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5 duplicate datasets
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[ "Yes this was documented in the PR that added this hf->paperswithcode mapping (https://github.com/huggingface/datasets/pull/2404) and AFAICT those are slightly distinct datasets so I think it's a wontfix\r\n\r\nFor context on the paperswithcode mapping you can also refer to https://github.com/huggingface/huggingface_hub/pull/43 which contains a lot of background discussion ", "Thanks for the antecedents. I close." ]
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## Describe the bug In 5 cases, I could find a dataset on Paperswithcode which references two Hugging Face datasets as dataset loaders. They are: - https://paperswithcode.com/dataset/multinli -> https://huggingface.co/datasets/multi_nli and https://huggingface.co/datasets/multi_nli_mismatch <img width="838" alt="Capture d’écran 2021-07-20 aΜ€ 16 33 58" src="https://user-images.githubusercontent.com/1676121/126342757-4625522a-f788-41a3-bd1f-2a8b9817bbf5.png"> - https://paperswithcode.com/dataset/squad -> https://huggingface.co/datasets/squad and https://huggingface.co/datasets/squad_v2 - https://paperswithcode.com/dataset/narrativeqa -> https://huggingface.co/datasets/narrativeqa and https://huggingface.co/datasets/narrativeqa_manual - https://paperswithcode.com/dataset/hate-speech-and-offensive-language -> https://huggingface.co/datasets/hate_offensive and https://huggingface.co/datasets/hate_speech_offensive - https://paperswithcode.com/dataset/newsph-nli -> https://huggingface.co/datasets/newsph and https://huggingface.co/datasets/newsph_nli Possible solutions: - don't fix (it works) - for each pair of duplicate datasets, remove one, and create an alias to the other. ## Steps to reproduce the bug Visit the Paperswithcode links, and look at the "Dataset Loaders" section ## Expected results There should only be one reference to a Hugging Face dataset loader ## Actual results Two Hugging Face dataset loaders
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The reverse reference exists on paperswithcode: https://paperswithcode.com/dataset/qasper
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Cannot load the blog_authorship_corpus due to codec errors
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[ "Hi @izaskr, thanks for reporting.\r\n\r\nHowever the traceback you joined does not correspond to the codec error message: it is about other error `NonMatchingSplitsSizesError`. Maybe you missed some important part of your traceback...\r\n\r\nI'm going to have a look at the dataset anyway...", "Hi @izaskr, thanks again for having reported this issue.\r\n\r\nAfter investigation, I have created a Pull Request (#2685) to fix several issues with this dataset:\r\n- the `NonMatchingSplitsSizesError`\r\n- the `UnicodeDecodeError`\r\n\r\nOnce the Pull Request merged into master, you will be able to load this dataset if you install `datasets` from our GitHub repository master branch. Otherwise, you will be able to use it after our next release, by updating `datasets`: `pip install -U datasets`.", "@albertvillanova \r\nCan you shed light on how this fix works?\r\n\r\nWe're experiencing a similar issue. \r\n\r\nIf we run several runs (eg in a Wandb sweep) the first run \"works\" but then we get `NonMatchingSplitsSizesError`\r\n\r\n| run num | actual train examples # | expected example # | recorded example # |\r\n| ------- | -------------- | ----------------- | -------- |\r\n| 1 | 100 | 100 | 100 |\r\n| 2 | 102 | 100 | 102 |\r\n| 3 | 100 | 100 | 202 | \r\n| 4 | 40 | 100 | 40 |\r\n| 5 | 40 | 100 | 40 |\r\n| 6 | 40 | 100 | 40 | \r\n\r\n\r\nThe second through the nth all crash with \r\n\r\n```\r\ndatasets.utils.info_utils.NonMatchingSplitsSizesError: [{'expected': SplitInfo(name='train', num_bytes=19980970, num_examples=100, dataset_name='cies'), 'recorded': SplitInfo(name='train', num_bytes=40163811, num_examples=202, dataset_name='cies')}]\r\n\r\n```" ]
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## Describe the bug A codec error is raised while loading the blog_authorship_corpus. ## Steps to reproduce the bug ``` from datasets import load_dataset raw_datasets = load_dataset("blog_authorship_corpus") ``` ## Expected results Loading the dataset without errors. ## Actual results An error similar to the one below was raised for (what seems like) every XML file. /home/izaskr/.cache/huggingface/datasets/downloads/extracted/7cf52524f6517e168604b41c6719292e8f97abbe8f731e638b13423f4212359a/blogs/788358.male.24.Arts.Libra.xml cannot be loaded. Error message: 'utf-8' codec can't decode byte 0xe7 in position 7551: invalid continuation byte Traceback (most recent call last): File "<stdin>", line 1, in <module> File "/home/izaskr/anaconda3/envs/local_vae_older/lib/python3.8/site-packages/datasets/load.py", line 856, in load_dataset builder_instance.download_and_prepare( File "/home/izaskr/anaconda3/envs/local_vae_older/lib/python3.8/site-packages/datasets/builder.py", line 583, in download_and_prepare self._download_and_prepare( File "/home/izaskr/anaconda3/envs/local_vae_older/lib/python3.8/site-packages/datasets/builder.py", line 671, in _download_and_prepare verify_splits(self.info.splits, split_dict) File "/home/izaskr/anaconda3/envs/local_vae_older/lib/python3.8/site-packages/datasets/utils/info_utils.py", line 74, in verify_splits raise NonMatchingSplitsSizesError(str(bad_splits)) datasets.utils.info_utils.NonMatchingSplitsSizesError: [{'expected': SplitInfo(name='train', num_bytes=610252351, num_examples=532812, dataset_name='blog_authorship_corpus'), 'recorded': SplitInfo(name='train', num_bytes=614706451, num_examples=535568, dataset_name='blog_authorship_corpus')}, {'expected': SplitInfo(name='validation', num_bytes=37500394, num_examples=31277, dataset_name='blog_authorship_corpus'), 'recorded': SplitInfo(name='validation', num_bytes=32553710, num_examples=28521, dataset_name='blog_authorship_corpus')}] ## Environment info <!-- You can run the command `datasets-cli env` and copy-and-paste its output below. --> - `datasets` version: 1.9.0 - Platform: Linux-4.15.0-132-generic-x86_64-with-glibc2.10 - Python version: 3.8.8 - PyArrow version: 4.0.1
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Import Error in Kaggle notebook
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[ "This looks like an issue with PyArrow. Did you try reinstalling it ?", "@lhoestq I did, and then let pip handle the installation in `pip import datasets`. I also tried using conda but it gives the same error.\r\n\r\nEdit: pyarrow version on kaggle is 4.0.0, it gets replaced with 4.0.1. So, I don't think uninstalling will change anything.\r\n```\r\nInstall Trace of datasets:\r\n\r\nCollecting datasets\r\n Downloading datasets-1.9.0-py3-none-any.whl (262 kB)\r\n |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–ˆ| 262 kB 834 kB/s eta 0:00:01\r\nRequirement already satisfied: dill in /opt/conda/lib/python3.7/site-packages (from datasets) (0.3.4)\r\nCollecting pyarrow!=4.0.0,>=1.0.0\r\n Downloading pyarrow-4.0.1-cp37-cp37m-manylinux2014_x86_64.whl (21.8 MB)\r\n |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–ˆ| 21.8 MB 6.2 MB/s eta 0:00:01\r\nRequirement already satisfied: importlib-metadata in /opt/conda/lib/python3.7/site-packages (from datasets) (3.4.0)\r\nRequirement already satisfied: huggingface-hub<0.1.0 in /opt/conda/lib/python3.7/site-packages (from datasets) (0.0.8)\r\nRequirement already satisfied: pandas in /opt/conda/lib/python3.7/site-packages (from datasets) (1.2.4)\r\nRequirement already satisfied: requests>=2.19.0 in /opt/conda/lib/python3.7/site-packages (from datasets) (2.25.1)\r\nRequirement already satisfied: fsspec>=2021.05.0 in /opt/conda/lib/python3.7/site-packages (from datasets) (2021.6.1)\r\nRequirement already satisfied: multiprocess in /opt/conda/lib/python3.7/site-packages (from datasets) (0.70.12.2)\r\nRequirement already satisfied: packaging in /opt/conda/lib/python3.7/site-packages (from datasets) (20.9)\r\nCollecting xxhash\r\n Downloading xxhash-2.0.2-cp37-cp37m-manylinux2010_x86_64.whl (243 kB)\r\n |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–ˆ| 243 kB 23.7 MB/s eta 0:00:01\r\nRequirement already satisfied: numpy>=1.17 in /opt/conda/lib/python3.7/site-packages (from datasets) (1.19.5)\r\nRequirement already satisfied: tqdm>=4.27 in /opt/conda/lib/python3.7/site-packages (from datasets) (4.61.1)\r\nRequirement already satisfied: filelock in /opt/conda/lib/python3.7/site-packages (from huggingface-hub<0.1.0->datasets) (3.0.12)\r\nRequirement already satisfied: urllib3<1.27,>=1.21.1 in /opt/conda/lib/python3.7/site-packages (from requests>=2.19.0->datasets) (1.26.5)\r\nRequirement already satisfied: idna<3,>=2.5 in /opt/conda/lib/python3.7/site-packages (from requests>=2.19.0->datasets) (2.10)\r\nRequirement already satisfied: certifi>=2017.4.17 in /opt/conda/lib/python3.7/site-packages (from requests>=2.19.0->datasets) (2021.5.30)\r\nRequirement already satisfied: chardet<5,>=3.0.2 in /opt/conda/lib/python3.7/site-packages (from requests>=2.19.0->datasets) (4.0.0)\r\nRequirement already satisfied: typing-extensions>=3.6.4 in /opt/conda/lib/python3.7/site-packages (from importlib-metadata->datasets) (3.7.4.3)\r\nRequirement already satisfied: zipp>=0.5 in /opt/conda/lib/python3.7/site-packages (from importlib-metadata->datasets) (3.4.1)\r\nRequirement already satisfied: pyparsing>=2.0.2 in /opt/conda/lib/python3.7/site-packages (from packaging->datasets) (2.4.7)\r\nRequirement already satisfied: python-dateutil>=2.7.3 in /opt/conda/lib/python3.7/site-packages (from pandas->datasets) (2.8.1)\r\nRequirement already satisfied: pytz>=2017.3 in /opt/conda/lib/python3.7/site-packages (from pandas->datasets) (2021.1)\r\nRequirement already satisfied: six>=1.5 in /opt/conda/lib/python3.7/site-packages (from python-dateutil>=2.7.3->pandas->datasets) (1.15.0)\r\nInstalling collected packages: xxhash, pyarrow, datasets\r\n Attempting uninstall: pyarrow\r\n Found existing installation: pyarrow 4.0.0\r\n Uninstalling pyarrow-4.0.0:\r\n Successfully uninstalled pyarrow-4.0.0\r\nSuccessfully installed datasets-1.9.0 pyarrow-4.0.1 xxhash-2.0.2\r\nWARNING: Running pip as root will break packages and permissions. You should install packages reliably by using venv: https://pip.pypa.io/warnings/venv\r\n```", "You may need to restart your kaggle notebook after installing a newer version of `pyarrow`.\r\n\r\nIf it doesn't work we'll probably have to create an issue on [arrow's JIRA](https://issues.apache.org/jira/projects/ARROW/issues/), and maybe ask kaggle why it could fail", "> You may need to restart your kaggle notebook before after installing a newer version of `pyarrow`.\r\n> \r\n> If it doesn't work we'll probably have to create an issue on [arrow's JIRA](https://issues.apache.org/jira/projects/ARROW/issues/), and maybe ask kaggle why it could fail\r\n\r\nIt works after restarting.\r\nMy bad, I forgot to restart the notebook. Sorry for the trouble!" ]
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## Describe the bug Not able to import datasets library in kaggle notebooks ## Steps to reproduce the bug ```python !pip install datasets import datasets ``` ## Expected results No such error ## Actual results ``` ImportError Traceback (most recent call last) <ipython-input-9-652e886d387f> in <module> ----> 1 import datasets /opt/conda/lib/python3.7/site-packages/datasets/__init__.py in <module> 31 ) 32 ---> 33 from .arrow_dataset import Dataset, concatenate_datasets 34 from .arrow_reader import ArrowReader, ReadInstruction 35 from .arrow_writer import ArrowWriter /opt/conda/lib/python3.7/site-packages/datasets/arrow_dataset.py in <module> 36 import pandas as pd 37 import pyarrow as pa ---> 38 import pyarrow.compute as pc 39 from multiprocess import Pool, RLock 40 from tqdm.auto import tqdm /opt/conda/lib/python3.7/site-packages/pyarrow/compute.py in <module> 16 # under the License. 17 ---> 18 from pyarrow._compute import ( # noqa 19 Function, 20 FunctionOptions, ImportError: /opt/conda/lib/python3.7/site-packages/pyarrow/_compute.cpython-37m-x86_64-linux-gnu.so: undefined symbol: _ZNK5arrow7compute15KernelSignature8ToStringEv ``` ## Environment info <!-- You can run the command `datasets-cli env` and copy-and-paste its output below. --> - `datasets` version: 1.9.0 - Platform: Kaggle - Python version: 3.7.10 - PyArrow version: 4.0.1
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Error when downloading C4
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[ "Hi Thanks for reporting !\r\nIt looks like these files are not correctly reported in the list of expected files to download, let me fix that ;)", "Alright this is fixed now. We'll do a new release soon to make the fix available.\r\n\r\nIn the meantime feel free to simply pass `ignore_verifications=True` to `load_dataset` to skip this error", "@lhoestq thank you for such a quick feedback!" ]
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Hi, I am trying to download `en` corpus from C4 dataset. However, I get an error caused by validation files download (see image). My code is very primitive: `datasets.load_dataset('c4', 'en')` Is this a bug or do I have some configurations missing on my server? Thanks! <img width="1014" alt="Π‘Π½ΠΈΠΌΠΎΠΊ экрана 2021-07-20 Π² 11 37 17" src="https://user-images.githubusercontent.com/36672861/126289448-6e0db402-5f3f-485a-bf74-eb6e0271fc25.png">
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2,676
Increase json reader block_size automatically
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Currently some files can't be read with the default parameters of the JSON lines reader. For example this one: https://huggingface.co/datasets/thomwolf/codeparrot/resolve/main/file-000000000006.json.gz raises a pyarrow error: ```python ArrowInvalid: straddling object straddles two block boundaries (try to increase block size?) ``` The block size that is used is the default one by pyarrow (related to this [jira issue](https://issues.apache.org/jira/browse/ARROW-9612)). To fix this issue I changed the block_size to increase automatically if there is a straddling issue when parsing a batch of json lines. By default the value is `chunksize // 32` in order to leverage multithreading, and it doubles every time a straddling issue occurs. The block_size is then reset for each file. cc @thomwolf @albertvillanova
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Parallelize ETag requests
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Since https://github.com/huggingface/datasets/pull/2628 we use the ETag or the remote data files to compute the directory in the cache where a dataset is saved. This is useful in order to reload the dataset from the cache only if the remote files haven't changed. In this I made the ETag requests parallel using multithreading. There is also a tqdm progress bar that shows up if there are more than 16 data files.
