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[ "There was an issue in the format, thanks.\r\nNow you can do\r\n```python3\r\nsquad_dset = nlp.load_dataset(\"squad\")\r\nsquad_metric = nlp.load_metric(\"/Users/quentinlhoest/Desktop/hf/nlp-bis/metrics/squad\")\r\npredictions = [\r\n {\"id\": v[\"id\"], \"prediction_text\": v[\"answers\"][\"text\"][0]} # take first possible answer\r\n for v in squad_dset[\"validation\"]\r\n]\r\nsquad_metric.compute(predictions, squad_dset[\"validation\"])\r\n```\r\n\r\nand the expected format is \r\n```\r\nArgs:\r\n predictions: List of question-answers dictionaries with the following key-values:\r\n - 'id': id of the question-answer pair as given in the references (see below)\r\n - 'prediction_text': the text of the answer\r\n references: List of question-answers dictionaries with the following key-values:\r\n - 'id': id of the question-answer pair (see above),\r\n - 'answers': a Dict {'text': list of possible texts for the answer, as a list of strings}\r\n```" ]

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[ "Same for the dataset_infos.json at the project root no ?", "Sorry guys, I haven't noticed. Thank you for mentioning it." ]

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[ "You are right and I think those are usual best practice :) I'm 100% fine with this^^", "Merging this for now to unblock other PRs." ]

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[ "Had really fun playing around with this new library :heart: ", "That's awesome - thanks @stefan-it :-) \r\n\r\nCould you maybe rebase to master and check if all dummy data tests are fine. I should have included the local tests directly in the test suite so that all PRs are fully checked: #140 - sorry :D ", "@patrickvonplaten Rebased it 😅\r\n\r\nHow can it test 🤔 I used:\r\n\r\n```bash\r\nRUN_SLOW=1 RUN_LOCAL=1 pytest tests/test_dataset_common.py::DatasetTest::test_load_real_dataset_local_germeval_14\r\n# and\r\nRUN_SLOW=1 RUN_LOCAL=1 pytest tests/test_dataset_common.py::DatasetTest::test_load_dataset_all_configs_local_germeval_14\r\n```\r\n\r\nand the tests still pass :)", "Perfect, if these tests pass that's great - I'll merge the PR then :-) Was it very difficult to create the dummy data structure? " ]

