Datasets:

LukeSajkowski

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[ "That's great!\r\n\r\nI think it would be nice to test \"deterministic-ness\" of datasets in CI if we can do it (should be left for future PR of course)\r\n\r\nHere is a possibility (maybe there are other ways to do it): given that we may soon have efficient and large-scale hashing (cf our discussion on versioning/tracability), we could incorporate a hash of the final Arrow Dataset to the `dataset.json` file and have a test on it as well as CI on a diversity of platform to test the hash (Win/Mac/Linux + various python/env).\r\nWhat do you think @lhoestq @patrickvonplaten?", "> That's great!\r\n> \r\n> I think it would be nice to test \"deterministic-ness\" of datasets in CI if we can do it (should be left for future PR of course)\r\n> \r\n> Here is a possibility (maybe there are other ways to do it): given that we may soon have efficient and large-scale hashing (cf our discussion on versioning/tracability), we could incorporate a hash of the final Arrow Dataset to the `dataset.json` file and have a test on it as well as CI on a diversity of platform to test the hash (Win/Mac/Linux + various python/env).\r\n> What do you think @lhoestq @patrickvonplaten?\r\n\r\nI think that's a great idea! The test should be a `RUN_SLOW` test, since it takes a considerable amount of time to download the dataset and generate the examples, but I think we should add some kind of hash test for each dataset.", "Really nice!!" ]

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[ "Everything is already cached by default in 🤗nlp (in particular dataset\nloading and all the “map()” operations) so I don’t think you need to do any\nspecific caching in streamlit.\n\nTell us if you feel like it’s not the case.\n\nOn Sat, 6 Jun 2020 at 13:02, Fabrizio Milo <[email protected]> wrote:\n\n> For example if I want to use streamlit with a nlp dataset:\n>\n> @st.cache\n> def load_data():\n> return nlp.load_dataset('squad')\n>\n> This code raises the error \"uncachable object\"\n>\n> Right now I just fixed with a constant for my specific case:\n>\n> @st.cache(hash_funcs={pyarrow.lib.Buffer: lambda b: 0})\n>\n> But I was curious to know what is the best way in general\n>\n> —\n> You are receiving this because you are subscribed to this thread.\n> Reply to this email directly, view it on GitHub\n> <https://github.com/huggingface/nlp/issues/246>, or unsubscribe\n> <https://github.com/notifications/unsubscribe-auth/ABYDIHKAKO7CWGX2QY55UXLRVIO3ZANCNFSM4NV333RQ>\n> .\n>\n", "Closing this one. Feel free to re-open if you have other questions !" ]

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[ "this also happened to me with `nlp.load_dataset('glue', 'mnli')`", "Yes, this is because the test sets for glue are hidden so the labels are\nnot publicly available. You can read the glue paper for more details.\n\nOn Sat, 6 Jun 2020 at 18:16, Jack Morris <[email protected]> wrote:\n\n> this also happened to me with nlp.load_datasets('glue', 'mnli')\n>\n> —\n> You are receiving this because you are subscribed to this thread.\n> Reply to this email directly, view it on GitHub\n> <https://github.com/huggingface/nlp/issues/245#issuecomment-640083980>,\n> or unsubscribe\n> <https://github.com/notifications/unsubscribe-auth/ABYDIHMVQD2EDX2HTZUXG5DRVJTWRANCNFSM4NVG3AKQ>\n> .\n>\n", "Thanks @thomwolf!", "@thomwolf shouldn't this be visible in the .info and/or in the .features?", "It should be in the datasets card (the README.md and on the hub) in my opinion. What do you think @yjernite?", "I checked both before I got to looking at issues, so that would be fine as well.\r\n\r\nSome additional thoughts on this: Is there a specific reason why the \"test\" split even has a \"label\" column if it isn't tagged. Shouldn't there just not be any. Seems more transparent", "I'm a little confused with the data size.\r\n`sst2` dataset is referenced to `Recursive Deep Models for Semantic Compositionality Over a Sentiment Treebank` and the link of the dataset in the paper is https://nlp.stanford.edu/sentiment/index.html which is often shown in GLUE/SST2 reference.\r\nFrom the original data, the standard train/dev/test splits split is 6920/872/1821 for binary classification. \r\nWhy in GLUE/SST2 the train/dev/test split is 67,349/872/1,821 ? \r\n\r\n", "> I'm a little confused with the data size.\r\n> `sst2` dataset is referenced to `Recursive Deep Models for Semantic Compositionality Over a Sentiment Treebank` and the link of the dataset in the paper is https://nlp.stanford.edu/sentiment/index.html which is often shown in GLUE/SST2 reference.\r\n> From the original data, the standard train/dev/test splits split is 6920/872/1821 for binary classification.\r\n> Why in GLUE/SST2 the train/dev/test split is 67,349/872/1,821 ?\r\n\r\nHave you figured out this problem? AFAIK, the original sst-2 dataset is totally different from the GLUE/sst-2. Do you think so?", "@yc1999 Sorry, I didn't solve this conflict. In the end, I just use a local data file provided by the previous work I followed(for consistent comparison), not use `datasets` package.\r\n\r\nRelated information: https://github.com/thunlp/OpenAttack/issues/146#issuecomment-766323571", "@yc1999 I find that the original SST-2 dataset (6,920/872/1,821) can be loaded from https://huggingface.co/datasets/gpt3mix/sst2 or built with SST data and the scripts in https://github.com/prrao87/fine-grained-sentiment/tree/master/data/sst.\r\nThe GLUE/SST-2 dataset (67,349/872/1,821) should be a completely different version.\r\n" ]

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[ "great work @TheophileBlard ", "LGTM, thanks a lot for adding dummy data tests :-) Was it difficult to create the correct dummy data folder? ", "It was pretty easy actually. Documentation is on point !" ]

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[ "Thanks for fixing the encoding :)" ]

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[ "It should be good now, thanks for noticing and fixing it ! I would say that it was because you are on windows but not 100% sure", "On Windows Python supports Unicode almost everywhere, but one of the notable exceptions is open() where it uses the locale encoding schema. So platform independent python scripts would always set the encoding='utf-8' in calls to open explicitly. \r\nIn the meantime: since Python 3.7 Windows users can set the default encoding for everything including open() to Unicode by setting this environment variable: set PYTHONUTF8=1 (details can be found in [PEP 540](https://www.python.org/dev/peps/pep-0540/))\r\n\r\nFor me this fixed the problem described by the OP." ]

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[ "Looks great! Will this change force all cached datasets to be redownloaded? But even if it does, it shoud not be a big problem, I think", "> Looks great! Will this change force all cached datasets to be redownloaded? But even if it does, it shoud not be a big problem, I think\r\n\r\nNo it shouldn't force to redownload" ]

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[ "Yes good point !", "I think using `sorted(glob.glob())` would actually solve this problem. Can you think of other reasons why dataset loading might not be deterministic? @mariamabarham @yjernite @lhoestq @thomwolf . \r\n\r\nI can do a sweep through the dataset scripts and fix the glob.glob() if you guys are ok with it", "I'm pretty sure it would solve the problem too.\r\n\r\nThe only other dataset that is not deterministic right now is `blog_authorship_corpus` (see #215) but this is a problem related to string encodings.", "I think we should do the same also for `os.list_dir`" ]

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[ "I think you can just `rm` this directory and it should be good :)", "@lhoestq - this seems to happen quite often (already the 2nd issue). Can we maybe delete this automatically?", "Yes I have an idea of what's going on. I'm sure I can fix that", "Hi, I rebase my local copy to `fix-empty-cache-dir`, and try to run again `python nlp-cli test datasets/bookcorpus --save_infos --all_configs`.\r\n\r\nI got this, \r\n```\r\nTraceback (most recent call last):\r\n File \"nlp-cli\", line 10, in <module>\r\n from nlp.commands.run_beam import RunBeamCommand\r\n File \"/home/yisiang/nlp/src/nlp/commands/run_beam.py\", line 6, in <module>\r\n import apache_beam as beam\r\nModuleNotFoundError: No module named 'apache_beam'\r\n```\r\nAnd after I installed it. I got this\r\n```\r\nFile \"/home/yisiang/nlp/src/nlp/datasets/bookcorpus/aea0bd5142d26df645a8fce23d6110bb95ecb81772bb2a1f29012e329191962c/bookcorpus.py\", line 88, in _split_generators\r\n downloaded_path_or_paths = dl_manager.download_custom(_GDRIVE_FILE_ID, download_file_from_google_drive)\r\n File \"/home/yisiang/nlp/src/nlp/utils/download_manager.py\", line 128, in download_custom\r\n downloaded_path_or_paths = map_nested(url_to_downloaded_path, url_or_urls)\r\n File \"/home/yisiang/nlp/src/nlp/utils/py_utils.py\", line 172, in map_nested\r\n return function(data_struct)\r\n File \"/home/yisiang/nlp/src/nlp/utils/download_manager.py\", line 126, in url_to_downloaded_path\r\n return os.path.join(self._download_config.cache_dir, hash_url_to_filename(url))\r\n File \"/home/yisiang/miniconda3/envs/nlppr/lib/python3.7/posixpath.py\", line 80, in join\r\n a = os.fspath(a)\r\n```\r\nThe problem is when I print `self._download_config.cache_dir` using pdb, it is `None`.\r\n\r\nDid I miss something ? Or can you provide a workaround first so I can keep testing my script ?", "I'll close this issue because I brings more reports in another issue #249 ." ]

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[ "This print statement comes from the official implementation of bert_score (see [here](https://github.com/Tiiiger/bert_score/blob/master/bert_score/utils.py#L343)). The warning shows up only if the attention mask outputs no candidate.\r\nRight now we want to only use official code for metrics to have fair evaluations, so I'm not sure we can do anything about it. Maybe you can try to create an issue on their [repo](https://github.com/Tiiiger/bert_score) ?" ]

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[ "You should use `load_dataset('glue', 'mnli')`", "Thanks! I thought I had to use the same code displayed in the live viewer:\r\n```python\r\n!pip install nlp\r\nfrom nlp import load_dataset\r\ndataset = load_dataset('multi_nli', 'plain_text')\r\n```\r\nYour suggestion works, even if then I got a different issue (#242). ", "Glad it helps !\nThough I am not one of hf team, but maybe you should close this issue first." ]