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947,338,202
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2,674
Fix sacrebleu parameter name
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DONE: - Fix parameter name: `smooth` to `smooth_method`. - Improve kwargs description. - Align docs on using a metric. - Add example of passing additional arguments in using metrics. Related to #2669.
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947,300,008
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2,673
Fix potential DuplicatedKeysError in SQuAD
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DONE: - Fix potential DiplicatedKeysError by ensuring keys are unique. - Align examples in the docs with SQuAD code. We should promote as a good practice, that the keys should be programmatically generated as unique, instead of read from data (which might be not unique).
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Fix potential DuplicatedKeysError in LibriSpeech
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DONE: - Fix unnecessary path join. - Fix potential DiplicatedKeysError by ensuring keys are unique. We should promote as a good practice, that the keys should be programmatically generated as unique, instead of read from data (which might be not unique).
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Mesinesp development and training data sets have been added.
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[ "It'll be new pull request with new commits." ]
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https://zenodo.org/search?page=1&size=20&q=mesinesp, Mesinesp has Medical Semantic Indexed records in Spanish. Indexing is done using DeCS codes, a sort of Spanish equivalent to MeSH terms. The Mesinesp (Spanish BioASQ track, see https://temu.bsc.es/mesinesp) development set has a total of 750 records. The Mesinesp (Spanish BioASQ track, see https://temu.bsc.es/mesinesp) training set has a total of 369,368 records.
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Using sharding to parallelize indexing
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**Is your feature request related to a problem? Please describe.** Creating an elasticsearch index on large dataset could be quite long and cannot be parallelized on shard (the index creation is colliding) **Describe the solution you'd like** When working on dataset shards, if an index already exists, its mapping should be checked and if compatible, the indexing process should continue with the shard data. Additionally, at the end of the process, the `_indexes` dict should be send back to the original dataset object (from which the shards have been created) to allow to use the index for later filtering on the whole dataset. **Describe alternatives you've considered** Each dataset shard could created independent partial indices. then on the whole dataset level, indices should be all referred in `_indexes` dict and be used in querying through `get_nearest_examples()`. The drawback is that the scores will be computed independently on the partial indices leading to inconsistent values for most scoring based on corpus level statistics (tf/idf, BM25). **Additional context** The objectives is to parallelize the index creation to speed-up the process (ie surcharging the ES server which is fine to handle large load) while later enabling search on the whole dataset.
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Metric kwargs are not passed to underlying external metric f1_score
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[ "Hi @BramVanroy, thanks for reporting.\r\n\r\nFirst, note that `\"min\"` is not an allowed value for `average`. According to scikit-learn [documentation](https://scikit-learn.org/stable/modules/generated/sklearn.metrics.f1_score.html), `average` can only take the values: `{\"micro\", \"macro\", \"samples\", \"weighted\", \"binary\"} or None, default=\"binary\"`.\r\n\r\nSecond, you should take into account that all additional metric-specific argument should be passed in the method `compute` (and not in the method `load_metric`). You can find more information in our documentation: https://huggingface.co/docs/datasets/using_metrics.html#computing-the-metric-scores\r\n\r\nSo for example, if you would like to calculate the macro-averaged F1 score, you should use:\r\n```python\r\nimport datasets\r\n\r\nf1 = datasets.load_metric(\"f1\", keep_in_memory=True)\r\nf1.add_batch(predictions=[0,2,3], references=[1, 2, 3])\r\nf1.compute(average=\"macro\")\r\n```", "Thanks, that was it. A bit strange though, since `load_metric` had an argument `metric_init_kwargs`. I assume that that's for specific initialisation arguments whereas `average` is for the function itself." ]
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## Describe the bug When I want to use F1 score with average="min", this keyword argument does not seem to be passed through to the underlying sklearn metric. This is evident because [sklearn](https://scikit-learn.org/stable/modules/generated/sklearn.metrics.f1_score.html) throws an error telling me so. ## Steps to reproduce the bug ```python import datasets f1 = datasets.load_metric("f1", keep_in_memory=True, average="min") f1.add_batch(predictions=[0,2,3], references=[1, 2, 3]) f1.compute() ``` ## Expected results No error, because `average="min"` should be passed correctly to f1_score in sklearn. ## Actual results ``` Traceback (most recent call last): File "<stdin>", line 1, in <module> File "C:\Users\bramv\.virtualenvs\pipeline-TpEsXVex\lib\site-packages\datasets\metric.py", line 402, in compute output = self._compute(predictions=predictions, references=references, **kwargs) File "C:\Users\bramv\.cache\huggingface\modules\datasets_modules\metrics\f1\82177930a325d4c28342bba0f116d73f6d92fb0c44cd67be32a07c1262b61cfe\f1.py", line 97, in _compute "f1": f1_score( File "C:\Users\bramv\.virtualenvs\pipeline-TpEsXVex\lib\site-packages\sklearn\utils\validation.py", line 63, in inner_f return f(*args, **kwargs) File "C:\Users\bramv\.virtualenvs\pipeline-TpEsXVex\lib\site-packages\sklearn\metrics\_classification.py", line 1071, in f1_score return fbeta_score(y_true, y_pred, beta=1, labels=labels, File "C:\Users\bramv\.virtualenvs\pipeline-TpEsXVex\lib\site-packages\sklearn\utils\validation.py", line 63, in inner_f return f(*args, **kwargs) File "C:\Users\bramv\.virtualenvs\pipeline-TpEsXVex\lib\site-packages\sklearn\metrics\_classification.py", line 1195, in fbeta_score _, _, f, _ = precision_recall_fscore_support(y_true, y_pred, File "C:\Users\bramv\.virtualenvs\pipeline-TpEsXVex\lib\site-packages\sklearn\utils\validation.py", line 63, in inner_f return f(*args, **kwargs) File "C:\Users\bramv\.virtualenvs\pipeline-TpEsXVex\lib\site-packages\sklearn\metrics\_classification.py", line 1464, in precision_recall_fscore_support labels = _check_set_wise_labels(y_true, y_pred, average, labels, File "C:\Users\bramv\.virtualenvs\pipeline-TpEsXVex\lib\site-packages\sklearn\metrics\_classification.py", line 1294, in _check_set_wise_labels raise ValueError("Target is %s but average='binary'. Please " ValueError: Target is multiclass but average='binary'. Please choose another average setting, one of [None, 'micro', 'macro', 'weighted']. ``` ## Environment info <!-- You can run the command `datasets-cli env` and copy-and-paste its output below. --> - `datasets` version: 1.9.0 - Platform: Windows-10-10.0.19041-SP0 - Python version: 3.9.2 - PyArrow version: 4.0.1
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Add Russian SuperGLUE
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[ "Added the missing label classes and their explanations (to the best of my understanding)", "Thanks a lot ! Once the last comment about the label names is addressed we can merge :)" ]
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Hi, This adds the [Russian SuperGLUE](https://russiansuperglue.com/) dataset. For the most part I reused the code for the original SuperGLUE, although there are some relatively minor differences in the structure that I accounted for.
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Use tqdm from tqdm_utils
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[ "The current CI failure is due to modifications in the dataset script.", "Merging since the CI is only failing because of dataset card issues, which is unrelated to this PR" ]
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This PR replaces `tqdm` from the `tqdm` lib with `tqdm` from `datasets.utils.tqdm_utils`. With this change, it's possible to disable progress bars just by calling `disable_progress_bar`. Note this doesn't work on Windows when using multiprocessing due to how global variables are shared between processes. Currently, there is no easy way to disable progress bars in a multiprocess setting on Windows (patching logging with `datasets.utils.logging.get_verbosity = lambda: datasets.utils.logging.NOTSET` doesn't seem to work as well), so adding support for this is a future goal. Additionally, this PR adds a unit ("ba" for batches) to the bar printed by `Dataset.to_json` (this change is motivated by https://github.com/huggingface/datasets/issues/2657).