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[ "I would suggest `nlds`. NLP is a very general, broad and ambiguous term, the library is not about NLP (as in processing) per se, it is about accessing Natural Language related datasets. So the name should reflect its purpose.\r\n", "Chiming in to second everything @honnibal said, and to add that I think the current name is going to impact the discoverability of this library. People who are looking for \"NLP Datasets\" through a search engine are going to see a library called `nlp` and think it's too broad. People who are looking to do NLP in python are going to search \"Python NLP\" and end up here, confused that this is a collection of datasets.\r\n\r\nThe names of the other huggingface libraries work because they're the only game in town: there are not very many robust, distinct libraries for `tokenizers` or `transformers` in python, for example. But there are several options for NLP in python, and adding this as a possible search result for \"python nlp\" when datasets are likely not what someone is searching for adds noise and frustrates potential users.", "I'm also not sure whether the naming of `nlp` is the problem itself, as long as it comes with the appropriate identifier, so maybe something like `huggingface_nlp`? This is analogous to what @honnibal and spacy are doing for `spacy-transformers`. Of course, this is a \"step back\" from the recent changes/renaming of transformers, but may be some middle ground between a complete rebranding, and keeping it identifiable.", "Interesting, thanks for sharing your thoughts.\r\n\r\nAs we’ll move toward a first non-beta release, we will pool the community of contributors/users of the library for their opinions on a good final name (like when we renamed the beautifully (?) named `pytorch-pretrained-bert`)\r\n\r\nIn the meantime, using `from nlp import load_dataset, load_metric` should work 😉", "I feel like we are conflating two distinct subjects here:\r\n\r\n1. @honnibal's point is that using `nlp` as a package name might break existing code and bring developer usability issues in the future\r\n2. @pmbaumgartner's point is that the `nlp` package name is too broad and shouldn't be used by a package that exposes only datasets and metrics\r\n\r\n(let me know if I mischaracterize your point)\r\n\r\nI'll chime in to say that the first point is a bit silly IMO. As Python developers due to the limitations of the import system we already have to share:\r\n- a single flat namespace for packages\r\n- which also conflicts with local modules i.e. local files\r\n\r\nIf we add the constraint that this flat namespace also be shared with variable names this gets untractable pretty fast :)\r\n\r\nI also think all Python software developers/ML engineers/scientists are capable of at least a subset of:\r\n- importing only the methods that they need like @thomwolf suggested\r\n- aliasing their import\r\n- renaming a local variable", "By the way, `nlp` will very likely not be only about datasets, and not even just about datasets and metrics.\r\n\r\nI see it as a laboratory for testing several long-term ideas about how we could do NLP in terms of research as well as open-source and community sharing, most of these ideas being too experimental/big to fit in `transformers`.\r\n\r\nSome of the directions we would like to explore are about sharing, traceability and more experimental models, as well as seeing a model as the community-based process of creating a composite entity from data, optimization, and code.\r\n\r\nWe'll see how these ideas end up being implemented and we'll better know how we should define the library when we start to dive into these topics. I'll try to get the `nlp` team to draft a roadmap on these topics at some point.", "> If we add the constraint that this flat namespace also be shared with variable names this gets untractable pretty fast :)\r\n\r\nI'm sort of confused by your point here. The namespace *is* shared by variable names. You should not use local variables that are named the same as modules, because then you cannot use the module within the scope of your function.\r\n\r\nFor instance,\r\n\r\n```python\r\n\r\nimport nlp\r\nimport transformers\r\n\r\nnlp = transformers.pipeline(\"sentiment-analysis\")\r\n```\r\n\r\nThis is a bug: you've just overwritten the module, so now you can't use it. Or instead:\r\n\r\n```python\r\n\r\nimport transformers\r\n\r\nnlp = transformers.pipeline(\"sentiment-analysis\")\r\n# (Later, e.g. in a notebook)\r\nimport nlp\r\n```\r\n\r\nThis is also a bug: you've overwritten your variable with an import.\r\n\r\nIf you have a module named `nlp`, you should avoid using `nlp` as a variable, or you'll have bugs in some contexts and inconsistencies in other contexts. You'll have situations where you need to import differently in one module vs another, or name variables differently in one context vs another, which is bad.\r\n\r\n> importing only the methods that they need like @thomwolf suggested\r\n\r\nOkay but the same logic applies to naming the module *literally anything else*. There's absolutely no point in having a module name that's 3 letters if you always plan to do `import from`! It would be entirely better to name it `nlp_datasets` if you don't want people to do `import nlp`.\r\n\r\nAnd finally:\r\n\r\n> By the way, nlp will very likely not be only about datasets, and not even just about datasets and metrics.\r\n\r\nSo...it isn't a datasets library? https://twitter.com/Thom_Wolf/status/1261282491622731781\r\n\r\nI'm confused 😕 ", "Dropping by as I noticed that the library has been renamed `datasets` so I wonder if the conversation above is settled (`nlp` not used anymore) :) ", "I guess indeed", "I'd argue that `datasets` is worse than `nlp`. Datasets should be a user specific decision and not encapsulate all of python (`pip install datasets`). If this package contained every dataset in the world (NLP / vision / etc) then it would make sense =/", "I can't speak for the HF team @jramapuram, but as member of the community it looks to me that HF wanted to avoid the past path of changing names as scope broadened over time:\r\n\r\nRemember\r\nhttps://github.com/huggingface/pytorch-openai-transformer-lm\r\nhttps://github.com/huggingface/pytorch-pretrained-BERT\r\nhttps://github.com/huggingface/pytorch-transformers\r\nand now\r\nhttps://github.com/huggingface/transformers\r\n\r\n;) \r\n\r\nJokes aside, seems that the library is growing in a multi-modal direction (https://github.com/huggingface/datasets/pull/363) so the current name is not that implausible. Possibly HF ambition is really to grow its community and bring here a large chunk of datasets of the world (including tabular / vision / audio?).", "Yea I see your point. However, wouldn't scoping solve the entire problem? \r\n\r\n```python\r\nimport huggingface.datasets as D\r\nimport huggingface.transformers as T\r\n```\r\n\r\nCalling something `datasets` is akin to saying I'm going to name my package `python` --> `import python` " ]

