Datasets:

spasis

/

datasets-github-issues

like 0

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[ "Awesome!\r\nLet's have this in the doc at the end :-)" ]

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[ "Test failure is not related to change in test file.\r\n" ]

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[ "Whoops I fixed it directly on master before checking that you have done it in this PR. We may close it", "Yeah, I saw :-) But I think we should add this as well since some datasets have an empty list of configs and then as the code is now it would fail. \r\n\r\nIn this PR, I just make sure that the code jumps in the correct else if \"there are no configs\" as is the case for some datasets @mariamabarham ", "Sorry, I thought you meant a different commit . Just saw this one: https://github.com/huggingface/nlp/commit/7c644f284e2303b57612a6e7c904fe13906d893f\r\n.\r\n\r\nAll good then :-) " ]

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[ "@lhoestq - could you take a look? It's not very urgent though!", "Fixed with 06882b4\r\n\r\nNow your command works :)\r\nNote that you can also do\r\n```\r\nnlp-cli test datasets/nlp/xnli --save_checksums\r\n```\r\nSo that it will save the checksums directly in the right directory.", "Awesome!" ]

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[ "Note:\r\n```\r\nnlp-cli test datasets/nlp/<your-dataset-folder> --save_checksums --all_configs\r\n```\r\ndirectly saves the checksums in the right place, and runs for all the dataset configurations.", "@patrickvonplaten can you provide the add the link to the PR for the dummy data? ", "https://github.com/huggingface/nlp/pull/15 - But it's probably best to checkout into this branch and look how the dummy data strtucture is for `squad` for example.", "are lock files supposed to stay ?", "> are lock files supposed to stay ?\r\n\r\nNot sure! I think the checksum command creates them, so I just uploaded them as well.", "We can trash the `lock` file, they are dummy file that are only used to avoid concurrent access when the library is run.\r\nYou can read the filelock readme and code, it's a very simple single-file library: https://github.com/benediktschmitt/py-filelock", "The testing design was slightly changed as explained in https://github.com/huggingface/nlp/pull/51 . \r\nIf creating the dummy folder is too confusing it helps to upload everything else to AWS, then run the test and check the INFO when testing on how to create the dummy folder structure.", "Closing because we can now work on master" ]

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[ "Ok, this one seems to be ready to merge.", "> Really cool, I love it! I would just raise a tiny point, the distributive version of the metrics might not work properly with TF because it is a different way to do, why not to add a \"framework\" detection and raise warning when TF is used, saying something like \"not available yet in TF switch to non distributive metric computation\".\r\n> \r\n> What do you think?\r\n\r\nGood point @jplu I'm not sure how you should do distributed metrics evaluation for TF.\r\nThere is only one python script, right?\r\nMaybe it's just the same as in the not-distributed case?", "I think non-distributed case should work in TF for both cases indeed, but this needs to be tested." ]

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[ "Great! I think when this merged, we can merge sure that Circle Ci stays happy when uploading new datasets. " ]

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[ "Ok merging! I think it's good now" ]