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[ "Hi @aleSuglia, thanks for this great PR. Indeed you can have both datasets in one file. You need to add a config class which will allows you to specify the different subdataset names and then you will be able to load them as follow.\r\nnlp.load_dataset(\"compguesswhat\", \"compguesswhat-gameplay\") \r\nnlp.load_dataset(\"compguesswhat\", \"compguesswhat-zs-gameplay\").\r\n\r\nMaybe you can refer to this file https://github.com/huggingface/nlp/blob/master/datasets/discofuse/discofuse.py", "@mariamabarham Thanks for your suggestions. I've followed your advice and integrated the additional dataset using another `DatasetConfig` class. It looks like all tests passed. What do you think?", "great @aleSuglia. I requested an additional review from @thomwolf @lhoestq and @patrickvonplaten @jplu . You can merge it after an approval from one of them", "Looks great! Thanks for adding the dummy data :-) ", "Not sure whether it's the most appropriate place but I'll ask another design question. For Vision+Language dataset, is very common to have visual features associated with each example. At the moment, for instance, I'm only integrating the image identifier so that people can later on lookup the image features during training. Do you recommend this approach or do you think it should be done in a different way?\r\n\r\nThank you for your answer!", "Hi @aleSuglia your remark on the visual features is a good point.\r\n\r\nWe haven't started to dive deeply into how CV datasets are usually structured (cc @sgugger)\r\n\r\nDo you have a pointer to how visual features are currently loaded and accessed by people using GuessCompWhat? ", "@thomwolf As far as I know, people using Language+Vision tasks they typically have their reference dataset (either in JSON or JSONL format) and for each example in it they have an identifier that specifies the reference image. Currently, images are represented by either pooling-based visual features (average pooling of ResNet or VGGNet features, see [DeVries et.al, 2017](https://arxiv.org/abs/1611.08481), [Shekhar et.al, 2019](https://www.aclweb.org/anthology/N19-1265.pdf)) where you have a single vector for every image. Another option is to use a set of feature maps for every image extracted from a specific layer of a CNN (see [Xu et.al, 2015](https://arxiv.org/abs/1502.03044)). A more common and recent option, especially with large-scale multi-modal transformers [Li et. al, 2019](https://arxiv.org/abs/1908.03557), is to use FastRCNN features. \r\n\r\nFor all these types of features, people use either HD5F or NumPy compressed representations. In my personal projects, I've ditched altogether HD5F because it doesn't have out-of-the-box support for multi-processing (unless you have an ad-hoc installation of it). I've been successfully using a NumPy compressed file for each image so that I can store any sort of information in it (see [numpy.savez](https://numpy.org/doc/stable/reference/generated/numpy.savez.html)). However, I believe that Apache Arrow would be a really good fit for this type of features. \r\n\r\nLooking forward to hearing your thoughts about it!", "Awesome work on this one thanks :)", "@thomwolf I was thinking that I should create an issue regarding the visual features so that we can keep track of it for future work. I think it would be great to have it in NLP and I'll be happy to contribute. Let me know what you think :) " ]

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[ "I think it would be nicer to not create a new folder `datasets_experimental` , but just put your datasets also into the folder `datasets` for the following reasons:\r\n\r\n- From my point of view, the datasets are not very different from the other datasets (assuming that we soon have C4, and the beam datasets) so I don't see why we require a new dataset folder\r\n\r\n- I'm not a big fan of adding a boolean flag to the `load_dataset()` function that basically switches between folder names on S3. The user has to know whether a dataset script is experimental or not. User installing nlp with pip won't see that there are folders called `datasets` and `datasets_experimental`\r\n\r\n- If we do this just to bypass the test, I think a good solution could be: For all tests that are too complicated to be currently tested with the testing framework, we can add a class variable called `do_test = False` to the dataset builder class and a default `do_test = True` to the abstract dataset class and skip all tests that have that variable in the dataset test framework similar to what is done to beam datasets: https://github.com/huggingface/nlp/blob/2e0a8639a79b1abc848cff5c669094d40bba0f63/tests/test_dataset_common.py#L79 \r\nWe can also print a warning for all dataset tests having `do_test = False`. This variable would only concern testing and we would not have a problem removing it at a later stage IMO.\r\n\r\n- This way the datascripts are backward compatible and can be used with earlier versions of `nlp` (not that this matters too much atm) \r\n\r\nWhat is your opinion on this @lhoestq @thomwolf ?", "Very cool to have add those datasets :)\r\nI understand that making the dummy data for this case is not fun. I'm sure we'll be able to add them soon. For now it's still interesting to have them in the library, even if we can't test all the code with dummy data.\r\n\r\nI like the idea of the `do_tests=False` class variable. \r\nHowever it would be cool to test at least that we can load the module and instantiate the builder (only ignore the dummy data test for now). In that case a better name could be `test_dummy_data=False` or something like that.\r\n\r\nIf we want to be picky we can also add a warning in `_download_and_prepare` to tell the user that datasets with `test_dummy_data=False` are still experimental.", "Yeah I really like the idea of a partial test.\r\n\r\nMy main concern with the class variable is visibility, but having a warning would help with that. Maybe even get the user to agree > \"are you sure you want to go ahead?\"", "> Very cool to have add those datasets :)\r\n> I understand that making the dummy data for this case is not fun. I'm sure we'll be able to add them soon. For now it's still interesting to have them in the library, even if we can't test all the code with dummy data.\r\n> \r\n> I like the idea of the `do_tests=False` class variable.\r\n> However it would be cool to test at least that we can load the module and instantiate the builder (only ignore the dummy data test for now). In that case a better name could be `test_dummy_data=False` or something like that.\r\n> \r\n> If we want to be picky we can also add a warning in `_download_and_prepare` to tell the user that datasets with `test_dummy_data=False` are still experimental.\r\n\r\n`test_dummy_data=False` sounds good to me!", "There we go: added a `test_dummy_data` class variable that is `False` by default for the `BeamBasedBuilder` and `True` for everyone else (except the new `explainlikeimfive` and `wiki_snippets`)\r\n\r\nNote that `wiki_snippets` should become obsolete as soon as @lhoestq adds in the `IndexedDataset` class", "Great! LGTM!" ]

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[ "What do you mean 'custom' ? You may want to elaborate on it when ask a question.\r\n\r\nAnyway, there are two things you may interested\r\n`nlp.Dataset.from_file` and `load_dataset(..., cache_dir=)`", "To load a dataset you need to have a script that defines the format of the examples, the splits and the way to generate examples. As your dataset has the same format of squad, you can just copy the squad script (see the [datasets](https://github.com/huggingface/nlp/tree/master/datasets) forlder) and just replace the url to load the data to your local or remote path.\r\n\r\nThen what you can do is `load_dataset(<path/to/your/script>)`", "Also if you want to upload your script, you should be able to use the `nlp-cli`.\r\n\r\nUnfortunately the upload feature was not shipped in the latest version 0.2.0. so right now you can either clone the repo to use it or wait for the next release. We will add some docs to explain how to upload datasets.\r\n", "Since the latest release 0.2.1 you can use \r\n```bash\r\nnlp-cli upload_dataset <path/to/dataset>\r\n```\r\nwhere `<path/to/dataset>` is a path to a folder containing your script (ex: `squad.py`).\r\nThis will upload the script under your namespace on our S3.\r\n\r\nOptionally the folder can also contain `dataset_infos.json` generated using\r\n```bash\r\nnlp-cli test <path/to/dataset> --all_configs --save_infos\r\n```\r\n\r\nThen you should be able to do\r\n```python\r\nnlp.load_dataset(\"my_namespace/dataset_name\")\r\n```" ]

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[ "Hello ! Thanks for noticing this bug, let me fix that.\r\n\r\nAlso for information, as specified in the changelog of the latest release, C4 currently needs to have a runtime for apache beam to work on. Apache beam is used to process this very big dataset and it can work on dataflow, spark, flink, apex, etc. You can find more info on beam datasets [here](https://github.com/huggingface/nlp/blob/master/docs/beam_dataset.md).\r\n\r\nOur goal in the future is to make available an already-processed version of C4 (as we do for wikipedia for example) so that users without apache beam runtimes can load it.", "@lhoestq I am facing `IsADirectoryError` while downloading with this command.\r\nCan you pls look into it & help me.\r\nI'm using version 0.4.0 of `nlp`.\r\n\r\n```\r\ndataset = load_dataset(\"c4\", 'en', data_dir='.', beam_runner='DirectRunner')\r\n```\r\n\r\nHere's the complete stack trace.\r\n\r\n```\r\nDownloading and preparing dataset c4/en (download: Unknown size, generated: Unknown size, post-processed: Unknown sizetotal: Unknown size) to /home/devops/.cache/huggingface/datasets/c4/en/2.3.0/096df5a27756d51957c959a2499453e60a08154971fceb017bbb29f54b11bef7...\r\n\r\n---------------------------------------------------------------------------\r\nIsADirectoryError Traceback (most recent call last)\r\n<ipython-input-11-f622e6705e03> in <module>\r\n----> 1 dataset = load_dataset(\"c4\", 'en', data_dir='.', beam_runner='DirectRunner')\r\n\r\n/data/anaconda/envs/hf/lib/python3.6/site-packages/nlp/load.py in load_dataset(path, name, version, data_dir, data_files, split, cache_dir, features, download_config, download_mode, ignore_verifications, save_infos, **config_kwargs)\r\n 547 # Download and prepare data\r\n 548 builder_instance.download_and_prepare(\r\n--> 549 download_config=download_config, download_mode=download_mode, ignore_verifications=ignore_verifications,\r\n 550 )\r\n 551 \r\n\r\n/data/anaconda/envs/hf/lib/python3.6/site-packages/nlp/builder.py in download_and_prepare(self, download_config, download_mode, ignore_verifications, try_from_hf_gcs, dl_manager, **download_and_prepare_kwargs)\r\n 461 if not downloaded_from_gcs:\r\n 462 self._download_and_prepare(\r\n--> 463 dl_manager=dl_manager, verify_infos=verify_infos, **download_and_prepare_kwargs\r\n 464 )\r\n 465 # Sync info\r\n\r\n/data/anaconda/envs/hf/lib/python3.6/site-packages/nlp/builder.py in _download_and_prepare(self, dl_manager, verify_infos)\r\n 964 pipeline = beam_utils.BeamPipeline(runner=beam_runner, options=beam_options,)\r\n 965 super(BeamBasedBuilder, self)._download_and_prepare(\r\n--> 966 dl_manager, verify_infos=False, pipeline=pipeline,\r\n 967 ) # TODO handle verify_infos in beam datasets\r\n 968 # Run pipeline\r\n\r\n/data/anaconda/envs/hf/lib/python3.6/site-packages/nlp/builder.py in _download_and_prepare(self, dl_manager, verify_infos, **prepare_split_kwargs)\r\n 516 split_dict = SplitDict(dataset_name=self.name)\r\n 517 split_generators_kwargs = self._make_split_generators_kwargs(prepare_split_kwargs)\r\n--> 518 split_generators = self._split_generators(dl_manager, **split_generators_kwargs)\r\n 519 # Checksums verification\r\n 520 if verify_infos:\r\n\r\n/data/anaconda/envs/hf/lib/python3.6/site-packages/nlp/datasets/c4/096df5a27756d51957c959a2499453e60a08154971fceb017bbb29f54b11bef7/c4.py in _split_generators(self, dl_manager, pipeline)\r\n 187 if self.config.realnewslike:\r\n 188 files_to_download[\"realnews_domains\"] = _REALNEWS_DOMAINS_URL\r\n--> 189 file_paths = dl_manager.download_and_extract(files_to_download)\r\n 190 \r\n 191 if self.config.webtextlike:\r\n\r\n/data/anaconda/envs/hf/lib/python3.6/site-packages/nlp/utils/download_manager.py in download_and_extract(self, url_or_urls)\r\n 218 extracted_path(s): `str`, extracted paths of given URL(s).\r\n 219 \"\"\"\r\n--> 220 return self.extract(self.download(url_or_urls))\r\n 221 \r\n 222 def get_recorded_sizes_checksums(self):\r\n\r\n/data/anaconda/envs/hf/lib/python3.6/site-packages/nlp/utils/download_manager.py in download(self, url_or_urls)\r\n 156 lambda url: cached_path(url, download_config=self._download_config,), url_or_urls,\r\n 157 )\r\n--> 158 self._record_sizes_checksums(url_or_urls, downloaded_path_or_paths)\r\n 159 return downloaded_path_or_paths\r\n 160 \r\n\r\n/data/anaconda/envs/hf/lib/python3.6/site-packages/nlp/utils/download_manager.py in _record_sizes_checksums(self, url_or_urls, downloaded_path_or_paths)\r\n 106 flattened_downloaded_path_or_paths = flatten_nested(downloaded_path_or_paths)\r\n 107 for url, path in zip(flattened_urls_or_urls, flattened_downloaded_path_or_paths):\r\n--> 108 self._recorded_sizes_checksums[url] = get_size_checksum_dict(path)\r\n 109 \r\n 110 def download_custom(self, url_or_urls, custom_download):\r\n\r\n/data/anaconda/envs/hf/lib/python3.6/site-packages/nlp/utils/info_utils.py in get_size_checksum_dict(path)\r\n 77 \"\"\"Compute the file size and the sha256 checksum of a file\"\"\"\r\n 78 m = sha256()\r\n---> 79 with open(path, \"rb\") as f:\r\n 80 for chunk in iter(lambda: f.read(1 << 20), b\"\"):\r\n 81 m.update(chunk)\r\n\r\nIsADirectoryError: [Errno 21] Is a directory: '/'\r\n\r\n```\r\n\r\nCan anyone please try to see what I am doing wrong or is this a bug?", "I have the same problem as @prashant-kikani", "Looks like a bug in the dataset script, can you open an issue ?", "I see the same issue as @prashant-kikani. I'm using `datasets` version 1.2.0 to download C4." ]

