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Request to add StrategyQA dataset
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## Request to add StrategyQA dataset - **Name:** StrategyQA - **Description:** open-domain QA [(project page)](https://allenai.org/data/strategyqa) - **Paper:** [url](https://arxiv.org/pdf/2101.02235.pdf) - **Data:** [here](https://allenai.org/data/strategyqa) - **Motivation:** uniquely-formulated dataset that also includes a question-decomposition breakdown and associated Wikipedia annotations for each step. Good for multi-hop reasoning modeling.
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Update README.md
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Adding relevant citations (paper accepted at AAAI 2020 & EMNLP 2020) to the benchmark
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Fix bash snippet formatting in ADD_NEW_DATASET.md
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This PR indents the paragraphs around the bash snippets in ADD_NEW_DATASET.md to fix formatting.
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Fix `xnli` dataset tuple key
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Closes #2229 The `xnli` dataset yields a tuple key in case of `ar` which is inconsistant with the acceptable key types (str/int). The key was thus ported to `str` keeping the original information intact.
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Start filling GLUE dataset card
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[ "I replaced all the \"we\" and applied your suggestion", "Merging this for now, we can continue improving this card in other PRs :)" ]
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The dataset card was pretty much empty. I added the descriptions (mainly from TFDS since the script is the same), and I also added the tasks tags as well as examples for a subset of the tasks. cc @sgugger
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Fix map when removing columns on a formatted dataset
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This should fix issue #2226 The `remove_columns` argument was ignored on formatted datasets
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Keys yielded while generating dataset are not being checked
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[ "Hi ! Indeed there's no verification on the uniqueness nor the types of the keys.\r\nDo you already have some ideas of what you would like to implement and how ?", "Hey @lhoestq, thank you so much for the opportunity.\r\nAlthough I haven't had much experience with the HF Datasets code, after a careful look at how the `ArrowWriter` functions, I think we can implement this as follows:\r\n\r\n1. First, we would have to update the `ArrowWriter.write()` function here:\r\nhttps://github.com/huggingface/datasets/blob/fcd3c3c8e3b1d9a2f3686a496082e21f06591380/src/datasets/arrow_writer.py#L296\r\nso that it accepts an additional argument `key` which would be appended along with the example here after hashing.\r\n\r\n2. Then, we would need to create a `Hasher` class which will take the key as its input and return a hash for it (We might need to use some hash salt which can be passed to the ArrowWriter.writer() with value equal to the `split_name` for differentiating between same keys of different splits)\r\n\r\n We can use the `hashlib.md5` function for hashing which will conert each key to its byte code before hashing (depending on the data type of the key) **Thus, the `key` type will be verified here**.\r\n\r\n3. Now, we would have to edit this\r\nhttps://github.com/huggingface/datasets/blob/fcd3c3c8e3b1d9a2f3686a496082e21f06591380/src/datasets/arrow_writer.py#L257\r\n so that it iterates over each `(hash, example)` pair (sorted according to hash). We can then simply **check whether each hash is different from the previous hash** (since they will be sorted)\r\n\r\nHowever, since I'm not very familiar with how the data is being written on disk in the form of a table, I might need some guidance for Step 3. \r\nPlease let me know your thought on this. Thanks!", "Interesting !\r\nWe keep the dataset sorted in the order examples are generated by the builder (we expect the dataset builders to generate examples in deterministic order). Therefore I don't think we should shuffle the examples with the hashing. Let me know what you think.\r\nOther that that, I really like the idea of checking for keys duplicates in `write_examples_on_file` :)\r\n\r\nThis looks like a great plan ! Feel free to open a PR and ping me if you have questions or if I can help\r\n", "@lhoestq I'm glad you liked the idea!\r\nI think that since the keys will be unique and deterministic in the nature themselves, so even if we shuffle the examples according to the hash, a deterministic order would still be maintained (as the keys will always have the same hash, whenever the dataset is generated). \r\nAnd since, we are not dealing with time series data (which would require the data to be in original order), I don't think the order of examples would matter much, as long as the order is deterministic and constant for all users.\r\n\r\nI think that this is also what was originally envisioned as mentioned in the documentation here:\r\nhttps://github.com/huggingface/datasets/blob/6775661b19d2ec339784f3d84553a3996a1d86c3/src/datasets/builder.py#L973\r\n\r\nAlso, if we avoid this, we would need to keep track of all the hashed keys in some place and compare each individual key with all others. This can cause some major overhead as each dataset consists of tens of thousands of examples.\r\nLet me know your thoughts in it! I would be opening a PR soon :)", "When users load their own data, they expect the order to stay the same. I think that shuffling the data can make things inconvenient.\r\n\r\n> I think that this is also what was originally envisioned as mentioned in the documentation here:\r\n\r\nThis part was originally developed by tensorflow datasets, and tensorflow datasets indeed does the shuffling. However in this library this is probably not what we want in the general case. But if @albertvillanova and @thomwolf you have opinions on this please let us know.\r\n\r\n> Also, if we avoid this, we would need to keep track of all the hashed keys in some place and compare each individual key with all others. This can cause some major overhead as each dataset consists of tens of thousands of examples.\r\n\r\nMaybe we cam simply keep track of the hashes of of each batch being written ? The size of the batch when the data are save in arrow is 10 000 examples. This would only ensure that we don't have duplicates in each batch, but there might still be duplicates across batches. For 10 000 examples the hashes can just be stored as a python `set`.\r\n\r\nOtherwise if we want full deduplication, we need an extra tool that allows to temporarily save and query hashes that may need to use disk space rather than memory.", "Yes I think we want to keep the original order by default and only shuffle when the user ask for it (for instance by calling `dataset.shuffle()`). That’s how I had it in mind originally.", "Hey @lhoestq, I just had a more in-depth look at the original TFDS code about why the keys and hash were used in the first place.\r\n\r\nIn my opinion, the only use that the `hash(key)` serves is that it allows us to shuffle the examples in a deterministic order (as each example will always yield the same key and thus, the same hash on every system) so that the same dataset is generated for each user, irrespective of the order the examples are yielded by the dataset builder on different user systems.\r\n\r\nOtherwise, if we are not shuffling, then while yielding and writing the data, after getting the key and hashing it for an example, I can't quite see the use of the hash or the key. The hash will simply be generated for each example but not actually used anywhere?\r\n\r\n@lhoestq @thomwolf It would be great if you could explain a bit more about the usage of keys. Thanks!\r\n", "In `datasets` the keys are currently ignored.\r\nFor shuffling we don't use the keys. Instead we shuffle an array of indices. Since both the original order of the dataset and the indices shuffling are deterministic, then `dataset.shuffle` is deterministic as well.\r\nWe can use it to:\r\n1. detect duplicates\r\n2. verify that the generation order is indeed deterministic\r\n3. maybe more ?", "Thanks a lot @lhoestq. I think I understand what we need to do now. The keys can indeed be used for detecting duplicates in generated examples as well as ensuring the order.\r\n\r\n> Maybe we cam simply keep track of the hashes of of each batch being written ? The size of the batch when the data are save in arrow is 10 000 examples. This would only ensure that we don't have duplicates in each batch,\r\n\r\nI think that checking for duplicates in every batch independently would be sufficient as the probability of collisions using something like `MD5` is very low. I would be opening a draft PR soon. It would be great to have your guidance. Thanks!" ]
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The keys used in the dataset generation script to ensure the same order is generated on every user's end should be checked for their types (i.e either `str` or `int`) as well as whether they are unique or not. Currently, the keys are not being checked for any of these, as evident from `xnli' dataset generation: https://github.com/huggingface/datasets/blob/56346791aed417306d054d89bd693d6b7eab17f7/datasets/xnli/xnli.py#L196 Even after having a tuple as key, the dataset is generated without any warning. Also, as tested in the case of `anli` dataset (I tweeked the dataset script to use `1` as a key for every example): ``` >>> import datasets >>> nik = datasets.load_dataset('anli') Downloading and preparing dataset anli/plain_text (download: 17.76 MiB, generated: 73.55 MiB, post-processed: Unknown size, total: 91.31 MiB) to C:\Users\nikhil\.cache\huggingface\datasets\anli\plain_text\0.1.0\43fa2c99c10bf8478f1fa0860f7b122c6b277c4c41306255b7641257cf4e3299... 0 examples [00:00, ? examples/s]1 {'uid': '0fd0abfb-659e-4453-b196-c3a64d2d8267', 'premise': 'The Parma trolleybus system (Italian: "Rete filoviaria di Parma" ) forms part of the public transport network of the city and "comune" of Parma, in the region of Emilia-Romagna, northern Italy. In operation since 1953, the system presently comprises four urban routes.', 'hypothesis': 'The trolleybus system has over 2 urban routes', 'label': 'entailment', 'reason': ''} 2021-04-16 12:38:14.483968: I tensorflow/stream_executor/platform/default/dso_loader.cc:49] Successfully opened dynamic library cudart64_110.dll 1 examples [00:01, 1.87s/ examples]1 {'uid': '7ed72ff4-40b7-4f8a-b1b9-6c612aa62c84', 'premise': 'Alexandra Lendon Bastedo (9 March 1946 – 12 January 2014) was a British actress, best known for her role as secret agent Sharron Macready in the 1968 British espionage/science fiction adventure series "The Champions". She has been cited as a sex symbol of the 1960s and 1970s. Bastedo was a vegetarian and animal welfare advocate.', 'hypothesis': "Sharron Macready was a popular character through the 1980's.", 'label': 'neutral', 'reason': ''} 1 {'uid': '5d2930a3-62ac-485d-94d7-4e36cbbcd7b5', 'premise': 'Alexandra Lendon Bastedo (9 March 1946 – 12 January 2014) was a British actress, best known for her role as secret agent Sharron Macready in the 1968 British espionage/science fiction adventure series "The Champions". She has been cited as a sex symbol of the 1960s and 1970s. Bastedo was a vegetarian and animal welfare advocate.', 'hypothesis': "Bastedo didn't keep any pets because of her views on animal rights.", 'label': 'neutral', 'reason': ''} 1 {'uid': '324db753-ddc9-4a85-a825-f09e2e5aebdd', 'premise': 'Alexandra Lendon Bastedo (9 March 1946 – 12 January 2014) was a British actress, best known for her role as secret agent Sharron Macready in the 1968 British espionage/science fiction adventure series "The Champions". She has been cited as a sex symbol of the 1960s and 1970s. Bastedo was a vegetarian and animal welfare advocate.', 'hypothesis': 'Alexandra Bastedo was named by her mother.', 'label': 'neutral', 'reason': ''} 1 {'uid': '4874f429-da0e-406a-90c7-22240ff3ddf8', 'premise': 'Alexandra Lendon Bastedo (9 March 1946 – 12 January 2014) was a British actress, best known for her role as secret agent Sharron Macready in the 1968 British espionage/science fiction adventure series "The Champions". She has been cited as a sex symbol of the 1960s and 1970s. Bastedo was a vegetarian and animal welfare advocate.', 'hypothesis': 'Bastedo cared for all the animals that inhabit the earth.', 'label': 'neutral', 'reason': ''} ``` Here also, the dataset was generated successfuly even hough it had same keys without any warning. The reason appears to stem from here: https://github.com/huggingface/datasets/blob/56346791aed417306d054d89bd693d6b7eab17f7/src/datasets/builder.py#L988 Here, although it has access to every key, but it is not being checked and the example is written directly: https://github.com/huggingface/datasets/blob/56346791aed417306d054d89bd693d6b7eab17f7/src/datasets/builder.py#L992 I would like to take this issue if you allow me. Thank You!
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`xnli` dataset creating a tuple key while yielding instead of `str` or `int`
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[ "Hi ! Sure sounds good. Also if you find other datasets that use tuples instead of str/int, you can also fix them !\r\nthanks :)", "@lhoestq I have sent a PR for fixing the issue. Would be great if you could have a look! Thanks!" ]
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When using `ds = datasets.load_dataset('xnli', 'ar')`, the dataset generation script uses the following section of code in the egging, which yields a tuple key instead of the specified `str` or `int` key: https://github.com/huggingface/datasets/blob/56346791aed417306d054d89bd693d6b7eab17f7/datasets/xnli/xnli.py#L196 Since, community datasets in Tensorflow Datasets also use HF datasets, this causes a Tuple key error while loading HF's `xnli` dataset. I'm up for sending a fix for this, I think we can simply use `file_idx + "_" + row_idx` as a unique key instead of a tuple.
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[WIP] Add ArrayXD support for fixed size list.
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[ "Awesome thanks ! To fix the CI you just need to merge master into your branch.\r\nThe error is unrelated to your PR" ]
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Add support for fixed size list for ArrayXD when shape is known . See https://github.com/huggingface/datasets/issues/2146 Since offset are not stored anymore, the file size is now roughly equal to the actual data size.
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Use update_metadata_with_features decorator in class_encode_column method
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Following @mariosasko 's comment
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Batched map fails when removing all columns
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[ "I found the problem. I called `set_format` on some columns before. This makes it crash. Here is a complete example to reproduce:\r\n\r\n```python\r\nfrom datasets import load_dataset\r\nsst = load_dataset(\"sst\")\r\nsst.set_format(\"torch\", columns=[\"label\"], output_all_columns=True)\r\nds = sst[\"train\"]\r\n\r\n# crashes\r\nds.map(\r\n lambda x: {\"a\": list(range(20))},\r\n remove_columns=ds.column_names,\r\n load_from_cache_file=False,\r\n num_proc=1,\r\n batched=True,\r\n)\r\n```", "Thanks for reporting and for providing this code to reproduce the issue, this is really helpful !", "I merged a fix, it should work on `master` now :)\r\nWe'll do a new release soon !" ]
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Hi @lhoestq , I'm hijacking this issue, because I'm currently trying to do the approach you recommend: > Currently the optimal setup for single-column computations is probably to do something like > > ```python > result = dataset.map(f, input_columns="my_col", remove_columns=dataset.column_names) > ``` Here is my code: (see edit, in which I added a simplified version ``` This is the error: ```bash pyarrow.lib.ArrowInvalid: Column 1 named tokens expected length 8964 but got length 1000 ``` I wonder why this error occurs, when I delete every column? Can you give me a hint? ### Edit: I preprocessed my dataset before (using map with the features argument) and saved it to disk. May this be part of the error? I can iterate over the complete dataset and print every sample before calling map. There seems to be no other problem with the dataset. I tried to simplify the code that crashes: ```python # works log.debug(dataset.column_names) log.debug(dataset) for i, sample in enumerate(dataset): log.debug(i, sample) # crashes counted_dataset = dataset.map( lambda x: {"a": list(range(20))}, input_columns=column, remove_columns=dataset.column_names, load_from_cache_file=False, num_proc=num_workers, batched=True, ) ``` ``` pyarrow.lib.ArrowInvalid: Column 1 named tokens expected length 20 but got length 1000 ``` Edit2: May this be a problem with a schema I set when preprocessing the dataset before? I tried to add the `features` argument to the function and then I get a new error: ```python # crashes counted_dataset = dataset.map( lambda x: {"a": list(range(20))}, input_columns=column, remove_columns=dataset.column_names, load_from_cache_file=False, num_proc=num_workers, batched=True, features=datasets.Features( { "a": datasets.Sequence(datasets.Value("int32")) } ) ) ``` ``` File "env/lib/python3.8/site-packages/datasets/arrow_dataset.py", line 1704, in _map_single writer.write_batch(batch) File "env/lib/python3.8/site-packages/datasets/arrow_writer.py", line 312, in write_batch col_type = schema.field(col).type if schema is not None else None File "pyarrow/types.pxi", line 1341, in pyarrow.lib.Schema.field KeyError: 'Column tokens does not exist in schema' ``` _Originally posted by @villmow in https://github.com/huggingface/datasets/issues/2193#issuecomment-820230874_
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fixed one instance of 'train' to 'test'
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[ "Thanks ! good catch\r\n\r\nCould you also update the metadata of this dataset ?\r\nYou can do so by running\r\n```\r\ndatasets-cli test ./datasets/newsgroup --all_configs --save_infos --ignore_verifications\r\n```\r\nThis should update the dataset_infos.json file that contains the size of all the splits for example.", "Hi,\r\n`dataset_infos.json` should be updated now.\r\n" ]
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I believe this should be 'test' instead of 'train'
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Raise error if Windows max path length is not disabled
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MEMBER
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On startup, raise an error if Windows max path length is not disabled; ask the user to disable it. Linked to discussion in #2220.
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2,223
Set test cache config
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[ "> why a cache dir per test function does not work?\r\n\r\nProbably because we end up with multiple `datasets_module` in the python path. This breaks the import of all the datasets/metrics modules.\r\nIf you want to use one modules cache per test, you may need remove the `datasets_module` that was added to the python path during the test.\r\nIndeed if the module cache hasn't been initialized, then it's added to the python path by calling `init_dynamic_modules`:\r\n\r\nhttps://github.com/huggingface/datasets/blob/ba76012a19193a35053b9e20243ff40c2b4204ab/src/datasets/load.py#L291-L291", "@lhoestq, for the moment, this PR avoids populating the `~/.cache` dir during training, which is already an improvement, isn't it?", "Yes we can merge it this way if you're fine with it !\r\nThis is a good improvement", "I will eventually try to implement a `cache_dir` per test function in another PR, but I think I should first fix some side effects in tests: each test function should be atomic and able to have its own `cache_dir` without being affected by the `cache_dir` set in other test functions.", "Yes this would be ideal !" ]
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Currently, running the tests populates the default cache directory `"~/.cache"`. This PR monkey-patches the config to set the cache directory within the temporary test directory, avoiding side effects.
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2,222
Fix too long WindowsFileLock name
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[ "Windows users should disable the max path length limit. It's a nightmare to handle it.\r\nAlso the lock path must not be changed in a random way. Otherwise from another process the lock path might not be the same and the locking mechanism won't work.", "Do you agree with handling the case where MAX_PATH is not disabled? If not, we can close this PR.\r\n\r\nIf so, would it work a deterministic lock path instead of random?", "I'd rather not handle this at all, since there will be other places in the code where the limit will break things" ]
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Fix WindowsFileLock name longer than allowed MAX_PATH by shortening the basename.
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2,221
Add SLR70 - SLR80 and SLR86 to OpenSLR dataset
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I would like to add SLR70, SLR71, SLR72, SLR73, SLR74, SLR75, SLR76, SLR77, SLR78, SLR79, SLR80 and SLR86 to OpenSLR dataset. The languages are: Nigerian English, Chilean Spanish, Columbian Spanish, Peruvian Spanish, Puerto Rico Spanish, Venezuelan Spanish, Basque, Galician, Gujarati and Kannada.