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[ "Very interesting thoughts, we should think deeper about all what you raised indeed.", "Ok here is a proposal for a more general API and workflow.\r\n\r\n# New `ArrowBasedBuilder`\r\n\r\nFor all the formats that can be directly and efficiently loaded by Arrow (CSV, JSON, Parquet, Arrow), we don't really want to have to go through a conversion to python and back to Arrow. This new builder has a `_generate_tables` method to yield `Arrow.Tables` instead of single examples.\r\nThe tables can be directly casted in Arrow so it's not necessary to supply `Features`, they can be deduced from the `Table` column.\r\n\r\n# Central role of the `BuilderConfig` to store all the arguments necessary for the Dataset creation.\r\n \r\n`BuilderConfig` provide a few defaults fields `name`, `version`, `description`, `data_files` and `data_dir` which can be used to store values necessary for the creation of the dataset. It can be freely extended to store additional information (see the example for `CsvConfig`).\r\n\r\nOn the contrary, `DatasetInfo` is designed as an organized and delimited information storage class with predefined fields.\r\n\r\n`DatasetInfo` now store two names:\r\n- `builder_name`: Name of the builder script used to create the dataset\r\n- `config_name`: Name of the configuration used to create the dataset.\r\n\r\n# Refactoring `load()` arguments and all the chain of processing including the `DownloadManager`\r\n\r\n`load()` now accept a selection of arguments which are used to update the `BuilderConfig` and some kwargs which are used to handle the download process.\r\n\r\nSupplying a `BuilderConfig` as `config` will override the config provided in the dataset. Supplying a `str` will get the associated config from the dataset. Default is to fetch the first config of the dataset.\r\n\r\nGiving additional arguments to `load()` will override the arguments in the `BuilderConfig`.\r\n\r\n# CSV script\r\n\r\nThe `csv.py` script is provided as an example, usage is:\r\n```python\r\nbbc = nlp.load('/Users/thomwolf/Documents/GitHub/datasets/datasets/nlp/csv',\r\n name='bbc',\r\n version=\"1.0.1\",\r\n split='train',\r\n data_files={'train': ['/Users/thomwolf/Documents/GitHub/datasets/datasets/dummy_data/csv/test.csv']},\r\n skip_rows=10,\r\n download_mode='force_redownload')\r\n```\r\n\r\n# Checksums\r\n\r\nWe now don't raise an error if the checksum file is not found.\r\n\r\n# `DownloadConfig`\r\n\r\nWe now have a download configuration class to handle all the specific arguments for file caching like proxies, using only local files or user-agents.", "Ok merging this for now.\r\n\r\nOne general note is that it's a bit hard to handle the `ClassLabel` generally in both `nlp` and `Arrow` since a class label typically need some metadata for the class names. For now, I raise a `NotImplementedError` when an `ArrowBuilder` output a table with a `DictionaryType` is encountered (which could be a simple equivalent for a `ClassLabel` Feature in Arrow tables).\r\n\r\nIn general and if we need this in the future for some Beam Datasets for instance, I think we should use one of the `metadata` fields in the `Arrow` type or table's schema to store the relation with indices and class names.\r\n\r\nSo ping me if you meet Beam datasets which uses `ClassLabels` (cc @lhoestq @patrickvonplaten @mariamabarham)." ]

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[ "Looks good to me :-) \r\n\r\nJust would prefer to get rid of the `_DYNAMICALLY_IMPORTED_MODULE` attribute and replace it by a `get_imported_module()` function. Maybe there is something I'm not seeing here though - what do you think? ", "> * I'm not sure I understand the general organization of checksums. I see we have a checksum folder with potentially several checksum files but I also see that checksum files can potentially contain several checksums. Could you explain a bit more how this is organized?\r\n\r\nIt should look like this:\r\nsquad/\r\n├── squad.py/\r\n└── urls_checksums/\r\n...........└── checksums.txt\r\n\r\nIn checksums.txt, the format is one line per (url, size, checksum)\r\n\r\nI don't have a strong opinion between `urls_checksums/checksums.txt` or directly `checksums.txt` (not inside the `urls_checksums` folder), let me know what you think.\r\n\r\n\r\n> * Also regarding your comment on checksum files for \"canonical\" datasets. I understand we can just create these with `nlp-cli test` and then upload them manually to our S3, right?\r\n\r\nYes you're right", "Update of the commands:\r\n\r\n- nlp-cli test \\<dataset\\> : Run download_and_prepare and verify checksums\r\n * --name \\<name\\> : run only for the name\r\n * --all_configs : run for all configs\r\n * --save_checksums : instead of verifying checksums, compute and save them\r\n * --ignore_checksums : don't do checksums verification\r\n\r\n- nlp-cli upload \\<dataset_folder\\> : Upload a dataset\r\n * --upload_checksums : compute and upload checksums for uploaded files\r\n\r\nTODO:\r\n- don't overwrite checksums files on S3, to let the user upload a dataset in several steps if needed\r\n\r\nQuestion:\r\n- One idea from @patrickvonplaten : shall we upload checksums everytime we upload files ? (and therefore remove the upload_checksums parameter)", "Ok, ready to merge, then @lhoestq ?", "Yep :)" ]