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[ "LGTM 👍 " ]

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[ "\r\nThis was actually the right name. `dataset_infos.json` is used to have the infos of all the dataset configurations.\r\n\r\nOn the other hand `dataset_info.json` (without 's') is a cache file with the info of one specific configuration.\r\n\r\nTo fix #228, we probably just have to clear and reload the nlp-viewer cache." ]

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[ "Added pull request to change the name of the file from dataset_infos.json to dataset_info.json", "Thanks for reporting this bug !\r\nAs it seems to be just a cache problem, I closed your PR.\r\nI think we might just need to clear and reload the `xnli` cache @srush ? ", "Update: The dataset_info.json error is gone, but we have a new one instead:\r\n```\r\nConnectionError: Couldn't reach https://www.nyu.edu/projects/bowman/xnli/XNLI-1.0.zip\r\n```\r\nI am not able to reproduce on my side unfortunately. Any idea @srush ?", "xnli is now properly shown in the viewer.\r\nClosing this one." ]

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[ "Thanks for your message 😊 \r\nIndeed users shouldn't have to install those dependencies", "Got it, feel free to close this issue when you think it’s resolved.", "It should be good now :)" ]

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[ "Awesome :D" ]

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[ "@Colanim unfortunately there are different implementations of the ROUGE metric floating around online which yield different results, and we had to chose one for the package :) We ended up including the one from the google-research repository, which does minimal post-processing before computing the P/R/F scores. If I recall correctly, files2rouge relies on the Perl, script, which among other things normalizes all numbers to a special token: in the case you presented, this should account for a good chunk of the difference.\r\n\r\nWe may end up adding in more versions of the metric, but probably not for a while (@lhoestq correct me if I'm wrong). However, feel free to take a stab at adding it in yourself and submitting a PR if you're interested!", "Thank you for your kind answer.\r\n\r\nAs a side question : Isn't it better to have a package that normalize more ?\r\n\r\nI understand to idea of having a package that does minimal post-processing for transparency.\r\n\r\nBut it means that people using different architecture (with different tokenizers for example) will have difference in ROUGE scores even if their predictions are actually similar. \r\nThe goal of `nlp` is to have _one package to rule them all_, right ?\r\n\r\nI will look into it but I'm not sure I have the required skill for this ^^ ", "You're right, there's a pretty interesting trade-off here between robustness and sensitivity :) The flip side of your argument is that we also still want the metric to be sensitive to model mistakes. How we think about number normalization for example has evolved a fair bit since the Perl script was written: at the time, ROUGE was used mostly to evaluate short-medium text summarization systems, where there were only a few numbers in the input and it was assumed that the most popular methods in use at the time would get those right. However, as your example showcases, that assumption does not hold any more, and we do want to be able to penalize a model that generates a wrong numerical value.\r\n\r\nAlso, we think that abstracting away tokenization differences is the role of the model/tokenizer: if you use the 🤗Tokenizers library for example, it will handle that for you ;)\r\n\r\nFinally, there is a lot of active research on developing model-powered metrics that are both more sensitive and more robust than ROUGE. Check out for example BERTscore, which is implemented in this library!" ]

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[ "Is there any update on this? \r\n\r\nThanks!", "Hitting this error when using bleurt with PyTorch ...\r\n\r\n```\r\nUnrecognizedFlagError: Unknown command line flag 'f'\r\n```\r\n... and I'm assuming because it was built for TF specifically. Is there a way to use this metric in PyTorch?", "We currently provide a wrapper on the TensorFlow implementation: https://huggingface.co/metrics/bleurt\r\n\r\nWe have long term plans to better handle model-based metrics, but they probably won't be implemented right away\r\n\r\n@adamwlev it would still be cool to add the BLEURT checkpoints to the transformers repo if you're interested, but that would best be discussed there :) \r\n\r\nclosing for now", "Hi there. We ran into the same problem this year (converting BLEURT to PyTorch) and thanks to @adamwlev found his colab notebook which didn't work but served as a good starting point. Finally, we **made it work** by doing just two simple conceptual fixes: \r\n\r\n1. Transposing 'kernel' layers instead of 'dense' ones when copying params from the original model;\r\n2. Taking pooler_output as a cls_state in forward function of the BleurtModel class.\r\n\r\nPlus few minor syntactical fixes for the outdated parts. The result is still not exactly the same, but is very close to the expected one (1.0483 vs 1.0474).\r\n\r\nFind the fixed version here (fixes are commented): https://colab.research.google.com/drive/1KsCUkFW45d5_ROSv2aHtXgeBa2Z98r03?usp=sharing \r\n" ]

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[ "Hi @lbourdois, yes please share it with us", "@mariamabarham \r\nI put all the datasets on this drive: https://1drv.ms/u/s!Ao2Rcpiny7RFinDypq7w-LbXcsx9?e=iVsEDh\r\n\r\n\r\nSome information : \r\n• For FLUE, the quote used is\r\n\r\n> @misc{le2019flaubert,\r\n> title={FlauBERT: Unsupervised Language Model Pre-training for French},\r\n> author={Hang Le and Loïc Vial and Jibril Frej and Vincent Segonne and Maximin Coavoux and Benjamin Lecouteux and Alexandre Allauzen and Benoît Crabbé and Laurent Besacier and Didier Schwab},\r\n> year={2019},\r\n> eprint={1912.05372},\r\n> archivePrefix={arXiv},\r\n> primaryClass={cs.CL}\r\n> }\r\n\r\n• The Github repo of FLUE is avaible here : https://github.com/getalp/Flaubert/tree/master/flue\r\n\r\n\r\n\r\nInformation related to the different tasks of FLUE : \r\n\r\n**1. Classification**\r\nThree dataframes are available: \r\n- Book\r\n- DVD\r\n- Music\r\nFor each of these dataframes is available a set of training and test data, and a third one containing unlabelled data.\r\n\r\nCitation : \r\n>@dataset{prettenhofer_peter_2010_3251672,\r\n author = {Prettenhofer, Peter and\r\n Stein, Benno},\r\n title = {{Webis Cross-Lingual Sentiment Dataset 2010 (Webis- \r\n CLS-10)}},\r\n month = jul,\r\n year = 2010,\r\n publisher = {Zenodo},\r\n doi = {10.5281/zenodo.3251672},\r\n url = {https://doi.org/10.5281/zenodo.3251672}\r\n}\r\n\r\n\r\n**2. Paraphrasing** \r\nFrench part of the PAWS-X dataset (https://github.com/google-research-datasets/paws).\r\nThree dataframes are available: \r\n- train\r\n- dev\r\n- test \r\n\r\nCitation : \r\n> @InProceedings{pawsx2019emnlp,\r\n> title = {{PAWS-X: A Cross-lingual Adversarial Dataset for Paraphrase Identification}},\r\n> author = {Yang, Yinfei and Zhang, Yuan and Tar, Chris and Baldridge, Jason},\r\n> booktitle = {Proc. of EMNLP},\r\n> year = {2019}\r\n> }\r\n\r\n\r\n\r\n**3. Natural Language Inference**\r\nFrench part of the XNLI dataset (https://github.com/facebookresearch/XNLI).\r\nThree dataframes are available: \r\n- train\r\n- dev\r\n- test \r\n\r\nFor the dev and test datasets, extra columns compared to the train dataset were available so I left them in the dataframe (I didn't know if these columns could be useful for other tasks or not). \r\nIn the context of the FLUE benchmark, only the columns gold_label, sentence1 and sentence2 are useful.\r\n\r\n\r\nCitation : \r\n\r\n> @InProceedings{conneau2018xnli,\r\n> author = \"Conneau, Alexis\r\n> and Rinott, Ruty\r\n> and Lample, Guillaume\r\n> and Williams, Adina\r\n> and Bowman, Samuel R.\r\n> and Schwenk, Holger\r\n> and Stoyanov, Veselin\",\r\n> title = \"XNLI: Evaluating Cross-lingual Sentence Representations\",\r\n> booktitle = \"Proceedings of the 2018 Conference on Empirical Methods\r\n> in Natural Language Processing\",\r\n> year = \"2018\",\r\n> publisher = \"Association for Computational Linguistics\",\r\n> location = \"Brussels, Belgium\",\r\n\r\n\r\n**4. Parsing**\r\nThe dataset used by the FLUE authors for this task is not freely available.\r\nUsers of your library will therefore not be able to access it.\r\nNevertheless, I think maybe it is useful to add a link to the site where to request this dataframe: http://ftb.linguist.univ-paris-diderot.fr/telecharger.php?langue=en \r\n(personally it was sent to me less than 48 hours after I requested it).\r\n\r\n\r\n**5. Word Sense Disambiguation Tasks**\r\n5.1 Verb Sense Disambiguation\r\n\r\nTwo dataframes are available: train and test\r\nFor both dataframes, 4 columns are available: document, sentence, lemma and word.\r\nI created the document column thinking that there were several documents in the dataset but afterwards it turns out that there were not: several sentences but only one document. It's up to you to keep it or not when importing these two dataframes.\r\n\r\nThe sentence column is used to determine to which sentence the word in the word column belongs. It is in the form of a dictionary {'id': 'd000.s001', 'idx': '1'}. I thought for a while to keep only the idx because the id doesn't matter any more information. Nevertheless for the test dataset, the dictionary has an extra value indicating the source of the sentence. I don't know if it's useful or not, that's why I left the dictionary just in case. The user is free to do what he wants with it.\r\n\r\nCitation : \r\n\r\n> Segonne, V., Candito, M., and Crabb ́e, B. (2019). Usingwiktionary as a resource for wsd: the case of frenchverbs. InProceedings of the 13th International Confer-ence on Computational Semantics-Long Papers, pages259–270\r\n\r\n5.2 Noun Sense Disambiguation\r\nTwo dataframes are available: 2 train and 1 test\r\n\r\nI confess I didn't fully understand the procedure for this task.\r\n\r\nCitation : \r\n\r\n> @dataset{loic_vial_2019_3549806,\r\n> author = {Loïc Vial},\r\n> title = {{French Word Sense Disambiguation with Princeton \r\n> WordNet Identifiers}},\r\n> month = nov,\r\n> year = 2019,\r\n> publisher = {Zenodo},\r\n> version = {1.0},\r\n> doi = {10.5281/zenodo.3549806},\r\n> url = {https://doi.org/10.5281/zenodo.3549806}\r\n> }\r\n\r\nFinally, additional information about FLUE is available in the FlauBERT publication : \r\nhttps://arxiv.org/abs/1912.05372 (p. 4).\r\n\r\n\r\nHoping to have provided you with everything you need to add this benchmark :) \r\n", "https://github.com/huggingface/datasets/pull/943" ]