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Adds CodeClippy dataset [WIP]
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CodeClippy is an opensource code dataset scrapped from github during flax-jax-community-week https://the-eye.eu/public/AI/training_data/code_clippy_data/
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Adds APPS dataset to the hub [WIP]
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A loading script for [APPS dataset](https://github.com/hendrycks/apps)
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[`to_json`] add multi-proc sharding support
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[ "Hi @stas00, \r\nI want to work on this issue and I was thinking why don't we use `imap` [in this loop](https://github.com/huggingface/datasets/blob/440b14d0dd428ae1b25881aa72ba7bbb8ad9ff84/src/datasets/io/json.py#L99)? This way, using offset (which is being used to slice the pyarrow table) we can convert pyarrow table to `json` using multiprocessing. I've a small code snippet for some clarity:\r\n```\r\nresult = list(\r\n pool.imap(self._apply_df, [(offset, batch_size) for offset in range(0, len(self.dataset), batch_size)])\r\n )\r\n```\r\n`_apply_df` is a function which will return `batch.to_pandas().to_json(path_or_buf=None, orient=\"records\", lines=True)` which is basically json version of the batched pyarrow table. Later on we can concatenate it to form json file? \r\n\r\nI think the only downside here is to write file from `imap` output (output would be a list and we'll need to iterate over it and write in a file) which might add a little overhead cost. What do you think about this?", "Followed up in https://github.com/huggingface/datasets/pull/2747" ]
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As discussed on slack it appears that `to_json` is quite slow on huge datasets like OSCAR. I implemented sharded saving, which is much much faster - but the tqdm bars all overwrite each other, so it's hard to make sense of the progress, so if possible ideally this multi-proc support could be implemented internally in `to_json` via `num_proc` argument. I guess `num_proc` will be the number of shards? I think the user will need to use this feature wisely, since too many processes writing to say normal style HD is likely to be slower than one process. I'm not sure whether the user should be responsible to concatenate the shards at the end or `datasets`, either way works for my needs. The code I was using: ``` from multiprocessing import cpu_count, Process, Queue [...] filtered_dataset = concat_dataset.map(filter_short_documents, batched=True, batch_size=256, num_proc=cpu_count()) DATASET_NAME = "oscar" SHARDS = 10 def process_shard(idx): print(f"Sharding {idx}") ds_shard = filtered_dataset.shard(SHARDS, idx, contiguous=True) # ds_shard = ds_shard.shuffle() # remove contiguous=True above if shuffling print(f"Saving {DATASET_NAME}-{idx}.jsonl") ds_shard.to_json(f"{DATASET_NAME}-{idx}.jsonl", orient="records", lines=True, force_ascii=False) queue = Queue() processes = [Process(target=process_shard, args=(idx,)) for idx in range(SHARDS)] for p in processes: p.start() for p in processes: p.join() ``` Thank you! @lhoestq
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Load Dataset from the Hub (NO DATASET SCRIPT)
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[ "This is ready for review now :)\r\n\r\nI would love to have some feedback on the changes in load.py @albertvillanova. There are many changes so if you have questions let me know, especially on the `resolve_data_files` functions and on the changes in `prepare_module`.\r\n\r\nAnd @thomwolf if you want to take a look at the documentation, feel free to share your suggestions :)", "I took your comments into account thanks !\r\nAnd I made `aiohttp` a required dependency :)", "Just updated the documentation :)\r\n[share_datasets.html](https://45532-250213286-gh.circle-artifacts.com/0/docs/_build/html/share_dataset.html)\r\n\r\nLet me know if you have some comments", "Merging this one :) \r\n\r\nWe can try to integrate the changes in the docs to #2718 @stevhliu !", "Baked this into the [docs](https://44335-250213286-gh.circle-artifacts.com/0/docs/_build/html/loading.html#hugging-face-hub) already, let me know if there is anything else I should add! :)" ]
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## Load the data from any Dataset repository on the Hub This PR adds support for loading datasets from any dataset repository on the hub, without requiring any dataset script. As a user it's now possible to create a repo and upload some csv/json/text/parquet files, and then be able to load the data in one line. Here is an example with the `allenai/c4` repository that contains a lot of compressed json lines files: ```python from datasets import load_dataset data_files = {"train": "en/c4-train.*.json.gz"} c4 = load_dataset("allenai/c4", data_files=data_files, split="train", streaming=True) print(c4.n_shards) # 1024 print(next(iter(c4))) # {'text': 'Beginners BBQ Class Takin...'} ``` By default it loads all the files, but as shown in the example you can choose the ones you want with unix style patterns. Of course it's still possible to use dataset scripts since they offer the most flexibility. ## Implementation details It uses `huggingface_hub` to list the files in a dataset repository. If you provide a path to a local directory instead of a repository name, it works the same way but it uses `glob`. Depending on the data files available, or passed in the `data_files` parameter, one of the available builders will be used among the csv, json, text and parquet builders. Because of this, it's not possible to load both csv and json files at once. In this case you have to load them separately and then concatenate the two datasets for example. ## TODO - [x] tests - [x] docs - [x] when huggingface_hub gets a new release, update the CI and the setup.py Close https://github.com/huggingface/datasets/issues/2629
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Add SD task for SUPERB
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[ "I make a summary about our discussion with @lewtun and @Narsil on the agreed schema for this dataset and the additional steps required to generate the 2D array labels:\r\n- The labels for this dataset are a 2D array:\r\n Given an example:\r\n ```python\r\n {\"record_id\": record_id, \"file\": file, \"start\": start, \"end\": end, \"speakers\": [...]}\r\n ```\r\n the labels are a 2D array of shape `(num_frames, num_speakers)` where `num_frames = end - start` and `num_speakers = 2`.\r\n- In order to avoid a too large dataset (too large disk space), `datasets` does not store the 2D array label. Instead, we store a compact form:\r\n ```\r\n \"speakers\": [\r\n {\"speaker_id\": speaker_0_id, \"start\": start_0_speaker_0, \"end\": end_0_speaker_0},\r\n {\"speaker_id\": speaker_0_id, \"start\": start_1_speaker_0, \"end\": end_1_speaker_0},\r\n {\"speaker_id\": speaker_1_id, \"start\": start_0_speaker_1, \"end\": end_0_speaker_1},\r\n ],\r\n ```\r\n - Once loaded the dataset, an additional step is required to generate the 2D array label from this compact form\r\n - This additional step should be a modified version of the s3prl method `_get_labeled_speech`:\r\n - Original s3prl `_get_labeled_speech` includes 2 functionalities: reading the audio file and transforming it into an array, and generating the label 2D array; I think we should separate these 2 functionalities\r\n - Original s3prl `_get_labeled_speech` performs 2 steps to generate the labels:\r\n - Transform start/end seconds (float) into frame numbers (int): I have already done this step to generate the dataset\r\n - Generate the 2D array label from the frame numbers\r\n\r\nI also ping @osanseviero and @lhoestq to include them in the loop.", "Here I would like to discuss (and agree) one of the decisions I made, as I'm not completely satisfied with it: to transform the seconds (float) into frame numbers (int) to generate this dataset.\r\n\r\n- A priori, the most natural and general choice would be to preserve the seconds (float), because:\r\n - this is the way the raw data comes from\r\n - the transformation into frame numbers depends on the sample rate, frame_shift and subsampling\r\n\r\nHowever, I finally decided to transform seconds into frame numbers because:\r\n- for SUPERB, sampling rate, frame_shift and subsampling are fixed (`rate = 16_000`, `frame_shift = 160`, `subsampling = 1`)\r\n- it makes easier the post-processing, as labels are generated from sample numbers: labels are a 2D array of shape `(num_frames, num_speakers)`\r\n- the number of examples depends on the number of frames:\r\n - if an example has more than 2_000 frames, then it is split into 2 examples. This is the case for `record_id = \"7859-102521-0017_3983-5371-0014\"`, which has 2_452 frames and it is split into 2 examples:\r\n ```\r\n {\"record_id\": \"7859-102521-0017_3983-5371-0014\", \"start\"= 0, \"end\": 2_000,...},\r\n {\"record_id\": \"7859-102521-0017_3983-5371-0014\", \"start\"= 2_000, \"end\": 2_452,...},\r\n ```\r\n\r\nAs I told you, I'm not totally convinced of this decision, and I would really appreciate your opinion.\r\n\r\ncc: @lewtun @Narsil @osanseviero @lhoestq ", "It makes total sense to prepare the data to be in a format that can actually be used for model training and evaluation. That's one of the roles of this lib :)\r\n\r\nSo for me it's ok to use frames as a unit instead of seconds. Just pinging @patrickvonplaten in case he has ever played with such audio tasks and has some advice. For the context: the task is to classify which speaker is speaking, let us know if you are aware of any convenient/standard format for this.\r\n\r\nAlso I'm not sure why you have to split an example if it's longer that 2,000 frames ?", "> Also I'm not sure why you have to split an example if it's longer that 2,000 frames ?\r\n\r\nIt is a convention in SUPERB benchmark.", "Note that if we agree to leave the dataset as it is now, 2 additional custom functions must be used:\r\n- one to generate the 2D array labels\r\n- one to load the audio file into an array, but taking into account start/end to cut the audio\r\n\r\nIs there a way we can give these functions ready to be used? Or should we leave this entirely to the end user? This is not trivial...", "You could add an example of usage in the dataset card, as it is done for other audio datasets", "@albertvillanova this simple function can be edited simply to add the start/stop cuts \r\n\r\nhttps://github.com/huggingface/transformers/blob/master/src/transformers/pipelines/automatic_speech_recognition.py#L29 ", "Does this function work on windows ?", "Windows ? What is it ? (Not sure not able to test, it's directly calling ffmpeg binary, so depending on the setup it could but can't say for sure without testing)\r\n", "It's one of the OS we're supposed to support :P (for the better and for the worse)", "> Note that if we agree to leave the dataset as it is now, 2 additional custom functions must be used:\r\n> \r\n> * one to generate the 2D array labels\r\n> * one to load the audio file into an array, but taking into account start/end to cut the audio\r\n> \r\n> Is there a way we can give these functions ready to be used? Or should we leave this entirely to the end user? This is not trivial...\r\n\r\n+1 on providing the necessary functions on the dataset card. aside from that, the current implementation looks great from my perspective!" ]
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Include the SD (Speaker Diarization) task as described in the [SUPERB paper](https://arxiv.org/abs/2105.01051) and `s3prl` [instructions](https://github.com/s3prl/s3prl/tree/master/s3prl/downstream#sd-speaker-diarization). TODO: - [x] Generate the LibriMix corpus - [x] Prepare the corpus for diarization - [x] Upload these files to the superb-data repo - [x] Transcribe the corresponding s3prl processing of these files into our superb loading script - [x] README: tags + description sections - ~~Add DER metric~~ (we leave the DER metric for a follow-up PR) Related to #2619. Close #2653. cc: @lewtun
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Move checks from _map_single to map
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[ "@lhoestq This one has been open for a while. Could you please take a look?", "@lhoestq Ready for the final review!", "I forgot to update the signature of `DatasetDict.map`, so did that now." ]
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The goal of this PR is to remove duplicated checks in the `map` logic to execute them only once whenever possible (`fn_kwargs`, `input_columns`, ...). Additionally, this PR improves the consistency (to align it with `input_columns`) of the `remove_columns` check by adding support for a single string value, which is then wrapped into a list.
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Allow dataset config kwargs to be None
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Close https://github.com/huggingface/datasets/issues/2658 The dataset config kwargs that were set to None we simply ignored. This was an issue when None has some meaning for certain parameters of certain builders, like the `sep` parameter of the "csv" builder that allows to infer to separator. cc @SBrandeis
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Can't pass `sep=None` to load_dataset("csv", ...) to infer the separator via pandas.read_csv
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When doing `load_dataset("csv", sep=None)`, the `sep` passed to `pd.read_csv` is still the default `sep=","` instead, which makes it impossible to make the csv loader infer the separator. Related to https://github.com/huggingface/datasets/pull/2656 cc @SBrandeis
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`to_json` reporting enhancements
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While using `to_json` 2 things came to mind that would have made the experience easier on the user: 1. Could we have a `desc` arg for the tqdm use and a fallback to just `to_json` so that it'd be clear to the user what's happening? Surely, one can just print the description before calling json, but I thought perhaps it'd help to have it self-identify like you did for other progress bars recently. 2. It took me a while to make sense of the reported numbers: ``` 22%|β–ˆβ–ˆβ– | 1536/7076 [12:30:57<44:09:42, 28.70s/it] ``` So iteration here happens to be 10K samples, and the total is 70M records. But the user does't know that, so the progress bar is perfect, but the numbers it reports are meaningless until one discovers that 1it=10K samples. And one still has to convert these in the head - so it's not quick. Not exactly sure what's the best way to approach this, perhaps it can be part of `desc`? or report M or K, so it'd be built-in if it were to print, e.g.: ``` 22%|β–ˆβ–ˆβ– | 15360K/70760K [12:30:57<44:09:42, 28.70s/it] ``` or ``` 22%|β–ˆβ–ˆβ– | 15.36M/70.76M [12:30:57<44:09:42, 28.70s/it] ``` (while of course remaining friendly to small datasets) I forget if tqdm lets you add a magnitude identifier to the running count. Thank you!