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Fix infinite loop in WindowsFileLock
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[ "How is it possible to get an infinite loop ? Can you add more details ?", "Yes, in Windows, if the filename is too long, a `FileNotFoundError` is raised. The exception should be raised in this case. Otherwise, we get into an infinite loop.\r\n\r\nIf other process has the file locked, then `PermissionError` is raised. In this case, `pass` is OK.", "Note that the filelock module comes from this project that hasn't changed in years - while still being used by ten of thousands of projects:\r\nhttps://github.com/benediktschmitt/py-filelock\r\n\r\nUnless we have proper tests for this, I wouldn't recommend to change it", "I'm pretty sure many things from the library could break for windows users that haven't disabled the max path length limit.\r\nMaybe it would be simpler to simply raise an error on startup. For exampe, for windows users the error could ask them to disable the limit if it's not been disabled yet ?" ]
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Raise exception to avoid infinite loop.
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Added CUAD dataset
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[ "1) Changed the language in a few places apart from those you mentioned in README\r\n2) Reduced the size of dummy data folder by removing all other entries except the first\r\n3) Updated YAML tags by using to the past version of `datasets-tagging` app. Will update the quick fix on that repository too in a while", "@bhavitvyamalik Thanks for adding the dataset on huggingface! Can you please add a metric also for the dataset using the squad_v2 metric file? ", "@MohammedRakib you can check [#2257](https://github.com/huggingface/datasets/pull/2257)" ]
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Dataset link : https://github.com/TheAtticusProject/cuad/ Working on README.md currently. Closes #2084 and [#1](https://github.com/TheAtticusProject/cuad/issues/1).
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Duplicates in the LAMA dataset
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[ "Hi,\r\n\r\ncurrently the datasets API doesn't have a dedicated function to remove duplicate rows, but since the LAMA dataset is not too big (it fits in RAM), we can leverage pandas to help us remove duplicates:\r\n```python\r\n>>> from datasets import load_dataset, Dataset\r\n>>> dataset = load_dataset('lama', split='train')\r\n>>> dataset = Dataset.from_pandas(dataset.to_pandas().drop_duplicates(subset=...)) # specify a subset of the columns to consider in a list or use all of the columns if None\r\n```\r\n\r\nNote that the same can be achieved with the `Dataset.filter` method but this would requrie some extra work (filter function, speed?).", "Oh, seems like my question wasn't specified well. I'm _not_ asking how to remove duplicates, but whether duplicates should be removed if I want to do the evaluation on the LAMA dataset as it was proposed in the original paper/repository? In other words, will I get the same result if evaluate on the de-duplicated dataset loaded from HF's `datasets` as the results I'd get if I use the original data format and data processing script in https://github.com/facebookresearch/LAMA? ", "So it looks like the person who added LAMA to the library chose to have one item per piece of evidence rather than one per relation - and in this case, there are duplicate pieces of evidence for the target relation\r\n\r\nIf I understand correctly, to reproduce reported results, you would have to aggregate predictions for the several pieces of evidence provided for each relation (each unique `uuid`), but the original authors will know better \r\n\r\ncc @fabiopetroni " ]
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I observed duplicates in the LAMA probing dataset, see a minimal code below. ``` >>> import datasets >>> dataset = datasets.load_dataset('lama') No config specified, defaulting to: lama/trex Reusing dataset lama (/home/anam/.cache/huggingface/datasets/lama/trex/1.1.0/97deffae13eca0a18e77dfb3960bb31741e973586f5c1fe1ec0d6b5eece7bddc) >>> train_dataset = dataset['train'] >>> train_dataset[0] {'description': 'language or languages a person has learned from early childhood', 'label': 'native language', 'masked_sentence': 'Louis Jules Trochu ([lwi Κ’yl tʁɔʃy]; 12 March 1815 – 7 October 1896) was a [MASK] military leader and politician.', 'obj_label': 'French', 'obj_surface': 'French', 'obj_uri': 'Q150', 'predicate_id': 'P103', 'sub_label': 'Louis Jules Trochu', 'sub_surface': 'Louis Jules Trochu', 'sub_uri': 'Q441235', 'template': 'The native language of [X] is [Y] .', 'template_negated': '[X] is not owned by [Y] .', 'type': 'N-1', 'uuid': '40b2ed1c-0961-482e-844e-32596b6117c8'} >>> train_dataset[1] {'description': 'language or languages a person has learned from early childhood', 'label': 'native language', 'masked_sentence': 'Louis Jules Trochu ([lwi Κ’yl tʁɔʃy]; 12 March 1815 – 7 October 1896) was a [MASK] military leader and politician.', 'obj_label': 'French', 'obj_surface': 'French', 'obj_uri': 'Q150', 'predicate_id': 'P103', 'sub_label': 'Louis Jules Trochu', 'sub_surface': 'Louis Jules Trochu', 'sub_uri': 'Q441235', 'template': 'The native language of [X] is [Y] .', 'template_negated': '[X] is not owned by [Y] .', 'type': 'N-1', 'uuid': '40b2ed1c-0961-482e-844e-32596b6117c8'} ``` I checked the original data available at https://dl.fbaipublicfiles.com/LAMA/data.zip. This particular duplicated comes from: ``` {"uuid": "40b2ed1c-0961-482e-844e-32596b6117c8", "obj_uri": "Q150", "obj_label": "French", "sub_uri": "Q441235", "sub_label": "Louis Jules Trochu", "predicate_id": "P103", "evidences": [{"sub_surface": "Louis Jules Trochu", "obj_surface": "French", "masked_sentence": "Louis Jules Trochu ([lwi \u0292yl t\u0281\u0254\u0283y]; 12 March 1815 \u2013 7 October 1896) was a [MASK] military leader and politician."}, {"sub_surface": "Louis Jules Trochu", "obj_surface": "French", "masked_sentence": "Louis Jules Trochu ([lwi \u0292yl t\u0281\u0254\u0283y]; 12 March 1815 \u2013 7 October 1896) was a [MASK] military leader and politician."}]} ``` What is the best way to deal with these duplicates if I want to use `datasets` to probe with LAMA?
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Revert breaking change in cache_files property
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#2025 changed the format of `Dataset.cache_files`. Before it was formatted like ```python [{"filename": "path/to/file.arrow", "start": 0, "end": 1337}] ``` and it was changed to ```python ["path/to/file.arrow"] ``` since there's no start/end offsets available anymore. To make this less breaking, I'm setting the format back to a list of dicts: ```python [{"filename": "path/to/file.arrow"}] ```
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added real label for glue/mrpc to test set
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Added real label to `glue.py` `mrpc` task for test split.
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Add datasets SLR35 and SLR36 to OpenSLR
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[ "Hi @lhoestq,\r\nCould you please help me, I got this error message in all \"ci/circleci: run_dataset_script_tests_pyarrow*\" tests:\r\n```\r\n...\r\n \"\"\"Wrapper classes for various types of tokenization.\"\"\"\r\n \r\n from bleurt.lib import bert_tokenization\r\n import tensorflow.compat.v1 as tf\r\n> import sentencepiece as spm\r\nE ModuleNotFoundError: No module named 'sentencepiece'\r\n...\r\n```\r\nI am not sure why I do get it. Thanks.\r\n", "Hi ! This issue appeared on master since the last update of `BLEURT`.\r\nI'm working on a fix. You can ignore this issue for this PR", "> Hi ! This issue appeared on master since the last update of `BLEURT`.\r\n> I'm working on a fix. You can ignore this issue for this PR\r\n\r\nThanks for the info", "Merging since the CI is fixed on master" ]
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I would like to add [SLR35](https://openslr.org/35/) (18GB) and [SLR36](https://openslr.org/36/) (22GB) which are Large Javanese and Sundanese ASR training data set collected by Google in collaboration with Reykjavik University and Universitas Gadjah Mada in Indonesia.
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load_metric error: module 'datasets.utils.file_utils' has no attribute 'add_start_docstrings'
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[ "Hi @nsaphra, thanks for reporting.\r\n\r\nThis issue was fixed in `datasets` version 1.3.0. Could you please update `datasets` and tell me if the problem persists?\r\n```shell\r\npip install -U datasets\r\n```", "There might be a bug in the conda version of `datasets` 1.2.1 where the datasets/metric scripts are downloaded from `master` instead of the `1.2.1` repo.\r\n\r\nYou can try setting the env var `HF_SCRIPTS_VERSION=\"1.2.1\"` as a workaround. Let me know if that helps.", "I just faced the same issue. I was using 1.2.1 from conda and received the same AttributeError complaining about 'add_start_docstrings'. Uninstalling the conda installed datasets and then installing the latest datasets (version 1.5.0) using pip install solved the issue for me. I don't like mixing up conda and pip installs in the same environments but this will have to do for now, until 1.5.0 is made available through conda.", "Yep, seems to have fixed things! The conda package could really do with an update. Thanks!" ]
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I'm having the same problem as [Notebooks issue 10](https://github.com/huggingface/notebooks/issues/10) on datasets 1.2.1, and it seems to be an issue with the datasets package. ```python >>> from datasets import load_metric >>> metric = load_metric("glue", "sst2") Traceback (most recent call last): File "<stdin>", line 1, in <module> File "/ext3/miniconda3/lib/python3.8/site-packages/datasets-1.2.1-py3.8.egg/datasets/load.py", line 502, in load_metric File "/ext3/miniconda3/lib/python3.8/site-packages/datasets-1.2.1-py3.8.egg/datasets/load.py", line 66, in import_main_class File "/ext3/miniconda3/lib/python3.8/importlib/__init__.py", line 127, in import_module return _bootstrap._gcd_import(name[level:], package, level) File "<frozen importlib._bootstrap>", line 1014, in _gcd_import File "<frozen importlib._bootstrap>", line 991, in _find_and_load File "<frozen importlib._bootstrap>", line 975, in _find_and_load_unlocked File "<frozen importlib._bootstrap>", line 671, in _load_unlocked File "<frozen importlib._bootstrap_external>", line 783, in exec_module File "<frozen importlib._bootstrap>", line 219, in _call_with_frames_removed File "/home/ns4008/.cache/huggingface/modules/datasets_modules/metrics/glue/e4606ab9804a36bcd5a9cebb2cb65bb14b6ac78ee9e6d5981fa679a495dd55de/glue.py", line 105, in <module> @datasets.utils.file_utils.add_start_docstrings(_DESCRIPTION, _KWARGS_DESCRIPTION) AttributeError: module 'datasets.utils.file_utils' has no attribute 'add_start_docstrings' ```
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Fix lc_quad download checksum
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Fixes #2211
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Can't reach "https://storage.googleapis.com/illuin/fquad/train.json.zip" when trying to load fquad dataset
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[ "Hi ! Apparently the data are not available from this url anymore. We'll replace it with the new url when it's available", "I saw this on their website when we request to download the dataset:\r\n![image](https://user-images.githubusercontent.com/19718818/114879600-fa458680-9e1e-11eb-9e05-f0963d68ff0f.png)\r\n\r\nCan we still request them link for the dataset and make a PR? @lhoestq @yjernite ", "I've contacted Martin (first author of the fquad paper) regarding a possible new url. Hopefully we can get one soon !", "They now made a website to force people who want to use the dataset for commercial purposes to seek a commercial license from them ..." ]
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I'm trying to load the [fquad dataset](https://huggingface.co/datasets/fquad) by running: ```Python fquad = load_dataset("fquad") ``` which produces the following error: ``` Using custom data configuration default Downloading and preparing dataset fquad/default (download: 3.14 MiB, generated: 6.62 MiB, post-processed: Unknown size, total: 9.76 MiB) to /root/.cache/huggingface/datasets/fquad/default/0.1.0/778dc2c85813d05ddd0c17087294d5f8f24820752340958070876b677af9f061... --------------------------------------------------------------------------- ConnectionError Traceback (most recent call last) <ipython-input-48-a2721797e23b> in <module>() ----> 1 fquad = load_dataset("fquad") 11 frames /usr/local/lib/python3.7/dist-packages/datasets/utils/file_utils.py in get_from_cache(url, cache_dir, force_download, proxies, etag_timeout, resume_download, user_agent, local_files_only, use_etag, max_retries, use_auth_token) 614 raise FileNotFoundError("Couldn't find file at {}".format(url)) 615 _raise_if_offline_mode_is_enabled(f"Tried to reach {url}") --> 616 raise ConnectionError("Couldn't reach {}".format(url)) 617 618 # Try a second time ConnectionError: Couldn't reach https://storage.googleapis.com/illuin/fquad/train.json.zip ``` Does anyone know why that is and how to fix it?
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Getting checksum error when trying to load lc_quad dataset
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[ "Hi,\r\n\r\nI've already opened a PR with the fix. If you are in a hurry, just build the project from source and run:\r\n```bash\r\ndatasets-cli test datasets/lc_quad --save_infos --all_configs --ignore_verifications\r\n```\r\n\r\n", "Ah sorry, I tried searching but couldn't find any related PR. \r\n\r\nThank you! " ]
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I'm having issues loading the [lc_quad](https://huggingface.co/datasets/fquad) dataset by running: ```Python lc_quad = load_dataset("lc_quad") ``` which is giving me the following error: ``` Using custom data configuration default Downloading and preparing dataset lc_quad/default (download: 3.69 MiB, generated: 19.77 MiB, post-processed: Unknown size, total: 23.46 MiB) to /root/.cache/huggingface/datasets/lc_quad/default/2.0.0/5a98fe174603f5dec6df07edf1c2b4d2317210d2ad61f5a393839bca4d64e5a7... --------------------------------------------------------------------------- NonMatchingChecksumError Traceback (most recent call last) <ipython-input-42-404ace83f73c> in <module>() ----> 1 lc_quad = load_dataset("lc_quad") 3 frames /usr/local/lib/python3.7/dist-packages/datasets/utils/info_utils.py in verify_checksums(expected_checksums, recorded_checksums, verification_name) 37 if len(bad_urls) > 0: 38 error_msg = "Checksums didn't match" + for_verification_name + ":\n" ---> 39 raise NonMatchingChecksumError(error_msg + str(bad_urls)) 40 logger.info("All the checksums matched successfully" + for_verification_name) 41 NonMatchingChecksumError: Checksums didn't match for dataset source files: ['https://github.com/AskNowQA/LC-QuAD2.0/archive/master.zip'] ``` Does anyone know why this could be and how I fix it?
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dataloading slow when using HUGE dataset
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[ "Hi ! Yes this is an issue with `datasets<=1.5.0`\r\nThis issue has been fixed by #2122 , we'll do a new release soon :)\r\nFor now you can test it on the `master` branch.", "Hi, thank you for your answer. I did not realize that my issue stems from the same problem. " ]
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Hi, When I use datasets with 600GB data, the dataloading speed increases significantly. I am experimenting with two datasets, and one is about 60GB and the other 600GB. Simply speaking, my code uses `datasets.set_format("torch")` function and let pytorch-lightning handle ddp training. When looking at the pytorch-lightning supported profile of two different runs, I see that fetching a batch(`get_train_batch`) consumes an unreasonable amount of time when data is large. What could be the cause? * 60GB data ``` Action | Mean duration (s) |Num calls | Total time (s) | Percentage % | ------------------------------------------------------------------------------------------------------------------------------------ Total | - |_ | 200.33 | 100 % | ------------------------------------------------------------------------------------------------------------------------------------ run_training_epoch | 71.994 |1 | 71.994 | 35.937 | run_training_batch | 0.64373 |100 | 64.373 | 32.133 | optimizer_step_and_closure_0 | 0.64322 |100 | 64.322 | 32.108 | training_step_and_backward | 0.61004 |100 | 61.004 | 30.452 | model_backward | 0.37552 |100 | 37.552 | 18.745 | model_forward | 0.22813 |100 | 22.813 | 11.387 | training_step | 0.22759 |100 | 22.759 | 11.361 | get_train_batch | 0.066385 |100 | 6.6385 | 3.3138 | ``` * 600GB data ``` Action | Mean duration (s) |Num calls | Total time (s) | Percentage % | ------------------------------------------------------------------------------------------------------------------------------------ Total | - |_ | 3285.6 | 100 % | ------------------------------------------------------------------------------------------------------------------------------------ run_training_epoch | 1397.9 |1 | 1397.9 | 42.546 | run_training_batch | 7.2596 |100 | 725.96 | 22.095 | optimizer_step_and_closure_0 | 7.2589 |100 | 725.89 | 22.093 | training_step_and_backward | 7.223 |100 | 722.3 | 21.984 | model_backward | 6.9662 |100 | 696.62 | 21.202 | get_train_batch | 6.322 |100 | 632.2 | 19.241 | model_forward | 0.24902 |100 | 24.902 | 0.75789 | training_step | 0.2485 |100 | 24.85 | 0.75633 | ```
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Add code of conduct to the project
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Add code of conduct to the project and link it from README and CONTRIBUTING. This was already done in `transformers`.