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[ "The notebook forces version 0.1.0. If I use the latest, things work, I'll run the whole notebook and create a PR.\r\n\r\nBut in the meantime, this issue gets fixed by changing:\r\n`!pip install nlp==0.1.0`\r\nto\r\n`!pip install nlp`", "It still breaks very near the end\r\n\r\n\r\n", "When you install `nlp` for the first time on a Colab runtime, it updates the `pyarrow` library that was already on colab. This update shows this message on colab:\r\n```\r\nWARNING: The following packages were previously imported in this runtime:\r\n [pyarrow]\r\nYou must restart the runtime in order to use newly installed versions.\r\n```\r\nYou just have to restart the runtime and it should be fine.\r\nIf you don't restart, then it breaks like in your first message ", "Thanks for reporting the second one ! We'll update the notebook to fix this one :)", "This trick from @thomwolf seems to be the most reliable solution to fix this colab notebook issue:\r\n\r\n```python\r\n# install nlp\r\n!pip install -qq nlp==0.2.0\r\n\r\n# Make sure that we have a recent version of pyarrow in the session before we continue - otherwise reboot Colab to activate it\r\nimport pyarrow\r\nif int(pyarrow.__version__.split('.')[1]) < 16:\r\n import os\r\n os.kill(os.getpid(), 9)\r\n```", "The second part got fixed here: 2cbc656d6fc4b18ce57eac070baec05b31180d39\r\n\r\nThanks! I'm then closing this issue." ]

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[ "Thanks ! Good catch :)\r\n\r\nTo fix the CI you can do:\r\n1 - rebase from master\r\n2 - then run `make style` as specified in [CONTRIBUTING.md](https://github.com/huggingface/nlp/blob/master/CONTRIBUTING.md) ?" ]

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[ "you can rebase from master to fix the CI error :)", "Awesome !" ]