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Change `from_csv` default arguments
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[ "This is not the default in pandas right ?\r\nWe try to align our CSV loader with the pandas API.\r\n\r\nMoreover according to their documentation, the python parser is used when sep is None, which might not be the fastest one.\r\n\r\nMaybe users could just specify `sep=None` themselves ?\r\nIn this case we should add some documentation about this" ]
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Passing `sep=None` to pandas's `read_csv` lets pandas guess the CSV file's separator This PR allows users to use this pandas's feature by passing `sep=None` to `Dataset.from_csv`: ```python Dataset.from_csv( ..., sep=None ) ```
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Allow the selection of multiple columns at once
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[ "Hi! I was looking into this and hope you can clarify a point. Your my_dataset variable would be of type DatasetDict which means the alternative you've described (dict comprehension) is what makes sense. \r\nIs there a reason why you wouldn't want to convert my_dataset to a pandas df if you'd like to use it like one? Please let me know if I'm missing something.", "Hi! Sorry for the delay.\r\n\r\nIn this case, the dataset would be a `datasets.Dataset` and we want to select multiple columns, the `idx` and `label` columns for example.\r\n\r\nMy issue is that my dataset is too big for memory if I load everything into pandas." ]
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**Is your feature request related to a problem? Please describe.** Similar to pandas, it would be great if we could select multiple columns at once. **Describe the solution you'd like** ```python my_dataset = ... # Has columns ['idx', 'sentence', 'label'] idx, label = my_dataset[['idx', 'label']] ``` **Describe alternatives you've considered** we can do `[dataset[col] for col in ('idx', 'label')]` **Additional context** This is of course very minor.
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Give a user feedback if the dataset he loads is streamable or not
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[ "#self-assign", "I understand it already raises a `NotImplementedError` exception, eg:\r\n\r\n```\r\n>>> dataset = load_dataset(\"journalists_questions\", name=\"plain_text\", split=\"train\", streaming=True)\r\n\r\n[...]\r\nNotImplementedError: Extraction protocol for file at https://drive.google.com/uc?export=download&id=1CBrh-9OrSpKmPQBxTK_ji6mq6WTN_U9U is not implemented yet\r\n```\r\n" ]
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**Is your feature request related to a problem? Please describe.** I would love to know if a `dataset` is with the current implementation streamable or not. **Describe the solution you'd like** We could show a warning when a dataset is loaded with `load_dataset('...',streaming=True)` when its lot streamable, e.g. if it is an archive. **Describe alternatives you've considered** Add a new metadata tag for "streaming"
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Add SD task for SUPERB
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[ "Note that this subset requires us to:\r\n\r\n* generate the LibriMix corpus from LibriSpeech\r\n* prepare the corpus for diarization\r\n\r\nAs suggested by @lhoestq we should perform these steps locally and add the prepared data to this public repo on the Hub: https://huggingface.co/datasets/superb/superb-data\r\n\r\nThen we can use the URLs for the files to load the data in `superb`'s dataset loading script.\r\n\r\nFor consistency, I suggest we name the folders in `superb-data` in the same way as the configs in the dataset loading script - e.g. use `sd` for speech diarization in both places :)", "@lewtun @lhoestq: \r\n\r\nI have already generated the LibriMix corpus and prepared the corpus for diarization. The output is 3 dirs (train, dev, test), each one containing 6 files: reco2dur rttm segments spk2utt utt2spk wav.scp\r\n\r\nNext steps:\r\n- Upload these files to the superb-data repo\r\n- Transcribe the corresponding s3prl processing of these files into our superb loading script\r\n\r\nNote that processing of these files is a bit more intricate than usual datasets: https://github.com/s3prl/s3prl/blob/master/s3prl/downstream/diarization/dataset.py#L233\r\n\r\n" ]
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Include the SD (Speaker Diarization) task as described in the [SUPERB paper](https://arxiv.org/abs/2105.01051) and `s3prl` [instructions](https://github.com/s3prl/s3prl/tree/master/s3prl/downstream#sd-speaker-diarization). Steps: - [x] Generate the LibriMix corpus - [x] Prepare the corpus for diarization - [x] Upload these files to the superb-data repo - [x] Transcribe the corresponding s3prl processing of these files into our superb loading script - [ ] README: tags + description sections Related to #2619. cc: @lewtun
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Fix logging docstring
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Remove "no tqdm bars" from the docstring in the logging module to align it with the changes introduced in #2534.
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Setting log level higher than warning does not suppress progress bar
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[ "Hi,\r\n\r\nyou can suppress progress bars by patching logging as follows:\r\n```python\r\nimport datasets\r\nimport logging\r\ndatasets.logging.get_verbosity = lambda: logging.NOTSET\r\n# map call ...\r\n```", "Thank you, it worked :)", "See https://github.com/huggingface/datasets/issues/2528 for reference", "Note also that you can disable the progress bar with\r\n\r\n```python\r\nfrom datasets.utils import disable_progress_bar\r\ndisable_progress_bar()\r\n```\r\n\r\nSee https://github.com/huggingface/datasets/blob/8814b393984c1c2e1800ba370de2a9f7c8644908/src/datasets/utils/tqdm_utils.py#L84", "Now the library officially recommends `set_progress_bar_enabled(False)`\r\n\r\n```py\r\nfrom datasets.utils import set_progress_bar_enabled\r\n\r\nset_progress_bar_enabled(False)\r\n```\r\n\r\nsource:\r\n\r\nhttps://github.com/huggingface/datasets/blob/1fd47120ace13626c528367787ffa13e1a26e6c0/src/datasets/utils/tqdm_utils.py#L83-L88\r\n\r\n" ]
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## Describe the bug I would like to disable progress bars for `.map` method (and other methods like `.filter` and `load_dataset` as well). According to #1627 one can suppress it by setting log level higher than `warning`, however doing so doesn't suppress it with version 1.9.0. I also tried to set `DATASETS_VERBOSITY` environment variable to `error` or `critical` but it also didn't work. ## Steps to reproduce the bug ```python import datasets from datasets.utils.logging import set_verbosity_error set_verbosity_error() def dummy_map(batch): return batch common_voice_train = datasets.load_dataset("common_voice", "de", split="train") common_voice_test = datasets.load_dataset("common_voice", "de", split="test") common_voice_train.map(dummy_map) ``` ## Expected results - The progress bar for `.map` call won't be shown ## Actual results - The progress bar for `.map` is still shown ## Environment info <!-- You can run the command `datasets-cli env` and copy-and-paste its output below. --> - `datasets` version: 1.9.0 - Platform: Linux-5.4.0-1045-aws-x86_64-with-Ubuntu-18.04-bionic - Python version: 3.7.5 - PyArrow version: 4.0.1
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[load_dataset] shard and parallelize the process
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- Some huge datasets take forever to build the first time. (e.g. oscar/en) as it's done in a single cpu core. - If the build crashes, everything done up to that point gets lost Request: Shard the build over multiple arrow files, which would enable: - much faster build by parallelizing the build process - if the process crashed, the completed arrow files don't need to be re-built again Thank you! @lhoestq
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adding progress bar / ETA for `load_dataset`
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CONTRIBUTOR
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Please consider: ``` Downloading and preparing dataset oscar/unshuffled_deduplicated_en (download: 462.40 GiB, generated: 1.18 TiB, post-processed: Unknown size, total: 1.63 TiB) to cache/oscar/unshuffled_deduplicated_en/1.0.0/84838bd49d2295f62008383b05620571535451d84545037bb94d6f3501651df2... HF google storage unreachable. Downloading and preparing it from source ``` and no indication whatsoever of whether things work well or when it'll be done. It's important to have an estimated completion time for when doing slurm jobs since some instances have a cap on run-time. I think for this particular job it sat for 30min in total silence and then after 30min it started generating: ``` 897850 examples [07:24, 10286.71 examples/s] ``` which is already great! Request: 1. ETA - knowing how many hours to allocate for a slurm job 2. progress bar - helps to know things are working and aren't stuck and where we are at. Thank you! @lhoestq
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Add web_split dataset for Paraphase and Rephrase benchmark
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## Describe: For getting simple sentences from complex sentence there are dataset and task like wiki_split that is available in hugging face datasets. This web_split is a very similar dataset. There some research paper which states that by combining these two datasets we if we train the model it will yield better results on both tests data. This dataset is made from web NLG data. All the dataset related details are provided in the below repository Github link: https://github.com/shashiongithub/Split-and-Rephrase
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944,424,941
MDExOlB1bGxSZXF1ZXN0Njg5OTExMzky
2,647
Fix anchor in README
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I forgot to push this fix in #2611, so I'm sending it now.