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[ "I second this request. The bottom line is that **scores produced with different reference tokenizations are not comparable**. To discourage (even inadvertent) cheating, the user should never touch the reference. The `v13a` tokenization standard is not ideal, but at least it has been consistently used at matrix.statmt.org, facilitating comparisons.\r\n\r\nSacrebleu exposes [all its data sources](https://github.com/mjpost/sacrebleu/blob/master/sacrebleu/dataset.py) and additionally provides [an API](https://github.com/mjpost/sacrebleu/blob/master/sacrebleu/__init__.py) to accessing the references, which seem to fit within the spirit of your codebase.", "Didn't we have a slide and discussion at WMT admitting that, for production-quality models, BLEU doesn't correlate with human eval anyway?\r\n", "Yes, there are slides like that at WMT every year :) BLEU correlates with human judgment only at coarse levels, and it seems to be getting worse when people try to use it to do model selection among high-performing neural systems.\r\n\r\nHowever, the point isn't whether BLEU is a good metric, but whether your BLEU score can be compared to other BLEU scores. They only can be compared if you use the same reference tokenization (similar to how you [can't compare LM perplexities across different segmentations](https://sjmielke.com/comparing-perplexities.htm)). sacrebleu was an attempt to get everyone to use WMT's reference tokenization (meaning, your system has to first remove its own tokenization) so that you could just compare across papers. This also prevents scores from being gamed.", "I do not consider as a sufficient solution switching this library's default metric from BLEU to the wrapper around SacreBLEU. \r\n\r\nAs currently implemented, the wrapper allows end users to toggle SacreBLEU options, but doesn't pass along the SacreBLEU signature. As @mjpost showed in [Post18](https://www.aclweb.org/anthology/W18-6319.pdf), it's simply not credible to assume that people will stick to the defaults, therefore, the signature is necessary to be explicit about what options were used. \r\n\r\nIn addition to the `v13a` or `intl` options for the SacreBLEU `tokenize` argument, which was pointed out earlier, papers frequently differ on whether they lowercase text before scoring (`lowercase`) and the smoothing method used (`smooth_method`). BLEU scores can differ substantially (over 1 BLEU) just by changing these options. \r\n\r\nLosing the SacreBLEU signature is a regression in reproducibility and clarity.\r\n\r\n(Perhaps this should belong in a separate issue?)", "Thanks for sharing your thoughts. This is a very important discussion.\r\n\r\nAlso one of the first items on our mid-term roadmap (we will try to clean it and share it soon) is to introduce mechanisms to get high-quality traceability and reproducibility for all the processes related to the library.\r\n\r\nSo having the signature for `sacrebleu` is really important!\r\n\r\nRegarding BLEU, I guess we can just remove it from the canonical metrics included in the repo itself (it won't prevent people to add it as \"user-metrics\" but at least we won't be promoting it).\r\n\r\nOn a more general note (definitely too large for the scope of this issue) we are wondering, with @srush in particular, how we could handle the selection of metrics/datasets with the most community-based and bottom-up approach possible. If you have opinions on this, please share!", "Yeah, I would love to have discussions about ways this project can have an community-based, transparent process to arrive at strong default metrics. @kpu / @mjpost do you have any suggestions of how that might work or pointers to places where this is done right? Perhaps this question can be template for what is likely to be repeated for other datasets.", "I think @bittlingmayer is referring to Figure 6 in http://statmt.org/wmt19/pdf/53/WMT02.pdf . When you look at Appendix A there are some cases where metrics fall apart at the high end and some where they correlate well. en-zh is arguably production-quality. \r\n\r\nThis could evolve into a metrics Bazaar where the value add is really the packaging and consistency: it installs/compiles the metrics for me, gives a reproducible name to use in publication (involve the authors; you don't want a different sacrebleu hash system), a version number, and evaluation of the metrics like http://ufallab.ms.mff.cuni.cz/~bojar/wmt19-metrics-task-package.tgz but run when code changes rather than once a year. ", "While a Bazaar setup works for models / datasets, I am not sure it is ideal for metrics ? Ideal from my perspective would be to have tasks with metrics moderated by experts who document, cite, and codify known pitchfalls (as above^) and make it non-trivial for beginners to mess it up. ", "@srush @thomwolf \r\n\r\nModelFront could provide (automated, \"QE-based\") evaluation for all the pretrained translation models you host. Not bottom-up and not valid for claiming SoTA, but independent, practical for builders and not top-down.\r\n\r\nFor that I would also suggest some diverse benchmarks (so split it out into datasets with only user-generated data, or only constants, or only UI strings, or only READMEs) which tease out known trade-offs. Even hypothetical magic eval is limited if we always reduce it to a single number.\r\n\r\nRealistically people want to know how a model compares to an API like Google Translate, Microsoft Translator, DeepL or Yandex (especially for a language pair like EN:RU, or for the many languages that only Yandex supports), and that could be done too.\r\n", "Very important discussion.\r\nI am trying to understand the effects of tokenization.\r\nI wanted to ask which is a good practice.\r\nSacrebleu should be used on top of the tokenized output, or detokenized(raw) text?", "Use sacrebleu on detokenized output and raw unmodified references. " ]