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[ "I like this feature! I think the first question we should decide on is how to convert all datasets into the same format. In T5, the authors decided to format every dataset into a text-to-text format. If the dataset had \"multiple\" inputs like MNLI, the inputs were concatenated. So in MNLI the input:\r\n\r\n> - **Hypothesis**: The St. Louis Cardinals have always won.\r\n> \r\n> - **Premise**: yeah well losing is i mean i’m i’m originally from Saint Louis and Saint Louis Cardinals when they were there were uh a mostly a losing team but \r\n\r\nwas flattened to a single input:\r\n\r\n> mnli hypothesis: The St. Louis Cardinals have always won. premise:\r\n> yeah well losing is i mean i’m i’m originally from Saint Louis and Saint Louis Cardinals\r\n> when they were there were uh a mostly a losing team but.\r\n\r\nThis flattening is actually a very simple operation in `nlp` already. You would just need to do the following:\r\n\r\n```python \r\ndef flatten_inputs(example):\r\n return {\"input\": \"mnli hypothesis: \" + example['hypothesis'] + \" premise: \" + example['premise']}\r\n\r\nt5_ready_mnli_ds = mnli_ds.map(flatten_inputs, remove_columns=[<all columns except output>])\r\n```\r\n\r\nSo I guess converting the datasets into the same format can be left to the user for now. \r\nThen the question is how we can merge the datasets. I would probably be in favor of a simple \r\n\r\n```python \r\ndataset.add()\r\n```\r\n\r\nfunction that checks if the dataset is of the same format and if yes merges the two datasets. Finally, how should the sampling be implemented? **Examples-proportional mixing** corresponds to just merging the datasets and shuffling. For the other two sampling approaches we would need some higher-level features, maybe even a `dataset.sample()` function for merged datasets. \r\n\r\nWhat are your thoughts on this @thomwolf @lhoestq @ghomasHudson @enzoampil ?", "I agree that we should leave the flattening of the dataset to the user for now. Especially because although the T5 framing seems obvious, there are slight variations on how the T5 authors do it in comparison to other approaches such as gpt-3 and decaNLP.\r\n\r\nIn terms of sampling, Examples-proportional mixing does seem the simplest to implement so would probably be a good starting point.\r\n\r\nTemperature-scaled mixing would probably most useful, offering flexibility as it can simulate the other 2 methods by setting the temperature parameter. There is a [relevant part of the T5 repo](https://github.com/google-research/text-to-text-transfer-transformer/blob/03c94165a7d52e4f7230e5944a0541d8c5710788/t5/data/utils.py#L889-L1118) which should help with implementation.\r\n\r\nAccording to the T5 authors, equal-mixing performs worst. Among the other two methods, tuning the K value (the artificial dataset size limit) has a large impact.\r\n", "I agree with going with temperature-scaled mixing for its flexibility!\r\n\r\nFor the function that combines the datasets, I also find `dataset.add()` okay while also considering that users may want it to be easy to combine a list of say 10 data sources in one go.\r\n\r\n`dataset.sample()` should also be good. By the looks of it, we're planning to have as main parameters: `temperature`, and `K`.\r\n\r\nOn converting the datasets to the same format, I agree that we can leave these to the users for now. But, I do imagine it'd be an awesome feature for the future to have this automatically handled, based on a chosen *approach* to formatting :smile: \r\n\r\nE.g. T5, GPT-3, decaNLP, original raw formatting, or a contributed way of formatting in text-to-text. ", "This is an interesting discussion indeed and it would be nice to make multi-task easier.\r\n\r\nProbably the best would be to have a new type of dataset especially designed for that in order to easily combine and sample from the multiple datasets.\r\n\r\nThis way we could probably handle the combination of datasets with differing schemas as well (unlike T5).", "@thomwolf Are you suggesting making a wrapper class which can take existing datasets as arguments and do all the required sampling/combining, to present the same interface as a normal dataset?\r\n\r\nThat doesn't seem too complicated to implement.\r\n", "I guess we're looking at the end user writing something like:\r\n``` python\r\nds = nlp.load_dataset('multitask-t5',datasets=[\"squad\",\"cnn_dm\",...], k=1000, t=2.0)\r\n```\r\nUsing the t5 method of combining here (or this could be a function passed in as an arg) \r\n\r\nPassing kwargs to each 'sub-dataset' might become tricky.", "From thinking upon @thomwolf 's suggestion, I've started experimenting:\r\n```python\r\nclass MultitaskDataset(DatasetBuilder):\r\n def __init__(self, *args, **kwargs):\r\n super(MultitaskDataset, self).__init__(*args, **kwargs)\r\n self._datasets = kwargs.get(\"datasets\")\r\n\r\n def _info(self):\r\n return nlp.DatasetInfo(\r\n description=_DESCRIPTION,\r\n features=nlp.Features({\r\n \"source\": nlp.Value(\"string\"),\r\n \"target\": nlp.Sequence(nlp.Value(\"string\"))\r\n })\r\n )\r\n\r\n def _get_common_splits(self):\r\n '''Finds the common splits present in all self._datasets'''\r\n min_set = None\r\n for dataset in self._datasets:\r\n if min_set != None:\r\n min_set.intersection(set(dataset.keys()))\r\n else:\r\n min_set = set(dataset.keys())\r\n return min_set\r\n\r\n....\r\n\r\n# Maybe this?:\r\nsquad = nlp.load_dataset(\"squad\")\r\ncnn_dm = nlp.load_dataset(\"cnn_dailymail\",\"3.0.0\")\r\nmultitask_dataset = nlp.load_dataset(\r\n 'multitask_dataset',\r\n datasets=[squad,cnn_dailymail], \r\n k=1000, \r\n t=2.0\r\n)\r\n\r\n```\r\n\r\nDoes anyone know what methods of `MultitaskDataset` I would need to implement? Maybe `as_dataset` and `download_and_prepare`? Most of these should be just calling the methods of the sub-datasets. \r\n\r\nI'm assuming DatasetBuilder is better than the more specific `GeneratorBasedBuilder`, `BeamBasedBuilder`, etc....\r\n\r\nOne of the other problems is that the dataset size is unknown till you construct it (as you can pick the sub-datasets). Am hoping not to need to make changes to `nlp.load_dataset` just for this class.\r\n\r\nI'd appreciate it if anyone more familiar with nlp's internal workings could tell me if I'm on the right track!", "I think I would probably go for a `MultiDataset` wrapper around a list of `Dataset`.\r\n\r\nI'm not sure we need to give it `k` and `t` parameters at creation, it can maybe be something along the lines of:\r\n```python\r\nsquad = nlp.load_dataset(\"squad\")\r\ncnn_dm = nlp.load_dataset(\"cnn_dailymail\",\"3.0.0\")\r\n\r\nmultitask_dataset = nlp.MultiDataset(squad, cnn_dm)\r\n\r\nbatch = multitask_dataset.sample(10, temperature=2.0, k=1000)\r\n```\r\n\r\nThe first proof-of-concept for multi-task datasets could definitely require that the provided datasets have the same name/type for columns (if needed you easily rename/cast a column prior to instantiating the `MultiDataset`).\r\n\r\nIt's good to think about it for some time though and don't overfit too much on the T5 examples (in particular for the ways/kwargs for sampling among datasets).", "The problem with changing `k` and `t` per sampling is that you'd have to somehow remember which examples you'd already returned while re-weighting the remaining examples based on the new `k` and `t`values. It seems possible but complicated (I can't really see a reason why you'd want to change the weighting of datasets after you constructed the multidataset).\r\n\r\nWouldn't it be convenient if it implemented the dataset interface? Then if someone has code using a single nlp dataset, they can replace it with a multitask combination of more datasets without having to change other code. We would at least need to be able to pass it into a `DataLoader`.\r\n\r\n", "A very janky (but working) implementation of `multitask_dataset.sample()` could be something like this:\r\n```python\r\nimport nlp\r\nimport torch\r\n\r\nclass MultiDataset():\r\n def __init__(self, *args, temperature=2.0, k=1000, maximum=None, scale=1):\r\n self.datasets = args\r\n self._dataloaders = {}\r\n for split in self._get_common_splits():\r\n split_datasets = [ds[split] for ds in self.datasets]\r\n mixing_rates = self._calc_mixing_rates(split_datasets,temperature, k, maximum, scale)\r\n weights = []\r\n for i in range(len(self.datasets)):\r\n weights += [mixing_rates[i]]*len(self.datasets[i][split])\r\n self._dataloaders[split] = torch.utils.data.DataLoader(torch.utils.data.ConcatDataset(split_datasets),\r\n sampler=torch.utils.data.sampler.WeightedRandomSampler(\r\n num_samples=len(weights),\r\n weights = weights,\r\n replacement=True),\r\n shuffle=False)\r\n\r\n def _get_common_splits(self):\r\n '''Finds the common splits present in all self.datasets'''\r\n min_set = None\r\n for dataset in self.datasets:\r\n if min_set != None:\r\n min_set.intersection(set(dataset.keys()))\r\n else:\r\n min_set = set(dataset.keys())\r\n return min_set\r\n\r\n\r\n def _calc_mixing_rates(self,datasets, temperature=2.0, k=1000, maximum=None, scale=1):\r\n '''Work out the weighting of each dataset based on t and k'''\r\n mixing_rates = []\r\n for dataset in datasets:\r\n rate = len(dataset)\r\n rate *= scale\r\n if maximum:\r\n rate = min(rate, maximum)\r\n if temperature != 1.0:\r\n rate = rate ** (1.0/temperature)\r\n mixing_rates.append(rate)\r\n return mixing_rates\r\n\r\n def sample(self,n,split):\r\n batch = []\r\n for example in self._dataloaders[split]:\r\n batch.append(example)\r\n n -= 1\r\n if n == 0:\r\n return batch\r\n\r\n\r\ndef flatten(dataset,flatten_fn):\r\n for k in dataset.keys():\r\n if isinstance(dataset[k],nlp.Dataset):\r\n dataset[k] = dataset[k].map(flatten_fn,remove_columns=dataset[k].column_names)\r\n\r\n# Squad\r\ndef flatten_squad(example):\r\n return {\"source\": \"squad context: \" + example['context'] + \" question: \" + example['question'],\"target\":example[\"answers\"][\"text\"]}\r\nsquad = nlp.load_dataset(\"squad\")\r\nflatten(squad,flatten_squad)\r\n\r\n# CNN_DM\r\ndef flatten_cnn_dm(example):\r\n return {\"source\": \"cnn_dm: \" + example['article'],\"target\":[example[\"highlights\"]]}\r\ncnn_dm = nlp.load_dataset(\"cnn_dailymail\", \"3.0.0\")\r\nflatten(cnn_dm,flatten_cnn_dm)\r\n\r\nmultitask_dataset = MultiDataset(squad, cnn_dm)\r\nbatch = multitask_dataset.sample(100,\"train\")\r\n```\r\n\r\nThere's definitely a more sensible way than embedding `DataLoader`s inside. ", "There is an interesting related investigation by @zphang here https://colab.research.google.com/github/zphang/zphang.github.io/blob/master/files/notebooks/Multi_task_Training_with_Transformers_NLP.ipynb", "Good spot! Here are my thoughts:\r\n\r\n- Aside: Adding `MultitaskModel` to transformers might be a thing to raise - even though having task-specific heads has become unfashionable in recent times in favour of text-to-text type models.\r\n- Adding the task name as an extra field also seems useful for these kind of models which have task-specific heads\r\n- There is some validation of our approach that the user should be expected to `map` datasets into a common form.\r\n- The size-proportional sampling (also called \"Examples-proportional mixing\") used here doesn't perform too badly in the T5 paper (it's comparable to temperature-scaled mixing in many cases but less flexible. This is only reasonable with a `K` maximum size parameter to prevent very large datasets dominating). This might be good for a first prototype using:\r\n ```python\r\n def __iter__(self):\r\n \"\"\"\r\n For each batch, sample a task, and yield a batch from the respective\r\n task Dataloader.\r\n\r\n We use size-proportional sampling, but you could easily modify this\r\n to sample from some-other distribution.\r\n \"\"\"\r\n task_choice_list = []\r\n for i, task_name in enumerate(self.task_name_list):\r\n task_choice_list += [i] * self.num_batches_dict[task_name]\r\n task_choice_list = np.array(task_choice_list)\r\n np.random.shuffle(task_choice_list)\r\n\r\n dataloader_iter_dict = {\r\n task_name: iter(dataloader) \r\n for task_name, dataloader in self.dataloader_dict.items()\r\n }\r\n for task_choice in task_choice_list:\r\n task_name = self.task_name_list[task_choice]\r\n yield next(dataloader_iter_dict[task_name]) \r\n ```\r\n We'd just need to pull samples from the raw datasets and not from `DataLoader`s for each task. We can assume the user has done `dataset.shuffle()` if they want to.\r\n\r\n Other sampling methods can later be implemented by changing how the `task_choice_list` is generated. This should allow more flexibility and not tie us to specific methods for sampling among datasets.\r\n", "Another thought: Multitasking over benchmarks (represented as Meta-datasets in nlp) is probably a common use case. Would be nice to pass an entire benchmark to our `MultiDataset` wrapper rather than having to pass individual components.", "Here's a fully working implementation based on the `__iter__` function of @zphang.\r\n\r\n- I've generated the task choice list in the constructor as it allows us to index into the MultiDataset just like a normal dataset. I'm changing `task_choice_list` into a list of `(dataset_idx, example_idx)` so each entry references a unique dataset example. The shuffling has to be done before this as we don't want to shuffle within each task (we assume this is done by the user if this is what they intend).\r\n- I'm slightly concerned this list could become very large if many large datasets were used. Can't see a way round it at the moment though.\r\n- I've used `task.info.builder_name` as the dataset name. Not sure if this is correct.\r\n- I'd love to add some of the other `Dataset` methods (map, slicing by column, etc...). Would be great to implement the whole interface so a single dataset can be simply replaced by this.\r\n- This does everything on the individual example-level. If some application required batches all from a single task in turn we can't really do that.\r\n\r\n```python\r\nimport nlp\r\nimport numpy as np\r\n\r\nclass MultiDataset:\r\n def __init__(self,tasks):\r\n self.tasks = tasks\r\n\r\n # Create random order of tasks\r\n # Using size-proportional sampling\r\n task_choice_list = []\r\n for i, task in enumerate(self.tasks):\r\n task_choice_list += [i] * len(task)\r\n task_choice_list = np.array(task_choice_list)\r\n np.random.shuffle(task_choice_list)\r\n\r\n # Add index into each dataset\r\n # - We don't want to shuffle within each task\r\n counters = {}\r\n self.task_choice_list = []\r\n for i in range(len(task_choice_list)):\r\n idx = counters.get(task_choice_list[i],0)\r\n self.task_choice_list.append((task_choice_list[i],idx))\r\n counters[task_choice_list[i]] = idx + 1\r\n\r\n\r\n def __len__(self):\r\n return np.sum([len(t) for t in self.tasks])\r\n\r\n def __repr__(self):\r\n task_str = \", \".join([str(t) for t in self.tasks])\r\n return f\"MultiDataset(tasks: {task_str})\"\r\n\r\n def __getitem__(self,key):\r\n if isinstance(key, int):\r\n task_idx, example_idx = self.task_choice_list[key]\r\n task = self.tasks[task_idx]\r\n example = task[example_idx]\r\n example[\"task_name\"] = task.info.builder_name\r\n return example\r\n elif isinstance(key, slice):\r\n raise NotImplementedError()\r\n\r\n def __iter__(self):\r\n for i in range(len(self)):\r\n yield self[i]\r\n\r\n\r\ndef load_multitask(*datasets):\r\n '''Create multitask datasets per split'''\r\n\r\n def _get_common_splits(datasets):\r\n '''Finds the common splits present in all self.datasets'''\r\n min_set = None\r\n for dataset in datasets:\r\n if min_set != None:\r\n min_set.intersection(set(dataset.keys()))\r\n else:\r\n min_set = set(dataset.keys())\r\n return min_set\r\n\r\n common_splits = _get_common_splits(datasets)\r\n out = {}\r\n for split in common_splits:\r\n out[split] = MultiDataset([d[split] for d in datasets])\r\n return out\r\n\r\n\r\n##########################################\r\n# Dataset Flattening\r\n\r\ndef flatten(dataset,flatten_fn):\r\n for k in dataset.keys():\r\n if isinstance(dataset[k],nlp.Dataset):\r\n dataset[k] = dataset[k].map(flatten_fn,remove_columns=dataset[k].column_names)\r\n\r\n# Squad\r\ndef flatten_squad(example):\r\n return {\"source\": \"squad context: \" + example['context'] + \" question: \" + example['question'],\r\n \"target\":example[\"answers\"][\"text\"]}\r\nsquad = nlp.load_dataset(\"squad\")\r\nflatten(squad,flatten_squad)\r\n\r\n# CNN_DM\r\ndef flatten_cnn_dm(example):\r\n return {\"source\": \"cnn_dm: \" + example['article'],\"target\":[example[\"highlights\"]]}\r\ncnn_dm = nlp.load_dataset(\"cnn_dailymail\", \"3.0.0\")\r\nflatten(cnn_dm,flatten_cnn_dm)\r\n\r\n#############################################\r\n\r\nmtds = load_multitask(squad,cnn_dm)\r\n\r\nfor example in mtds[\"train\"]:\r\n print(example[\"task_name\"],example[\"target\"])\r\n```\r\nLet me know if you have any thoughts. I've started using this in some of my projects and it seems to work. If people are happy with the general approach for a first version, I can make a pull request.", "Hey! Happy to jump into the discussion here. I'm still getting familiar with bits of this code, but the reasons I sampled over data loaders rather than datasets is 1) ensuring that each sampled batch corresponds to only 1 task (in case of different inputs formats/downstream models) and 2) potentially having different batch sizes per task (e.g. some tasks have very long/short inputs). How are you currently dealing with these in your PR?", "The short answer is - I'm not! Everything is currently on a per-example basis. It would be fairly simple to add a `batch_size` argument which would ensure that every `batch_size` examples come from the same task. That should suit most use-cases (unless you wanted to ensure batches all came from the same task and apply something like `SortishSampler` on each task first)\r\n\r\nYour notebook was really inspiring by the way - thanks!", "@zphang is having different batch sizes per task actually helpful? Would be interesting to know as it's not something I've come across as a technique used by any MTL papers.", "mt-dnn's [batcher.py](https://github.com/namisan/mt-dnn/blob/master/mt_dnn/batcher.py) might be worth looking at.", "> @zphang is having different batch sizes per task actually helpful? Would be interesting to know as it's not something I've come across as a technique used by any MTL papers.\r\n\r\nI think having different batch sizes per task is particularly helpful in some scenarios where each task has different amount of data. For example, the problem I'm currently facing is one task has tens of thousands of samples while one task has a couple hundreds. I think in this case different batch size could help. But if using the same batch size is a lot simpler to implement, I guess it makes sense to go with that.", "I think that instead of proportional to size sampling you should specify weights or probabilities for drawing a batch from each dataset. We should also ensure that the smaller datasets are repeated so that the encoder layer doesn't overtrain on the largest dataset.", "Are there any references for people doing different batch sizes per task in the literature? I've only seen constant batch sizes with differing numbers of batches for each task which seems sufficient to prevent the impact of large datasets (Read 3.5.3 of the [T5 paper](https://arxiv.org/pdf/1910.10683.pdf) for example).\r\n\r\n", "Hi,\r\nregarding building T5 dataset , I think we can use datasets https://github.com/huggingface/datasets and then need something similar to tf.data.experimental.sample_from_datasets, do you know if similar functionality exist in pytorch? Which can sample multiple datasets with the given rates. thanks. " ]