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downloading of yahoo_answers_topics dataset failed
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[ "Hi ! I just tested and it worked fine today for me.\r\n\r\nI think this is because the dataset is stored on Google Drive which has a quota limit for the number of downloads per day, see this similar issue https://github.com/huggingface/datasets/issues/996 \r\n\r\nFeel free to try again today, now that the quota was reset" ]
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## Describe the bug I get an error datasets.utils.info_utils.NonMatchingChecksumError: Checksums didn't match for dataset source files when I try to download the yahoo_answers_topics dataset ## Steps to reproduce the bug self.dataset = load_dataset( 'yahoo_answers_topics', cache_dir=self.config['yahoo_cache_dir'], split='train[:90%]') # Sample code to reproduce the bug self.dataset = load_dataset( 'yahoo_answers_topics', cache_dir=self.config['yahoo_cache_dir'], split='train[:90%]') ## Expected results A clear and concise description of the expected results. ## Actual results Specify the actual results or traceback. datasets.utils.info_utils.NonMatchingChecksumError: Checksums didn't match for dataset source files
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load_dataset processing failed with OS error after downloading a dataset
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[ "Hi ! It looks like an issue with pytorch.\r\n\r\nCould you try to run `import torch` and see if it raises an error ?", "> Hi ! It looks like an issue with pytorch.\r\n> \r\n> Could you try to run `import torch` and see if it raises an error ?\r\n\r\nIt works. Thank you!" ]
1,626,265,433,000
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## Describe the bug After downloading a dataset like opus100, there is a bug that OSError: Cannot find data file. Original error: dlopen: cannot load any more object with static TLS ## Steps to reproduce the bug ```python from datasets import load_dataset this_dataset = load_dataset('opus100', 'af-en') ``` ## Expected results there is no error when running load_dataset. ## Actual results Specify the actual results or traceback. Traceback (most recent call last): File "/home/anaconda3/lib/python3.6/site-packages/datasets/builder.py", line 652, in _download_and_prep self._prepare_split(split_generator, **prepare_split_kwargs) File "/home/anaconda3/lib/python3.6/site-packages/datasets/builder.py", line 989, in _prepare_split example = self.info.features.encode_example(record) File "/home/anaconda3/lib/python3.6/site-packages/datasets/features.py", line 952, in encode_example example = cast_to_python_objects(example) File "/home/anaconda3/lib/python3.6/site-packages/datasets/features.py", line 219, in cast_to_python_ob return _cast_to_python_objects(obj)[0] File "/home/anaconda3/lib/python3.6/site-packages/datasets/features.py", line 165, in _cast_to_python_o import torch File "/home/anaconda3/lib/python3.6/site-packages/torch/__init__.py", line 188, in <module> _load_global_deps() File "/home/anaconda3/lib/python3.6/site-packages/torch/__init__.py", line 141, in _load_global_deps ctypes.CDLL(lib_path, mode=ctypes.RTLD_GLOBAL) File "/home/anaconda3/lib/python3.6/ctypes/__init__.py", line 348, in __init__ self._handle = _dlopen(self._name, mode) OSError: dlopen: cannot load any more object with static TLS During handling of the above exception, another exception occurred: Traceback (most recent call last): File "download_hub_opus100.py", line 9, in <module> this_dataset = load_dataset('opus100', language_pair) File "/home/anaconda3/lib/python3.6/site-packages/datasets/load.py", line 748, in load_dataset use_auth_token=use_auth_token, File "/home/anaconda3/lib/python3.6/site-packages/datasets/builder.py", line 575, in download_and_prepa dl_manager=dl_manager, verify_infos=verify_infos, **download_and_prepare_kwargs File "/home/anaconda3/lib/python3.6/site-packages/datasets/builder.py", line 658, in _download_and_prep + str(e) OSError: Cannot find data file. Original error: dlopen: cannot load any more object with static TLS ## Environment info - `datasets` version: 1.8.0 - Platform: Linux-3.13.0-32-generic-x86_64-with-debian-jessie-sid - Python version: 3.6.6 - PyArrow version: 3.0.0
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944,254,748
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2,644
Batched `map` not allowed to return 0 items
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[ "Hi ! Thanks for reporting. Indeed it looks like type inference makes it fail. We should probably just ignore this step until a non-empty batch is passed.", "Sounds good! Do you want me to propose a PR? I'm quite busy right now, but if it's not too urgent I could take a look next week.", "Sure if you're interested feel free to open a PR :)\r\n\r\nYou can also ping me anytime if you have questions or if I can help !", "Sorry to ping you, @lhoestq, did you have a chance to take a look at the proposed PR? Thank you!", "Yes and it's all good, thank you :)\r\n\r\nFeel free to close this issue if it's good for you", "Everything's good, thanks!" ]
1,626,256,699,000
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CONTRIBUTOR
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## Describe the bug I'm trying to use `map` to filter a large dataset by selecting rows that match an expensive condition (files referenced by one of the columns need to exist in the filesystem, so we have to `stat` them). According to [the documentation](https://huggingface.co/docs/datasets/processing.html#augmenting-the-dataset), `a batch mapped function can take as input a batch of size N and return a batch of size M where M can be greater or less than N and can even be zero`. However, when the returned batch has a size of zero (neither item in the batch fulfilled the condition), we get an `index out of bounds` error. I think that `arrow_writer.py` is [trying to infer the returned types using the first element returned](https://github.com/huggingface/datasets/blob/master/src/datasets/arrow_writer.py#L100), but no elements were returned in this case. For this error to happen, I'm returning a dictionary that contains empty lists for the keys I want to keep, see below. If I return an empty dictionary instead (no keys), then a different error eventually occurs. ## Steps to reproduce the bug ```python def select_rows(examples): # `key` is a column name that exists in the original dataset # The following line simulates no matches found, so we return an empty batch result = {'key': []} return result filtered_dataset = dataset.map( select_rows, remove_columns = dataset.column_names, batched = True, num_proc = 1, desc = "Selecting rows with images that exist" ) ``` The code above immediately triggers the exception. If we use the following instead: ```python def select_rows(examples): # `key` is a column name that exists in the original dataset result = {'key': []} # or defaultdict or whatever # code to check for condition and append elements to result # some_items_found will be set to True if there were any matching elements in the batch return result if some_items_found else {} ``` Then it _seems_ to work, but it eventually fails with some sort of schema error. I believe it may happen when an empty batch is followed by a non-empty one, but haven't set up a test to verify it. In my opinion, returning a dictionary with empty lists and valid column names should be accepted as a valid result with zero items. ## Expected results The dataset would be filtered and only the matching fields would be returned. ## Actual results An exception is encountered, as described. Using a workaround makes it fail further along the line. ## Environment info <!-- You can run the command `datasets-cli env` and copy-and-paste its output below. --> - `datasets` version: 1.9.1.dev0 - Platform: Linux-5.4.0-53-generic-x86_64-with-glibc2.17 - Python version: 3.8.10 - PyArrow version: 4.0.1
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Enum used in map functions will raise a RecursionError with dill.
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[ "I'm running into this as well. (Thank you so much for reporting @jorgeecardona β€” was staring at this massive stack trace and unsure what exactly was wrong!)", "Hi ! Thanks for reporting :)\r\n\r\nUntil this is fixed on `dill`'s side, we could implement a custom saving in our Pickler indefined in utils.py_utils.py\r\nThere is already a suggestion in this message about how to do it:\r\nhttps://github.com/uqfoundation/dill/issues/250#issuecomment-852566284\r\n\r\nLet me know if such a workaround could help, and feel free to open a PR if you want to contribute !", "I have the same bug.\r\nthe code is as follows:\r\n![image](https://user-images.githubusercontent.com/84262181/139785849-620dd4ac-86ce-4212-8163-942bbca305aa.png)\r\nthe error is: \r\n![image](https://user-images.githubusercontent.com/84262181/139785899-88a9bd75-c60b-45a5-b819-830c7c096f3d.png)\r\n\r\nLook for the solution for this bug.", "Hi ! I think your RecursionError comes from a different issue @BitcoinNLPer , could you open a separate issue please ?\r\n\r\nAlso which dataset are you using ? I tried loading `CodedotAI/code_clippy` but I get a different error\r\n```python\r\nTraceback (most recent call last):\r\n File \"<stdin>\", line 1, in <module>\r\n File \"/Users/quentinlhoest/Desktop/hf/datasets/src/datasets/load.py\", line 1615, in load_dataset\r\n **config_kwargs,\r\n File \"/Users/quentinlhoest/Desktop/hf/datasets/src/datasets/load.py\", line 1446, in load_dataset_builder\r\n builder_cls = import_main_class(dataset_module.module_path)\r\n File \"/Users/quentinlhoest/Desktop/hf/datasets/src/datasets/load.py\", line 101, in import_main_class\r\n module = importlib.import_module(module_path)\r\n File \"/Users/quentinlhoest/.virtualenvs/hf-datasets/lib/python3.7/importlib/__init__.py\", line 127, in import_module\r\n return _bootstrap._gcd_import(name[level:], package, level)\r\n File \"<frozen importlib._bootstrap>\", line 1006, in _gcd_import\r\n File \"<frozen importlib._bootstrap>\", line 983, in _find_and_load\r\n File \"<frozen importlib._bootstrap>\", line 967, in _find_and_load_unlocked\r\n File \"<frozen importlib._bootstrap>\", line 677, in _load_unlocked\r\n File \"<frozen importlib._bootstrap_external>\", line 728, in exec_module\r\n File \"<frozen importlib._bootstrap>\", line 219, in _call_with_frames_removed\r\n File \"/Users/quentinlhoest/.cache/huggingface/modules/datasets_modules/datasets/CodedotAI___code_clippy/d332f69d036e8c80f47bc9a96d676c3fa30cb50af7bb81e2d4d12e80b83efc4d/code_clippy.py\", line 66, in <module>\r\n url_elements = results.find_all(\"a\")\r\nAttributeError: 'NoneType' object has no attribute 'find_all'\r\n```" ]
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## Describe the bug Enums used in functions pass to `map` will fail at pickling with a maximum recursion exception as described here: https://github.com/uqfoundation/dill/issues/250#issuecomment-852566284 In my particular case, I use an enum to define an argument with fixed options using the `TraininigArguments` dataclass as base class and the `HfArgumentParser`. In the same file I use a `ds.map` that tries to pickle the content of the module including the definition of the enum that runs into the dill bug described above. ## Steps to reproduce the bug ```python from datasets import load_dataset from enum import Enum class A(Enum): a = 'a' def main(): a = A.a def f(x): return {} if a == a.a else x ds = load_dataset('cnn_dailymail', '3.0.0')['test'] ds = ds.map(f, num_proc=15) if __name__ == "__main__": main() ``` ## Expected results The known problem with dill could be prevented as explained in the link above (workaround.) Since `HFArgumentParser` nicely uses the enum class for choices it makes sense to also deal with this bug under the hood. ## Actual results ```python File "/home/xxxx/miniconda3/lib/python3.8/site-packages/dill/_dill.py", line 1373, in save_type pickler.save_reduce(_create_type, (type(obj), obj.__name__, File "/home/xxxx/miniconda3/lib/python3.8/pickle.py", line 690, in save_reduce save(args) File "/home/xxxx/miniconda3/lib/python3.8/pickle.py", line 558, in save f(self, obj) # Call unbound method with explicit self File "/home/xxxx/miniconda3/lib/python3.8/pickle.py", line 899, in save_tuple save(element) File "/home/xxxx/miniconda3/lib/python3.8/pickle.py", line 534, in save self.framer.commit_frame() File "/home/xxxx/miniconda3/lib/python3.8/pickle.py", line 220, in commit_frame if f.tell() >= self._FRAME_SIZE_TARGET or force: RecursionError: maximum recursion depth exceeded while calling a Python object ``` ## Environment info - `datasets` version: 1.8.0 - Platform: Linux-5.9.0-4-amd64-x86_64-with-glibc2.10 - Python version: 3.8.5 - PyArrow version: 3.0.0
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Support multi-worker with streaming dataset (IterableDataset).
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[ "Hi ! This is a great idea :)\r\nI think we could have something similar to what we have in `datasets.Dataset.map`, i.e. a `num_proc` parameter that tells how many processes to spawn to parallelize the data processing. \r\n\r\nRegarding AUTOTUNE, this could be a nice feature as well, we could see how to add it in a second step" ]
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**Is your feature request related to a problem? Please describe.** The current `.map` does not support multi-process, CPU can become bottleneck if the pre-processing is complex (e.g. t5 span masking). **Describe the solution you'd like** Ideally `.map` should support multi-worker like tfds, with `AUTOTUNE`. **Describe alternatives you've considered** A simpler solution is to shard the dataset and process it in parallel with pytorch dataloader. The shard does not need to be of equal size. * https://pytorch.org/docs/stable/data.html#torch.utils.data.IterableDataset **Additional context**
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load_dataset("financial_phrasebank") NonMatchingChecksumError
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[ "Hi! It's probably because this dataset is stored on google drive and it has a per day quota limit. It should work if you retry, I was able to initiate the download.\r\n\r\nSimilar issue [here](https://github.com/huggingface/datasets/issues/2646)", "Hi ! Loading the dataset works on my side as well.\r\nFeel free to try again and let us know if it works for you know", "Thank you! I've been trying periodically for the past month, and no luck yet with this particular dataset. Just tried again and still hitting the checksum error.\r\n\r\nCode:\r\n\r\n`dataset = load_dataset(\"financial_phrasebank\", \"sentences_allagree\") `\r\n\r\nTraceback:\r\n\r\n```\r\n---------------------------------------------------------------------------\r\nNonMatchingChecksumError Traceback (most recent call last)\r\n<ipython-input-2-55cc2144f31e> in <module>\r\n----> 1 dataset = load_dataset(\"financial_phrasebank\", \"sentences_allagree\")\r\n\r\n/opt/conda/lib/python3.7/site-packages/datasets/load.py in load_dataset(path, name, data_dir, data_files, split, cache_dir, features, download_config, download_mode, ignore_verifications, keep_in_memory, save_infos, script_version, use_auth_token, task, streaming, **config_kwargs)\r\n 859 ignore_verifications=ignore_verifications,\r\n 860 try_from_hf_gcs=try_from_hf_gcs,\r\n--> 861 use_auth_token=use_auth_token,\r\n 862 )\r\n 863 \r\n\r\n/opt/conda/lib/python3.7/site-packages/datasets/builder.py in download_and_prepare(self, download_config, download_mode, ignore_verifications, try_from_hf_gcs, dl_manager, base_path, use_auth_token, **download_and_prepare_kwargs)\r\n 582 if not downloaded_from_gcs:\r\n 583 self._download_and_prepare(\r\n--> 584 dl_manager=dl_manager, verify_infos=verify_infos, **download_and_prepare_kwargs\r\n 585 )\r\n 586 # Sync info\r\n\r\n/opt/conda/lib/python3.7/site-packages/datasets/builder.py in _download_and_prepare(self, dl_manager, verify_infos, **prepare_split_kwargs)\r\n 642 if verify_infos:\r\n 643 verify_checksums(\r\n--> 644 self.info.download_checksums, dl_manager.get_recorded_sizes_checksums(), \"dataset source files\"\r\n 645 )\r\n 646 \r\n\r\n/opt/conda/lib/python3.7/site-packages/datasets/utils/info_utils.py in verify_checksums(expected_checksums, recorded_checksums, verification_name)\r\n 38 if len(bad_urls) > 0:\r\n 39 error_msg = \"Checksums didn't match\" + for_verification_name + \":\\n\"\r\n---> 40 raise NonMatchingChecksumError(error_msg + str(bad_urls))\r\n 41 logger.info(\"All the checksums matched successfully\" + for_verification_name)\r\n 42 \r\n\r\nNonMatchingChecksumError: Checksums didn't match for dataset source files:\r\n['https://www.researchgate.net/profile/Pekka_Malo/publication/251231364_FinancialPhraseBank-v10/data/0c96051eee4fb1d56e000000/FinancialPhraseBank-v10.zip']\r\n```" ]
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## Describe the bug Attempting to download the financial_phrasebank dataset results in a NonMatchingChecksumError ## Steps to reproduce the bug ```python from datasets import load_dataset dataset = load_dataset("financial_phrasebank", 'sentences_allagree') ``` ## Expected results I expect to see the financial_phrasebank dataset downloaded successfully ## Actual results NonMatchingChecksumError: Checksums didn't match for dataset source files: ['https://www.researchgate.net/profile/Pekka_Malo/publication/251231364_FinancialPhraseBank-v10/data/0c96051eee4fb1d56e000000/FinancialPhraseBank-v10.zip'] ## Environment info - `datasets` version: 1.9.0 - Platform: Linux-4.14.232-177.418.amzn2.x86_64-x86_64-with-debian-10.6 - Python version: 3.7.10 - PyArrow version: 4.0.1
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Fix docstrings
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Fix rendering of some docstrings.