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Remove Python2 leftovers
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[ "merging since the CI is fixed on master" ]
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This PR removes Python2 leftovers since this project aims for Python3.6+ (and as of 2020 Python2 is no longer officially supported)
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making labels consistent across the datasets
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[ "Hi ! The ClassLabel feature type encodes the labels as integers.\r\nThe integer corresponds to the index of the label name in the `names` list of the ClassLabel.\r\nHere that means that the labels are 'entailment' (0), 'neutral' (1), 'contradiction' (2).\r\n\r\nYou can get the label names back by using `a.features['label'].int2str(i)`.\r\n" ]
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Hi For accessing the labels one can type ``` >>> a.features['label'] ClassLabel(num_classes=3, names=['entailment', 'neutral', 'contradiction'], names_file=None, id=None) ``` The labels however are not consistent with the actual labels sometimes, for instance in case of XNLI, the actual labels are 0,1,2, but if one try to access as above they are entailment, neutral,contradiction, it would be great to have the labels consistent. thanks
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Got pyarrow error when loading a dataset while adding special tokens into the tokenizer
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[ "Hi,\r\n\r\nthe output of the tokenizers is treated specially in the lib to optimize the dataset size (see the code [here](https://github.com/huggingface/datasets/blob/master/src/datasets/arrow_writer.py#L138-L141)). It looks like that one of the values in a dictionary returned by the tokenizer is out of the assumed range.\r\nCan you please provide a minimal reproducible example for more help?", "Hi @yana-xuyan, thanks for reporting.\r\n\r\nAs clearly @mariosasko explained, `datasets` performs some optimizations in order to reduce the size of the dataset cache files. And one of them is storing the field `special_tokens_mask` as `int8`, which means that this field can only contain integers between `-128` to `127`. As your message error states, one of the values of this field is `50259`, and therefore it cannot be stored as an `int8`.\r\n\r\nMaybe we could implement a way to disable this optimization and allow using any integer value; although the size of the cache files would be much larger." ]
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I added five more special tokens into the GPT2 tokenizer. But after that, when I try to pre-process the data using my previous code, I got an error shown below: Traceback (most recent call last): File "/home/xuyan/anaconda3/envs/convqa/lib/python3.7/site-packages/datasets/arrow_dataset.py", line 1687, in _map_single writer.write(example) File "/home/xuyan/anaconda3/envs/convqa/lib/python3.7/site-packages/datasets/arrow_writer.py", line 296, in write self.write_on_file() File "/home/xuyan/anaconda3/envs/convqa/lib/python3.7/site-packages/datasets/arrow_writer.py", line 270, in write_on_file pa_array = pa.array(typed_sequence) File "pyarrow/array.pxi", line 222, in pyarrow.lib.array File "pyarrow/array.pxi", line 110, in pyarrow.lib._handle_arrow_array_protocol File "/home/xuyan/anaconda3/envs/convqa/lib/python3.7/site-packages/datasets/arrow_writer.py", line 108, in __arrow_array__ out = out.cast(pa.list_(self.optimized_int_type)) File "pyarrow/array.pxi", line 810, in pyarrow.lib.Array.cast File "/home/xuyan/anaconda3/envs/convqa/lib/python3.7/site-packages/pyarrow/compute.py", line 281, in cast return call_function("cast", [arr], options) File "pyarrow/_compute.pyx", line 465, in pyarrow._compute.call_function File "pyarrow/_compute.pyx", line 294, in pyarrow._compute.Function.call File "pyarrow/error.pxi", line 122, in pyarrow.lib.pyarrow_internal_check_status File "pyarrow/error.pxi", line 84, in pyarrow.lib.check_status pyarrow.lib.ArrowInvalid: Integer value 50259 not in range: -128 to 127 Do you have any idea about it?
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Updating citation information on LinCE readme
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Hi! I just updated the citation information in this PR. It had an additional bibtex from one of the datasets used in LinCE and then the LinCE bibtex. I removed the former and added a link that shows the full list of citations for each dataset. Thanks!
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Add configurable options to `seqeval` metric
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Fixes #2148 Adds options to use strict mode, different schemes of evaluation, sample weight and adjust zero_division behavior, if encountered. `seqeval` provides schemes as objects, hence dynamic import from string, to avoid making the user do the import (thanks to @albertvillanova for the `importlib` idea).
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updated banking77 train and test data
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[ "Hi ! Can you add a description regarding this PR ? Why do you think we need to update the dummy data used to test the `banking77` dataset loading script ?", "Closing for inactivity. Feel free to re-open if you want to push this change" ]
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Add classes GenerateMode, DownloadConfig and Version to the documentation
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Add documentation for classes `GenerateMode`, `DownloadConfig` and `Version`. Update the docstring of `load_dataset` to create cross-reference links to the classes. Related to #2187.
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Fix ArrowWriter overwriting features in ArrowBasedBuilder
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This should fix the issues with CSV loading experienced in #2153 and #2200. The CSV builder is an ArrowBasedBuilder that had an issue with its ArrowWriter used to write the arrow file from the csv data. The writer wasn't initialized with the features passed by the user. Therefore the writer was inferring the features from the arrow data, discarding the features passed by the user. I fixed that and I updated the tests
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_prepare_split will overwrite DatasetBuilder.info.features
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[ "Hi ! This might be related to #2153 \r\n\r\nYou're right the ArrowWriter should be initialized with `features=self.info.features` ! Good catch\r\nI'm opening a PR to fix this and also to figure out how it was not caught in the tests\r\n\r\nEDIT: opened #2201", "> Hi ! This might be related to #2153\r\n> \r\n> You're right the ArrowWriter should be initialized with `features=self.info.features` ! Good catch\r\n> I'm opening a PR to fix this and also to figure out how it was not caught in the tests\r\n> \r\n> EDIT: opened #2201\r\n\r\nGlad to hear that! Thank you for your fix, I'm new to huggingface, it's a fantastic project 😁" ]
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Hi, here is my issue: I initialized a Csv datasetbuilder with specific features: ``` def get_dataset_features(data_args): features = {} if data_args.text_features: features.update({text_feature: hf_features.Value("string") for text_feature in data_args.text_features.strip().split(",")}) if data_args.num_features: features.update({text_feature: hf_features.Value("float32") for text_feature in data_args.num_features.strip().split(",")}) if data_args.label_classes: features["label"] = hf_features.ClassLabel(names=data_args.label_classes.strip().split(",")) else: features["label"] = hf_features.Value("float32") return hf_features.Features(features) datasets = load_dataset(extension, data_files=data_files, sep=data_args.delimiter, header=data_args.header, column_names=data_args.column_names.split(",") if data_args.column_names else None, features=get_dataset_features(data_args=data_args)) ``` The `features` is printout as below before `builder_instance.as_dataset` is called: ``` {'label': ClassLabel(num_classes=2, names=['unacceptable', 'acceptable'], names_file=None, id=None), 'notated': Value(dtype='string', id=None), 'sentence': Value(dtype='string', id=None), 'src_code': Value(dtype='string', id=None)} ```` But after the `builder_instance.as_dataset` is called for Csv dataset builder, the `features` is changed to: ``` {'label': Value(dtype='int64', id=None), 'notated': Value(dtype='string', id=None), 'sentence': Value(dtype='string', id=None), 'src_code': Value(dtype='string', id=None)} ``` After digged into the code, I releazed that in `ArrowBasedBuilder._prepare_split`, the DatasetBuilder's info's features will be overwrited by `ArrowWriter`'s `_features`. But `ArrowWriter` is initailized without passing `features`. So my concern is: It's this overwrite must be done, or, should it be an option to pass features in `_prepare_split` function?
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Fix backward compatibility in Dataset.load_from_disk
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[ "Hi @lhoestq, could you please check if this makes sense? Thanks.", "What about using `_indices_data_files` field in save_to_disk instead of `_indices_files` ?\r\nThis way future datasets can also be reloaded from older versions of the lib\r\n\r\n`_indices_files` was introduced in a recent PR and was not released", "Yes, I have seen it is not released yet...\r\n\r\nYou are right! It was your awesome PR on Tables which renamed this. If there is no particular reason for this renaming, yes, we could switch it back to the previous `_indices_data_files`. ;)" ]
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Fix backward compatibility when loading from disk an old dataset saved to disk with indices using key "_indices_data_files". Related to #2195.
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2,198
added file_permission in load_dataset
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[ "From offline discussions: we want to make the permissions handling consistent with `transformers`. However from discussion in https://github.com/huggingface/transformers/pull/11119 it looks like it might not be a good solution to provide this argument. Users should use umask for now, and we'll see how things evolve.\r\n\r\n@bhavitvyamalik I'm closing the PR for now if you don't mind" ]
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As discussed in #2065 I've added `file_permission` argument in `load_dataset`. Added mainly 2 things here: 1) Permission of downloaded datasets when converted to .arrow files can be changed with argument `file_permission` argument in `load_dataset` (default is 0o644 only) 2) Incase the user uses `map` later on to generate another cache file of dataset, it ensures the permissions of newly generated file are similar to that of` *-train.arrow` file inside cache_dir for that dataset.
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2,197
fix missing indices_files in load_form_disk
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This should fix #2195 `load_from_disk` was failing if there was no "_indices_files" field in state.json. This can happen if the dataset has no indices mapping
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`load_dataset` caches two arrow files?
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[ "Hi ! Files that starts with `cache-*` are cached computation files, i.e. they are the cached results of map/filter/cast/etc. operations. For example if you used `map` on your dataset to transform it, then the resulting dataset is going to be stored and cached in a `cache-*` file. These files are used to avoid having to load the dataset in RAM, even after many transforms", "Thanks @lhoestq! Hmm.. that's strange because I specifically turned off auto caching, and saved mapped result, using `save_to_disk`, to another location. At this location, the following file is created:`355G\tcache-ed205e500a7dc44c.arrow`\r\n\r\nTo my observation, both `load_dataset` and `map` creates `cache-*` files, and I wonder what the `cache-*` file from `load_dataset` is for (as I believe the same information is stored in `json-train.arrow`.", "This is a wrong report -- `cache-*` files are created only my `map`, not by `load_dataset`. " ]
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Hi, I am using datasets to load large json file of 587G. I checked the cached folder and found that there are two arrow files created: * `cache-ed205e500a7dc44c.arrow` - 355G * `json-train.arrow` - 582G Why is the first file created? If I delete it, would I still be able to `load_from_disk`?
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KeyError: '_indices_files' in `arrow_dataset.py`
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[ "Thanks for reporting @samsontmr.\r\n\r\nIt seems a backward compatibility issue...", "Thanks @samsontmr this should be fixed on master now\r\n\r\nFeel free to reopen if you're still having issues" ]
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After pulling the latest master, I'm getting a crash when `load_from_disk` tries to load my local dataset. Trace: ``` Traceback (most recent call last): File "load_data.py", line 11, in <module> dataset = load_from_disk(SRC) File "/opt/conda/envs/py38/lib/python3.8/site-packages/datasets/load.py", line 784, in load_from_disk return DatasetDict.load_from_disk(dataset_path, fs, keep_in_memory=keep_in_memory) File "/opt/conda/envs/py38/lib/python3.8/site-packages/datasets/dataset_dict.py", line 692, in load_from_disk dataset_dict[k] = Dataset.load_from_disk(dataset_dict_split_path, fs, keep_in_memory=keep_in_memory) File "/opt/conda/envs/py38/lib/python3.8/site-packages/datasets/arrow_dataset.py", line 634, in load_from_disk if state["_indices_files"]: KeyError: '_indices_files' ``` I believe this is the line causing the error since there may not be a `_indices_files` key in the older versions: https://github.com/huggingface/datasets/blob/b70141e3c5149430951773aaa0155555c5fb3e76/src/datasets/arrow_dataset.py#L634 May I suggest using `state.get()` instead of directly indexing the dictionary? @lhoestq
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py3.7: TypeError: can't pickle _LazyModule objects
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[ "\r\nThis wasn't a `datasets` problem, but `transformers`' and it was solved here https://github.com/huggingface/transformers/pull/11168\r\n" ]
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While this works fine with py3.8, under py3.7, with a totally new conda env and transformers install: ``` git clone https://github.com/huggingface/transformers cd transformers pip install -e .[testing] export BS=1; rm -rf /tmp/test-clm; PYTHONPATH=src USE_TF=0 CUDA_VISIBLE_DEVICES=0 python \ examples/language-modeling/run_clm.py --model_name_or_path distilgpt2 --dataset_name wikitext \ --dataset_config_name wikitext-2-raw-v1 --do_train --max_train_samples 1 \ --per_device_train_batch_size $BS --output_dir /tmp/test-clm --block_size 128 --logging_steps 1 \ --fp16 ``` ``` Traceback (most recent call last): File "examples/language-modeling/run_clm.py", line 453, in <module> main() File "examples/language-modeling/run_clm.py", line 336, in main load_from_cache_file=not data_args.overwrite_cache, File "/home/stas/anaconda3/lib/python3.7/site-packages/datasets/dataset_dict.py", line 303, in map for k, dataset in self.items() File "/home/stas/anaconda3/lib/python3.7/site-packages/datasets/dataset_dict.py", line 303, in <dictcomp> for k, dataset in self.items() File "/home/stas/anaconda3/lib/python3.7/site-packages/datasets/arrow_dataset.py", line 1259, in map update_data=update_data, File "/home/stas/anaconda3/lib/python3.7/site-packages/datasets/arrow_dataset.py", line 157, in wrapper out: Union["Dataset", "DatasetDict"] = func(self, *args, **kwargs) File "/home/stas/anaconda3/lib/python3.7/site-packages/datasets/fingerprint.py", line 158, in wrapper self._fingerprint, transform, kwargs_for_fingerprint File "/home/stas/anaconda3/lib/python3.7/site-packages/datasets/fingerprint.py", line 105, in update_fingerprint hasher.update(transform_args[key]) File "/home/stas/anaconda3/lib/python3.7/site-packages/datasets/fingerprint.py", line 57, in update self.m.update(self.hash(value).encode("utf-8")) File "/home/stas/anaconda3/lib/python3.7/site-packages/datasets/fingerprint.py", line 53, in hash return cls.hash_default(value) File "/home/stas/anaconda3/lib/python3.7/site-packages/datasets/fingerprint.py", line 46, in hash_default return cls.hash_bytes(dumps(value)) File "/home/stas/anaconda3/lib/python3.7/site-packages/datasets/utils/py_utils.py", line 389, in dumps dump(obj, file) File "/home/stas/anaconda3/lib/python3.7/site-packages/datasets/utils/py_utils.py", line 361, in dump Pickler(file, recurse=True).dump(obj) File "/home/stas/anaconda3/lib/python3.7/site-packages/dill/_dill.py", line 454, in dump StockPickler.dump(self, obj) File "/home/stas/anaconda3/lib/python3.7/pickle.py", line 437, in dump self.save(obj) File "/home/stas/anaconda3/lib/python3.7/pickle.py", line 504, in save f(self, obj) # Call unbound method with explicit self File "/home/stas/anaconda3/lib/python3.7/site-packages/datasets/utils/py_utils.py", line 556, in save_function obj=obj, File "/home/stas/anaconda3/lib/python3.7/pickle.py", line 638, in save_reduce save(args) File "/home/stas/anaconda3/lib/python3.7/pickle.py", line 504, in save f(self, obj) # Call unbound method with explicit self File "/home/stas/anaconda3/lib/python3.7/pickle.py", line 789, in save_tuple save(element) File "/home/stas/anaconda3/lib/python3.7/pickle.py", line 504, in save f(self, obj) # Call unbound method with explicit self File "/home/stas/anaconda3/lib/python3.7/site-packages/dill/_dill.py", line 941, in save_module_dict StockPickler.save_dict(pickler, obj) File "/home/stas/anaconda3/lib/python3.7/pickle.py", line 859, in save_dict self._batch_setitems(obj.items()) File "/home/stas/anaconda3/lib/python3.7/pickle.py", line 885, in _batch_setitems save(v) File "/home/stas/anaconda3/lib/python3.7/pickle.py", line 524, in save rv = reduce(self.proto) TypeError: can't pickle _LazyModule objects ``` ``` $ python --version Python 3.7.4 $ python -m torch.utils.collect_env Collecting environment information... PyTorch version: 1.8.0.dev20210110+cu110 Is debug build: False CUDA used to build PyTorch: 11.0 ROCM used to build PyTorch: N/A OS: Ubuntu 20.04.2 LTS (x86_64) GCC version: (Ubuntu 9.3.0-17ubuntu1~20.04) 9.3.0 Clang version: 10.0.0-4ubuntu1 CMake version: version 3.16.3 ``` Thanks.