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[ "ROUGE-1 and ROUGE-L shouldn't return the same thing. This is weird", "For the rouge2 metric you can do\r\n\r\n```python\r\nrouge = nlp.load_metric('rouge')\r\nwith open(\"pred.txt\") as p, open(\"ref.txt\") as g:\r\n for lp, lg in zip(p, g):\r\n rouge.add(lp, lg)\r\nscore = rouge.compute(rouge_types=[\"rouge2\"])\r\n```\r\n\r\nNote that I just did a PR to have both `.add` and `.add_batch` for metrics, that's why now this is `rouge.add(lp, lg)` and not `rouge.add([lp], [lg])`", "Well I just tested with the official script and both rouge1 and rougeL return exactly the same thing for the input you gave, so this is actually fine ^^\r\n\r\nI hope it helped :)" ]

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[ "I just ran it on colab and got this\r\n```\r\n[{'expected': SplitInfo(name='train', num_bytes=610252351, num_examples=532812,\r\ndataset_name='blog_authorship_corpus'), 'recorded': SplitInfo(name='train',\r\nnum_bytes=611607465, num_examples=533285, dataset_name='blog_authorship_corpus')},\r\n{'expected': SplitInfo(name='validation', num_bytes=37500394, num_examples=31277,\r\ndataset_name='blog_authorship_corpus'), 'recorded': SplitInfo(name='validation',\r\nnum_bytes=35652716, num_examples=30804, dataset_name='blog_authorship_corpus')}]\r\n```\r\nwhich is different from the `dataset_infos.json` and also different from yours.\r\n\r\nIt looks like the script for generating examples is not consistent", "The files provided by the authors are corrupted and the script seems to ignore the xml files that can't be decoded (it does `try:... except UnicodeDecodeError`). Maybe depending of the environment some files can be opened and some others don't but not sure why", "Feel free to do `ignore_verifications=True` for now... The verifications only include a check on the checksums of the downloaded files, and a check on the number of examples in each splits.", "I'm getting this same issue when loading the `imdb` corpus via `dataset = load_dataset(\"imdb\")`. When I try `ignore_verifications=True`, no examples are read into the `train` portion of the dataset. ", "> I'm getting this same issue when loading the `imdb` corpus via `dataset = load_dataset(\"imdb\")`. When I try `ignore_verifications=True`, no examples are read into the `train` portion of the dataset.\r\n\r\nWhen the checksums don't match, it may mean that the file you downloaded is corrupted. In this case you can try to load the dataset again `load_dataset(\"imdb\", download_mode=\"force_redownload\")`\r\n\r\nAlso I just checked on my side and it worked fine:\r\n\r\n```python\r\nfrom datasets import load_dataset\r\n\r\ndataset = load_dataset(\"imdb\")\r\nprint(len(dataset[\"train\"]))\r\n# 25000\r\n```\r\n\r\nLet me know if redownloading fixes your issue @EmilyAlsentzer .\r\nIf not, feel free to open a separate issue.", "It doesn't seem to fix the problem. I'll open a separate issue. Thanks. ", "I wasn't aware of the \"force_redownload\" option and manually removed the '/home/me/.cache/huggingface/datasets/' dir, this worked for me (dataset 'cnn_dailymail')", "Yes I think this might not be documented well enough. Let’s add it to the doc @lhoestq @SBrandeis.\r\nAnd everything on how to control the cache behavior better (removing, overriding, changing the path, etc)", "Already fixed:\r\n```python\r\nIn [1]: from datasets import load_dataset\r\n\r\nIn [2]: ds = load_dataset(\"blog_authorship_corpus\")\r\n\r\nIn [3]: ds\r\nOut[3]: \r\nDatasetDict({\r\n train: Dataset({\r\n features: ['text', 'date', 'gender', 'age', 'horoscope', 'job'],\r\n num_rows: 689793\r\n })\r\n validation: Dataset({\r\n features: ['text', 'date', 'gender', 'age', 'horoscope', 'job'],\r\n num_rows: 37919\r\n })\r\n})\r\n" ]

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[ "I agree that a `.filter` method would be VERY useful and appreciated. I'm not a big fan of using `flatten_nested` as it completely breaks down the structure of the example and it may create bugs. Right now I think it may not work for nested structures. Maybe there's a simpler way that we've not figured out yet.", "Instead of flattening everything and rebuilding the example, maybe we can try to access the examples like this:\r\n```python\r\nfor i in range(num_examples):\r\n example = map_nested(lambda x: x[i], batch)\r\n # ... then test to keep it or not\r\n```", "> Instead of flattening everything and rebuilding the example, maybe we can try to access the examples like this:\r\n> \r\n> ```python\r\n> for i in range(num_examples):\r\n> example = map_nested(lambda x: x[i], batch)\r\n> # ... then test to keep it or not\r\n> ```\r\n\r\nAwesome I'll check it out :-) ", "> Instead of flattening everything and rebuilding the example, maybe we can try to access the examples like this:\r\n> \r\n> ```python\r\n> for i in range(num_examples):\r\n> example = map_nested(lambda x: x[i], batch)\r\n> # ... then test to keep it or not\r\n> ```\r\n\r\nAwesome this function is definitely much nicer!", "Actually I just realized that `map_nested` might not work either as it applies the function at the very last list of the structure. However we can imagine that a single example has also a list in its structure:\r\n```python\r\none_example = {\r\n \"title\": \"blabla\",\r\n \"paragraphs\": [\r\n \"p1\", \"p2\", ...\r\n ]\r\n}\r\n```", "We'll probably have to take into account the `dset._data.schema` to extract the examples from the batch.", "> Actually I just realized that `map_nested` might not work either as it applies the function at the very last list of the structure. However we can imagine that a single example has also a list in its structure:\r\n> \r\n> ```python\r\n> one_example = {\r\n> \"title\": \"blabla\",\r\n> \"paragraphs\": [\r\n> \"p1\", \"p2\", ...\r\n> ]\r\n> }\r\n> ```\r\n\r\nThey both work. I'm using it on trivia_qa which is pretty nested. If you use the option `dict_only=True` I think it's fine.", "> We'll probably have to take into account the `dset._data.schema` to extract the examples from the batch.\r\n\r\nWhy? ", "Actually it's fine. I guess this is going to be yet another thing to be unit-tested just to make sure ^^", "Yes, I will need to add tests and documentation! \r\n@thomwolf - would a function like this be ok? It abstracts `.map()` a bit which might be hard to understand. ", "I tried on some datasets with nested structure and it works fine ! Great work :D \r\n", "Awesome :-), I will add documentation and some simple unittests", "Ok merging!" ]