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[ "For conflicts, I think the mention hint \"This should be modified because it mentions ...\" is missing.", "Looks great!" ]

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[ "Awesome!" ]

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[ "LGTM" ]

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[ "Looks great to me! ", "The new download manager is ready. I removed the old folder and I fixed a few remaining dependencies.\r\nI tested it on squad and a few others from the dataset folder and it works fine.\r\n\r\nThe only impact of these changes is that it breaks the `download_and_prepare` script that was used to register the checksums when we create a dataset, as the checksum logic is not implemented.\r\n\r\nLet me know if you have remarks", "Ok merged it (a bit fast for you to update the copyright, now I see that. but it's ok, we'll do a pass on these doc/copyright before releasing anyway)", "Actually two additional things here @lhoestq (I merged too fast sorry, let's make a new PR for additional developments):\r\n- I think we can remove some dependencies now (e.g. `promises`) in setup.py, can you have a look?\r\n- also, I think we can remove the boto3 dependency like here: https://github.com/huggingface/transformers/pull/3968" ]

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[ "> I think I'm fine with this.\r\n> \r\n> The alternative would be to host a small subset of the dataset on the S3 together with the testing script. But I think having all (test file creation + actual tests) in one file is actually quite convenient.\r\n> \r\n> Good for me!\r\n> \r\n> One question though, will we have to create one test file for each of the 100+ datasets or could we make some automatic conversion from tfds dataset test files?\r\n\r\nI think if we go the way shown in the PR we would have to create one test file for each of the 100+ datasets. \r\n\r\nAs far as I know the tfds test files all rely on the user having created a special download folder structure in `tensorflow-datasets/tensorflow_datasets/testing/test_data/fake_examples`. \r\n\r\nMy hypothesis was: \r\nBecasue, we don't want to work with PRs, no `dataset_script` is going to be in the official repo, so no `dataset_script_test` can be in the repo either. Therefore we can also not have any \"fake\" test folder structure in the repo. \r\n\r\n**BUT:** As you mentioned @thom, we could have a fake data structure on AWS. To add a test the user has to upload multiple small test files when uploading his data set script. \r\n\r\nSo for a cli this could look like:\r\n`python nlp-cli upload <data_set_script> --testfiles <relative path to test file 1> <relative path to test file 2> ...` \r\n\r\nor even easier if the user just creates the dataset folder with the script inside and the testing folder structure, then the API could look like:\r\n\r\n`python nlp-cli upload <path/to/dataset/folder>`\r\n\r\nand the dataset folder would look like\r\n```\r\nsquad\r\n- squad.py\r\n- fake_data # this dir would have to have the exact same structure we get when downloading from the official squad data url\r\n```\r\n\r\nThis way I think we wouldn't even need any test files at all for each dataset script. For special datasets like `c4` or `wikipedia` we could then allow to optionally upload another test script. \r\nWe just assume that this is our downloaded `url` and check all functionality from there. \r\n\r\nThinking a bit more about this solution sounds a) much less work and b) even easier for the user.\r\n\r\nA small problem I see here though:\r\n1) What do we do when the depending on the config name the downloaded folder structure is very different? I think for each dataset config name we should have one test, which could correspond to one \"fake\" folder structure on AWS\r\n\r\n@thomwolf What do you think? I would actually go for this solution instead now.