`).\r\nDo you know if there exist some copies without this issue ?\r\nHow would you fix this issue on the current data exactly ? I can see that the data is raw text (not tokenized) so I'm not sure I understand how you would do it. Could you provide more details ?", "I'm afraid that I don't know how to obtain the original BookCorpus data. I believe this version came from an anonymous Google Drive link posted in another issue.\r\n\r\nGoing through the raw text in this version, it's apparent that NLTK's TreebankWordTokenizer was applied on it (I gave some examples in my original post), followed by:\r\n`' '.join(tokens)`\r\nYou can retrieve the tokenization by splitting on whitespace. You can then \"detokenize\" it with TreebankWordDetokenizer class of NLTK (though, as I suggested, use the fixed version in my repo). This will bring the text closer to its original form, but some steps of TreebankWordTokenizer are destructive, so it wouldn't be one-to-one. Something along the lines of the following should work:\r\n```\r\ntreebank_detokenizer = nltk.tokenize.treebank.TreebankWordDetokenizer()\r\ndb = nlp.load_dataset('bookcorpus', split=nlp.Split.TRAIN)\r\ndb = db.map(lambda x: treebank_detokenizer.detokenize(x['text'].split()))\r\n```\r\n\r\nRegarding other issues beyond the above, I'm afraid that I can't help with that.", "Ok I get it, that would be very cool indeed\r\n\r\nWhat kinds of patterns the detokenizer can't retrieve ?", "The TreebankTokenizer makes some assumptions about whitespace, parentheses, quotation marks, etc. For instance, while tokenizing the following text:\r\n```\r\nDwayne \"The Rock\" Johnson\r\n```\r\nwill result in:\r\n```\r\nDwayne `` The Rock '' Johnson\r\n```\r\nwhere the left and right quotation marks are turned into distinct symbols. Upon reconstruction, we can attach the left part to its token on the right, and respectively for the right part. However, the following texts would be tokenized exactly the same:\r\n```\r\nDwayne \" The Rock \" Johnson\r\nDwayne \" The Rock\" Johnson\r\nDwayne \" The Rock\" Johnson\r\n...\r\n```\r\nIn the above examples, the detokenizer would correct these inputs into the canonical text\r\n```\r\nDwayne \"The Rock\" Johnson\r\n```\r\nHowever, there are cases where there the solution cannot easily be inferred (at least without a true LM - this tokenizer is just a bunch of regexes). For instance, in cases where you have a fragment that contains the end of quote, but not its beginning, plus an accidental space:\r\n```\r\n... and it sounds fantastic, \" he said.\r\n```\r\nIn the above case, the tokenizer would assume that the quotes refer to the next token, and so upon detokenization it will result in the following mistake:\r\n```\r\n... and it sounds fantastic, \"he said.\r\n```\r\n\r\nWhile these are all odd edge cases (the basic assumptions do make sense), in noisy data they can occur, which is why I mentioned that the detokenizer cannot restore the original perfectly.\r\n", "To confirm, since this is preprocessed, this was not the exact version of the Book Corpus used to actually train the models described here (particularly Distilbert)? https://huggingface.co/datasets/bookcorpus\r\n\r\nOr does this preprocessing exactly match that of the papers?", "I believe these are just artifacts of this particular source. It might be better to crawl it again, or use another preprocessed source, as found here: https://github.com/soskek/bookcorpus ", "Yes actually the BookCorpus on hugginface is based on [this](https://github.com/soskek/bookcorpus/issues/24#issuecomment-643933352). And I kind of regret naming it as \"BookCorpus\" instead of something like \"BookCorpusLike\".\r\n\r\nBut there is a good news ! @shawwn has replicated BookCorpus in his way, and also provided a link to download the plain text files. see [here](https://github.com/soskek/bookcorpus/issues/27). There is chance we can have a \"OpenBookCorpus\" !", "Resolved via #856" ]

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[ "Thanks for adding this mirror link :)\r\n\r\nCould you run the following command to update the json file `dataset_infos.json` used to verify the integrity of the downloaded file ?\r\n\r\n```\r\nnlp-cli test ./datasets/rotten_tomatoes --save_infos --ignore_verifications\r\n```", "done! @lhoestq ", "the build_doc CI fail comes from master and has been fixed on master", "done @thomwolf @lhoestq " ]