https://api.github.com/repos/huggingface/datasets/issues/2194/timeline
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Filtering/mapping on one column is very slow
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[ "Hi ! Yes we are working on making `filter` significantly faster. You can look at related PRs here: #2060 #2178 \r\n\r\nI think you can expect to have the fast version of `filter` available next week.\r\n\r\nWe'll make it only select one column, and we'll also make the overall filtering operation way faster by avoiding many arrow<->python conversions especially during writing.\r\n\r\nI'll let you know how it goes !", "@lhoestq Thanks for the responseβ€” it's great to hear that we'll be getting a much faster `filter` method soon. However, my use case does also involve using `map` over a single column in order to pre-compute roughly uniformly sized batches, and right now that is also very slow. Is there any plan to make `map` faster for single column operations?\r\n\r\nIf that's not a priority for the maintainers right now, I could try my hand at adding the feature, but I can't guarantee I would do a good job given my lack of familiarity with pyarrow.", "Currently the optimal setup for single-column computations is probably to do something like\r\n```python\r\nresult = dataset.map(f, input_columns=\"my_col\", remove_columns=dataset.column_names)\r\n```\r\nThis has two advantages:\r\n- input_columns=\"my_col\" allows to only read the column \"my_col\"\r\n- remove_columns=dataset.column_names makes `map` only keep the output of your function `f`, and it drops the other columns of the dataset instead of keeping them.\r\n\r\nLet me know if it improves speed on your side.\r\n\r\nYou can also get more speed by using `batched=True` and setting `num_proc=` for multiprocessing", "Hi @lhoestq ,\r\n\r\nI'm hijacking this issue, because I'm currently trying to do the approach you recommend:\r\n\r\n> Currently the optimal setup for single-column computations is probably to do something like\r\n> \r\n> ```python\r\n> result = dataset.map(f, input_columns=\"my_col\", remove_columns=dataset.column_names)\r\n> ```\r\n\r\nHere is my code: (see edit, in which I added a simplified version\r\n\r\n```\r\nThis is the error:\r\n```bash\r\npyarrow.lib.ArrowInvalid: Column 1 named tokens expected length 8964 but got length 1000\r\n```\r\nI wonder why this error occurs, when I delete every column? Can you give me a hint?\r\n\r\n### Edit:\r\nI preprocessed my dataset before (using map with the features argument) and saved it to disk. May this be part of the error? I can iterate over the\r\ncomplete dataset and print every sample before calling map. There seems to be no other problem with the dataset.\r\n\r\nI tried to simplify the code that crashes:\r\n\r\n```python\r\n# works\r\nlog.debug(dataset.column_names)\r\nlog.debug(dataset)\r\nfor i, sample in enumerate(dataset):\r\n log.debug(i, sample)\r\n\r\n# crashes\r\ncounted_dataset = dataset.map(\r\n lambda x: {\"a\": list(range(20))},\r\n input_columns=column,\r\n remove_columns=dataset.column_names,\r\n load_from_cache_file=False,\r\n num_proc=num_workers,\r\n batched=True,\r\n)\r\n```\r\n\r\n```\r\npyarrow.lib.ArrowInvalid: Column 1 named tokens expected length 20 but got length 1000\r\n```\r\n\r\nEdit2: \r\n\r\nMay this be a problem with a schema I set when preprocessing the dataset before? I tried to add the `features` argument to the function and then I get a new error:\r\n\r\n```python\r\n# crashes\r\ncounted_dataset = dataset.map(\r\n lambda x: {\"a\": list(range(20))},\r\n input_columns=column,\r\n remove_columns=dataset.column_names,\r\n load_from_cache_file=False,\r\n num_proc=num_workers,\r\n batched=True,\r\n features=datasets.Features(\r\n {\r\n \"a\": datasets.Sequence(datasets.Value(\"int32\"))\r\n }\r\n )\r\n)\r\n```\r\n\r\n```\r\n File \"env/lib/python3.8/site-packages/datasets/arrow_dataset.py\", line 1704, in _map_single\r\n writer.write_batch(batch)\r\n File \"env/lib/python3.8/site-packages/datasets/arrow_writer.py\", line 312, in write_batch\r\n col_type = schema.field(col).type if schema is not None else None\r\n File \"pyarrow/types.pxi\", line 1341, in pyarrow.lib.Schema.field\r\nKeyError: 'Column tokens does not exist in schema'\r\n```", "Hi ! Can you open a separate issue for that ?\r\nAlso if you could provide a google colab or a sample code to reproduce this issue that would be helpful.\r\nOn my side I was not able to reproduce this error.", "@lhoestq Sorry I'm just responding now. I'm currently using your recommendation for the map on a single column, and I've gotten it to be fast enough to sort of work for my use case by just setting `num_proc=10`, although it's still quite slow. It's clear that it is still loading the entirety of each row into memory and then discarding everything except the selected column, instead of exploiting the columnar data format to only load the selected column.\r\n\r\nMy code is like this:\r\n```\r\n self.dataset = self.dataset.sort('num_tokens')\r\n batch_dataset = self.dataset.map(\r\n\tcompute_uniform_sized_batches,\r\n\tbatched=True, batch_size=10_000, num_proc=10, input_columns=['num_tokens'],\r\n\tremove_columns=get_columns_all_equal(self.dataset),\r\n\twith_indices=True,\r\n\tfn_kwargs=dict(max_size=tokens_per_batch)\r\n)\r\nself.batches = {\r\n\tname: list(zip(split['start'], split['length']))\r\n\tfor name, split in batch_dataset.items()\r\n}\r\n```\r\nI find that the processes with higher IDs take significantly longer to complete, presumably because the dataset is sorted by article length and they're loading the entire article text into memory, instead of just the 'num_tokens' column.\r\n\r\nI should note that my batching procedure would work best if I just used `batch_size=None` and loaded the whole column into memory at once, but I found that this was intolerably slow and gave me no progress information, so I'm using the less than ideal `batch_size=10_000`.", "Hi @norabelrose ! I'm glad you managed to make this work on your side.\r\nRegarding memory usage, you can try to drop the columns that you don't want to use for your `map` for now.\r\n\r\nIn the future we'll try to find a way to not load unnecessary columns in memory in `map`. Currently the way it works is that it gets the batch as a python dict, then it updates it using the output of your mapping function, and finally it removes columns from `remove_columns`. Therefore for a moment some columns are loaded in memory even if you remove them or don't use them for your mapping function.\r\n\r\nIt would be nice to have a way to optimize memory for cases such as yours !", "@lhoestq After looking through the source code, it looks like the following solution has at least some chance of working:\r\n- refactor `Dataset.map()` so that the `input_columns` parameter is implemented by using the `self.formatted_as()` context manager with `columns=input_columns`\r\n- change `Dataset._getitem()` so that it passes `self._data.drop(drop_columns)` to the `query_table()` function whenever `format_columns` is non-None and `output_all_columns` is False, instead of `self._data` itself", "Looks like a great direction :)\r\nNote that `query_table` doesn't bring data into memory. Only `format_table` does.\r\nAlso the dataset may already have a format with `columns=` already defined so we would need to define the formatted `input_dataset` like:\r\n```python\r\n# before the `map` main for loop\r\ninput_columns = input_columns if input_columns is not None else self.column_names\r\nif not self._output_all_columns:\r\n columns = [col for col in input_columns if self._format_columns is None or col in self._format_columns]\r\n input_dataset = self.with_format(\r\n type=self._format_type,\r\n columns=columns\r\n )\r\nelse:\r\n # in this case we could find a way to filter both format_columns and unformatted columns eventually\r\n input_dataset = self\r\n# then input_dataset can be used in the main for loop of `map`\r\n```\r\n\r\nEDIT: oh and regarding streaming format versus file format for arrow, we plan to start using the file format #1933 at one point (though I'm not sure if it would improve performance)", "Good to know about `query_table` not bringing anything into memory. I was under the impression that it did because a while back I looked at my `map` operation in pdb and it looked like it was spending forever in line 93 of formatting.py, `return pa.concat_tables(....)`, although that was before the `fast_slice` interpolation search was implemented, so it may have had more to do with the slow ChunkedArray slice implementation than anything else.\r\n\r\nIf `query_table` is I/O free then the fix may be as simple as just adding this to line 1779 of arrow_dataset.py:\r\n```python\r\n# Only load the columns we actually need\r\nif input_columns:\r\n stack.enter_context(self.formatted_as(\r\n self._format_type,\r\n columns=input_columns,\r\n output_all_columns=False,\r\n **self._format_kwargs\r\n ))\r\n```\r\nIt's not clear to me why the `[col for col in input_columns if self._format_columns is None or col in self._format_columns]` check would be necessaryβ€” it seems like either `input_columns` should simply temporarily override the `_format_columns` within the `map` operation, or we should throw an error if there are any conflicts. Currently it doesn't look like this case is checked for at all within `map`, but maybe I'm just missing it.", "`query_table` simply slices/concatenates parts of the table. The actual data inside the table is not brought in memory.\r\nAlso I'm more in favor of declaring `input_dataset = self.with_format(...)` since `formatted_as` may update the dataset fingerprint of `self`, which is not expected when someone runs `map`.\r\n\r\n> It's not clear to me why the [col for col in input_columns if self._format_columns is None or col in self._format_columns] check would be necessaryβ€” it seems like either input_columns should simply temporarily override the _format_columns within the map operation, or we should throw an error if there are any conflicts. Currently it doesn't look like this case is checked for at all within map, but maybe I'm just missing it.\r\n\r\nActually yes we can just use input_columns. And we do need to add a check to make sure there are not conflicts or this could lead to confusing errors.", "That sounds good to me! I just submitted a PR (#2246) implementing your approach. I also changed how `_query_table` handles Iterable keys since it still seemed like `pa.concat_tables` was taking a long time to create the table for each batch. Now my whole `map()` operation takes 1 min 46 seconds where it used to take somewhere on the order of 10 minutes." ]
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I'm currently using the `wikipedia` datasetβ€” I'm tokenizing the articles with the `tokenizers` library using `map()` and also adding a new `num_tokens` column to the dataset as part of that map operation. I want to be able to _filter_ the dataset based on this `num_tokens` column, but even when I specify `input_columns=['num_tokens']`, it seems that the entirety of each row is loaded into memory, which makes the operation take much longer than it should. Indeed, `filter` currently just calls `map`, and I found that in `_map_single` on lines 1690-1704 of `arrow_dataset.py`, the method is just grabbing slices of _all the rows_ of the dataset and then passing only the specified columns to the map function. It seems that, when the user passes a value for `input_columns`, the `map` function should create a temporary pyarrow table by selecting just those columns, and then get slices from that table. Or something like thatβ€” I'm not very familiar with the pyarrow API. I know that in the meantime I can sort of get around this by simply only returning the rows that match my filter criterion from the tokenizing function I pass to `map()`, but I actually _also_ want to map on just the `num_tokens` column in order to compute batches with a roughly uniform number of tokens per batch. I would also ideally like to be able to change my minimum and maximum article lengths without having to re-tokenize the entire dataset. PS: This is definitely not a "dataset request." I'm realizing that I don't actually know how to remove labels from my own issues on other people's repos, if that is even possible.
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Fix typo in huggingface hub
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pip knows how to resolve to `huggingface_hub`, but conda doesn't! The `packaging` dependency is also required for the build to complete.
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Refactorize tests to use Dataset as context manager
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[ "I find very interesting that idea of using a fixture instead!\r\n\r\nLet me rework a little bit this PR, @lhoestq.", "@lhoestq, as this is a big refactoring, I had many problems to solve the conflicts with the master branch...\r\n\r\nTherefore, I think it is better to merge this as it is, and then to make other PRs with additional refactorings, before I get conflicts again with the master branch...", "There are still some conflicts that prevent merging.\r\nMoreover I noticed that you added one fixture per method of the Dataset object to be mocked. The code of all these fixtures is pretty much the same, feel free to factorize them into one fixture.\r\n\r\nAlso feel free to create another branch from `master` if you don't want to fix the conflicts of this branch.\r\nLet me know if I can help you on this", "@lhoestq, yes, the new conflicts appeared after today merge commits on master...\r\n\r\nI am definitely going to split this PR into smaller ones in order to avoid having to resolve many conflicts after each commit on master. There are lots of conflicts and these are painful to resolve." ]
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Refactorize Dataset tests to use Dataset as context manager.
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News_commentary Dataset Translation Pairs are of Incorrect Language Specified Pairs
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[ "Hi @anassalamah,\r\n\r\nCould you please try with this:\r\n```python\r\ntrain_ds = load_dataset(\"news_commentary\", lang1=\"ar\", lang2=\"en\", split='train[:98%]')\r\nval_ds = load_dataset(\"news_commentary\", lang1=\"ar\", lang2=\"en\", split='train[98%:]')\r\n```", "Hello @albertvillanova, \r\n\r\nThanks for the suggestion. I didn't know you could do that. however, it didn't resolve the issue\r\n\r\n![image](https://user-images.githubusercontent.com/8571003/114169966-ec819400-993a-11eb-8a67-930f9a9b2290.png)\r\n" ]
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I used load_dataset to load the news_commentary dataset for "ar-en" translation pairs but found translations from Arabic to Hindi. ``` train_ds = load_dataset("news_commentary", "ar-en", split='train[:98%]') val_ds = load_dataset("news_commentary", "ar-en", split='train[98%:]') # filtering out examples that are not ar-en translations but ar-hi val_ds = val_ds.filter(lambda example, indice: indice not in chain(range(1312,1327) ,range(1384,1399), range(1030,1042)), with_indices=True) ``` * I'm fairly new to using datasets so I might be doing something wrong
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save_to_disk doesn't work when we use concatenate_datasets function before creating the final dataset_object.
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[ "Hi ! We refactored save_to_disk in #2025 so this doesn't happen.\r\nFeel free to try it on master for now\r\nWe'll do a new release soon" ]
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As you can see, it saves the entire dataset. @lhoestq You can check by going through the following example, ``` from datasets import load_from_disk,concatenate_datasets loaded_data=load_from_disk('/home/gsir059/HNSW-ori/my_knowledge_dataset') n=20 kb_list=[loaded_data.shard(n, i, contiguous=True) for i in range(n)] final_dataset=concatenate_datasets([kb_list[1],kb_list[2]]) final_dataset.save_to_disk('/home/gsir059/haha/k.arrow') ```
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Duplicate data in Timit dataset
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[ "Hi ! Thanks for reporting\r\nIf I recall correctly this has been recently fixed #1995\r\nCan you try to upgrade your local version of `datasets` ?\r\n```\r\npip install --upgrade datasets\r\n```", "Hi Ihoestq,\r\n\r\nThank you. It works after upgrading the datasets\r\n" ]
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I ran a simple code to list all texts in Timit dataset and the texts were all the same. Is this dataset corrupted? **Code:** timit = load_dataset("timit_asr") print(*timit['train']['text'], sep='\n') **Result:** Would such an act of refusal be useful? Would such an act of refusal be useful? Would such an act of refusal be useful? Would such an act of refusal be useful? ... ... Would such an act of refusal be useful?
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Question (potential issue?) related to datasets caching
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[ "An educated guess: does this refer to the fact that depending on the custom column names in the dataset files (csv in this case), there is a dataset loader being created? and this dataset loader - using the \"custom data configuration\" is used among all jobs running using this particular csv files? (thinking out loud here...)\r\n\r\nIf this is the case, it may be ok for my use case (have to think about it more), still a bit surprising given that datasets caching is disabled (or so I hope) by the lines I pasted above. ", "Hi ! Currently disabling the caching means that all the dataset transform like `map`, `filter` etc. ignore the cache: it doesn't write nor read processed cache files.\r\nHowever `load_dataset` reuses datasets that have already been prepared: it does reload prepared dataset files.\r\n\r\nIndeed from the documentation:\r\n> datasets.set_caching_enabled(boolean: bool)\r\n\r\n> When applying transforms on a dataset, the data are stored in cache files. The caching mechanism allows to reload an existing cache file if it’s already been computed.\r\n> Reloading a dataset is possible since the cache files are named using the dataset fingerprint, which is updated after each transform.\r\n> If disabled, the library will no longer reload cached datasets files when applying transforms to the datasets. More precisely, if the caching is disabled:\r\n> - cache files are always recreated\r\n> - cache files are written to a temporary directory that is deleted when session closes\r\n> - cache files are named using a random hash instead of the dataset fingerprint - use datasets.Dataset.save_to_disk() to save a transformed dataset or it will be deleted when session closes\r\n> - caching doesn’t affect datasets.load_dataset(). If you want to regenerate a dataset from scratch you should use the download_mode parameter in datasets.load_dataset().", "Thank you for the clarification. \r\n\r\nThis is a bit confusing. On one hand, it says that cache files are always recreated and written to a temporary directory that is removed; on the other hand the last bullet point makes me think that since the default according to the docs for `download_mode (Optional datasets.GenerateMode) – select the download/generate mode - Default to REUSE_DATASET_IF_EXISTS` => it almost sounds that it could reload prepared dataset files. Where are these files stored? I guess not in the temporary directory that is removed... \r\n\r\nI find this type of api design error-prone. When I see as a programmer `datasets.set_caching_enabled(False)` I expect no reuse of anything in the cache. ", "It would be nice if the documentation elaborated on all the possible values for `download_mode` and/or a link to `datasets.GenerateMode`. \r\nThis info here:\r\n```\r\n \"\"\"`Enum` for how to treat pre-existing downloads and data.\r\n The default mode is `REUSE_DATASET_IF_EXISTS`, which will reuse both\r\n raw downloads and the prepared dataset if they exist.\r\n The generations modes:\r\n | | Downloads | Dataset |\r\n | -----------------------------------|-----------|---------|\r\n | `REUSE_DATASET_IF_EXISTS` (default)| Reuse | Reuse |\r\n | `REUSE_CACHE_IF_EXISTS` | Reuse | Fresh |\r\n | `FORCE_REDOWNLOAD` | Fresh | Fresh |\r\n```", "I have another question. Assuming that I understood correctly and there is reuse of datasets files when caching is disabled (!), I'm guessing there is a directory that is created based on some information on the dataset file. I'm interested in the situation where I'm loading a (custom) dataset from local disk. What information is used to create the directory/filenames where the files are stored?\r\n\r\nI'm concerned about the following scenario: if I have a file, let's say `train.csv` at path `the_path`, run once, the dataset is prepared, some models are run, etc. Now let's say there is an issue and I recreate `train.csv` at the same path `the_path`. Is there enough information in the temporary name/hash to *not* reload the *old* prepared dataset (e.g., timestamp of the file)? Or is it going to reload the *old* prepared file? ", "Thanks for the feedback, we'll work in improving this aspect of the documentation.\r\n\r\n> Where are these files stored? I guess not in the temporary directory that is removed...\r\n\r\nWe're using the Arrow file format to load datasets. Therefore each time you load a dataset, it is prepared as an arrow file on your disk. By default the file is located in the ~/.cache/huggingface/datasets/<dataset_name>/<config_id>/<version> directory.\r\n\r\n> What information is used to create the directory/filenames where the files are stored?\r\n\r\nThe config_id contains a hash that takes into account:\r\n- the dataset loader used and its source code (e.g. the \"csv\" loader)\r\n- the arguments passed to the loader (e.g. the csv delimiter)\r\n- metadata of the local data files if any (e.g. their timestamps)\r\n\r\n> I'm concerned about the following scenario: if I have a file, let's say train.csv at path the_path, run once, the dataset is prepared, some models are run, etc. Now let's say there is an issue and I recreate train.csv at the same path the_path. Is there enough information in the temporary name/hash to not reload the old prepared dataset (e.g., timestamp of the file)? Or is it going to reload the old prepared file?\r\n\r\nYes the timestamp of the local csv file is taken into account. If you edit your csv file, the config_id will change and loading the dataset will create a new arrow file.", "Thank you for all your clarifications, really helpful! \r\n\r\nIf you have the bandwidth, please do revisit the api wrt cache disabling. Anywhere in the computer stack (hardware included) where you disable the cache, one assumes there is no caching that happens. ", "That makes total sense indeed !\r\nI think we can do the change", "I have another question about caching, this time in the case where FORCE_REDOWNLOAD is used to load the dataset, the datasets cache is one directory as defined by HF_HOME and there are multiple concurrent jobs running in a cluster using the same local dataset (i.e., same local files in the cluster). Does anything in the naming convention and/or file access/locking that you're using prevent race conditions between the concurrent jobs on the caching of the local dataset they all use?\r\n\r\nI noticed some errors (can provide more details if helpful) in load_dataset/prepare_split that lead to my question above. \r\n\r\nLet me know if my question is clear, I can elaborate more if needed @lhoestq Thank you!", "I got another error that convinces me there is a race condition (one of the test files had zero samples at prediction time). I think it comes down to the fact that the `config_id` above (used in the naming for the cache) has no information on who's touching the data. If I have 2 concurrent jobs, both loading the same dataset and forcing redownload, they may step on each other foot/caching of the dataset. ", "We're using a locking mechanism to prevent two processes from writing at the same time. The locking is based on the `filelock` module.\r\nAlso directories that are being written use a suffix \".incomplete\" so that reading is not possible on a dataset being written.\r\n\r\nDo you think you could provide a simple code to reproduce the race condition you experienced ?", "I can provide details about the code I'm running (it's really-really close to some official samples from the huggingface transformers examples, I can point to the exact sample file, I kept a record of that). I can also describe in which conditions this race occurs (I'm convinced it has to do with forcing the redownloading of the dataset, I've been running hundreds of experiments before and didn't have a problem before I forced the redownload). I also can provide samples of the different stack errors I get and some details about the level of concurrency of jobs I was running. I can also try to imagine how the race manifests (I'm fairly sure that it's a combo of one job cleaning up and another job being in the middle of the run).\r\n\r\nHowever, I have to cleanup all this to make sure I'm no spilling any info I shouldn't be spilling. I'll try to do it by the end of the week, if you think all this is helpful. \r\n\r\nFor now, I have a workaround. Don't use forcing redownloading. And to be ultra careful (although I don't think this is a problem), I run a series of jobs that will prepare the datasets and I know there is no concurrency wrt the dataset. Once that's done (and I believe even having multiple jobs loading the datasets at the same time doesn't create problems, as long as REUSE_DATASET_IF_EXISTS is the policy for loading the dataset, so the filelock mechanism you're using is working in that scenario), the prepared datasets will be reused, no race possible in any way. \r\n\r\nThanks for all the details you provided, it helped me understand the underlying implementation and coming up with workarounds when I ran into issues. " ]
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I thought I had disabled datasets caching in my code, as follows: ``` from datasets import set_caching_enabled ... def main(): # disable caching in datasets set_caching_enabled(False) ``` However, in my log files I see messages like the following: ``` 04/07/2021 18:34:42 - WARNING - datasets.builder - Using custom data configuration default-888a87931cbc5877 04/07/2021 18:34:42 - WARNING - datasets.builder - Reusing dataset csv (xxxx/cache-transformers/datasets/csv/default-888a87931cbc5877/0.0.0/965b6429be0fc05f975b608ce64e1fa941cc8fb4f30629b523d2390f3c0e1a93 ``` Can you please let me know what this reusing dataset csv means? I wouldn't expect any reusing with the datasets caching disabled. Thank you!