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[ "Here the full error trace:\r\n\r\n```\r\nArrowInvalid Traceback (most recent call last)\r\n<ipython-input-1-7aaf3f011358> in <module>\r\n 1 import nlp\r\n 2 ds = nlp.load_dataset(\"trivia_qa\", \"rc\", split=\"validation[:1%]\") # this might take 2.3 min to download but it's cached afterwards...\r\n----> 3 ds.map(lambda x: x, load_from_cache_file=False)\r\n\r\n~/python_bin/nlp/arrow_dataset.py in map(self, function, with_indices, batched, batch_size, remove_columns, keep_in_memory, load_from_cache_file, cache_file_name, writer_batch_size, arrow_schema, disable_nullable)\r\n 549\r\n 550 if update_data:\r\n--> 551 writer.finalize() # close_stream=bool(buf_writer is None)) # We only close if we are writing in a file\r\n 552\r\n 553 # Create new Dataset from buffer or file\r\n\r\n~/python_bin/nlp/arrow_writer.py in finalize(self, close_stream)\r\n 182 def finalize(self, close_stream=True):\r\n 183 if self.pa_writer is not None:\r\n--> 184 self.write_on_file()\r\n 185 self.pa_writer.close()\r\n 186 if close_stream:\r\n\r\n~/python_bin/nlp/arrow_writer.py in write_on_file(self)\r\n 104 \"\"\"\r\n 105 if self.current_rows:\r\n--> 106 pa_array = pa.array(self.current_rows, type=self._type)\r\n 107 first_example = pa.array(self.current_rows[0:1], type=self._type)[0]\r\n 108 # Sanity check\r\n\r\n~/hugging_face/venv_3.7/lib/python3.7/site-packages/pyarrow/array.pxi in pyarrow.lib.array()\r\n\r\n~/hugging_face/venv_3.7/lib/python3.7/site-packages/pyarrow/array.pxi in pyarrow.lib._sequence_to_array()\r\n\r\n~/hugging_face/venv_3.7/lib/python3.7/site-packages/pyarrow/error.pxi in pyarrow.lib.check_status()\r\n\r\nArrowInvalid: Could not convert TagMe with type str: converting to null type\r\n```", "Actually thinking a bit more about it, it's probably a data sample that is not correct in `trivia_qa`. But I'm a bit surprised though that we managed to write it in .arrow format and now cannot write it anymore after an \"identity\" mapping.", "I don't have this error :x", "Interesting, maybe I have a very old cache of trivia_qa...thanks for checking", "I'm running it right now on colab to double check", "Actually, I know what the problem is...I'm quite sure it's a bug. Here we take some test inputs: https://github.com/huggingface/nlp/blob/0e0ef12c14d2175e0b0bd7d8aa814b09e2cd7e1f/src/nlp/arrow_dataset.py#L472\r\n\r\nIt might be that in the test inputs, a `Sequence` type value is an emtpy list. So in my case I have `ds[0][\"entity_pages'][\"wiki_context\"] = []`. => this leads to an `arrow_schema` equal to `null` for `[\"entity_pages'][\"wiki_context\"]` => see line: https://github.com/huggingface/nlp/blob/0e0ef12c14d2175e0b0bd7d8aa814b09e2cd7e1f/src/nlp/arrow_dataset.py#L501 instead of list of string which it should for other examples. \r\n\r\nGuess it's an edge case, but it can happen.", "Good point, I think the schema should be infered at the writing stage where we have a `writer_batch_size` number of examples (typically 10k) so it's even less likely to run into this scenario." ]

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[ "Can you run the style formatting tools to pass the code quality test?\r\n\r\nYou can find all the details in CONTRIBUTING.md: https://github.com/huggingface/nlp/blob/master/CONTRIBUTING.md#how-to-contribute-to-nlp", "Nice ! ", "``make style`` done! Thanks for the approvals." ]

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[ "~is the viewer also open source?~\r\n[is a streamlit app!](https://docs.streamlit.io/en/latest/getting_started.html)", "Appears that [two thirds of those polled on Twitter](https://twitter.com/srush_nlp/status/1265734497632477185) are in favor of _some_ mechanism for averting eyeballs from the test data.", "We do no longer use datasets-viewer." ]

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[ "We are thinking about ways to combine datasets for T5 in #217, feel free to share your thoughts about this.", "Ok great! I will look at it. Thanks" ]

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[ "Indeed, good catch ! thanks\r\nFixing it right now" ]

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[ "Amazing, @LysandreJik!", "Really did my best!" ]

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[ "Good point!\r\n\r\nI guess we could add this to `write_batch` as well (before using `self._schema` in the first line of this method)?" ]

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[ "Hopefully. this also fixes the dataset view on https://huggingface.co/nlp/viewer/ :)", "Oh good catch ! This should fix it indeed" ]

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[ "Sounds like something related to the nlp viewer @srush ", "Fixed. " ]

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[ "Hi so there are indeed two configurations in the datasets as you can see [here](https://github.com/huggingface/nlp/blob/master/datasets/scientific_papers/scientific_papers.py#L81-L82).\r\n\r\nYou can load either one with:\r\n```python\r\ndataset = nlp.load_dataset('scientific_papers', 'pubmed')\r\ndataset = nlp.load_dataset('scientific_papers', 'arxiv')\r\n```\r\n\r\nThis issues is actually related to a similar user-experience issue with GLUE. When several configurations are available and the first configuration is loaded by default (see issue #152 and #130), it seems to be unexpected for users.\r\n\r\nI think we should maybe raise a (very explicit) error when there are several configurations available and the user doesn't specify one.\r\n\r\nWhat do you think @lhoestq @patrickvonplaten @mariamabarham ?", "Yes, it looks like the right thing to do ", "Now if you don't specify which part you want, it raises an error:\r\n```\r\nValueError: Config name is missing.\r\nPlease pick one among the available configs: ['pubmed', 'arxiv']\r\nExample of usage:\r\n\t`load_dataset('scientific_papers', 'pubmed')`\r\n```" ]

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[ "I think that's not related to the config name but the filenames in the dummy data. Mostly it occurs with files downloaded from drive. In that case the dummy file name is extracted from the google drive link and it corresponds to what comes after `https://drive.google.com/`\r\n\r\n", "> I think that's not related to the config name but the filenames in the dummy data. Mostly it occurs with files downloaded from drive. In that case the dummy file name is extracted from the google drive link and it corresponds to what comes after `https://drive.google.com/`\r\n\r\nThe filenames of the dummy data are now encoded (see #173). So this is not a problem anymore.\r\n\r\nThe problem here is different and comes from the directory names where we save the arrow files (basically `dataset_name/config_name/version`). In this case we could have invalid directory names because of the config name\r\n", "Okay great then.", "I like the method, but I'm wondering whether it should just be a test method instead of a `__post_init__` function. From a logical point of view the only reason this error would be thrown is because of an invalid config name introduced when creating the dataset script / adding a new dataset => so I think it might be better to write a simple test for this in `test_dataset_common.py`...what do you think @lhoestq ?", "`test_dataset_common.py` only tests canonical datasets no ? What if users wants to create their own script ?", "> `test_dataset_common.py` only tests canonical datasets no ? What if users wants to create their own script ?\r\n\r\nIt tests all dataset that can be loaded either locally or on AWS (which includes all non-canonical datasets as well)...by their own script you mean like a private dataset script that they don't want to be public? I guess even then they could locally run the test functions to check...", "We could have a bunch of simple consistency tests that run before uploading with the CLI (without loading data if we don't want to force the user to have dummy data)?", "Let's say someone want to create his own private script. As the script is not meant to be shared, it's not going to be placed in `/datasets` right ? Maybe the script is going to be inside another project. If I'm not wrong in this case the `test_dataset_common.py` is not going to test his script.\r\n\r\nRaising an error in the post init is a sanity check that would tell the user immediately what's wrong.\r\nThe error is raised if he tried to load the script or if he uses `nlp-cli test`", "> Let's say someone want to create his own private script. As the script is not meant to be shared, it's not going to be placed in `/datasets` right ? Maybe the script is going to be inside another project. If I'm not wrong in this case the `test_dataset_common.py` is not going to test his script.\r\n> \r\n> Raising an error in the post init is a sanity check that would tell the user immediately what's wrong.\r\n> The error is raised if he tried to load the script or if he uses `nlp-cli test`\r\n\r\nOK, fair point! I'm good with this then :-) ", "I'm fine with this as well (even though I understand what you meant @patrickvonplaten, we can still change it later if needed)", "> We could have a bunch of simple consistency tests that run before uploading with the CLI (without loading data if we don't want to force the user to have dummy data)?\r\n\r\nYes! I guess that's a big question whether we should force the user to add dummy data. It's probably too tedious for the user...so when uploading to circle ci should we just check \r\n- 1) All configs can be instantiated (if there are any)\r\n- 2) The BuilderClass can be instantiated ... \r\n- 3) ... maybe some more\r\n\r\nand maybe suggest to the user to add dummy data using the dummy data command?", "I really like that we have a test with dummy data for canonical datasets. This is insurance that they'll keep working in the long run. \r\n\r\nOn the other hand I understand that we will probably not force this practice for scripts uploaded on S3 by a user under his namespace (non-canonical), as it is tedious. As I understand right now the test is done for all the datasets on aws, even the non-canonical ? We should think about different tests for non-canonical datasets.\r\n\r\nI also like the idea of a simple consistency test !", "Merging this one for now, we can think about the test for non-canonical datasets later" ]

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[ "@lhoestq - tiny change in the dummy data command, should be good to merge." ]

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[ "@lhoestq - the config name is rather special here: *E.g.* `mode=first,char_skip=25`. It includes `=` and `,` - will that be a problem for windows folders, you think? \r\n\r\nApart from that good to merge for me.", "It's ok to have `=` and `,`.\r\nWindows doesn't like things like `?`, `:`, `/` etc.\r\n\r\nI'll add some lines to raise an error if the config name is invalid.", "Thanks for fixing things up! I'm curious to take a look at the zip files now to know the format for future reference." ]

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[ "I guess we can use `tf.data.Dataset.from_generator` instead. I'll give it a try.", "Is `tf.data.Dataset.from_generator` working on TPU ?", "`from_generator` is not working on TPU, I met the following error :\r\n\r\n```\r\nFile \"/usr/local/lib/python3.6/contextlib.py\", line 88, in __exit__\r\n next(self.gen)\r\n File \"/home/usr/.venv/bart/lib/python3.6/site-packages/tensorflow_core/python/eager/context.py\", line 1900, in execution_mode\r\n executor_new.wait()\r\n File \"/home/usr/.venv/bart/lib/python3.6/site-packages/tensorflow_core/python/eager/executor.py\", line 67, in wait\r\n pywrap_tensorflow.TFE_ExecutorWaitForAllPendingNodes(self._handle)\r\ntensorflow.python.framework.errors_impl.NotFoundError: No registered 'PyFunc' OpKernel for 'CPU' devices compatible with node {{node PyFunc}}\r\n . Registered: <no registered kernels>\r\n\r\n [[PyFunc]]\r\n```\r\n\r\n---\r\n\r\n@lhoestq It seems you merged some changes that allow lazy-loading. **Can you give an example of how to use ?** Maybe the Colab notebook should be updated with this method as well.", "Could you send me the code you used to run create the dataset using `.from_generator` ? What version of tensorflow are you using ?", "I'm using TF2.2\r\n\r\nHere is my code :\r\n```\r\nimport nlp\r\nfrom transformers import BartTokenizer\r\n\r\ntokenizer = BartTokenizer.from_pretrained('bart-large')\r\n\r\ndef encode(sample):\r\n article_inputs = tokenizer.encode_plus(sample[\"article\"], max_length=tokenizer.model_max_length, pad_to_max_length=True)\r\n summary_inputs = tokenizer.encode_plus(sample[\"highlights\"], max_length=tokenizer.model_max_length, pad_to_max_length=True)\r\n\r\n article_inputs.update({\"lm_labels\": summary_inputs['input_ids']})\r\n return article_inputs\r\n\r\ncnn_dm = nlp.load_dataset('cnn_dailymail', '3.0.0', split='test')\r\ncnn_dm = cnn_dm.map(encode)\r\n\r\ndef gen():\r\n for sample in cnn_dm:\r\n s = {}\r\n s['input_ids'] = sample['input_ids']\r\n s['attention_mask'] = sample['attention_mask']\r\n s['lm_labels'] = sample['lm_labels']\r\n yield s\r\n\r\ndataset = tf.data.Dataset.from_generator(gen, output_types={k: tf.int32 for k in ['input_ids', 'attention_mask', 'lm_labels']}, output_shapes={k: tf.TensorShape([tokenizer.model_max_length]) for k in ['input_ids', 'attention_mask', 'lm_labels']}\r\n```", "Apparently we'll have to wait for the next tensorflow release to use `.from_generator` and TPU. See https://github.com/tensorflow/tensorflow/issues/34346#issuecomment-598262489", "Fixed by https://github.com/huggingface/datasets/pull/339" ]