\r\n@mariamabarham You have written many more tfds dataset scripts and tests than I have - what do you think? \r\n\r\n", "Regarding the tfds tests, I don't really see a point in keeping them because:\r\n\r\n1) If you provide a fake data structure, IMO there is no need for each dataset to have an individual test file because (I think) most datasets have the same functions `_split_generators` and `_generate_examples` for which you can just test the functionality in a common test file. For special functions like these beam / pipeline functionality you probably need an extra test file. But @mariamabarham I think you have seen more than I have here as well \r\n\r\n2) The dataset test design is very much intertwined with the download manager design and contains a lot of code. I would like to seperate the tests into a) tests for downloading in general b) tests for post download data set pre-processing. Since we are going to change the download code anyways quite a lot, my plan was to focus on b) first. ", "I like the idea of having a fake data folder on S3. I have seen datasets with nested compressed files structures that would be tedious to generate with code. And for users it is probably easier to create a fake data folder by taking a subset of the actual data, and then upload it as you said.", "> > I think I'm fine with this.\r\n> > The alternative would be to host a small subset of the dataset on the S3 together with the testing script. But I think having all (test file creation + actual tests) in one file is actually quite convenient.\r\n> > Good for me!\r\n> > One question though, will we have to create one test file for each of the 100+ datasets or could we make some automatic conversion from tfds dataset test files?\r\n> \r\n> I think if we go the way shown in the PR we would have to create one test file for each of the 100+ datasets.\r\n> \r\n> As far as I know the tfds test files all rely on the user having created a special download folder structure in `tensorflow-datasets/tensorflow_datasets/testing/test_data/fake_examples`.\r\n> \r\n> My hypothesis was:\r\n> Becasue, we don't want to work with PRs, no `dataset_script` is going to be in the official repo, so no `dataset_script_test` can be in the repo either. Therefore we can also not have any \"fake\" test folder structure in the repo.\r\n> \r\n> **BUT:** As you mentioned @thom, we could have a fake data structure on AWS. To add a test the user has to upload multiple small test files when uploading his data set script.\r\n> \r\n> So for a cli this could look like:\r\n> `python nlp-cli upload <data_set_script> --testfiles <relative path to test file 1> <relative path to test file 2> ...`\r\n> \r\n> or even easier if the user just creates the dataset folder with the script inside and the testing folder structure, then the API could look like:\r\n> \r\n> `python nlp-cli upload <path/to/dataset/folder>`\r\n> \r\n> and the dataset folder would look like\r\n> \r\n> ```\r\n> squad\r\n> - squad.py\r\n> - fake_data # this dir would have to have the exact same structure we get when downloading from the official squad data url\r\n> ```\r\n> \r\n> This way I think we wouldn't even need any test files at all for each dataset script. For special datasets like `c4` or `wikipedia` we could then allow to optionally upload another test script.\r\n> We just assume that this is our downloaded `url` and check all functionality from there.\r\n> \r\n> Thinking a bit more about this solution sounds a) much less work and b) even easier for the user.\r\n> \r\n> A small problem I see here though:\r\n> \r\n> 1. What do we do when the depending on the config name the downloaded folder structure is very different? I think for each dataset config name we should have one test, which could correspond to one \"fake\" folder structure on AWS\r\n> \r\n> @thomwolf What do you think? I would actually go for this solution instead now.\r\n> @mariamabarham You have written many more tfds dataset scripts and tests than I have - what do you think?