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GEM: new challenge sets
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[ "cc @sebastiangehrmann" ]
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This PR updates the GEM dataset to: - remove extraneous fields in WikiAuto after https://github.com/huggingface/datasets/pull/2171 fixed the source - add context and services to Schema Guided Dialog - Add new or update challenge sets for MLSUM ES and DE, XSUM, and SGD
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.map() and distributed training
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[ "Hi, one workaround would be to save the mapped(tokenized in your case) file using `save_to_disk`, and having each process load this file using `load_from_disk`. This is what I am doing, and in this case, I turn off the ability to automatically load from the cache.\r\n\r\nAlso, multiprocessing the map function seems to be slower at the moment (#1992), hope this helps you.", "Thanks @hwijeen for the workaround, feels a bit prototypical but it works! (it seems files are written twice then though)\r\n\r\n(I haven't observed slowness using multiprocessed map function but I could be wrong)", "To my understanding, files are written twice anyhow(one after load_dataset, another aftet map). It's just that you now have it at the location where you can see, whereas it was secretlely saved at caching folder(.cache/huggingface/datasets by default)! Correct me if I'm wrong!", "Slowness in multiprocessing has been observed in certain environments but not others. We're investigating ;)", "So to answer my initial question, I was just doing something stupid as I was not re-giving the `preprocessing_num_workers` arguments when launching the distributed training (and it was then set to `None`). I initially thought the hash was computed only with the `tokenize_function` but it's all arguments. Thanks @lhoestq for clarifying!" ]
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Hi, I have a question regarding distributed training and the `.map` call on a dataset. I have a local dataset "my_custom_dataset" that I am loading with `datasets = load_from_disk(dataset_path=my_path)`. `dataset` is then tokenized: ```python datasets = load_from_disk(dataset_path=my_path) [...] def tokenize_function(examples): return tokenizer(examples[text_column_name]) logger.info("Mapping dataset to tokenized dataset.") tokenized_datasets = datasets.map( tokenize_function, batched=True, num_proc=preprocessing_num_workers, remove_columns=column_names, load_from_cache_file=True, ) ``` I am using 31 workers (`preprocessing_num_workers=31`) and thus it creates 31 `cache*.arrow` files in `my_path/train` (there is only a train split). When I relaunch the script, the map is tokenization is skipped in favor of loading the 31 previously cached files, and that's perfect. Everything so far was done by launching a **single process script**. I now launch the same training script in **distributed mode** (`pytorch -m torch.distributed.launch --nproc_per_node 2`). However, once it reaches the map call, it re-does the tokenization... instead of loading the 31 cached files. I tried adding the `cache_file_name` argument: `cache_file_name={"train": my_path/one_of_the_arrow_file}`, but I can't give the 31 cached files, so it probably isn't the right way to do it. **My question: what is the best way to load cached files if they were pre-processed and dumped in multiple arrow files?** It seems automatically handled for single processes but fails on distributed training. - I am following the same structure as the examples of transformers (more specifically [run_clm.py](https://github.com/huggingface/transformers/blob/master/examples/language-modeling/run_clm.py) in my case) - I am using 1.5.0 version of datasets if that matters.
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Implementation of class_encode_column
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[ "Made the required changes @lhoestq , sorry it took so much time!" ]
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Addresses #2176 I'm happy to discuss the API and internals!
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Fix s3fs tests for py36 and py37+
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Recently several changes happened: 1. latest versions of `fsspec` require python>3.7 for async features 2. `s3fs` added a dependency on `aiobotocore`, which is not compatible with the `moto` s3 mock context manager This PR fixes both issues, by pinning `fsspec` and `s3fs` for python 3.6, and by using `moto` in server mode to support running the tests on python>=3.7 with the latest version of `fsspec` and `s3fs`. cc @philschmid
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Set default in-memory value depending on the dataset size
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[ "I ping @krandiash to keep him up to date.", "TODO:\r\n- [x] Add a section in the docs about this.\r\n- ~Add a warning if someone tries to specify `cache_file_name=` in `map`, `filter` etc. on a dataset that is in memory, since the computation is not going to be cached in this case.~", "@lhoestq I have a question, regarding:\r\n> Also maybe we should add a warning if someone tries to specify cache_file_name= in map, filter etc. on a dataset that is in memory, since the computation is not going to be cached in this case.\r\n\r\n- It might be the case that the user has an in-memory dataset and might want to use `map` and cache it, by passing `cache_file_name=`\r\n- This is indeed allowed by the library and works as expected: the dataset is cached.\r\n\r\nWhy adding a warning?", "Yes right, I meant if `load_from_cache_file` is set to True and `cache_file_name ` is None. my bad :p" ]
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Set a default value for `in_memory` depending on the size of the dataset to be loaded. Close #2179. TODO: - [x] Add a section in the docs about this. - ~Add a warning if someone tries to specify `cache_file_name=` in `map`, `filter` etc. on a dataset that is in memory, since the computation is not going to be cached in this case.~
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Error when loading a HUGE json file (pyarrow.lib.ArrowInvalid: straddling object straddles two block boundaries)
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[ "Hi ! Can you try to increase the block size ? For example\r\n```python\r\nblock_size_10MB = 10<<20\r\nload_dataset(\"json\", ..., block_size=block_size_10MB)\r\n```\r\nThe block size corresponds to how much bytes to process at a time from the input stream.\r\nThis will determine multi-threading granularity as well as the size of individual chunks in the dataset.\r\n\r\nYou can also try with bigger block sizes if needed", "Hi @lhoestq! Thank you for your prompt reply.\r\nI have experimented with (10<<20, 10<<28, 10<<30, 10<<33, 10<<34), since my machine has 192G of memory, but it's either the above-mentioned error or processed killed because of OOM.\r\n\r\nCould you give me a bit of background on why block size needs to be exactly calibrated?\r\nTo my understanding, small block sized should run just fine despite its slowness..\r\n\r\n\r\n", "We're using the JSON loader of pyarrow. It parses the file chunk by chunk to load the dataset.\r\nThis issue happens when there's no delimiter in one chunk of data. For json line, the delimiter is the end of line.\r\nSo with a big value for chunk_size this should have worked unless you have one extremely long line in your file.\r\n\r\nAlso what version of pyarrow are you using ?\r\n\r\nFInally I wonder if it could be an issue on pyarrow's side when using big json files. (I haven't tested big json files like yours)", "I'm using `pyarrow==3.0.0` with `datasets==1.5.0`.\r\n\r\nYour point totally makes sense. I will check if my jsonl file contains an extremely long file and let you know. \r\n\r\nHere are some different error messages that I got when tweaking `block_size`. I also suspect that this is related to the pyarrow... but I guess it would be wonderful if datasesets could give a clear guide on how to play with large datasets! (I am suddenly experiencing various issue when working with large datasets.. e.g. #1992 )\r\n```python\r\n return paj.ReadOptions(use_threads=self.use_threads, block_size=self.block_size)\r\n File \"pyarrow/_json.pyx\", line 56, in pyarrow._json.ReadOptions.__init__\r\n File \"pyarrow/_json.pyx\", line 81, in pyarrow._json.ReadOptions.block_size.__set__\r\nOverflowError: value too large to convert to int32_t\r\n```\r\n\r\n```python\r\n\r\nline 83, in _generate_tables\r\n parse_options=self.config.pa_parse_options,\r\n File \"pyarrow/_json.pyx\", line 247, in pyarrow._json.read_json\r\n File \"pyarrow/error.pxi\", line 122, in pyarrow.lib.pyarrow_internal_check_status\r\n File \"pyarrow/error.pxi\", line 84, in pyarrow.lib.check_status\r\npyarrow.lib.ArrowInvalid: Exceeded maximum rows\r\n```", "I am getting the same error. When I tweak the block_size, I also find:\r\n`OverflowError: value too large to convert to int32_t`\r\nand \r\n`pyarrow.lib.ArrowInvalid: Exceeded maximum rows`\r\n", "I made more tests. I used a smaller dataset and I was getting the same error, which means that it was not necessarily linked to the dataset size. To make both my smaller and larger datasets work, I got rid of lists with the json file. I had the following data format:\r\n```python\r\n[\r\n {'key': \"a\", 'value': ['one', 'two', 'three']},\r\n {'key': \"b\", 'value': ['four', 'five', 'six']}\r\n]\r\n```\r\nI changed to:\r\n\r\n```python\r\n {'key': \"a\", 'value': 'one\\ntwo\\nthree'},\r\n {'key': \"b\", 'value': 'four\\nfive\\nsix']}\r\n```\r\nand that worked!\r\n\r\nI used the following to reformat my json file:\r\n```python\r\nwith open(file_name, \"w\", encoding=\"utf-8\") as f:\r\n for item in list_:\r\n f.write(json.dumps(item) + \"\\n\")\r\n```\r\nThis works with `block_size_10MB = 10 << 20` or without specifying `block_size`.", "Thanks @hwijeen for reporting and thanks @jpilaul for pointing this out.\r\n\r\nIndeed, those are different JSON-like formats:\r\n- the first one is the **standard JSON** format: all the file content is JSON-valid, thus all content is either a JSON object (between curly brackets `{...}`) or a JSON array (between square brackets `[...]`)\r\n- the second one is called **JSON Lines**: the entire file content is not JSON-valid, but only every line (newline-delimited) is JSON-valid\r\n\r\nCurrently PyArrow only supports **JSON Lines** format: \r\n- https://arrow.apache.org/docs/python/generated/pyarrow.json.read_json.html\r\n > Currently only the line-delimited JSON format is supported.\r\n- https://arrow.apache.org/docs/python/json.html\r\n > Arrow supports reading columnar data from line-delimited JSON files.", "Thanks @albertvillanova for your explanation, it is helpful to know (maybe add to docs?)!\r\nHowever, the problem I described above happened when I was dealing with jsonl files 😿\r\nAlthough I did not thoroughly inspect, I suspect the cause was the one extremely long document in my case.", "I see... I guess there is another problem going one then, related to the size." ]
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Hi, thanks for the great library. I have used the brilliant library for a couple of small projects, and now using it for a fairly big project. When loading a huge json file of 500GB, pyarrow complains as follows: ``` Traceback (most recent call last): File "/home/user/.pyenv/versions/3.7.9/lib/python3.7/site-packages/datasets/builder.py", line 531, in incomplete_dir yield tmp_dir File "/home/user/.pyenv/versions/3.7.9/lib/python3.7/site-packages/datasets/builder.py", line 573, in download_and_prepare dl_manager=dl_manager, verify_infos=verify_infos, **download_and_prepare_kwargs File "/home/user/.pyenv/versions/3.7.9/lib/python3.7/site-packages/datasets/builder.py", line 650, in _download_and_prepare self._prepare_split(split_generator, **prepare_split_kwargs) File "/home/user/.pyenv/versions/3.7.9/lib/python3.7/site-packages/datasets/builder.py", line 1027, in _prepare_split for key, table in utils.tqdm(generator, unit=" tables", leave=False, disable=not_verbose): File "/home/user/.pyenv/versions/3.7.9/lib/python3.7/site-packages/tqdm/std.py", line 1133, in __iter__ for obj in iterable: File "/app/.cache/huggingface/modules/datasets_modules/datasets/json/9498524fd296a6cca99c66d6c5be507d1c0991f5a814e535b507f4a66096a641/json.py", line 83, in _generate_tables parse_options=self.config.pa_parse_options, File "pyarrow/_json.pyx", line 247, in pyarrow._json.read_json File "pyarrow/error.pxi", line 122, in pyarrow.lib.pyarrow_internal_check_status File "pyarrow/error.pxi", line 84, in pyarrow.lib.check_status pyarrow.lib.ArrowInvalid: straddling object straddles two block boundaries (try to increase block size?) ``` When using only a small portion of the sample file, say first 100 lines, it works perfectly well.. I see that it is the error from pyarrow, but could you give me a hint or possible solutions? #369 describes the same error and #372 claims to have fixed the issue, but I have no clue why I am still getting this one. Thanks in advance!
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Add tel to xtreme tatoeba
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This should fix issue #2149
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Load small datasets in-memory instead of using memory map
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Currently all datasets are loaded using memory mapping by default in `load_dataset`. However this might not be necessary for small datasets. If a dataset is small enough, then it can be loaded in-memory and: - its memory footprint would be small so it's ok - in-memory computations/queries would be faster - the caching on-disk would be disabled, making computations even faster (no I/O bound because of the disk) - but running the same computation a second time would recompute everything since there would be no cached results on-disk. But this is probably fine since computations would be fast anyway + users should be able to provide a cache filename if needed. Therefore, maybe the default behavior of `load_dataset` should be to load small datasets in-memory and big datasets using memory mapping.
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Fix cast memory usage by using map on subtables
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[ "I addressed your comments about the docstrings and the output validation :)", "I updated the bleurt mocking method and bleurt test is passing now.\r\nI also ran the slow tests and they are passing for bleurt.", "Thanks @lhoestq and @albertvillanova !" ]
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The `cast` operation on a pyarrow Table may create new arrays in memory. This is an issue since users expect memory mapped datasets to not fill up the RAM. To fix that I used `map` to write a new arrow file on disk when cast is used. To make things more convenient I introduced the `arrow` formatting of a dataset, to make it return pyarrow tables instead of python dicts. This way one can use pyarrow transforms directly when using `map`. edit: we'll use the same mechanism for `filter`
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add social thumbnial
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# What does this PR do? I added OpenGraph/ Twitter Card support to the docs to create nice social thumbnails. ![Bildschirmfoto 2021-04-07 um 08 36 50](https://user-images.githubusercontent.com/32632186/113821698-bac2ce80-977c-11eb-81aa-d8f16355857e.png) To be able to add these I needed to install `sphinxext-opengraph`. I came across this [issue](https://github.com/readthedocs/readthedocs.org/issues/1758) on the readthedocs repo saying that since someone has built this plugin they are not integrating and providing documentation to it. That's why I added it for creating the documentation. The repository can be found [here](https://github.com/wpilibsuite/sphinxext-opengraph/tree/main). P.S. It seemed that `make style` never ran for `docs/` i hope the changes are okay otherwise I'll revert it.
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Converting a Value to a ClassLabel
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[ "Hi @nelson-liu!\r\nHere is what I do to convert a string to class label:\r\n\r\n```python\r\nfrom datasets import load_dataset, features\r\n\r\n\r\ndset = load_dataset(...)\r\ncol_name = \"the string column name\"\r\n\r\nclass_names = dset.unique(col_name)\r\nclass_feature = features.ClassLabel(names=sorted(class_names))\r\ndset = dset.map(lambda str_value: {col_name: class_feature.str2int(str_value)}, input_columns=col_name)\r\n\r\ndset = dset.cast(features.Features({\r\n ...\r\n col_name: class_feature\r\n})\r\n```\r\n" ]
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Hi! In the docs for `cast`, it's noted that `For non-trivial conversion, e.g. string <-> ClassLabel you should use map() to update the Dataset.` Would it be possible to have an example that demonstrates such a string <-> ClassLabel conversion using `map`? Thanks!
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dataset.search_batch() function outputs all -1 indices sometime.