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[ "Thanks, this was fixed with #135 :)" ]

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[ "Indeed, thanks!" ]

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[ "the readme got removed, closing this one" ]

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[ "Hi @zphang,\r\n\r\nSo you can just give the local path to a dataset script file and it should work.\r\n\r\nHere is an example:\r\n- you can download one of the scripts in the `datasets` folder of the present repo (or clone the repo)\r\n- then you can load it with `load_dataset('PATH/TO/YOUR/LOCAL/SCRIPT.py')`\r\n\r\nDoes it make sense?", "Could you give a more concrete example, please? \r\n\r\nI looked up wikitext dataset script from the repo. Should I just overwrite the `data_file` on line 98 to point to the local dataset directory? Would it work for different configurations of wikitext (wikitext2, wikitext103 etc.)?\r\n\r\nOr maybe we can use DownloadManager to specify local dataset location? In that case, where do we use DownloadManager instance?\r\n\r\nThanks", "Hi @MaveriQ , although what I am doing is to commit a new dataset, but I think looking at imdb script might help.\r\nYou may want to use `dl_manager.download_custom`, give it a url(arbitrary string), a custom_download(arbitrary function) and return a path, and finally use _get sample to fetch a sample.", "The download manager supports local directories. You can specify a local directory instead of a url and it should work.", "Closing this one.\r\nFeel free to re-open if you have other questions :)" ]

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[ "We're experimenting with hosting the OpenWebText corpus on Zenodo for easier downloading. https://zenodo.org/record/3834942#.Xs1w8i-z2J8", "Closing since it's been added in #660 " ]

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[ "As far as I understand, `wikitext` is refer to `WikiText-103` and `WikiText-2` that created by researchers in Salesforce, and mostly used in traditional language modeling.\r\n\r\nYou might want to say `wikipedia`, a dump from wikimedia foundation.\r\n\r\nAlso I would like to have Toronto BookCorpus too ! Though it involves copyright problem...", "Hi, @lhoestq, just a reminder that this is solved by #248 .😉 " ]

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[ "As a follow-up to this: It looks like the actual GLUE task name is supplied as the `name` argument. Is there a way to check what `name`s/sub-datasets are available under a grouping like GLUE? That information doesn't seem to be readily available in info from `nlp.list_datasets()`.\r\n\r\nEdit: I found the info under `Glue.BUILDER_CONFIGS`", "Yes so the first config is loaded by default when no `name` is supplied but for GLUE this should probably throw an error indeed.\r\n\r\nWe can probably just add an `__init__` at the top of the `class Glue(nlp.GeneratorBasedBuilder)` in the `glue.py` script which does this check:\r\n```\r\nclass Glue(nlp.GeneratorBasedBuilder):\r\n def __init__(self, *args, **kwargs):\r\n assert 'name' in kwargs and kwargs[name] is not None, \"Glue has to be called with a configuration name\"\r\n super(Glue, self).__init__(*args, **kwargs)\r\n```", "An error is raised if the sub-dataset is not specified :)\r\n```\r\nValueError: Config name is missing.\r\nPlease pick one among the available configs: ['cola', 'sst2', 'mrpc', 'qqp', 'stsb', 'mnli', 'mnli_mismatched', 'mnli_matched', 'qnli', 'rte', 'wnli', 'ax']\r\nExample of usage:\r\n\t`load_dataset('glue', 'cola')`\r\n```" ]