\r\n\r\nI'm agreed with you just one thing, for some dataset like glue or xtreme you may have multiple datasets in it. so I think a good way is to have one main fake folder and a subdirectory for each dataset inside", "> Regarding the tfds tests, I don't really see a point in keeping them because:\r\n> \r\n> 1. If you provide a fake data structure, IMO there is no need for each dataset to have an individual test file because (I think) most datasets have the same functions `_split_generators` and `_generate_examples` for which you can just test the functionality in a common test file. For special functions like these beam / pipeline functionality you probably need an extra test file. But @mariamabarham I think you have seen more than I have here as well\r\n> 2. The dataset test design is very much intertwined with the download manager design and contains a lot of code. I would like to seperate the tests into a) tests for downloading in general b) tests for post download data set pre-processing. Since we are going to change the download code anyways quite a lot, my plan was to focus on b) first.\r\n\r\nFor _split_generator, yes. But I'm not sure for _generate_examples because there is lots of things that should be taken into account such as feature names and types, data format (json, jsonl, csv, tsv,..)", "Sounds good to me!\r\n\r\nWhen testing, we could thus just override the prefix in the URL inside the download manager to have them point to the test directory on our S3.\r\n\r\nCc @lhoestq ", "Ok, here is a second draft for the testing structure. \r\n\r\nI think the big difficulty here is \"How can you generate tests on the fly from a given dataset name, *e.g.* `squad`\"?\r\n\r\nSo, this morning I did some research on \"parameterized testing\" and pure `unittest` or `pytest` didn't work very well. \r\nI found the lib https://github.com/wolever/parameterized, which works very nicely for our use case I think. \r\n@thomwolf - would it be ok to have a dependence on this lib for `nlp`? It seems like a light-weight lib to me. \r\n\r\nThis lib allows to add a `parameterization` decorator to a `unittest.TestCase` class so that the class can be instantiated for multiple different arguments (which are the dataset names `squad` etc. in our case).\r\n\r\nWhat I like about this lib is that one only has to add the decorator and the each of the parameterized tests are shown, like this: \r\n\r\n\r\n\r\nWith this structure we would only have to upload the dummy data for each dataset and would not require a specific testing file. \r\n\r\nWhat do you think @thomwolf @mariamabarham @lhoestq ?", "I think this is a nice solution.\r\n\r\nDo you think we could have the `parametrized` dependency in a `[test]` optional installation of `setup.py`? I would really like to keep the dependencies of the standard installation as small as possible. ", "> I think this is a nice solution.\r\n> \r\n> Do you think we could have the `parametrized` dependency in a `[test]` optional installation of `setup.py`? I would really like to keep the dependencies of the standard installation as small as possible.\r\n\r\nYes definitely!", "UPDATE: \r\n\r\nThis test design is ready now. I added dummy data to S3 for the dataests: `squad, crime_and_punish, sentiment140` . The structure can be seen on `https://s3.console.aws.amazon.com/s3/buckets/datasets.huggingface.co/nlp/squad/dummy/?region=us-east-1&tab=overview` for `squad`. \r\n\r\nAll dummy data files have to be in .zip format and called `dummy_data.zip`. The zip file should thereby have the exact same folder structure one gets from downloading the \"real\" data url(s). \r\n\r\nTo show how the .zip file looks like for the added datasets, I added the folder `nlp/datasets/dummy_data` in this PR. I think we can leave for the moment so that people can see better how to add dummy data tests and later delete it like `nlp/datasets/nlp`." ]