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[ "Actually, I found the answer [here](https://github.com/facebookresearch/faiss/wiki/FAQ#what-does-it-mean-when-a-search-returns--1-ids). \r\n\r\nSo we have to do some modifications to the code for instances where the index doesn't retrieve any IDs.", "@lhoestq @patrickvonplaten \r\n\r\nI also found another short bug in the retrieval part. Especially, when retrieving documents. If Faiss returns the -1 as the index, the retriever will always use the last element in the dataset.\r\n\r\nplease check [def get_doc_dicts function](https://github.com/huggingface/transformers/blob/master/src/transformers/models/rag/retrieval_rag.py#L222)\r\n\r\n\r\nDoes the use of the HNSW guarantee to retrieve valid indexes always? \r\n\r\n", "Hi !\r\nNo it happens sometimes to return -1, especially if your dataset is small.\r\nIf your dataset is big enough it shouldn't happen in my experience.\r\n\r\nIdeally we should ignore all the -1 that are returned. It should be possible to change that in RAG's code ", "I also checked with some indexes it returns more -1s. Specially with IVF\nwhen nprobr is very low. It doesn't happen when using HNSW though. But at\nthe moment if it happens, dataset will always return the last element.\nMaybe we should change it to repeat the most last valid retrieved doc id.\nWhat do you think?\n\nOn Wed, Apr 7, 2021, 21:09 Quentin Lhoest ***@***.***> wrote:\n\n> Hi !\n> No it happens sometimes to return -1, especially if your dataset is small.\n> If your dataset is big enough it shouldn't happen.\n>\n> Ideally we should ignore all the -1 that are returned. It should be\n> possible to change that in RAG's code\n>\n> β€”\n> You are receiving this because you authored the thread.\n> Reply to this email directly, view it on GitHub\n> <https://github.com/huggingface/datasets/issues/2175#issuecomment-814746509>,\n> or unsubscribe\n> <https://github.com/notifications/unsubscribe-auth/AEA4FGTENOTLBEZTXEO2RS3THQOMPANCNFSM42PRVYDA>\n> .\n>\n", "That would be an easy way to workaround this issue. Feel free to open a PR on `transformers` and ping me ! :)", "Sure. Will push everything together with RAG end to end. :) thanks a lot.\n\nOn Wed, Apr 7, 2021, 21:16 Quentin Lhoest ***@***.***> wrote:\n\n> That would be an easy way to workaround this issue. Feel free to open a PR\n> on transformers and ping me ! :)\n>\n> β€”\n> You are receiving this because you authored the thread.\n> Reply to this email directly, view it on GitHub\n> <https://github.com/huggingface/datasets/issues/2175#issuecomment-814752589>,\n> or unsubscribe\n> <https://github.com/notifications/unsubscribe-auth/AEA4FGWLROCGARKN7WOJYSTTHQPH5ANCNFSM42PRVYDA>\n> .\n>\n" ]
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I am working with RAG and playing around with different faiss indexes. At the moment I use **index = faiss.index_factory(768, "IVF65536_HNSW32,Flat")**. During the retrieval phase exactly in [this line of retrieval_rag.py](https://github.com/huggingface/transformers/blob/master/src/transformers/models/rag/retrieval_rag.py#L231) an error issue when all retrieved indices are -1. Please refer to the screenshot of a PID worker. ![image](https://user-images.githubusercontent.com/16892570/113782387-37a67600-9786-11eb-9c29-acad661a9648.png) Here, my retrieve batch size is 2 and n_docs is 5. I can solve this by working around np. stack, but I want to ask, why we get an output index of -1. Do you have any idea :) ? Is this a problem of the index, where the faiss can't find any similar vector? Is there documentation on the output index being -1? @lhoestq
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Pin docutils for better doc
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The latest release of docutils make the navbar in the documentation weird and the Markdown wrongly interpreted: ![image](https://user-images.githubusercontent.com/35901082/113711773-5be55280-96b3-11eb-9b3b-9794f17709aa.png) We had the same problem in Transformers and solved it by pinning docutils (a dep of sphinx). You can see the version after the change [here](https://32769-250213286-gh.circle-artifacts.com/0/docs/_build/html/index.html).
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Add OpenSLR dataset
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OpenSLR (https://openslr.org/) is a site devoted to hosting speech and language resources, such as training corpora for speech recognition, and software related to speech recognition. There are around 80 speech datasets listed in OpenSLR, currently this PR includes only 9 speech datasets SLR41, SLR42, SLR43, SLR44, SLR63, SLR64, SLR65, SLR66 and SLR69 (Javanese, Khmer, Nepali and Sundanese, Malayalam, Marathi, Tamil, Telugu and Catalan). I can add other speech datasets gradually next time.
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Pin fsspec lower than 0.9.0
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Today's release of `fsspec` 0.9.0 implied a new release of `s3fs` 0.6.0 but this version breaks the CI (see [here](https://app.circleci.com/pipelines/github/huggingface/datasets/5312/workflows/490f3240-cd1c-4dd1-bb60-b416771c5584/jobs/32734) for example) I'm pinning `fsspec` until this has been resolved
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2,171
Fixed the link to wikiauto training data.
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[ "Also you can ignore the CI failing on `docs`, this has been fixed on master :)", "@lhoestq I need to update other stuff on GEM later today too, so will merge this one and remove columns in the next PR!", "Ok !" ]
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Wikipedia historic dumps are deleted but hf/datasets hardcodes dump date
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[ "It seems that this can be fixed from user's end by including a `date` argument, like this:\r\n\r\n`dataset = datasets.load_dataset('wikipedia', '20200501.en', date='20210420')`\r\n\r\nYou can get available dates from [here](https://dumps.wikimedia.org/enwiki/).\r\n\r\nThis is not a proper fix however as all the files will still have '20200501' in their file names." ]
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Wikimedia does not keep all historical dumps. For example, as of today https://dumps.wikimedia.org/kowiki/ only provides ``` 20201220/ 02-Feb-2021 01:36 - 20210101/ 21-Feb-2021 01:26 - 20210120/ 02-Mar-2021 01:25 - 20210201/ 21-Mar-2021 01:26 - 20210220/ 02-Apr-2021 01:26 - 20210301/ 03-Mar-2021 08:10 - 20210320/ 21-Mar-2021 18:13 - 20210401/ 03-Apr-2021 10:08 - latest/ 03-Apr-2021 10:08 - ``` However, the wikipedia dataset provided in the library, only supports the following configs, none of which are applicable anymore when disregarding the cached datasets: ``` ValueError: BuilderConfig 20210401.ko not found. Available: ['20200501.aa', '20200501.ab', '20200501.ace', '20200501.ady', '20200501.af', '20200501.ak', '20200501.als', '20200501.am', '20200501.an', '20200501.ang', '20200501.ar', '20200501.arc', '20200501.arz', '20200501.as', '20200501.ast', '20200501.atj', '20200501.av', '20200501.ay', '20200501.az', '20200501.azb', '20200501.ba', '20200501.bar', '20200501.bat-smg', '20200501.bcl', '20200501.be', '20200501.be-x-old', '20200501.bg', '20200501.bh', '20200501.bi', '20200501.bjn', '20200501.bm', '20200501.bn', '20200501.bo', '20200501.bpy', '20200501.br', '20200501.bs', '20200501.bug', '20200501.bxr', '20200501.ca', '20200501.cbk-zam', '20200501.cdo', '20200501.ce', '20200501.ceb', '20200501.ch', '20200501.cho', '20200501.chr', '20200501.chy', '20200501.ckb', '20200501.co', '20200501.cr', '20200501.crh', '20200501.cs', '20200501.csb', '20200501.cu', '20200501.cv', '20200501.cy', '20200501.da', '20200501.de', '20200501.din', '20200501.diq', '20200501.dsb', '20200501.dty', '20200501.dv', '20200501.dz', '20200501.ee', '20200501.el', '20200501.eml', '20200501.en', '20200501.eo', '20200501.es', '20200501.et', '20200501.eu', '20200501.ext', '20200501.fa', '20200501.ff', '20200501.fi', '20200501.fiu-vro', '20200501.fj', '20200501.fo', '20200501.fr', '20200501.frp', '20200501.frr', '20200501.fur', '20200501.fy', '20200501.ga', '20200501.gag', '20200501.gan', '20200501.gd', '20200501.gl', '20200501.glk', '20200501.gn', '20200501.gom', '20200501.gor', '20200501.got', '20200501.gu', '20200501.gv', '20200501.ha', '20200501.hak', '20200501.haw', '20200501.he', '20200501.hi', '20200501.hif', '20200501.ho', '20200501.hr', '20200501.hsb', '20200501.ht', '20200501.hu', '20200501.hy', '20200501.ia', '20200501.id', '20200501.ie', '20200501.ig', '20200501.ii', '20200501.ik', '20200501.ilo', '20200501.inh', '20200501.io', '20200501.is', '20200501.it', '20200501.iu', '20200501.ja', '20200501.jam', '20200501.jbo', '20200501.jv', '20200501.ka', '20200501.kaa', '20200501.kab', '20200501.kbd', '20200501.kbp', '20200501.kg', '20200501.ki', '20200501.kj', '20200501.kk', '20200501.kl', '20200501.km', '20200501.kn', '20200501.ko', '20200501.koi', '20200501.krc', '20200501.ks', '20200501.ksh', '20200501.ku', '20200501.kv', '20200501.kw', '20200501.ky', '20200501.la', '20200501.lad', '20200501.lb', '20200501.lbe', '20200501.lez', '20200501.lfn', '20200501.lg', '20200501.li', '20200501.lij', '20200501.lmo', '20200501.ln', '20200501.lo', '20200501.lrc', '20200501.lt', '20200501.ltg', '20200501.lv', '20200501.mai', '20200501.map-bms', '20200501.mdf', '20200501.mg', '20200501.mh', '20200501.mhr', '20200501.mi', '20200501.min', '20200501.mk', '20200501.ml', '20200501.mn', '20200501.mr', '20200501.mrj', '20200501.ms', '20200501.mt', '20200501.mus', '20200501.mwl', '20200501.my', '20200501.myv', '20200501.mzn', '20200501.na', '20200501.nah', '20200501.nap', '20200501.nds', '20200501.nds-nl', '20200501.ne', '20200501.new', '20200501.ng', '20200501.nl', '20200501.nn', '20200501.no', '20200501.nov', '20200501.nrm', '20200501.nso', '20200501.nv', '20200501.ny', '20200501.oc', '20200501.olo', '20200501.om', '20200501.or', '20200501.os', '20200501.pa', '20200501.pag', '20200501.pam', '20200501.pap', '20200501.pcd', '20200501.pdc', '20200501.pfl', '20200501.pi', '20200501.pih', '20200501.pl', '20200501.pms', '20200501.pnb', '20200501.pnt', '20200501.ps', '20200501.pt', '20200501.qu', '20200501.rm', '20200501.rmy', '20200501.rn', '20200501.ro', '20200501.roa-rup', '20200501.roa-tara', '20200501.ru', '20200501.rue', '20200501.rw', '20200501.sa', '20200501.sah', '20200501.sat', '20200501.sc', '20200501.scn', '20200501.sco', '20200501.sd', '20200501.se', '20200501.sg', '20200501.sh', '20200501.si', '20200501.simple', '20200501.sk', '20200501.sl', '20200501.sm', '20200501.sn', '20200501.so', '20200501.sq', '20200501.sr', '20200501.srn', '20200501.ss', '20200501.st', '20200501.stq', '20200501.su', '20200501.sv', '20200501.sw', '20200501.szl', '20200501.ta', '20200501.tcy', '20200501.te', '20200501.tet', '20200501.tg', '20200501.th', '20200501.ti', '20200501.tk', '20200501.tl', '20200501.tn', '20200501.to', '20200501.tpi', '20200501.tr', '20200501.ts', '20200501.tt', '20200501.tum', '20200501.tw', '20200501.ty', '20200501.tyv', '20200501.udm', '20200501.ug', '20200501.uk', '20200501.ur', '20200501.uz', '20200501.ve', '20200501.vec', '20200501.vep', '20200501.vi', '20200501.vls', '20200501.vo', '20200501.wa', '20200501.war', '20200501.wo', '20200501.wuu', '20200501.xal', '20200501.xh', '20200501.xmf', '20200501.yi', '20200501.yo', '20200501.za', '20200501.zea', '20200501.zh', '20200501.zh-classical', '20200501.zh-min-nan', '20200501.zh-yue', '20200501.zu'] ``` The cached datasets: ``` % aws s3 --no-sign-request --endpoint-url https://storage.googleapis.com ls s3://huggingface-nlp/cache/datasets/wikipedia/ PRE 20200501.de/ PRE 20200501.en/ PRE 20200501.fr/ PRE 20200501.frr/ PRE 20200501.it/ PRE 20200501.simple/ ```
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Updated WER metric implementation to avoid memory issues
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[ "Hi ! Thanks for suggesting this fix \r\nUnfortunately it looks like it's already been fixed by #2111 \r\n\r\nFeel free to share your thoughts about this PR !\r\n\r\nI'm closing this one if you don't mind." ]
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This is in order to fix this issue: https://github.com/huggingface/datasets/issues/2078
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Preserve split type when realoding dataset
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[ "Thanks for diving into this !\r\n\r\nBefore going further, I just want to make sure if using `eval` is the right solution\r\nPersonally I'm not a big fan of `eval` since it has many security concerns. Also storing string representations of python objects in the json files is not ideal either IMO, so maybe it's possible to change this aspect instead.\r\n\r\nMaybe it would be better to convert the `_RelativeInstruction` to a string (or \"specs\") ?\r\nIt looks like `ReadInstruction.from_spec` already exists, but not the other way around.\r\nThe specs are the string representation of instructions. For example: `train+validation[:50%]`.\r\n\r\nLet me know what you think ! And thanks again, this issue has been here for a while now ^^", "@lhoestq Yes, before going with `eval`, I thought about this approach with the \"spec\". The only issue with this approach is that we have to come up with a represenation for the `rounding` arg.\r\n\r\nWhat do you think about this (maybe too verbose)?\r\n```python\r\n>>> print(ReadInstruction(\"train\", rounding=\"pct1_dropremainder\", from_=10, to=30).to_spec())\r\ntrain[10:30](pct1_dropremainder)", "Good idea !\r\n\r\nFirst we must note that the rounding is only used for percentage instructions.\r\nFor absolute instructions there's no rounding ambiguity.\r\n\r\nBy default the rounding is set to `closest`. For example if you have a train set of 999 examples and if you provide an instruction spec `\"train[:1%]\"`, you're going to get the first ten examples (while the `pct1_dropremainder ` rounding would return 9 examples).\r\n\r\nCurrently there's no way to get an instruction with a `pct1_dropremainder` rounding strategy from an instruction spec.\r\nSo we can either drop the support of `pct1_dropremainder` or define a way to use this strategy from a spec.\r\nI don't think dropping `pct1_dropremainder` would be a good idea since it allows to load each percent to all have the same number of examples (even the last one). Therefore I think your suggestion makes total sense and we should add a representation of this rounding strategy.\r\n\r\nI like what you suggested `train[10%:30%](pct1_dropremainder)` is fine, and it seems compatible with the regex that parses the instructions specs.", "@lhoestq I've made the changes as you suggested. Ready for the review.", "@lhoestq I've added a test and addressed the comments.\r\n\r\nAdditionally, `ReadInstruction` is converted to its spec form in `builder.py` to avoid a circular import that would happen if this logic was in `arrow_reader.py`. If you think it's better to have this logic in `arrow_reader.py`, the import can be delayed by putting it inside a function." ]
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Fixes #2167 Using `eval` is not ideal for security reasons (in web apps I assume), but without it the code would be much more complex IMO. In terms of style, instead of explicitly importing a private member (`_RelativeInstruction`), we can add these imports at the top of the module: ```python from . import arrow_reader # gives us access to ReadInstruction and _RelativeInstruction from . import splits # gives us access to NamedSplit ``` and then define the `eval` globals as follows: ```python {**arrow_reader.__dict__, **splits.__dict__} ```
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Split type not preserved when reloading the dataset
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A minimal reproducible example: ```python >>> from datasets import load_dataset, Dataset >>> dset = load_dataset("sst", split="train") >>> dset.save_to_disk("sst") >>> type(dset.split) <class 'datasets.splits.NamedSplit'> >>> dset = Dataset.load_from_disk("sst") >>> type(dset.split) # NamedSplit expected <class 'str'> ``` It seems like this bug was introduced in #2025.
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Regarding Test Sets for the GEM datasets
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[ "Hi @vyraun ! The test references for CommonGen are not publicly available: you can reach out to the original dataset authors if you would like to ask for them, but we will not be releasing them as part of GEM (March 31st was the release date for the test set inputs, references are incidentally released for some of the test sets but shouldn't really be used for benchmark submissions)\r\n\r\ncc @sebastiangehrmann", "Oh okay, thanks @yjernite ! " ]
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@yjernite Hi, are the test sets for the GEM datasets scheduled to be [added soon](https://gem-benchmark.com/shared_task)? e.g. ``` from datasets import load_dataset DATASET_NAME="common_gen" data = load_dataset("gem", DATASET_NAME) ``` The test set doesn't have the target or references. ``` data['test'][0] {'concept_set_id': 0, 'concepts': ['drill', 'field', 'run', 'team'], 'gem_id': 'common_gen-test-0', 'gem_parent_id': 'common_gen-test-0', 'references': [], 'target': ''} ```
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How to convert datasets.arrow_dataset.Dataset to torch.utils.data.Dataset
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[ "Hi,\r\n\r\na HF dataset can be converted to a Torch Dataset with a simple wrapper as follows:\r\n```python\r\nfrom torch.utils.data import Dataset\r\n \r\nclass HFDataset(Dataset):\r\n def __init__(self, dset):\r\n self.dset = dset\r\n\r\n def __getitem__(self, idx):\r\n return self.dset[idx]\r\n\r\n def __len__(self):\r\n return len(self.dset)\r\n\r\ntrain_ds = HFDataset(train_ds)\r\n```\r\n@lhoestq Since the Arrow Dataset already provides `__getitem__` and `__len__`, I think we could use the [virtual subclass](https://docs.python.org/3/library/abc.html#abc.ABCMeta.register) mechanism from the `abc` module to elegantly solve this issue. This mechanism would allow the Arrow Dataset to be used in place of the Torch Dataset because the `isinstance(instance of Arrow Dataset, TorchDataset)` check would return True (DeepSpeed has this check [here](https://github.com/microsoft/DeepSpeed/blob/ab5534fc4c0f8ca21ada321f9730d723aa31288b/deepspeed/runtime/engine.py#L823)).\r\n\r\nAnd it requires a minimal change in the `arrow_dataset.py` file:\r\n```python\r\nif config.TORCH_AVAILABLE:\r\n from torch.utils.data import Dataset as TorchDataset\r\n TorchDataset.register(Dataset)\r\n```", "Interesting ! Thanks for sharing this @mariosasko . I like the idea\r\nThis looks like something we should add IMO", "@mariosasko \r\nThx for your code!\r\nIt perfectly works with a small modification for HF NLP dataset:\r\n```\r\noriginal_ds = nlp.load_dataset('scientific_papers', 'arxiv')\r\ntrain_ds = HFDataset(train_ds['train']) # needs splitting\r\n```", "@lhoestq Sadly, from Python 3.7 onwards `torch.utils.data.Dataset` doesn't support the virtual subclass mechanism due to `typing.Generic` type no longer having `abc.ABCMeta` as its metaclass.\r\n\r\nWith that in mind, another option is to remove a direct type check (`isinstance(dataset, torch.utils.data.Dataset)`) in `deepspeed.initalize` and to rewrite the checks in a manner similar to `torch.utils.data.DataLoader` ([link](https://github.com/pytorch/pytorch/blob/b80c6f863f2327c712c478f67c248b94d66b65ac/torch/utils/data/dataloader.py#L197-L239)). This is exactly why the `DataLoader` works with arbitrary objects that provide `__getitem__` and `__len__` (and in our case, the `ArrowDataset`). By doing so, their code wouldn't be any stricter in comparison to the `DataLoader`.\r\n\r\nSo if you agree, I can open an issue in their repo and fix this if they like the idea.", "That makes sense ! Feel free to open an issue on their repo and discuss this idea", "@y-rokutan Hi, now if you install `deepspeed` from master (this feature will be available in the next official release), the code should work without subclassing. Let us know if you still have any issues.", "Worth mentioning that any function that expects a `torch..Dataset` (like `torch..DataLoader`) will fail a mypy-esque typecheck if a `datasets.Dataset` is passed, even though it implements the interface correctly (I think). The virtual subclass idea was a good one- I wonder if there's another workaround given the Generic issue. What we're really talking about is something similar to the structural subtyping semantics that `typing.Protocol` defines. If `torch..DataLoader` accepted anything that supports `__getitem__` and `__len__` methods this would be much easier. Not sure if there's a way to do this without the wrapper from the perspective of `datasets`." ]
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Hi, I'm trying to pretraine deep-speed model using HF arxiv dataset like: ``` train_ds = nlp.load_dataset('scientific_papers', 'arxiv') train_ds.set_format( type="torch", columns=["input_ids", "attention_mask", "global_attention_mask", "labels"], ) engine, _, _, _ = deepspeed.initialize( args=args, model=model, model_parameters=[p for p in model.parameters() if p.requires_grad], training_data=train_ds) ``` but deepspeed.initialize accepts torch.utils.data.Dataset only. How can I convert HF-style dataset to torch-style dataset?