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[ "found a mirror: https://storage.googleapis.com/seldon-datasets/sentence_polarity_v1/rt-polaritydata.tar.gz", "fixed in #484 ", "Closing this one. Thanks again @jxmorris12 for taking care of this :)" ]

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[ "This comes from an overflow in pyarrow's array.\r\nIt is stuck inside the loop that reduces the batch size to avoid the overflow.\r\nI'll take a look", "I created a PR to fix the issue.\r\nIt was due to an overflow check that handled badly an empty list.\r\n\r\nYou can try the changes by using \r\n```\r\n!pip install git+https://github.com/huggingface/nlp.git@fix-bad-type-in-overflow-check\r\n```\r\n\r\nAlso I noticed that the first 1000 examples have an empty list in the `title_urls` field. The feature type inference in `.map` will consider it `null` because of that, and it will crash when it encounter the next example with a `title_urls` that is not empty.\r\n\r\nTherefore to fix that, what you can do for now is increase the writer batch size so that the feature inference will take into account at least one example with a non-empty `title_urls`:\r\n\r\n```python\r\n# default batch size is 1_000 and it's not enough for feature type inference because of empty lists\r\nvalid_dataset = valid_dataset.map(make_input_target, writer_batch_size=3_000) \r\n```\r\n\r\nI was able to run the frozen cell with these changes.", "@lhoestq Perfect and thank you very much!!\r\nClose the issue.", "@lhoestq mapping the function `make_input_target` was passed by your fixing.\r\n\r\nHowever, there is another error in the final step of `valid_dataset.map(convert_to_features, batched=True)`\r\n\r\n`ArrowInvalid: Could not convert Thepiratebay.vg with type str: converting to null type`\r\n(The [same colab notebook above with new error message](https://colab.research.google.com/drive/14wttOTv3ky74B_c0kv5WrbgQjCF2fYQk?usp=sharing#scrollTo=5sRrJ3_C8rLt))\r\n\r\nDo you have some ideas? (I am really sorry I could not debug it by myself since I never used `pyarrow` before) \r\nNote that `train_dataset.map(convert_to_features, batched=True)` can be run successfully even though train_dataset is 27x bigger than `valid_dataset` so I believe the problem lies in some field of `valid_dataset` again .", "I got this issue too and fixed it by specifying `writer_batch_size=3_000` in `.map`.\r\nThis is because Arrow didn't expect `Thepiratebay.vg` in `title_urls `, as all previous examples have empty lists in `title_urls `", "I am clear now . Thank so much again Quentin!" ]

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[ "Not sure why the AWS test is failing - perhaps I made too many concurrent CI builds 😢. Can someone please rerun the CI to check the error is not on my end?", "I pushed an improved docstring and the unit tests now pass, which suggests the previous failure on AWS was simply a timeout error. \r\n\r\nFor some reason the docs are now failing to build, but does not seem related to my changes:\r\n```\r\nWarning, treated as error:\r\n/home/circleci/nlp/src/nlp/dataset_dict.py:docstring of nlp.DatasetDict.filter:27:Inline interpreted text or phrase reference start-string without end-string.\r\nmake: *** [Makefile:20: html] Error 2\r\n```\r\n\r\nAny ideas what's going wrong?", "The build_doc fail has been fixed on master.\r\nIt was due to the latest update of sphinx that has some issues, so I pinned the previous version for now.", "I noticed that you also changed the Apache Beam `open` to also use utf-8. However it doesn't have an `encoding` parameter.\r\nTherefore you should ignore lines like\r\n\r\n```python\r\nbeam.io.filesystems.FileSystems.open(filepath)\r\n```\r\n\r\nI guess you could add a rule to your regex to only include the `open` call that have a space right before it.", "Good catch @lhoestq! Your suggestion to match on `open(...)` with a whitespace was a great idea - it allowed me to simplify the regexp considerably 😄.\r\n\r\nI fixed the Apache Beam false positives and also caught a few problems in `json.load()`, e.g.\r\n```python\r\nrelation_name_map = json.load(open(rel_info), encoding='utf-8')\r\n```\r\n\r\nI've tested that the new regexp doesn't reintroduce these false positives, so I think the PR is ready for another review.", "Ok to merge this @lhoestq ?" ]

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[ "Looks good to me as well but we'll want to add a test indeed.\r\nYou can add one if you have time @TevenLeScao.\r\nOtherwise, we'll do it when we are back with Quentin. ", "I fixed it in #494 " ]