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Minor reorganization of the code, so that additional patching functions (not related to streaming) might be created. In relation with the initial approach followed in #2631.
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Streaming for the Json loader
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[ "A note is that I think we should add a few indicator of status (as mentioned by @stas00 in #2649), probably at the (1) downloading, (2) extracting and (3) reading steps. In particular when loading many very large files it's interesting to know a bit where we are in the process.", "I tested locally, and the builtin `json` loader is 4x slower than `pyarrow.json`. Thanks for the comment @albertvillanova !\r\n\r\nTherefore I switched back to using `pyarrow.json`, but only on the batch that is read. This way we don't have to deal with its `block_size`, and it only loads in memory one batch at a time." ]
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It was not using `open` in the builder. Therefore `pyarrow.json.read_json` was downloading the full file to start yielding rows. Moreover, it appeared that `pyarrow.json.read_json` was not really suited for streaming as it was downloading too much data and failing if `block_size` was not properly configured (related to #2573). So I switched to using `open` which is extended to support reading from remote file progressively, and I removed the pyarrow json reader which was not practical. Instead, I'm using the classical `json.loads` from the standard library.
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Add the CIDEr metric?
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Hi, I find the api in https://huggingface.co/metrics quite useful. I am playing around with video/image captioning task, where CIDEr is a popular metric. Do you plan to add this into the HF ```datasets``` library? Thanks.
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It was not using open in the builder. Therefore pd.read_pickle could fail when streaming from a private repo for example. Indeed, when streaming, open is extended to support reading from remote files and handles authentication to the HF Hub
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It was not using `open` in the builder. Therefore `pd.read_csv` was downloading the full file to start yielding rows. Indeed, when streaming, `open` is extended to support reading from remote file progressively.
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Related to: #2565, #2633. cc: @lewtun
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This PR updates the ASR tags of the 5 datasets added in #2565 following the change of task categories in #2620
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add image-classification task template
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[ "Awesome!", "Thanks for adding a new task template - great work @nateraw πŸš€ !" ]
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Snippet below is the tl;dr, but you can try it out directly here: [![Open In Collab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/gist/nateraw/005c025d41f0e48ae3d4ee61c0f20b70/image-classification-task-template-demo.ipynb) ```python from datasets import load_dataset ds = load_dataset('nateraw/image-folder', data_files='PetImages/') # DatasetDict({ # train: Dataset({ # features: ['file', 'labels'], # num_rows: 23410 # }) # }) ds = ds.prepare_for_task('image-classification') # DatasetDict({ # train: Dataset({ # features: ['image_file_path', 'labels'], # num_rows: 23410 # }) # }) ```
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Delete extracted files when loading dataset
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[ "Sure @stas00, it is still a draft pull request. :)", "Yes, I noticed it after reviewing - my apologies.", "The problem with this approach is that it also deletes the downloaded files (if they need not be extracted). 😟 ", "> The problem with this approach is that it also deletes the downloaded files (if they need not be extracted). worried\r\n\r\nRight! These probably should not be deleted by default, but having an option for those users who are tight on disc space?", "> Right! These probably should not be deleted by default, but having an option for those users who are tight on disc space?\r\n\r\nI propose leaving that for another PR, and leave this one handling only with \"extracted\" files. Is it OK for you? :) ", "Awesome thanks !\r\nI just have one question: what about image/audio datasets for which we store the path to the extracted file on the arrow data ?\r\nIn this case the default should be to keep the extracted files.\r\n\r\nSo for now I would just make `keep_extracted=True` by default until we have a way to separate extracted files that can be deleted and extracted files that are actual resources of the dataset.", "@lhoestq, current implementation only deletes extracted \"files\", not extracted \"directories\", as it uses: `os.remove(path)`. I'm going to add a filter on files, so that this line does not throw an exception when passed a directory.\r\n\r\nFor audio datasets, the audio files are inside the extracted \"directory\", so they are not deleted.", "I'm still more in favor of having `keep_extracted=True` by default:\r\n- When working with a dataset, you call `load_dataset` many times. By default we want to keep objects extracted to not extract them over and over again (it can take a long time). Then once you know what you're doing and you want to optimize disk space, you can do `keep_extracted=False`. Deleting the extracted files by default is a regression that can lead to slow downs for people calling `load_dataset` many times, which is common when experimenting\r\n- This behavior doesn't sound natural as a default behavior. In the rest of the library, things are cached and not removed unless you explicitly say do (`map` caching for example). Moreover the function in the download manager is called `download_and_extract`, not `download_and_extract_and_remove_extracted_files`\r\n\r\nLet me know what you think !", "I think the main issue is that after doing some work users typically move on to other datasets and the amount of disc space used keeps on growing. So your logic is very sound and perhaps what's really needed is a cleansweep function that can go through **all** datasets and clean them up to the desired degree:\r\n\r\n- delete all extracted files\r\n- delete all sources\r\n- delete all caches\r\n- delete all caches that haven't been accessed in 6 months\r\n- delete completely old datasets that haven't been accessed in 6 months\r\n- more?\r\n\r\nSo a user can launch a little application, choose what they want to clean up and voila they have just freed up a huge amount of disc space. Makes me think of Ubuntu Tweak's Janitor app - very useful.\r\n\r\nAt the moment, this process of linting is very daunting and error-prone, especially due to all those dirs/files with hash names.", "@stas00 I've had the same idea. Instead of the full-fledged app, a simpler approach would be to add a new command to the CLI.", "oh, CLI would be perfect. I didn't mean to request a GUI-one specifically, was just using it as an example.\r\n\r\nOne could even do a crontab to delete old datasets that haven't been accesses in X months.", "@lhoestq I totally agree with you. I'm addressing that change.\r\n\r\n@stas00, @mariosasko, that could eventually be addressed in another pull request. The objective of this PR is:\r\n- add an option to pass to `load_dataset`, so that extracted files are deleted\r\n- do this deletion file per file, once the file has been already used to generate the cache Arrow file", "I also like the idea of having a CLI tool to help users clean their cache and save disk space, good idea !" ]
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Close #2481, close #2604, close #2591. cc: @stas00, @thomwolf, @BirgerMoell
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Progress bars are not properly rendered in Jupyter notebook
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[ "To add my experience when trying to debug this issue:\r\n\r\nSeems like previously the workaround given [here](https://github.com/tqdm/tqdm/issues/485#issuecomment-473338308) worked around this issue. But with the latest version of jupyter/tqdm I still get terminal warnings that IPython tried to send a message from a forked process.", "Hi @mludv, thanks for the hint!!! :) \r\n\r\nWe will definitely take it into account to try to fix this issue... It seems somehow related to `multiprocessing` and `tqdm`..." ]
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## Describe the bug The progress bars are not Jupyter widgets; regular progress bars appear (like in a terminal). ## Steps to reproduce the bug ```python ds.map(tokenize, num_proc=10) ``` ## Expected results Jupyter widgets displaying the progress bars. ## Actual results Simple plane progress bars. cc: Reported by @thomwolf
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Load datasets from the Hub without requiring a dataset script
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[ "This is so cool, let us know if we can help with anything on the hub side (@Pierrci @elishowk) πŸŽ‰ " ]
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As a user I would like to be able to upload my csv/json/text/parquet/etc. files in a dataset repository on the Hugging Face Hub and be able to load this dataset with `load_dataset` without having to implement a dataset script. Moreover I would like to be able to specify which file goes into which split using the `data_files` argument. This feature should be compatible with private repositories and dataset streaming. This can be implemented by checking the extension of the files in the dataset repository and then by using the right dataset builder that is already packaged in the library (csv/json/text/parquet/etc.)
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Use ETag of remote data files
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Use ETag of remote data files to create config ID. Related to #2616.
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Minor fix tests with Windows paths
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Minor fix tests with Windows paths.
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Use correct logger in metrics.py
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Fixes #2624
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βš›οΈπŸ˜‡βš™οΈπŸ”‘
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can't set verbosity for `metric.py`
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[ "Thanks @thomas-happify for reporting and thanks @mariosasko for the fix." ]
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## Describe the bug ``` [2021-07-10 20:13:11,528][datasets.utils.filelock][INFO] - Lock 139705371374976 acquired on /root/.cache/huggingface/metrics/seqeval/default/default_experiment-1-0.arrow.lock [2021-07-10 20:13:11,529][datasets.arrow_writer][INFO] - Done writing 32 examples in 6100 bytes /root/.cache/huggingface/metrics/seqeval/default/default_experiment-1-0.arrow. [2021-07-10 20:13:11,531][datasets.arrow_dataset][INFO] - Set __getitem__(key) output type to python objects for no columns (when key is int or slice) and don't output other (un-formatted) columns. [2021-07-10 20:13:11,543][/conda/envs/myenv/lib/python3.8/site-packages/datasets/metric.py][INFO] - Removing /root/.cache/huggingface/metrics/seqeval/default/default_experiment-1-0.arrow ``` As you can see, `datasets` logging come from different places. `filelock`, `arrow_writer` & `arrow_dataset` comes from `datasets.*` which are expected However, `metric.py` logging comes from `/conda/envs/myenv/lib/python3.8/site-packages/datasets/` So when setting `datasets.utils.logging.set_verbosity_error()`, it still logs the last message which is annoying during evaluation. I had to do ``` logging.getLogger("/conda/envs/myenv/lib/python3.8/site-packages/datasets/metric").setLevel(logging.ERROR) ``` to fully mute these messages ## Expected results it shouldn't log these messages when setting `datasets.utils.logging.set_verbosity_error()` ## Environment info <!-- You can run the command `datasets-cli env` and copy-and-paste its output below. --> - `datasets` version: tried both 1.8.0 & 1.9.0 - Platform: Ubuntu 18.04.5 LTS - Python version: 3.8.10 - PyArrow version: 3.0.0
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[Metrics] added wiki_split metrics
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[ "Looks all good to me thanks :)\r\nJust did some minor corrections in the docstring" ]
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Fixes: #2606 This pull request adds combine metrics for the wikisplit or English sentence split task Reviewer: @patrickvonplaten
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Integration with AugLy
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[ "Hi,\r\n\r\nyou can define your own custom formatting with `Dataset.set_transform()` and then run the tokenizer with the batches of augmented data as follows:\r\n```python\r\ndset = load_dataset(\"imdb\", split=\"train\") # Let's say we are working with the IMDB dataset\r\ndset.set_transform(lambda ex: {\"text\": augly_text_augmentation(ex[\"text\"])}, columns=\"text\", output_all_columns=True)\r\ndataloader = torch.utils.data.DataLoader(dset, batch_size=32)\r\nfor epoch in range(5):\r\n for batch in dataloader:\r\n tokenizer_output = tokenizer(batch.pop(\"text\"), padding=True, truncation=True, return_tensors=\"pt\")\r\n batch.update(tokenizer_output)\r\n output = model(**batch)\r\n ...\r\n```" ]
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**Is your feature request related to a problem? Please describe.** Facebook recently launched a library, [AugLy](https://github.com/facebookresearch/AugLy) , that has a unified API for augmentations for image, video and text. It would be pretty exciting to have it hooked up to HF libraries so that we can make NLP models robust to misspellings or to punctuation, or emojis etc. Plus, with Transformers supporting more CV use cases, having augmentations support becomes crucial. **Describe the solution you'd like** The biggest difference between augmentations and preprocessing is that preprocessing happens only once, but you are running augmentations once per epoch. AugLy operates on text directly, so this breaks the typical workflow where we would run the tokenizer once, set format to pt tensors and be ready for the Dataloader. **Describe alternatives you've considered** One possible way of implementing these is to make a custom Dataset class where getitem(i) runs the augmentation and the tokenizer every time, though this would slow training down considerably given we wouldn't even run the tokenizer in batches.