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Replace assertTrue(isinstance with assertIsInstance in tests
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Replaces all the occurrences of the `assertTrue(isinstance(` pattern with `assertIsInstance`.
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Concat only unique fields in DatasetInfo.from_merge
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[ "Hi @mariosasko,\r\nJust came across this PR and I was wondering if we can use\r\n`description = \"\\n\\n\".join(OrderedDict.fromkeys([info.description for info in dataset_infos]))`\r\n\r\nThis will obviate the need for `unique` and is almost as fast as `set`. We could have used `dict` inplace of `OrderedDict` but it's available 3.7+ onwards", "Hi,\r\n\r\nlet's see what @lhoestq thinks. Although my approach adds more code, it's more readable IMO.", "Yeah, that's true. Your approach is more readable." ]
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I thought someone from the community with less experience would be interested in fixing this issue, but that wasn't the case. Fixes #2103
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visualization for cc100 is broken
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[ "This looks like an issue with the cc100 dataset itself but not sure\r\nDid you try loading cc100 on your machine ?", "Hi\nloading works fine, but the viewer only is broken\nthanks\n\nOn Wed, Apr 7, 2021 at 12:17 PM Quentin Lhoest ***@***.***>\nwrote:\n\n> This looks like an issue with the cc100 dataset itself but not sure\n> Did you try loading cc100 on your machine ?\n>\n> β€”\n> You are receiving this because you authored the thread.\n> Reply to this email directly, view it on GitHub\n> <https://github.com/huggingface/datasets/issues/2162#issuecomment-814793809>,\n> or unsubscribe\n> <https://github.com/notifications/unsubscribe-auth/AS37NMRUO33JSOYGT6RETWLTHQWNLANCNFSM42IUOR6Q>\n> .\n>\n" ]
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Hi visualization through dataset viewer for cc100 is broken https://huggingface.co/datasets/viewer/ thanks a lot
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any possibility to download part of large datasets only?
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[ "Not yet but it’s on the short/mid-term roadmap (requested by many indeed).", "oh, great, really awesome feature to have, thank you very much for the great, fabulous work", "We'll work on dataset streaming soon. This should allow you to only load the examples you need ;)", "thanks a lot Quentin, this would be really really a great feature to have\n\nOn Wed, Apr 7, 2021 at 12:14 PM Quentin Lhoest ***@***.***>\nwrote:\n\n> We'll work on dataset streaming soon. This should allow you to only load\n> the examples you need ;)\n>\n> β€”\n> You are receiving this because you authored the thread.\n> Reply to this email directly, view it on GitHub\n> <https://github.com/huggingface/datasets/issues/2161#issuecomment-814791922>,\n> or unsubscribe\n> <https://github.com/notifications/unsubscribe-auth/AS37NMROD62QAKIJMAKWISTTHQWBVANCNFSM42IUI5JQ>\n> .\n>\n", "Is streaming completed? On the 1.8.0 docs it is mentioned (https://huggingface.co/docs/datasets/dataset_streaming.html), but when following the example I get the following error:\r\n\r\n```\r\n>>> dataset2 = load_dataset(\"amazon_us_reviews\", \"Pet_Products_v1_00\", split='train', streaming=True)\r\n\r\n---------------------------------------------------------------------------\r\nValueError Traceback (most recent call last)\r\n<ipython-input-21-1eedab26cff1> in <module>()\r\n----> 1 en_dataset = load_dataset('oscar', \"unshuffled_deduplicated_en\", split='train', streaming=True)\r\n\r\n3 frames\r\n/usr/local/lib/python3.7/dist-packages/datasets/builder.py in _create_builder_config(self, name, custom_features, **config_kwargs)\r\n 339 if value is not None:\r\n 340 if not hasattr(builder_config, key):\r\n--> 341 raise ValueError(f\"BuilderConfig {builder_config} doesn't have a '{key}' key.\")\r\n 342 setattr(builder_config, key, value)\r\n 343 \r\n\r\nValueError: BuilderConfig OscarConfig(name='unshuffled_deduplicated_en', version=1.0.0, data_dir=None, data_files=None, description='Unshuffled and deduplicated, English OSCAR dataset') doesn't have a 'streaming' key.\r\n```\r\n\r\nUPDATE: Managed to get streaming working by building from source and installing the additional `datasets[streaming]` package:\r\n\r\n```\r\n!pip install git+https://github.com/huggingface/datasets.git\r\n!pip install datasets[streaming]\r\n```", "Hi ! Streaming is available on `master` only right now. We'll make a new release 1.9.0 on Monday :)" ]
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Hi Some of the datasets I need like cc100 are very large, and then I wonder if I can download first X samples of the shuffled/unshuffled data without going through first downloading the whole data then sampling? thanks
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data_args.preprocessing_num_workers almost freezes
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[ "Hi.\r\nI cannot always reproduce this issue, and on later runs I did not see it so far. Sometimes also I set 8 processes but I see less being showed, is this normal, here only 5 are shown for 8 being set, thanks\r\n\r\n```\r\n#3: 11%|β–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–Š | 172/1583 [00:46<06:21, 3.70ba/s]\r\n#4: 9%|β–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ– | 143/1583 [00:46<07:46, 3.09ba/s]\r\n#7: 6%|β–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆ | 98/1583 [00:45<11:34, 2.14ba/s]\r\n#5: 8%|β–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ– | 124/1583 [00:46<09:03, 2.68ba/s]\r\n#6: 7%|β–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ– \r\n```", "closing since I cannot reproduce it again, thanks " ]
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Hi @lhoestq I am running this code from huggingface transformers https://github.com/huggingface/transformers/blob/master/examples/language-modeling/run_mlm.py to speed up tokenization, since I am running on multiple datasets, I am using data_args.preprocessing_num_workers = 4 with opus100 corpus but this moves on till a point and then this freezes almost for sometime during tokenization steps and then this is back again, overall to me taking more time than normal case, I appreciate your advice on how I can use this option properly to speed up. thanks
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2,159
adding ccnet dataset
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[ "closing since I think this is cc100, just the name has been changed. thanks " ]
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## Adding a Dataset - **Name:** ccnet - **Description:** Common Crawl - **Paper:** https://arxiv.org/abs/1911.00359 - **Data:** https://github.com/facebookresearch/cc_net - **Motivation:** this is one of the most comprehensive clean monolingual datasets across a variety of languages. Quite important for cross-lingual reseach Instructions to add a new dataset can be found [here](https://github.com/huggingface/datasets/blob/master/ADD_NEW_DATASET.md). thanks
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viewer "fake_news_english" error
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[ "Thanks for reporting !\r\nThe viewer doesn't have all the dependencies of the datasets. We may add openpyxl to be able to show this dataset properly" ]
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When I visit the [Huggingface - viewer](https://huggingface.co/datasets/viewer/) web site, under the dataset "fake_news_english" I've got this error: > ImportError: To be able to use this dataset, you need to install the following dependencies['openpyxl'] using 'pip install # noqa: requires this pandas optional dependency for reading xlsx files' for instance' as well as the error Traceback.
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updated user permissions based on umask
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Updated user permissions based on running user's umask (#2065). Let me know if `0o666` is looking good or should I change it to `~umask` only (to give execute permissions as well)
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User permissions
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Updated user permissions based on running user's umask. Let me know if `0o666` is looking good or should I change it to `~umask` only (to give execute permissions as well)
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2,155
Add table classes to the documentation
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[ "Just note that docstrings injected from PyArrow do not follow the same convention for formatting types in `Args` or `Returns` as we do... Not a big problem, anyway! πŸ˜„ " ]
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Following #2025 , I added the table classes to the documentation cc @albertvillanova
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Adding the NorNE dataset for Norwegian POS and NER
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[ "Awesome!" ]
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NorNE is a manually annotated corpus of named entities which extends the annotation of the existing Norwegian Dependency Treebank. Comprising both of the official standards of written Norwegian (BokmΓ₯l and Nynorsk), the corpus contains around 600,000 tokens and annotates a rich set of entity types including persons, organizations, locations, geo-political entities, products, and events, in addition to a class corresponding to nominals derived from names. See #1720.
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load_dataset ignoring features
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[ "Hi ! Thanks for reporting. I opened a PR to fix this issue: #2201", "Nice question which helped me a lot! I have wasted a lot of time to the `DatasetDict` creation from a csv file. Hope the document of this module add some simple examples.", "Hi :) We're indeed working on tutorials that we will add to the docs !" ]
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First of all, I'm sorry if it is a repeated issue or the changes are already in master, I searched and I didn't find anything. I'm using datasets 1.5.0 ![image](https://user-images.githubusercontent.com/37592763/113114369-8f376580-920b-11eb-900d-94365b59f04b.png) As you can see, when I load the dataset, the ClassLabels are ignored, I have to cast the dataset in order to make it work. Code to reproduce: ```python import datasets data_location = "/data/prueba_multiclase" features = datasets.Features( {"texto": datasets.Value("string"), "label": datasets.features.ClassLabel(names=["false", "true"])} ) dataset = datasets.load_dataset( "csv", data_files=data_location, delimiter="\t", features=features ) ``` Dataset I used: [prueba_multiclase.zip](https://github.com/huggingface/datasets/files/6235022/prueba_multiclase.zip) (it has to be unzipped) Thank you! ❀️
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Update README.md
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Updated some descriptions of Wino_Bias dataset.
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Add support for axis in concatenate datasets
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[ "@lhoestq I am going to implement the consolidation step you mentioned in #1870.", "@lhoestq I was thinking that the order of the TableBlocks is not relevant, isn't it?\r\n\r\nI mean, in order to consolidate _consecutive_ in-memory table blocks, in this case:\r\n```\r\nblocks = [in_memory_1, memory_mapped, in_memory_2]\r\n```\r\nI could reorder the list:\r\n```\r\nblocks = [in_memory_1, in_memory_2, memory_mapped]\r\n```\r\nso that the first 2 can be consolidated into a single one:\r\n```\r\nblocks = [in_memory_3, memory_mapped]\r\n```", "I think the order is important, users won't expect the dataset to be \"shuffled\" when they add a new item", "> I think the order is important, users won't expect the dataset to be \"shuffled\" when they add a new item\r\n\r\nOK, therefore I leave `_consolidate_blocks` as it is, which currently keeps the order of the blocks (no shuffling).", "Thank you guys for implementing this. Minor thing I noticed in the [documentation](https://huggingface.co/docs/datasets/package_reference/main_classes.html#datasets.concatenate_datasets): it says \"Converts a list of Dataset with **the same schema** into a single Dataset\". With the addition of the axis parameter, perhaps this should be reworded, no?" ]
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Add support for `axis` (0 or 1) in `concatenate_datasets`. Close #853.
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Allow pickling of big in-memory tables
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This should fix issue #2134 Pickling is limited to <4GiB objects, it's not possible to pickle a big arrow table (for multiprocessing for example). For big tables, we have to write them on disk and only pickle the path to the table.
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Telugu subset missing for xtreme tatoeba dataset
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[ "Good catch ! Thanks for reporting\r\n\r\nI just opened #2180 to fix this" ]
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from nlp import load_dataset train_dataset = load_dataset('xtreme', 'tatoeba.tel')['validation'] ValueError: BuilderConfig tatoeba.tel not found. but language tel is actually included in xtreme: https://github.com/google-research/xtreme/blob/master/utils_preprocess.py def tatoeba_preprocess(args): lang3_dict = { 'afr':'af', 'ara':'ar', 'bul':'bg', 'ben':'bn', 'deu':'de', 'ell':'el', 'spa':'es', 'est':'et', 'eus':'eu', 'pes':'fa', 'fin':'fi', 'fra':'fr', 'heb':'he', 'hin':'hi', 'hun':'hu', 'ind':'id', 'ita':'it', 'jpn':'ja', 'jav':'jv', 'kat':'ka', 'kaz':'kk', 'kor':'ko', 'mal':'ml', 'mar':'mr', 'nld':'nl', 'por':'pt', 'rus':'ru', 'swh':'sw', 'tam':'ta', **_'tel':'te'_**, 'tha':'th', 'tgl':'tl', <----here 'tur':'tr', 'urd':'ur', 'vie':'vi', 'cmn':'zh', 'eng':'en', }
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Add configurable options to `seqeval` metric
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[ "Hi @marrodion. \r\n\r\nThanks for pointing this out. It would be great to incorporate this metric-specific enhancement.\r\n\r\nAnother possibility would be to require the user to input the scheme as a string `mode=\"strict\", scheme=\"IOB2\"` and then dynamically import the corresponding module using Python `importlib`:\r\n```python\r\nif scheme:\r\n scheme = importlib.import_module(f\"seqeval.scheme.{scheme}\")\r\n```\r\n\r\nFeel free to create a Pull Request to make this contribution." ]
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Right now `load_metric("seqeval")` only works in the default mode of evaluation (equivalent to conll evaluation). However, seqeval library [supports](https://github.com/chakki-works/seqeval#support-features) different evaluation schemes (IOB1, IOB2, etc.), which can be plugged in just by supporting additional kwargs in `Seqeval._compute` https://github.com/huggingface/datasets/blob/85cf7ff920c90ca2e12bedca12b36d2a043c3da2/metrics/seqeval/seqeval.py#L109 Things that would be relevant are, for example, supporting `mode="strict", scheme=IOB2` to count only full entity match as a true positive and omit partial matches. The only problem I see is that the spirit of `metrics` seems to not require additional imports from user. `seqeval` only supports schemes as objects, without any string aliases. It can be solved naively with mapping like `{"IOB2": seqeval.scheme.IOB2}`. Or just left as is and require user to explicitly import scheme from `seqeval` if he wants to configure it past the default implementation. If that makes sense, I am happy to implement the change.
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Render docstring return type as inline
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This documentation setting will avoid having the return type in a separate line under `Return type`. See e.g. current docs for `Dataset.to_csv`.
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Dataset file size on disk is very large with 3D Array
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[ "Hi ! In the arrow file we store all the integers as uint8.\r\nSo your arrow file should weigh around `height x width x n_channels x n_images` bytes.\r\n\r\nWhat feature type do your TFDS dataset have ?\r\n\r\nIf it uses a `tfds.features.Image` type, then what is stored is the encoded data (as png or jpg for example). Since these encodings are made for compression, the resulting tfrecord is smaller that the arrow file.\r\n\r\nWe are working on adding a similar feature in `datasets`: the ability to store the encoded data instead of the raw integers for images, but also for audio data. This way, arrow files will have similar sizes as tfrecords for images.", "Thanks for the prompt response. You're right about the encoding, I have the `tfds.features.Image` feature type you mentioned.\r\nHowever, as described in the `dataset_info.json`, my dataset is made of 1479 (224x224x3) images. 1479 x 224 x 224 x 3 = 222630912 bytes which is far from the actual size 520803408 bytes. \r\n\r\nAnyway I look forward to the Image feature type in `datasets`. ", "@lhoestq I changed the data structure so I have a 2D Array feature type instead of a 3D Array by grouping the two last dimensions ( a 224x672 2D Array instead of a 224x224x3 3D Array). The file size is now 223973964 bytes, nearly half the previous size! Which is around of what I would expect.\r\nI found similar behavior in existing `datasets` collection, when comparing black and white vs color image, for example MNIST vs CIFAR. ", "Interesting !\r\nThis may be because of the offsets that are stored with the array data.\r\n\r\nCurrently the offsets are stored even if the `shape` of the arrays is fixed. This was needed because of some issues with pyarrow a few months ago. I think these issues have been addressed now, so we can probably try to remove them to make the file lighter.\r\n\r\nIdeally in your case the floats data should be 220 MB for both Array2D and Array3D", "Yeah for sure, can you be a bit more specific about where the offset is stored in the code base ? And any reference to pyarrow issues if you have some. I would be very interested in contributing to `datasets` by trying to fix this issue. ", "Pyarrow has two types of lists: variable length lists and fixed size lists.\r\nCurrently we store the ArrayXD data as variable length lists. They take more disk space because they must store both actual data and offsets.\r\nIn the `datasets` code this is done here:\r\n\r\nhttps://github.com/huggingface/nlp/blob/dbac87c8a083f806467f5afc4ec9b401a7e4c15c/src/datasets/features.py#L346-L352\r\n\r\nTo use a fixed length list, one should use the `list_size` argument of `pyarrow.list_()`.\r\nI believe this would work directly modulo some changes in the numpy conversion here:\r\n\r\nhttps://github.com/huggingface/nlp/blob/dbac87c8a083f806467f5afc4ec9b401a7e4c15c/src/datasets/features.py#L381-L395" ]
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Hi, I have created my own dataset using the provided dataset loading script. It is an image dataset where images are stored as 3D Array with dtype=uint8. The actual size on disk is surprisingly large. It takes 520 MB. Here is some info from `dataset_info.json`. `{ "description": "", "citation": "", "homepage": "", "license": "", "features": { "image": { "shape": [224, 224, 3], "dtype": "uint8", "id": null, "_type": "Array3D", } }, "post_processed": null, "supervised_keys": null, "builder_name": "shot_type_image_dataset", "config_name": "default", "version": { "version_str": "0.0.0", "description": null, "major": 0, "minor": 0, "patch": 0, }, "splits": { "train": { "name": "train", "num_bytes": 520803408, "num_examples": 1479, "dataset_name": "shot_type_image_dataset", } }, "download_checksums": { "": { "num_bytes": 16940447118, "checksum": "5854035705efe08b0ed8f3cf3da7b4d29cba9055c2d2d702c79785350d72ee03", } }, "download_size": 16940447118, "post_processing_size": null, "dataset_size": 520803408, "size_in_bytes": 17461250526, }` I have created the same dataset with tensorflow_dataset and it takes only 125MB on disk. I am wondering, is it normal behavior ? I understand `Datasets` uses Arrow for serialization wheres tf uses TF Records. This might be a problem for large dataset. Thanks for your help.
https://api.github.com/repos/huggingface/datasets/issues/2146/timeline
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2,145
Implement Dataset add_column
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[ "#2274 has been merged. You can now merge master into this branch and use `assert_arrow_metadata_are_synced_with_dataset_features(dset)` to make sure that the metadata are good :)" ]
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Implement `Dataset.add_column`. Close #1954.