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[ "Indeed, I think this one is almost ready cc @lhoestq ", "I'm doing the latest adjustments to make the processing of the dataset run on Dataflow", "Is there an update to this? It will be very beneficial for the QA community!", "Still work in progress :)\r\nThe idea is to have the dataset already processed somewhere so that the user only have to download the processed files. I'm also doing it for wikipedia.", "Super appreciate your hard work !!\r\nI'll cross my fingers and hope easily loadable wikipedia dataset will come soon. ", "Quick update on NQ: due to some limitations I met using apache beam + parquet I was not able to use the dataset in a nested parquet structure in python to convert it to our Apache Arrow format yet.\r\nHowever we had planned to change this conversion step anyways so we'll make just sure that it enables to process and convert the NQ dataset to arrow.", "NQ was added in #427 🎉" ]

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[ "Google colab has an old version of Apache Arrow built-in.\r\nBe sure you execute the \"pip install\" cell and restart the notebook environment if the colab asks for it.", "Thanks for reply, worked fine!\r\n" ]

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[ "For each dataset, If there exist a `dataset_info.json`, then the command `nlp-cli test path/to/my/dataset --al_configs` is successful only if the `dataset_infos.json` is correct. The infos are correct if the size and checksums of the downloaded file are correct, and if the number of examples in each split are correct.\r\n\r\nNote: the `test` command is supposed to test the script, that's why it runs the script even if the cached files already exist. Let me know if it's good to you.", "> For each dataset, If there exist a `dataset_info.json`, then the command `nlp-cli test path/to/my/dataset --al_configs` is successful only if the `dataset_infos.json` is correct. The infos are correct if the size and checksums of the downloaded file are correct, and if the number of examples in each split are correct.\r\n> \r\n> Note: the `test` command is supposed to test the script, that's why it runs the script even if the cached files already exist. Let me know if it's good to you.\r\n\r\nDoes it have to download the whole data to check if the checksums are correct? I guess so no? ", "> > For each dataset, If there exist a `dataset_info.json`, then the command `nlp-cli test path/to/my/dataset --al_configs` is successful only if the `dataset_infos.json` is correct. The infos are correct if the size and checksums of the downloaded file are correct, and if the number of examples in each split are correct.\r\n> > Note: the `test` command is supposed to test the script, that's why it runs the script even if the cached files already exist. Let me know if it's good to you.\r\n> \r\n> Does it have to download the whole data to check if the checksums are correct? I guess so no?\r\n\r\nYes it has to download them all (unless they were already downloaded in which case it just uses the cached downloaded files)." ]

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[ "I didn't assign the output 🤦‍♂️\r\n\r\n```python\r\ndataset.map(test)\r\n```\r\n\r\nshould be :\r\n\r\n```python\r\ndataset = dataset.map(test)\r\n```" ]

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[ "It's strange, after installing `nlp` on Colab, the `pyarrow` version seems fine from `pip` but not from python :\r\n\r\n```python\r\nimport pyarrow\r\n\r\n!pip show pyarrow\r\nprint(\"version = {}\".format(pyarrow.__version__))\r\n```\r\n\r\n> Name: pyarrow\r\nVersion: 0.17.0\r\nSummary: Python library for Apache Arrow\r\nHome-page: https://arrow.apache.org/\r\nAuthor: None\r\nAuthor-email: None\r\nLicense: Apache License, Version 2.0\r\nLocation: /usr/local/lib/python3.6/dist-packages\r\nRequires: numpy\r\nRequired-by: nlp, feather-format\r\n> \r\n> version = 0.14.1", "Ok I just had to restart the runtime after installing `nlp`. After restarting, the version of `pyarrow` is fine." ]

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[ "That's the way!" ]

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[ "Hi, you can't do that at the moment." ]

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[ "Can you share your data files or a minimally reproducible example?", "Sure, [here is a Colab notebook](https://colab.research.google.com/drive/1uiS89fnHMG7HV_cYxp3r-_LqJQvNNKs9?usp=sharing) reproducing the error.\r\n\r\n> ArrowInvalid: Column 1 named references expected length 36 but got length 56", "This is because `add` takes as input a batch of elements and you provided only one. I think we should have `add` for one prediction/reference and `add_batch` for a batch of predictions/references. This would make it more coherent with the way we use Arrow.\r\n\r\nLet me do this change", "Thanks for noticing though. I was mainly used to do `.compute` directly ^^", "Thanks @lhoestq it works :)" ]