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Use prefix to allow exceed Windows MAX_PATH
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[ "Does this mean the `FileNotFoundError` that avoids infinite loop can be removed?", "Yes, I think so...", "Or maybe we could leave it in case a relative path exceeds the MAX_PATH limit?", " > Or maybe we could leave it in case a relative path exceeds the MAX_PATH limit?\r\n\r\nWhat about converting relative paths to absolute?", "Nice ! Have you had a chance to test it on a windows machine with the max path limit enabled ? Afaik the CI doesn't have the path limit", "Sure @lhoestq: I've tested on my machine... And this fixes most of the tests... πŸ˜… " ]
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By using this prefix, you can exceed the Windows MAX_PATH limit. See: https://docs.microsoft.com/en-us/windows/win32/fileio/naming-a-file?redirectedfrom=MSDN#win32-file-namespaces Related to #2524, #2220.
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Add speech processing tasks
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[ "Are there any `task_categories:automatic-speech-recognition` dataset for which we should update the tags ?", "> Are there any `task_categories:automatic-speech-recognition` dataset for which we should update the tags ?\r\n\r\nYes there's a few - I'll fix them tomorrow :)" ]
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This PR replaces the `automatic-speech-recognition` task category with a broader `speech-processing` category. The tasks associated with this category are derived from the [SUPERB benchmark](https://arxiv.org/abs/2105.01051), and ASR is included in this set.
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Add ASR task for SUPERB
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[ "Wait until #2620 is merged before pushing the README tags in this PR", "> Thanks!\r\n> \r\n> One question: aren't you adding `task_templates` to the `_info` method (and to the `dataset_infos.json`?\r\n\r\ngreat catch! i've now added the asr task template (along with a mapping from superb task -> template) and updated the `dataset_infos.json` :) ", "> Good!\r\n> \r\n> I have a suggested refactoring... Tell me what you think! :)\r\n\r\nyour approach is much more elegant - i've included your suggestions πŸ™ " ]
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This PR starts building up the SUPERB benchmark by including the ASR task as described in the [SUPERB paper](https://arxiv.org/abs/2105.01051) and `s3prl` [instructions](https://github.com/s3prl/s3prl/tree/v0.2.0/downstream#asr-automatic-speech-recognition). Usage: ```python from datasets import load_dataset asr = load_dataset("superb", "asr") # DatasetDict({ # train: Dataset({ # features: ['file', 'text', 'speaker_id', 'chapter_id', 'id'], # num_rows: 28539 # }) # validation: Dataset({ # features: ['file', 'text', 'speaker_id', 'chapter_id', 'id'], # num_rows: 2703 # }) # test: Dataset({ # features: ['file', 'text', 'speaker_id', 'chapter_id', 'id'], # num_rows: 2620 # }) # }) ``` I've used the GLUE benchmark as a guide for filling out the README. To move fast during the evaluation PoC I propose to merge one task at a time, so we can continue building the training / evaluation framework in parallel. Note: codewise this PR is ready for review - I'll add the missing YAML tags once #2620 is merged :)
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[ "Hi @liyucheng09, thanks for reporting.\r\n\r\nApparently this issue has to do with your environment setup. One question: is your data in an NFS share? Some people have reported this error when using `fcntl` to write to an NFS share... If this is the case, then it might be that your NFS just may not be set up to provide file locks. You should ask your system administrator, or try these commands in the terminal:\r\n```shell\r\nsudo systemctl enable rpc-statd\r\nsudo systemctl start rpc-statd\r\n```" ]
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## Describe the bug It seems that the `filelock.py` went error. ``` >>> ds=load_dataset('xsum') ^CTraceback (most recent call last): File "/user/HS502/yl02706/.conda/envs/lyc/lib/python3.6/site-packages/datasets/utils/filelock.py", line 402, in _acquire fcntl.flock(fd, fcntl.LOCK_EX | fcntl.LOCK_NB) OSError: [Errno 37] No locks available ``` According to error log, it is OSError, but there is an `except` in the `_acquire` function. ``` def _acquire(self): open_mode = os.O_WRONLY | os.O_CREAT | os.O_EXCL | os.O_TRUNC try: fd = os.open(self._lock_file, open_mode) except (IOError, OSError): pass else: self._lock_file_fd = fd return None ``` I don't know why it stucked rather than `pass` directly. I am not quite familiar with filelock operation, so any help is highly appriciated. ## Steps to reproduce the bug ```python ds = load_dataset('xsum') ``` ## Expected results A clear and concise description of the expected results. ## Actual results ``` >>> ds=load_dataset('xsum') ^CTraceback (most recent call last): File "/user/HS502/yl02706/.conda/envs/lyc/lib/python3.6/site-packages/datasets/utils/filelock.py", line 402, in _acquire fcntl.flock(fd, fcntl.LOCK_EX | fcntl.LOCK_NB) OSError: [Errno 37] No locks available During handling of the above exception, another exception occurred: Traceback (most recent call last): File "<stdin>", line 1, in <module> File "/user/HS502/yl02706/.conda/envs/lyc/lib/python3.6/site-packages/datasets/load.py", line 818, in load_dataset use_auth_token=use_auth_token, File "/user/HS502/yl02706/.conda/envs/lyc/lib/python3.6/site-packages/datasets/load.py", line 470, in prepare_module with FileLock(lock_path): File "/user/HS502/yl02706/.conda/envs/lyc/lib/python3.6/site-packages/datasets/utils/filelock.py", line 323, in __enter__ self.acquire() File "/user/HS502/yl02706/.conda/envs/lyc/lib/python3.6/site-packages/datasets/utils/filelock.py", line 272, in acquire self._acquire() File "/user/HS502/yl02706/.conda/envs/lyc/lib/python3.6/site-packages/datasets/utils/filelock.py", line 402, in _acquire fcntl.flock(fd, fcntl.LOCK_EX | fcntl.LOCK_NB) KeyboardInterrupt ``` ## Environment info <!-- You can run the command `datasets-cli env` and copy-and-paste its output below. --> - `datasets` version: 1.9.0 - Platform: Linux-4.15.0-135-generic-x86_64-with-debian-buster-sid - Python version: 3.6.13 - PyArrow version: 4.0.1
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Some versions of pandas don't add an EOL at the end of the output of `to_json`. Therefore users could end up having two samples in the same line Close https://github.com/huggingface/datasets/issues/2615
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[ "@lhoestq maybe we could also use (if available) the ETag of the remote file in `create_config_id`?", "> @lhoestq maybe we could also use (if available) the ETag of the remote file in `create_config_id`?\r\n\r\nSure ! We can get the ETag with\r\n```python\r\nheaders = get_authentication_headers_for_url(url, use_auth_token=use_auth_token) # auth for private repos\r\netag = http_head(url, headers=headers).headers.get(\"ETag\")\r\n```\r\n\r\nSince the computation of the `config_id` is done in the `DatasetBuilder.__init__`, then this means that we need to add a new parameter `use_auth_token` in `DatasetBuilder.__init__`\r\n\r\nDoes that sound good ? We can add this in a following PR" ]
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Add support for (streaming) remote data files: ```python data_files = f"https://huggingface.co/datasets/{repo_id}/resolve/main/{relative_file_path}" ds = load_dataset("json", split="train", data_files=data_files, streaming=True) ``` cc: @thomwolf
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Jsonlines export error
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[ "Thanks for reporting @TevenLeScao! I'm having a look...", "(not sure what just happened on the assignations sorry)", "For some reason this happens (both `datasets` version are on master) only on Python 3.6 and not Python 3.8.", "@TevenLeScao we are using `pandas` to serialize the dataset to JSON Lines. So it must be due to pandas. Could you please check the pandas version causing the issue?", "@TevenLeScao I have just checked it: this was a bug in `pandas` and it was fixed in version 1.2: https://github.com/pandas-dev/pandas/pull/36898", "Thanks ! I'm creating a PR", "Well I though it was me who has taken on this issue... πŸ˜… ", "Sorry, I was also talking to teven offline so I already had the PR ready before noticing x)", "I was also already working in my PR... Nevermind. Next time we should pay attention if there is somebody (self-)assigned to an issue and if he/she is still working on it before overtaking it... πŸ˜„ ", "The fix is available on `master` @TevenLeScao , thanks for reporting" ]
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## Describe the bug When exporting large datasets in jsonlines (c4 in my case) the created file has an error every 9999 lines: the 9999th and 10000th are concatenated, thus breaking the jsonlines format. This sounds like it is related to batching, which is by 10000 by default ## Steps to reproduce the bug This what I'm running: in python: ``` from datasets import load_dataset ptb = load_dataset("ptb_text_only") ptb["train"].to_json("ptb.jsonl") ``` then out of python: ``` head -10000 ptb.jsonl ``` ## Expected results Properly separated lines ## Actual results The last line is a concatenation of two lines ## Environment info <!-- You can run the command `datasets-cli env` and copy-and-paste its output below. --> - `datasets` version: 1.9.1.dev0 - Platform: Linux-5.4.0-1046-gcp-x86_64-with-Ubuntu-18.04-bionic - Python version: 3.6.9 - PyArrow version: 4.0.1
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Convert numpy scalar to python float in Pearsonr output
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Following of https://github.com/huggingface/datasets/pull/2612
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Use ndarray.item instead of ndarray.tolist
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This PR follows up on #2612 to use `numpy.ndarray.item` instead of `numpy.ndarray.tolist` as the latter is somewhat confusing to the developer (even though it works). Judging from the `numpy` docs, `ndarray.item` is closer to what we want: https://numpy.org/doc/stable/reference/generated/numpy.ndarray.item.html#numpy-ndarray-item PS. Sorry for the duplicate work here. I should have read the numpy docs more carefully in #2612
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Return Python float instead of numpy.float64 in sklearn metrics
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[ "I opened an issue on the `sklearn` repo to understand why `numpy.float64` is the default: https://github.com/scikit-learn/scikit-learn/discussions/20490", "It could be surprising at first to use `tolist()` on numpy scalars but it works ^^", "did the same for Pearsonr here: https://github.com/huggingface/datasets/pull/2614" ]
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This PR converts the return type of all `sklearn` metrics to be Python `float` instead of `numpy.float64`. The reason behind this is that our Hub evaluation framework relies on converting benchmark-specific metrics to YAML ([example](https://huggingface.co/datasets/autonlp/autonlp-benchmark-raft-neelalex__raft-test-neelalex__raft-predictions-3/blob/main/README.md#L11)) and the `numpy.float64` format produces garbage like: ```python import yaml from datasets import load_metric metric = load_metric("accuracy") score = metric.compute(predictions=[0,1], references=[0,1]) print(yaml.dump(score["accuracy"])) # output below # !!python/object/apply:numpy.core.multiarray.scalar # - !!python/object/apply:numpy.dtype # args: # - f8 # - false # - true # state: !!python/tuple # - 3 # - < # - null # - null # - null # - -1 # - -1 # - 0 # - !!binary | # AAAAAAAA8D8= ```
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More consistent naming
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As per @stas00's suggestion in #2500, this PR inserts a space between the logo and the lib name (`πŸ€—Datasets` -> `πŸ€— Datasets`) for consistency with the Transformers lib. Additionally, more consistent names are used for Datasets Hub, etc.