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[ "We store every dataset in the Arrow format. This is convenient as it supports nested types and memory mapping. If you are curious feel free to check the [pyarrow documentation](https://arrow.apache.org/docs/python/)\r\n\r\nYou can use this library to load your covid papers by creating a dataset script. You can find inspiration from the ones we've already written in `/datasets`. Here is a link to the steps to [add a dataset](https://github.com/huggingface/nlp/blob/master/CONTRIBUTING.md#how-to-add-a-dataset)", "Hello @mrm8488 and @lhoestq \r\n\r\nIs there a way to convert a dataset to Apache arrow format (locally/personal use) & use it before sending it to hugging face?\r\n\r\nThanks :)", "> Is there a way to convert a dataset to Apache arrow format (locally/personal use) & use it before sending it to hugging face?\r\n\r\nSure, to get a dataset in arrow format you can either:\r\n- [load from local files (txt, json, csv)](https://huggingface.co/nlp/loading_datasets.html?highlight=csv#from-local-files)\r\n- OR [load from python data (dict, pandas)](https://huggingface.co/nlp/loading_datasets.html?highlight=csv#from-in-memory-data)\r\n- OR [create your own dataset script](https://huggingface.co/nlp/loading_datasets.html?highlight=csv#using-a-custom-dataset-loading-script)\r\n", "> > Is there a way to convert a dataset to Apache arrow format (locally/personal use) & use it before sending it to hugging face?\r\n> \r\n> Sure, to get a dataset in arrow format you can either:\r\n> \r\n> * [load from local files (txt, json, csv)](https://huggingface.co/nlp/loading_datasets.html?highlight=csv#from-local-files)\r\n> \r\n> * OR [load from python data (dict, pandas)](https://huggingface.co/nlp/loading_datasets.html?highlight=csv#from-in-memory-data)\r\n> \r\n> * OR [create your own dataset script](https://huggingface.co/nlp/loading_datasets.html?highlight=csv#using-a-custom-dataset-loading-script)\r\n\r\nLinks were broken. \r\n\r\nUpdated links provided as below\r\n- [load from local files (txt, json, csv)](https://huggingface.co/docs/datasets/loading_datasets.html#from-local-or-remote-files)\r\n- [load from python data (dict, pandas)](https://huggingface.co/docs/datasets/loading_datasets.html#from-in-memory-data)\r\n- [create your own dataset script](https://huggingface.co/docs/datasets/loading_datasets.html#using-a-custom-dataset-loading-script)\r\n" ]

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[ "Nice ! :) \r\nCan we group them into one dataset with two versions, instead of having two datasets ?", "Yes sure, I can use the version as config name", "@lhoestq can you check? I grouped them", "Awesome :) feel free to merge after fixing the test in the CI", "@mariamabarham - feel free to merge when you're ready. I only checked the dummy files. I did not run the SLOW tests. " ]

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[ "Hi @sarahwie, can you details this a little more?\r\n\r\nI'm not sure I understand what you refer to and what you mean when you say \"Previously, this was done by passing a list of InputFeatures to the dataloader instead of a list of InputFeature\"", "I think I've resolved it. For others' reference: to convert from using the [`MultipleChoiceDataset` class](https://github.com/huggingface/transformers/blob/a34a9896ac2a4a33ff9cd805c76eed914c8d8965/examples/multiple-choice/utils_multiple_choice.py#L82)/[`run_multiple_choice.py`](https://github.com/huggingface/transformers/blob/a34a9896ac2a4a33ff9cd805c76eed914c8d8965/examples/multiple-choice/run_multiple_choice.py) script in Huggingface Transformers, I've done the following for hellaswag:\r\n\r\n1. converted the `convert_examples_to_features()` function to only take one input and return a dictionary rather than a list:\r\n```\r\ndef convert_examples_to_features(example, tokenizer, max_length):\r\n\r\n choices_inputs = defaultdict(list)\r\n for ending_idx, ending in enumerate(example['endings']['ending']):\r\n text_a = example['ctx']\r\n text_b = ending\r\n\r\n inputs = tokenizer.encode_plus(\r\n text_a,\r\n text_b,\r\n add_special_tokens=True,\r\n max_length=max_length,\r\n pad_to_max_length=True,\r\n return_overflowing_tokens=True,\r\n )\r\n if \"num_truncated_tokens\" in inputs and inputs[\"num_truncated_tokens\"] > 0:\r\n logger.info(\r\n \"Attention! you are cropping tokens (swag task is ok). \"\r\n \"If you are training ARC and RACE and you are poping question + options,\"\r\n \"you need to try to use a bigger max seq length!\"\r\n )\r\n\r\n for key in inputs:\r\n choices_inputs[key].append(inputs[key])\r\n \r\n choices_inputs['label'] = int(example['label'])\r\n\r\n return choices_inputs\r\n```\r\n2. apply this directly (instance-wise) to dataset, convert dataset to torch tensors. Dataset is then ready to be passed to `Trainer` instance.\r\n\r\n```\r\ndataset['train'] = dataset['train'].map(lambda x: convert_examples_to_features(x, tokenizer, max_length), batched=False)\r\ncolumns = ['input_ids', 'token_type_ids', 'attention_mask', 'label']\r\ndataset['train'].set_format(type='torch', columns=columns)\r\n```" ]

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[ "On a similar note it would be nice to differentiate between train-easy, train-medium, and train-hard", "Hi @tylerroost, we don't have a timeline for this at the moment.\r\nIf you want to give it a look we would be happy to review a PR on it.\r\nAlso, the library is one week old so everything is quite barebones, in particular the doc.\r\nYou should expect some bumps on the road.\r\n\r\nTo get you started, you can check the datasets scripts in the `./datasets` folder on the repo and find the one on math_datasets that will need to be modified. Then you should check the original repository on the math_dataset to see where the other files to download are located and what is the expected format for the various parts of the dataset.\r\n\r\nTo get a general overview on how datasets scripts are written and used, you can read the nice tutorial on how to add a new dataset for TensorFlow Dataset [here](https://www.tensorflow.org/datasets/add_dataset), our API is not exactly identical but it can give you a high-level overview.", "Thanks I'll give it a look" ]

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[ "I have seen that somebody is hard working on easierly loadable wikipedia. #129 \r\nMaybe I should wait a few days for that version ?", "Yes we (well @lhoestq) are very actively working on this." ]

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[ "Looks like a duplicate of #120.\r\nThis is already fixed on master. We'll do a new release on pypi soon", "Good catch, it looks fixed.\r\n" ]

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[ "awesome !" ]

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[ "This is the test set given by the Glue benchmark. The labels are not provided, and therefore set to -1.", "Ah, yeah. Why it didn’t occur to me. 😂\nThank you for your comment." ]

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[ "It's my misunderstanding. I cannot just upload a csv. I need to write a dataset loading script too.", "I now try with the sample `datasets/csv` folder. \r\n\r\n nlp-cli upload csv\r\n\r\nThe error is still the same\r\n\r\n```\r\n2020-05-21 17:20:56.394659: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcudart.so.10.1\r\nAbout to upload file /content/csv/csv.py to S3 under filename csv/csv.py and namespace korakot\r\nAbout to upload file /content/csv/dummy/0.0.0/dummy_data.zip to S3 under filename csv/dummy/0.0.0/dummy_data.zip and namespace korakot\r\nProceed? [Y/n] y\r\nUploading... This might take a while if files are large\r\nTraceback (most recent call last):\r\n File \"/usr/local/bin/nlp-cli\", line 33, in <module>\r\n service.run()\r\n File \"/usr/local/lib/python3.6/dist-packages/nlp/commands/user.py\", line 234, in run\r\n token=token, filename=filename, filepath=filepath, organization=self.args.organization\r\n File \"/usr/local/lib/python3.6/dist-packages/nlp/hf_api.py\", line 141, in presign_and_upload\r\n urls = self.presign(token, filename=filename, organization=organization)\r\n File \"/usr/local/lib/python3.6/dist-packages/nlp/hf_api.py\", line 132, in presign\r\n return PresignedUrl(**d)\r\nTypeError: __init__() got an unexpected keyword argument 'cdn'\r\n```\r\n", "We haven't tested the dataset upload feature yet cc @julien-c \r\nThis is on our short/mid-term roadmap though", "Even if I fix the `TypeError: __init__() got an unexpected keyword argument 'cdn'` error, it looks like it still uploads to `https://s3.amazonaws.com/models.huggingface.co/bert/<namespace>/<dataset_name>` instead of `https://s3.amazonaws.com/datasets.huggingface.co/nlp/<namespace>/<dataset_name>`", "@lhoestq The endpoints in https://github.com/huggingface/nlp/blob/master/src/nlp/hf_api.py should be (depending on the type of file):\r\n```\r\nPOST /api/datasets/presign\r\nGET /api/datasets/listObjs\r\nDELETE /api/datasets/deleteObj\r\nPOST /api/metrics/presign \r\nGET /api/metrics/listObjs\r\nDELETE /api/metrics/deleteObj\r\n```\r\n\r\nIn addition to this, @thomwolf cleaned up the objects with dataclasses but you should revert this and re-align to the hf_api that's in this branch of transformers: https://github.com/huggingface/transformers/pull/4632 (so that potential new JSON attributes in the API output don't break existing versions of any library)", "New commands are\r\n```\r\nnlp-cli upload_dataset <path/to/dataset>\r\nnlp-cli upload_metric <path/to/metric>\r\nnlp-cli s3_datasets {rm, ls}\r\nnlp-cli s3_metrics {rm, ls}\r\n```\r\nClosing this issue." ]

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[ "If your dataset is a collection of sub-datasets, you should probably consider having one config per sub-dataset. For example for Glue, we have sst2, mnli etc.\r\nIf you want to have multiple train sets (for example one per stage). The easiest solution would be to name them `nlp.Split(\"train_stage1\")`, `nlp.Split(\"train_stage2\")`, etc. or something like that.", "Thanks for the tip! I ended up setting up three different versions of the dataset with their own configs.\r\n\r\nfor the named splits, I was trying with `nlp.Split(\"train-stage1\")`, which fails. Changing to `nlp.Split(\"train_stage1\")` works :) I looked for examples of what works in the code comments, it may be worth adding some examples of valid/invalid names in there?" ]

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