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[ "I could access the info by first accessing the different splits :\r\n\r\n```python\r\nimport nlp\r\n\r\ncnn_dm = nlp.load_dataset('cnn_dailymail')\r\nprint(cnn_dm['train'].info)\r\n```\r\n\r\nInformation seems to be duplicated between the subsets :\r\n\r\n```python\r\nprint(cnn_dm[\"train\"].info == cnn_dm[\"test\"].info == cnn_dm[\"validation\"].info)\r\n# True\r\n```\r\n\r\nIs it expected ?", "Good point @Colanim ! What happens under the hood when running:\r\n\r\n```python\r\nimport nlp\r\n\r\ncnn_dm = nlp.load_dataset('cnn_dailymail')\r\n```\r\n\r\nis that for every split in `cnn_dailymail`, a different dataset object (which all holds the same info) is created. This has the advantages that the datasets are easily separable in a training setup. \r\nAlso note that you can load e.g. only the `train` split of the dataset via:\r\n\r\n```python\r\ncnn_dm_train = nlp.load_dataset('cnn_dailymail', split=\"train\")\r\nprint(cnn_dm_train.info)\r\n```\r\n\r\nI think we should make the `info` object slightly different when creating the dataset for each split - at the moment it contains for example the variable `splits` which should maybe be renamed to `split` and contain only one `SplitInfo` object ...\r\n" ]

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[ "Installing from source (instead of Pypi package) solved the problem." ]

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[ "Awesome that's great!" ]

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[ "Nice - I think we can merge this. I will update the checksums for `wikihow` then as well" ]

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[ "> Wikihow is an example that needs to manually download two files as stated in: https://github.com/mahnazkoupaee/WikiHow-Dataset.\r\n> \r\n> The user can then store these files under a hard-coded name: `wikihowAll.csv` and `wikihowSep.csv` in this case in a directory of his choice, e.g. `~/wikihow/manual_dir`.\r\n> \r\n> The dataset can then be loaded via:\r\n> \r\n> ```python\r\n> import nlp\r\n> nlp.load_dataset(\"wikihow\", data_dir=\"~/wikihow/manual_dir\")\r\n> ```\r\n> \r\n> I added/changed so that there are explicit error messages when using manually downloaded files.\r\n\r\nwouldn't be nicer if we can have `manual_dir/wikihow`? ", "> > Wikihow is an example that needs to manually download two files as stated in: https://github.com/mahnazkoupaee/WikiHow-Dataset.\r\n> > The user can then store these files under a hard-coded name: `wikihowAll.csv` and `wikihowSep.csv` in this case in a directory of his choice, e.g. `~/wikihow/manual_dir`.\r\n> > The dataset can then be loaded via:\r\n> > ```python\r\n> > import nlp\r\n> > nlp.load_dataset(\"wikihow\", data_dir=\"~/wikihow/manual_dir\")\r\n> > ```\r\n> > \r\n> > \r\n> > I added/changed so that there are explicit error messages when using manually downloaded files.\r\n> \r\n> wouldn't be nicer if we can have `manual_dir/wikihow`?\r\n\r\nSure, I mean the user can decide whatever he likes best :-) The path one puts in `data_dir` will be used as the path to the manual dir. `nlp.load_dataset(\"wikihow\", data_dir=\"~/manual_dir/wikihow\")` would work as well as any other path ;-) ", "Perfect! You can merge!" ]

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[ "Haha that cornell dialogue dataset - that ran for 3h on my computer as well. The `generate_examples` method in this script is one of the most inefficient code samples I've ever seen :D ", "Indeed it's been 3 hours already\r\n```73111 examples [3:07:48, 2.40 examples/s]```" ]

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[ "LGTM :-) Some small suggestions to shorten the code a bit :-) ", "Can you put the kwargs as normal kwargs instead of a dict? (And add them to the kwargs description As well)", "@thom Is-it what you meant?", "Yes and there is a dynamically generated doc string in the metric script KWARGS DESCRIPTION" ]