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[ "I think this can be quickly reproduced. \r\nI use `black, version 19.10b0`. \r\n\r\nWhen running: \r\n`black nlp/src/arrow_reader.py` \r\nit gives me: \r\n\r\n```\r\nerror: cannot format /home/patrick/hugging_face/nlp/src/nlp/arrow_reader.py: cannot use --safe with this file; failed to parse source file. AST error message: invalid syntax (<unknown>, line 78)\r\nOh no! 💥 💔 💥\r\n1 file failed to reformat.\r\n```\r\n\r\nThe line in question is: \r\nhttps://github.com/huggingface/nlp/blob/6922a16705e61f9e31a365f2606090b84d49241f/src/nlp/arrow_reader.py#L78\r\n\r\nWhat is weird is that the trainer file in `transformers` has more or less the same syntax and black does not fail there: \r\nhttps://github.com/huggingface/transformers/blob/cb3c2212c79d7ff0a4a4e84c3db48371ecc1c15d/src/transformers/trainer.py#L95\r\n\r\nI googled quite a bit about black & typing hints yesterday and didn't find anything useful. \r\nAny ideas @thomwolf @julien-c @LysandreJik ?", "> I think this can be quickly reproduced.\r\n> I use `black, version 19.10b0`.\r\n> \r\n> When running:\r\n> `black nlp/src/arrow_reader.py`\r\n> it gives me:\r\n> \r\n> ```\r\n> error: cannot format /home/patrick/hugging_face/nlp/src/nlp/arrow_reader.py: cannot use --safe with this file; failed to parse source file. AST error message: invalid syntax (<unknown>, line 78)\r\n> Oh no! 💥 💔 💥\r\n> 1 file failed to reformat.\r\n> ```\r\n> \r\n> The line in question is:\r\n> https://github.com/huggingface/nlp/blob/6922a16705e61f9e31a365f2606090b84d49241f/src/nlp/arrow_reader.py#L78\r\n> \r\n> What is weird is that the trainer file in `transformers` has more or less the same syntax and black does not fail there:\r\n> https://github.com/huggingface/transformers/blob/cb3c2212c79d7ff0a4a4e84c3db48371ecc1c15d/src/transformers/trainer.py#L95\r\n> \r\n> I googled quite a bit about black & typing hints yesterday and didn't find anything useful.\r\n> Any ideas @thomwolf @julien-c @LysandreJik ?\r\n\r\nOk I found the problem. It was the one Julien mentioned and has nothing to do with this line. Black's error message is a bit misleading here, I guess", "Ok, just had to remove the python 2 syntax comments `# type`. \r\n\r\nGood to merge for me now @thomwolf " ]