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Loading wikipedia 20200501.en throws pyarrow related error
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[ "That's how I loaded the dataset\r\n```python\r\nfrom datasets import load_dataset\r\nds = load_dataset('wikipedia', '20200501.en', cache_dir='/usr/local/workspace/NAS_NLP/cache')\r\n```", "Hi ! It looks like the arrow file in the folder\r\n`/usr/local/workspace/NAS_NLP/cache/wikipedia/20200501.en/1.0.0/50aa706aa417bb77d910ad61211cc672c0ef3e0f224225a5e0a18277ade8b931` is corrupted.\r\n\r\nCan you take a look and check that it's 18.3GB ?\r\n\r\nIf not, then maybe you need to redownload it:\r\n```python\r\nfrom datasets import load_dataset\r\nds = load_dataset('wikipedia', '20200501.en', cache_dir='/usr/local/workspace/NAS_NLP/cache', download_mode=\"force_redownload\")\r\n```", "> Hi ! It looks like the arrow file in the folder\r\n> `/usr/local/workspace/NAS_NLP/cache/wikipedia/20200501.en/1.0.0/50aa706aa417bb77d910ad61211cc672c0ef3e0f224225a5e0a18277ade8b931` is corrupted.\r\n> \r\n> Can you take a look and check that it's 18.3GB ?\r\n> \r\n> If not, then maybe you need to redownload it:\r\n> \r\n> ```python\r\n> from datasets import load_dataset\r\n> ds = load_dataset('wikipedia', '20200501.en', cache_dir='/usr/local/workspace/NAS_NLP/cache', download_mode=\"force_redownload\")\r\n> ```\r\n\r\nHi Ihoestq, thanks for the reply! Actually i think my issue is i couldn't download the dataset beyond 10.7G. It feels like the whole dataset is split into different volumes and after the first one was downloaded it crashed before proceeding to the next one. I did try 'force_redownload' mode but still got the same issue.", "I just tried on my side and got no issues.\r\nWhen downloading the dataset again, did it crash at 10.7GB as well ?", "> I just tried on my side and got no issues.\r\n> When downloading the dataset again, did it crash at 10.7GB as well ?\r\n\r\nYes i have tried it multiple times on different machines. I am wondering if you could share the screenshot of your dependency versions and i will try to make them the same as yours?", "I tried using `datasets` from `master` on macos with python 3.7.2\r\nI also have `requests==2.23.0` and `tqdm==4.45.0`." ]
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**Problem description** I am getting the following error when trying to load wikipedia/20200501.en dataset. **Error log** Downloading and preparing dataset wikipedia/20200501.en (download: 16.99 GiB, generated: 17.07 GiB, post-processed: Unknown size, total: 34.06 GiB) to /usr/local/workspace/NAS_NLP/cache/wikipedia/20200501.en/1.0.0/50aa706aa417bb77d910ad61211cc672c0ef3e0f224225a5e0a18277ade8b931... Downloading: 100%|β–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆ| 14.6k/14.6k [00:00<00:00, 5.41MB/s] Downloading: 59%|β–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–Š | 10.7G/18.3G [11:30<08:08, 15.5MB/s] Dataset wikipedia downloaded and prepared to /usr/local/workspace/NAS_NLP/cache/wikipedia/20200501.en/1.0.0/50aa706aa417bb77d910ad61211cc672c0ef3e0f224225a5e0a18277ade8b931. Subsequent calls will reuse this data. Traceback (most recent call last): File "load_wiki.py", line 2, in <module> ds = load_dataset('wikipedia', '20200501.en', cache_dir='/usr/local/workspace/NAS_NLP/cache') File "/usr/local/lib/python3.6/dist-packages/datasets/load.py", line 751, in load_dataset ds = builder_instance.as_dataset(split=split, ignore_verifications=ignore_verifications, in_memory=keep_in_memory) File "/usr/local/lib/python3.6/dist-packages/datasets/builder.py", line 746, in as_dataset map_tuple=True, File "/usr/local/lib/python3.6/dist-packages/datasets/utils/py_utils.py", line 204, in map_nested _single_map_nested((function, obj, types, None, True)) for obj in tqdm(iterable, disable=disable_tqdm) File "/usr/local/lib/python3.6/dist-packages/datasets/utils/py_utils.py", line 204, in <listcomp> _single_map_nested((function, obj, types, None, True)) for obj in tqdm(iterable, disable=disable_tqdm) File "/usr/local/lib/python3.6/dist-packages/datasets/utils/py_utils.py", line 142, in _single_map_nested return function(data_struct) File "/usr/local/lib/python3.6/dist-packages/datasets/builder.py", line 763, in _build_single_dataset in_memory=in_memory, File "/usr/local/lib/python3.6/dist-packages/datasets/builder.py", line 835, in _as_dataset in_memory=in_memory, File "/usr/local/lib/python3.6/dist-packages/datasets/arrow_reader.py", line 215, in read return self.read_files(files=files, original_instructions=instructions, in_memory=in_memory) File "/usr/local/lib/python3.6/dist-packages/datasets/arrow_reader.py", line 236, in read_files pa_table = self._read_files(files, in_memory=in_memory) File "/usr/local/lib/python3.6/dist-packages/datasets/arrow_reader.py", line 171, in _read_files pa_table: pa.Table = self._get_dataset_from_filename(f_dict, in_memory=in_memory) File "/usr/local/lib/python3.6/dist-packages/datasets/arrow_reader.py", line 302, in _get_dataset_from_filename pa_table = ArrowReader.read_table(filename, in_memory=in_memory) File "/usr/local/lib/python3.6/dist-packages/datasets/arrow_reader.py", line 324, in read_table pa_table = f.read_all() File "pyarrow/ipc.pxi", line 544, in pyarrow.lib.RecordBatchReader.read_all File "pyarrow/error.pxi", line 99, in pyarrow.lib.check_status OSError: Expected to be able to read 9176784 bytes for message body, got 4918712 **Detailed version info** datasets==1.5.0 - dataclasses [required: Any, installed: 0.8] - dill [required: Any, installed: 0.3.3] - fsspec [required: Any, installed: 0.8.7] - importlib-metadata [required: Any, installed: 1.7.0] - zipp [required: >=0.5, installed: 3.1.0] - huggingface-hub [required: <0.1.0, installed: 0.0.7] - filelock [required: Any, installed: 3.0.12] - importlib-metadata [required: Any, installed: 1.7.0] - zipp [required: >=0.5, installed: 3.1.0] - requests [required: Any, installed: 2.24.0] - certifi [required: >=2017.4.17, installed: 2020.6.20] - chardet [required: >=3.0.2,<4, installed: 3.0.4] - idna [required: >=2.5,<3, installed: 2.6] - urllib3 [required: >=1.21.1,<1.26,!=1.25.1,!=1.25.0, installed: 1.25.10] - tqdm [required: Any, installed: 4.49.0] - importlib-metadata [required: Any, installed: 1.7.0] - zipp [required: >=0.5, installed: 3.1.0] - multiprocess [required: Any, installed: 0.70.11.1] - dill [required: >=0.3.3, installed: 0.3.3] - numpy [required: >=1.17, installed: 1.17.0] - pandas [required: Any, installed: 1.1.5] - numpy [required: >=1.15.4, installed: 1.17.0] - python-dateutil [required: >=2.7.3, installed: 2.8.0] - six [required: >=1.5, installed: 1.15.0] - pytz [required: >=2017.2, installed: 2020.1] - pyarrow [required: >=0.17.1, installed: 3.0.0] - numpy [required: >=1.16.6, installed: 1.17.0] - requests [required: >=2.19.0, installed: 2.24.0] - certifi [required: >=2017.4.17, installed: 2020.6.20] - chardet [required: >=3.0.2,<4, installed: 3.0.4] - idna [required: >=2.5,<3, installed: 2.6] - urllib3 [required: >=1.21.1,<1.26,!=1.25.1,!=1.25.0, installed: 1.25.10] - tqdm [required: >=4.27,<4.50.0, installed: 4.49.0] - xxhash [required: Any, installed: 2.0.0]
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task casting via load_dataset
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wip not satisfied with the API, it means as a dataset implementer I need to write a function with boilerplate and write classes for each `<dataset><task>` "facet".
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Gem V1.1
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This branch updates the GEM benchmark to its 1.1 version which includes: - challenge sets for most tasks - detokenized TurkCorpus to match the rest of the text simplification subtasks - fixed inputs for TurkCorpus and ASSET test sets - 18 languages in WikiLingua cc @sebastianGehrmann
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2,141
added spans field for the wikiann datasets
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[ "Hi @lhoestq \r\nThanks a lot for taking time checking it. I update \"dataset_infos.json\", I added description to the function of _generate_samples in wikiann.py but I was not sure about the format to write in README. thanks. ", "Thanks !\r\n\r\nFor the fields description in the dataset card, something like this does the job:\r\n```\r\n- `tokens`: a `list` of `string` features.\r\n- `langs`: a `list` of `string` features that correspond to the language of each token.\r\n- `ner_tags`: a `list` of classification labels, with possible values including `O` (0), `B-PER` (1), `I-PER` (2), `B-ORG` (3), `I-ORG` (4), `B-LOC` (5), `I-LOC` (6).\r\n- `spans`: a `list` of `string` features, that is the list of named entities in the input text formatted as ``<TAG>: <mention>``\r\n```\r\n\r\nAlso for information, I think the trailer of rick and morty season 5 is out now :)", "Hi @lhoestq \r\nthank you! This is updated now, please feel free to let me know if I need to modify something :) thanks " ]
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Hi @lhoestq I tried to add spans to the wikiann datasets. Thanks a lot for kindly having a look. This addresses https://github.com/huggingface/datasets/issues/2130. Best regards Rabeeh
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2,140
add banking77 dataset
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[ "@lhoestq I updated files" ]
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Intent classification/detection dataset from banking category with 77 unique intents.
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2,139
TypeError when using save_to_disk in a dataset loaded with ReadInstruction split
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[ "Hi !\r\nI think this has been fixed recently on `master`.\r\nCan you try again by installing `datasets` from `master` ?\r\n```\r\npip install git+https://github.com/huggingface/datasets.git\r\n```", "Hi!\r\n\r\nUsing that version of the code solves the issue. Thanks!" ]
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Hi, Loading a dataset with `load_dataset` using a split defined via `ReadInstruction` and then saving it to disk results in the following error: `TypeError: Object of type ReadInstruction is not JSON serializable`. Here is the minimal reproducible example: ```python from datasets import load_dataset from datasets import ReadInstruction data_1 = load_dataset( "wikiann", "en", split="validation", ) data_1.save_to_disk("temporary_path_1") print("Save with regular split works.") data_2 = load_dataset( "wikiann", "en", split=ReadInstruction("validation", to=50, unit="%"), ) data_2.save_to_disk("temporary_path_2") ``` and the corresponding output: ``` Reusing dataset wikiann (/xxxxx/.cache/huggingface/datasets/wikiann/en/1.1.0/0b11a6fb31eea02f38ca17610657bfba3206100685283014daceb8da291c3be9) Save with regular split works. Reusing dataset wikiann (/xxxxx/.cache/huggingface/datasets/wikiann/en/1.1.0/0b11a6fb31eea02f38ca17610657bfba3206100685283014daceb8da291c3be9) Traceback (most recent call last): File "bug.py", line 20, in <module> data_2.save_to_disk("temporary_path_2") File "/xxxxx/lib/python3.7/site-packages/datasets/arrow_dataset.py", line 645, in save_to_disk json.dump(state, state_file, indent=2, sort_keys=True) File "/usr/lib/python3.7/json/__init__.py", line 179, in dump for chunk in iterable: File "/usr/lib/python3.7/json/encoder.py", line 431, in _iterencode yield from _iterencode_dict(o, _current_indent_level) File "/usr/lib/python3.7/json/encoder.py", line 405, in _iterencode_dict yield from chunks File "/usr/lib/python3.7/json/encoder.py", line 438, in _iterencode o = _default(o) File "/usr/lib/python3.7/json/encoder.py", line 179, in default raise TypeError(f'Object of type {o.__class__.__name__} ' TypeError: Object of type ReadInstruction is not JSON serializable ``` Let me know if there is some misuse from my end. Thanks in advance.
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2,138
Add CER metric
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Add Character Error Rate (CER) metric that is used in evaluation in ASR. I also have written unittests (hopefully thorough enough) but I'm not sure how to integrate them into the existed codebase. ```python from cer import CER cer = CER() class TestCER(unittest.TestCase): def test_cer_case_senstive(self): refs = ['White House'] preds = ['white house'] # S = 2, D = 0, I = 0, N = 11, CER = 2 / 11 char_error_rate = cer.compute(predictions=preds, references=refs) self.assertTrue(abs(char_error_rate - 0.1818181818) < 1e-6) def test_cer_whitespace(self): refs = ['were wolf'] preds = ['werewolf'] # S = 0, D = 0, I = 1, N = 9, CER = 1 / 9 char_error_rate = cer.compute(predictions=preds, references=refs) self.assertTrue(abs(char_error_rate - 0.1111111) < 1e-6) refs = ['werewolf'] preds = ['weae wolf'] # S = 1, D = 1, I = 0, N = 8, CER = 0.25 char_error_rate = cer.compute(predictions=preds, references=refs) self.assertTrue(abs(char_error_rate - 0.25) < 1e-6) # consecutive whitespaces case 1 refs = ['were wolf'] preds = ['were wolf'] # S = 0, D = 0, I = 0, N = 9, CER = 0 char_error_rate = cer.compute(predictions=preds, references=refs) self.assertTrue(abs(char_error_rate - 0.0) < 1e-6) # consecutive whitespaces case 2 refs = ['were wolf'] preds = ['were wolf'] # S = 0, D = 0, I = 0, N = 9, CER = 0 char_error_rate = cer.compute(predictions=preds, references=refs) self.assertTrue(abs(char_error_rate - 0.0) < 1e-6) def test_cer_sub(self): refs = ['werewolf'] preds = ['weaewolf'] # S = 1, D = 0, I = 0, N = 8, CER = 0.125 char_error_rate = cer.compute(predictions=preds, references=refs) self.assertTrue(abs(char_error_rate - 0.125) < 1e-6) def test_cer_del(self): refs = ['werewolf'] preds = ['wereawolf'] # S = 0, D = 1, I = 0, N = 8, CER = 0.125 char_error_rate = cer.compute(predictions=preds, references=refs) self.assertTrue(abs(char_error_rate - 0.125) < 1e-6) def test_cer_insert(self): refs = ['werewolf'] preds = ['wereolf'] # S = 0, D = 0, I = 1, N = 8, CER = 0.125 char_error_rate = cer.compute(predictions=preds, references=refs) self.assertTrue(abs(char_error_rate - 0.125) < 1e-6) def test_cer_equal(self): refs = ['werewolf'] char_error_rate = cer.compute(predictions=refs, references=refs) self.assertEqual(char_error_rate, 0.0) def test_cer_list_of_seqs(self): refs = ['werewolf', 'I am your father'] char_error_rate = cer.compute(predictions=refs, references=refs) self.assertEqual(char_error_rate, 0.0) refs = ['werewolf', 'I am your father', 'doge'] preds = ['werxwolf', 'I am your father', 'doge'] # S = 1, D = 0, I = 0, N = 28, CER = 1 / 28 char_error_rate = cer.compute(predictions=preds, references=refs) self.assertTrue(abs(char_error_rate - 0.03571428) < 1e-6) def test_cer_unicode(self): ref = [u'ζˆ‘θƒ½εžδΈ‹ηŽ»η’ƒθ€ŒδΈδΌ€θΊ«δ½“'] pred = [u' θƒ½εžθ™ΎηŽ»η’ƒθ€Œ δΈιœœθΊ«δ½“ε•¦'] # S = 3, D = 2, I = 0, N = 11 # CER = 5 / 11 char_error_rate = cer.compute(predictions=pred, references=ref) self.assertTrue(abs(char_error_rate - 0.4545454545) < 1e-6) ref = [u'ζˆ‘θƒ½εž', u'δΈ‹ηŽ»η’ƒθ€ŒδΈδΌ€θΊ«δ½“'] pred = [u'ζˆ‘ 能 吞 δΈ‹ 玻 η’ƒ', u'θ€ŒδΈδΌ€θΊ«δ½“'] # S = 0, D = 5, I = 0, N = 11 # CER = 5 / 11 char_error_rate = cer.compute(predictions=pred, references=ref) self.assertTrue(abs(char_error_rate - 0.454545454545) < 1e-6) ref = [u'ζˆ‘θƒ½εžδΈ‹ηŽ»η’ƒθ€ŒδΈδΌ€θΊ«δ½“'] char_error_rate = cer.compute(predictions=ref, references=ref) self.assertFalse(char_error_rate, 0.0) def test_cer_empty(self): ref = '' pred = 'Hypothesis' with self.assertRaises(ValueError): char_error_rate = cer.compute(predictions=pred, references=ref) if __name__ == '__main__': unittest.main() ```
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2,137
Fix missing infos from concurrent dataset loading
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This should fix issue #2131 When calling `load_dataset` at the same time from 2 workers, one of the worker could have missing split infos when reloading the dataset from the cache.
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