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[ "Should that rather be in an organization scope, @thomwolf @patrickvonplaten ?", "> Should that rather be in an organization scope, @thomwolf @patrickvonplaten ?\r\n\r\nI'm a bit indifferent - both would be fine for me!", "@jplu - if creating the dummy_data is too tedious, I can do it as well :-) ", "There is dummy_data here, no ?", "Yeah I think naming it webis/tl_dr would be best @jplu if that works for you", "No problem at all!! On it^^", "> There is dummy_data here, no ?\r\n\r\nSome paths were wrong - the structure is really confusing and the error messages don't really help either - I have to think about how to make this easier to understand!\r\n\r\nHope it was ok that I fiddled with your PR !", "> Some paths were wrong - the structure is really confusing and the error message don't really help either - I have to think about how to make this easier to understand!\r\n\r\nOh ok! I haven't noticed that sorry :(\r\n\r\n> Hope it was ok that I fiddled with your PR !\r\n\r\nOf course it was ok :)", "@julien-c Looks like what you have in mind?\r\n\r\n```python\r\nimport nlp\r\nnlp.load_dataset(\"datasets/webis\", \"tl_dr\")\r\n\r\n#Output: Downloading and preparing dataset webis/tl_dr (download: Unknown size, generated: Unknown size, total: Unknown size) to /home/jplu/.cache/huggingface/datasets/webis/tl_dr/1.0.0...\r\n```", "Merging this for now. Maybe we can see whether to rename it in a different PR @julien-c ? \r\n", "Hi, \r\nAuthor here of the webis-tldr corpus. Any plans on integrating this dataset into the hub? I remember we could access it in the previous versions of the library. If there is a particular issue that I can help with, do let me know.\r\n\r\nThanks!", "Hi @shahbazsyed, this dataset _is_ inside the hub but it's namespaced by the organization name `webis`.\r\n\r\nYou can load it following the steps described in https://huggingface.co/datasets/webis/tl_dr\r\n\r\nHere's a Colab showcasing that it works: https://colab.research.google.com/drive/11IrzRVpnMLJZ8_UFFHLR8FhiajjAHRUU?usp=sharing\r\n\r\nThe reason the code is in S3 and not in this repo is that the dataset is namespaced under the `webis` organization. We don't have a lot of namespaced datasets yet but this should become the main way we add more datasets in the future.\r\nLet us know if that's an issue for you. Thank you!" ]

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[ "@thomwolf - can you check and merge if ok? " ]

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[ "I might have a different version of `isort` than others. It seems like I'm always reordering the imports of others. But isn't really a problem..." ]

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[ "Great! LGTM :-) ", "> Ok, really clean!\r\n> I like the logic (not a huge fan of using `_asdict_inner` but it makes sense).\r\n> I think it's a nice improvement!\r\n> \r\n> How should we update the files in the repo? Run a big job on a server or on somebody's computer who has most of the datasets already downloaded?\r\n\r\nMaybe we can split the updates among us...IMO most datasets run very quickly. \r\nI think I've downloaded 50 datasets and 80% are loaded in <5min, 15% in <1h and then `wmt` which is still downloading (since 12h). \r\nI deleted my cache because the `wmt` downloads require quite a lot of space, so I only have parts of the `wmt` datasets on my computer. \r\n\r\n@mariamabarham I guess you have downloaded most of the datasets no? " ]

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[ "@jplu - I changed this weird archieve - iter method to something simpler. It's only one file to download anyways so I don't see the point of using weird iter methods...It's a huge file though :D 30 million lines of text. Took me quite some time to download :D " ]

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[ "awesome - so no manual downloaded needed here? ", "Yes exactly. It works like a standard download" ]

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[ "Looks good to me. I have not really looked too much into the Beam Datasets yet though - so I think you can merge whenever you think is good for Beam datasets :-) " ]

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[ "Awesome :-) Thanks for adding the function to the Mock DL Manager" ]

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[ "Looks good to me! Should we hard code those numbers in the config classes and make sure that when loading a dataset that the numbers match? " ]

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[ "@lhoestq - I moved the wkipedia file to the \"correct\" folder. ", "Nice thanks !" ]

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[ "It's all about my metric stuff so I'll probably merge it unless you want to have a look.\r\n\r\nTook the occasion to remove the old doc and requirements.txt" ]

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[ "The tests for the Wikipedia dataset do not pass anymore with the error:\r\n```\r\nTo be able to use this dataset, you need to install the following dependencies ['mwparserfromhell'] using 'pip install mwparserfromhell' for instance'\r\n```", "This was an issue on master. You can just rebase from master.", "Perfect! Indeed, it worked^^ Thanks @lhoestq ", "Currently the dummy_data tests are always green because in a PR the dataset is not yet synchronized with aws. This PR fixes this: https://github.com/huggingface/nlp/pull/140 . \r\n\r\nCould you test `json` locally or wait until the PR: https://github.com/huggingface/nlp/pull/140 is merged ? :-) ", "Ok, I will wait #140 to be merged and then rebase :) " ]

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