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[ "Also added to an assert statement that if a dict is returned by function, all values of `dicts` are `lists`", "Wait to merge until `make style` is set in place.", "Updated the assert statements. Played around with multiple cases and it should be good now IMO. " ]

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[ "Yes!" ]

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[ "Yes looks good to me.\r\nNote that we may refactor quite strongly the `info.py` to make it a lot simpler (it's very complicated for basically a dictionary of info I think)", "No, problem ^^ It might just be a temporary fix :)", "Fixed." ]

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[ "To fix this I propose to modify only the file `arrow_reader.py` with few updates. First update, the following method:\r\n```python\r\ndef _make_file_instructions_from_absolutes(\r\n name,\r\n name2len,\r\n absolute_instructions,\r\n):\r\n \"\"\"Returns the files instructions from the absolute instructions list.\"\"\"\r\n # For each split, return the files instruction (skip/take)\r\n file_instructions = []\r\n num_examples = 0\r\n for abs_instr in absolute_instructions:\r\n length = name2len[abs_instr.splitname]\r\n if not length:\r\n raise ValueError(\r\n 'Split empty. This might means that dataset hasn\\'t been generated '\r\n 'yet and info not restored from GCS, or that legacy dataset is used.')\r\n filename = filename_for_dataset_split(\r\n dataset_name=name,\r\n split=abs_instr.splitname,\r\n filetype_suffix='arrow')\r\n from_ = 0 if abs_instr.from_ is None else abs_instr.from_\r\n to = length if abs_instr.to is None else abs_instr.to\r\n num_examples += to - from_\r\n single_file_instructions = [{\"filename\": filename, \"skip\": from_, \"take\": to - from_}]\r\n file_instructions.extend(single_file_instructions)\r\n return FileInstructions(\r\n num_examples=num_examples,\r\n file_instructions=file_instructions,\r\n )\r\n```\r\nBecomes:\r\n```python\r\ndef _make_file_instructions_from_absolutes(\r\n name,\r\n name2len,\r\n absolute_instructions,\r\n):\r\n \"\"\"Returns the files instructions from the absolute instructions list.\"\"\"\r\n # For each split, return the files instruction (skip/take)\r\n file_instructions = []\r\n num_examples = 0\r\n for abs_instr in absolute_instructions:\r\n length = name2len[abs_instr.splitname]\r\n ## Delete the if not length and the raise\r\n filename = filename_for_dataset_split(\r\n dataset_name=name,\r\n split=abs_instr.splitname,\r\n filetype_suffix='arrow')\r\n from_ = 0 if abs_instr.from_ is None else abs_instr.from_\r\n to = length if abs_instr.to is None else abs_instr.to\r\n num_examples += to - from_\r\n single_file_instructions = [{\"filename\": filename, \"skip\": from_, \"take\": to - from_}]\r\n file_instructions.extend(single_file_instructions)\r\n return FileInstructions(\r\n num_examples=num_examples,\r\n file_instructions=file_instructions,\r\n )\r\n```\r\n\r\nSecond update the following method:\r\n```python\r\ndef _read_files(files, info):\r\n \"\"\"Returns Dataset for given file instructions.\r\n\r\n Args:\r\n files: List[dict(filename, skip, take)], the files information.\r\n The filenames contain the absolute path, not relative.\r\n skip/take indicates which example read in the file: `ds.slice(skip, take)`\r\n \"\"\"\r\n pa_batches = []\r\n for f_dict in files:\r\n pa_table: pa.Table = _get_dataset_from_filename(f_dict)\r\n pa_batches.extend(pa_table.to_batches())\r\n pa_table = pa.Table.from_batches(pa_batches)\r\n ds = Dataset(arrow_table=pa_table, data_files=files, info=info)\r\n return ds\r\n```\r\nBecomes:\r\n```python\r\ndef _read_files(files, info):\r\n \"\"\"Returns Dataset for given file instructions.\r\n\r\n Args:\r\n files: List[dict(filename, skip, take)], the files information.\r\n The filenames contain the absolute path, not relative.\r\n skip/take indicates which example read in the file: `ds.slice(skip, take)`\r\n \"\"\"\r\n pa_batches = []\r\n for f_dict in files:\r\n pa_table: pa.Table = _get_dataset_from_filename(f_dict)\r\n pa_batches.extend(pa_table.to_batches())\r\n ## we modify the table only if there are some batches\r\n if pa_batches:\r\n pa_table = pa.Table.from_batches(pa_batches)\r\n ds = Dataset(arrow_table=pa_table, data_files=files, info=info)\r\n return ds\r\n```\r\n\r\nWith these two updates it works now. @thomwolf are you ok with this changes?", "Yes sounds good to me!\r\nDo you want to make a PR? or I can do it as well", "Fixed." ]

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[ "Yes! I see mostly two options for this:\r\n- a `Feature` approach like currently (but we might deprecate features)\r\n- wrapping in a smart way the Dictionary arrays of Arrow: https://arrow.apache.org/docs/python/data.html?highlight=dictionary%20encode#dictionary-arrays", "I would have a preference for the second bullet point.", "This should be accessible now as a feature in dataset.info.features (and even have the mapping methods).", "Perfect! Well done!!", "Hi,\r\nI hope we could get a better documentation.\r\nIt took me more than 1 hour to found this way to get the label information.", "Yes we are working on the doc right now, should be in the next release quite soon." ]

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[ "Yes!\r\n- pandas will be a one-liner in `arrow_dataset`: https://arrow.apache.org/docs/python/generated/pyarrow.Table.html#pyarrow.Table.to_pandas\r\n- for Spark I have no idea. let's investigate that at some point", "For Spark it looks to be pretty straightforward as well https://spark.apache.org/docs/latest/sql-pyspark-pandas-with-arrow.html but looks to be having a dependency to Spark is necessary, then nevermind we can skip it", "Now Pandas is available." ]

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[ "My first bug report ❤️\r\nLooking into this right now!", "Ok, there are some news, most good than bad :laughing: \r\n\r\nThe dataset script now became:\r\n```python\r\nimport csv\r\n\r\nimport nlp\r\n\r\n\r\nclass Bbc(nlp.GeneratorBasedBuilder):\r\n VERSION = nlp.Version(\"1.0.0\")\r\n\r\n def __init__(self, **config):\r\n self.train = config.pop(\"train\", None)\r\n self.validation = config.pop(\"validation\", None)\r\n super(Bbc, self).__init__(**config)\r\n\r\n def _info(self):\r\n return nlp.DatasetInfo(builder=self, description=\"bla\", features=nlp.features.FeaturesDict({\"id\": nlp.int32, \"text\": nlp.string, \"label\": nlp.string}))\r\n\r\n def _split_generators(self, dl_manager):\r\n return [nlp.SplitGenerator(name=nlp.Split.TRAIN, gen_kwargs={\"filepath\": self.train}),\r\n nlp.SplitGenerator(name=nlp.Split.VALIDATION, gen_kwargs={\"filepath\": self.validation})]\r\n\r\n def _generate_examples(self, filepath):\r\n with open(filepath) as f:\r\n reader = csv.reader(f, delimiter=',', quotechar=\"\\\"\")\r\n lines = list(reader)[1:]\r\n\r\n for idx, line in enumerate(lines):\r\n yield idx, {\"id\": idx, \"text\": line[1], \"label\": line[0]}\r\n\r\n```\r\n\r\nAnd the dataset folder becomes:\r\n```\r\n.\r\n├── bbc\r\n│ ├── bbc.py\r\n│ └── data\r\n│ ├── test.csv\r\n│ └── train.csv\r\n```\r\nI can load the dataset by using the keywords arguments like this:\r\n```python\r\nimport nlp\r\ndataset = nlp.load(\"bbc\", builder_kwargs={\"train\": \"bbc/data/train.csv\", \"validation\": \"bbc/data/test.csv\"})\r\n```\r\n\r\nThat was the good part ^^ Because it took me some time to understand that the script itself is put in cache in `datasets/src/nlp/datasets/some-hash/bbc.py` which is very difficult to discover without checking the source code. It means that doesn't matter the changes you do to your original script it is taken into account. I think instead of doing a hash on the name (I suppose it is the name), a hash on the content of the script itself should be a better solution.\r\n\r\nThen by diving a bit in the code I found the `force_reload` parameter [here](https://github.com/huggingface/datasets/blob/master/src/nlp/load.py#L50) but the call of this `load_dataset` method is done with the `builder_kwargs` as seen [here](https://github.com/huggingface/datasets/blob/master/src/nlp/load.py#L166) which is ok until the call to the builder is done as the builder do not have this `force_reload` parameter. To show as example, the previous load becomes:\r\n```python\r\nimport nlp\r\ndataset = nlp.load(\"bbc\", builder_kwargs={\"train\": \"bbc/data/train.csv\", \"validation\": \"bbc/data/test.csv\", \"force_reload\": True})\r\n```\r\nRaises\r\n```\r\nTraceback (most recent call last):\r\n File \"<stdin>\", line 1, in <module>\r\n File \"/home/jplu/dev/jplu/datasets/src/nlp/load.py\", line 283, in load\r\n dbuilder: DatasetBuilder = builder(path, name, data_dir=data_dir, **builder_kwargs)\r\n File \"/home/jplu/dev/jplu/datasets/src/nlp/load.py\", line 170, in builder\r\n builder_instance = builder_cls(**builder_kwargs)\r\n File \"/home/jplu/dev/jplu/datasets/src/nlp/datasets/84d638d2a8ca919d1021a554e741766f50679dc6553d5a0612b6094311babd39/bbc.py\", line 12, in __init__\r\n super(Bbc, self).__init__(**config)\r\nTypeError: __init__() got an unexpected keyword argument 'force_reload'\r\n```\r\nSo yes the cache is refreshed with the new script but then raises this error.", "Ok great, so as discussed today, let's:\r\n- have a main dataset directory inside the lib with sub-directories hashed by the content of the file\r\n- keep a cache for downloading the scripts from S3 for now\r\n- later: add methods to list and clean the local versions of the datasets (and the distant versions on S3 as well)\r\n\r\nSide question: do you often use `builder_kwargs` for other things than supplying file paths? I was thinking about having a more easy to read and remember `data_files` argument maybe.", "Good plan!\r\n\r\nYes I do use `builder_kwargs` for other things such as:\r\n- dataset name\r\n- properties to know how to properly read a CSV file: do I have to skip the first line in a CSV, which delimiter is used, and the columns ids to use.\r\n- properties to know how to properly read a JSON file: which properties in a JSON object to read", "Done!" ]

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