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Add missing WikiANN language tags
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Add missing language tags for WikiANN datasets.
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Fix potential DiplicatedKeysError by ensuring keys are unique. We should promote as a good practice, that the keys should be programmatically generated as unique, instead of read from data (which might be not unique).
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Support streaming JSON files
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Use open in JSON dataset builder, so that it can be patched with xopen for streaming. Close #2607.
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Streaming local gzip compressed JSON line files is not working
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[ "Updating to pyarrow-4.0.1 didn't fix the issue", "Here is an exemple dataset with 2 of these compressed JSON files: https://huggingface.co/datasets/thomwolf/github-python", "Hi @thomwolf, thanks for reporting.\r\n\r\nIt seems this might be due to the fact that the JSON Dataset builder uses `pyarrow.json` (`paj.read_json`) to read the data without using the Python standard `open(file,...` (which is the one patched with `xopen` to work in streaming mode).\r\n\r\nThis has to be fixed.", "Sorry for reopening this, but I'm having the same issue as @thomwolf when streaming a gzipped JSON Lines file from the hub. Or is that just not possible by definition?\r\nI installed `datasets`in editable mode from source (so probably includes the fix from #2608 ?): \r\n```\r\n>>> datasets.__version__\r\n'1.9.1.dev0'\r\n```\r\n\r\n```\r\n>>> msmarco = datasets.load_dataset(\"webis/msmarco\", \"corpus\", streaming=True)\r\nUsing custom data configuration corpus-174d3b7155eb68db\r\n>>> msmarco_iter = iter(msmarco['train'])\r\n>>> print(next(msmarco_iter))\r\nTraceback (most recent call last):\r\n File \"<stdin>\", line 1, in <module>\r\n File \"/media/ssd/TREC/msmarco/datasets/src/datasets/iterable_dataset.py\", line 338, in __iter__\r\n for key, example in self._iter():\r\n File \"/media/ssd/TREC/msmarco/datasets/src/datasets/iterable_dataset.py\", line 335, in _iter\r\n yield from ex_iterable\r\n File \"/media/ssd/TREC/msmarco/datasets/src/datasets/iterable_dataset.py\", line 78, in __iter__\r\n for key, example in self.generate_examples_fn(**self.kwargs):\r\n File \"/home/christopher/.cache/huggingface/modules/datasets_modules/datasets/msmarco/eb63dff8d83107168e973c7a655a6082d37e08d71b4ac39a0afada479c138745/msmarco.py\", line 96, in _generate_examples\r\n with gzip.open(file, \"rt\", encoding=\"utf-8\") as f:\r\n File \"/usr/lib/python3.6/gzip.py\", line 53, in open\r\n binary_file = GzipFile(filename, gz_mode, compresslevel)\r\n File \"/usr/lib/python3.6/gzip.py\", line 163, in __init__\r\n fileobj = self.myfileobj = builtins.open(filename, mode or 'rb')\r\nFileNotFoundError: [Errno 2] No such file or directory: 'https://huggingface.co/datasets/webis/msmarco/resolve/main/msmarco_doc_00.gz'\r\n```\r\n\r\nLoading the dataset without streaming set to True, works fine.", "Hi ! To make the streaming work, we extend `open` in the dataset builder to work with urls.\r\n\r\nTherefore you just need to use `open` before using `gzip.open`:\r\n```diff\r\n- with gzip.open(file, \"rt\", encoding=\"utf-8\") as f:\r\n+ with gzip.open(open(file, \"rb\"), \"rt\", encoding=\"utf-8\") as f:\r\n```\r\n\r\nYou can see that it is the case for oscar.py and c4.py for example:\r\n\r\nhttps://github.com/huggingface/datasets/blob/8814b393984c1c2e1800ba370de2a9f7c8644908/datasets/oscar/oscar.py#L358-L358\r\n\r\nhttps://github.com/huggingface/datasets/blob/8814b393984c1c2e1800ba370de2a9f7c8644908/datasets/c4/c4.py#L88-L88\r\n\r\n", "@lhoestq Sorry I missed that. Thank you Quentin!" ]
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## Describe the bug Using streaming to iterate on local gzip compressed JSON files raise a file not exist error ## Steps to reproduce the bug ```python from datasets import load_dataset streamed_dataset = load_dataset('json', split='train', data_files=data_files, streaming=True) next(iter(streamed_dataset)) ``` ## Actual results ``` FileNotFoundError Traceback (most recent call last) <ipython-input-6-27a664e29784> in <module> ----> 1 next(iter(streamed_dataset)) ~/Documents/GitHub/datasets/src/datasets/iterable_dataset.py in __iter__(self) 336 337 def __iter__(self): --> 338 for key, example in self._iter(): 339 if self.features: 340 # we encode the example for ClassLabel feature types for example ~/Documents/GitHub/datasets/src/datasets/iterable_dataset.py in _iter(self) 333 else: 334 ex_iterable = self._ex_iterable --> 335 yield from ex_iterable 336 337 def __iter__(self): ~/Documents/GitHub/datasets/src/datasets/iterable_dataset.py in __iter__(self) 76 77 def __iter__(self): ---> 78 for key, example in self.generate_examples_fn(**self.kwargs): 79 yield key, example 80 ~/Documents/GitHub/datasets/src/datasets/iterable_dataset.py in wrapper(**kwargs) 282 def wrapper(**kwargs): 283 python_formatter = PythonFormatter() --> 284 for key, table in generate_tables_fn(**kwargs): 285 batch = python_formatter.format_batch(table) 286 for i, example in enumerate(_batch_to_examples(batch)): ~/Documents/GitHub/datasets/src/datasets/packaged_modules/json/json.py in _generate_tables(self, files, original_files) 85 file, 86 read_options=self.config.pa_read_options, ---> 87 parse_options=self.config.pa_parse_options, 88 ) 89 except pa.ArrowInvalid as err: ~/miniconda2/envs/datasets/lib/python3.7/site-packages/pyarrow/_json.pyx in pyarrow._json.read_json() ~/miniconda2/envs/datasets/lib/python3.7/site-packages/pyarrow/_json.pyx in pyarrow._json._get_reader() ~/miniconda2/envs/datasets/lib/python3.7/site-packages/pyarrow/io.pxi in pyarrow.lib.get_input_stream() ~/miniconda2/envs/datasets/lib/python3.7/site-packages/pyarrow/io.pxi in pyarrow.lib.get_native_file() ~/miniconda2/envs/datasets/lib/python3.7/site-packages/pyarrow/io.pxi in pyarrow.lib.OSFile.__cinit__() ~/miniconda2/envs/datasets/lib/python3.7/site-packages/pyarrow/io.pxi in pyarrow.lib.OSFile._open_readable() ~/miniconda2/envs/datasets/lib/python3.7/site-packages/pyarrow/error.pxi in pyarrow.lib.pyarrow_internal_check_status() ~/miniconda2/envs/datasets/lib/python3.7/site-packages/pyarrow/error.pxi in pyarrow.lib.check_status() FileNotFoundError: [Errno 2] Failed to open local file 'gzip://file-000000000000.json::/Users/thomwolf/github-dataset/file-000000000000.json.gz'. Detail: [errno 2] No such file or directory ``` ## Environment info - `datasets` version: 1.9.1.dev0 - Platform: Darwin-19.6.0-x86_64-i386-64bit - Python version: 3.7.7 - PyArrow version: 1.0.0
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[Metrics] addition of wiki_split metrics
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**Is your feature request related to a problem? Please describe.** While training the model on sentence split the task in English we require to evaluate the trained model on `Exact Match`, `SARI` and `BLEU` score like this ![image](https://user-images.githubusercontent.com/26653468/124746876-ff5a3380-df3e-11eb-9a01-4b48db7a6694.png) While training we require metrics which can give all the output Currently, we don't have an exact match for text normalized data **Describe the solution you'd like** A custom metrics for wiki_split that can calculate these three values and provide it in the form of a single dictionary For exact match, we can refer to [this](https://github.com/huggingface/transformers/blob/master/src/transformers/data/metrics/squad_metrics.py) **Describe alternatives you've considered** Two metrics are already present one more can be added for an exact match then we can run all three metrics in training script #self-assign
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Make any ClientError trigger retry in streaming mode (e.g. ClientOSError)
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During the FLAX sprint some users have this error when streaming datasets: ```python aiohttp.client_exceptions.ClientOSError: [Errno 104] Connection reset by peer ``` This error must trigger a retry instead of directly crashing Therefore I extended the error type that triggers the retry to be the base aiohttp error type: `ClientError` In particular both `ClientOSError` and `ServerDisconnectedError` inherit from `ClientError`.
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Add option to delete temporary files (e.g. extracted files) when loading dataset
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[ "Hi !\r\nIf we want something more general, we could either\r\n1. delete the extracted files after the arrow data generation automatically, or \r\n2. delete each extracted file during the arrow generation right after it has been closed.\r\n\r\nSolution 2 is better to save disk space during the arrow generation. Is it what you had in mind ?\r\n\r\nThe API could look like\r\n```python\r\nload_dataset(..., delete_extracted_files_after_usage=True)\r\n```\r\n\r\nIn terms of implementation, here are some directions we could take for each solution:\r\n1. get the list of the extracted files from the DownloadManager and then delete them after the dataset is processed. This can be implemented in `download_and_prepare` I guess\r\n2. maybe wrap and mock `open` in the builder to make it delete the file when the file is closed.", "Also, if I delete the extracted files they need to be re-extracted again instead of loading from the Arrow cache files", "I think we already opened an issue about this topic (suggested by @stas00): duplicated of #2481?\r\n\r\nThis is in our TODO list... πŸ˜… ", "I think the deletion of each extracted file could be implemented in our CacheManager and ExtractManager (once merged to master: #2295, #2277). πŸ˜‰ ", "Oh yes sorry, I didn't check if this was a duplicate", "Nevermind @thomwolf, I just mentioned the other issue so that both appear linked in GitHub and we do not forget to close both once we make the corresponding Pull Request... That was the main reason! πŸ˜„ ", "Ok yes. I think this is an important feature to be able to use large datasets which are pretty much always compressed files.\r\n\r\nIn particular now this requires to keep the extracted file on the drive if you want to avoid reprocessing the dataset so in my case, this require using always ~400GB of drive instead of just 200GB (which is already significant). \r\n\r\nTwo nice features would be to:\r\n- allow to delete the extracted files without loosing the ability to load the dataset from the cached arrow-file\r\n- streamlined decompression when only the currently read file is extracted - this might require to read the list of files from the extracted archives before processing them?", "Here is a sample dataset with 2 such large compressed JSON files for debugging: https://huggingface.co/datasets/thomwolf/github-python", "Note that I'm confirming that with the current master branch of dataset, deleting extracted files (without deleting the arrow cache file) lead to **re-extracting** these files when reloading the dataset instead of directly loading the arrow cache file.", "Hi ! That's weird, it doesn't do that on my side (tested on master on my laptop by deleting the `extracted` folder in the download cache directory). You tested with one of the files at https://huggingface.co/datasets/thomwolf/github-python that you have locally ?", "Yes it’s when I load local compressed JSON line files with load_dataset(β€˜json’, data_files=…) ", "@thomwolf I'm sorry but I can't reproduce this problem. I'm also using: \r\n```python\r\nds = load_dataset(\"json\", split=\"train\", data_files=data_files, cache_dir=cache_dir)\r\n```\r\nafter having removed the extracted files:\r\n```python\r\nassert sorted((cache_dir / \"downloads\" / \"extracted\").iterdir()) == []\r\n```\r\n\r\nI get the logging message:\r\n```shell\r\nWARNING datasets.builder:builder.py:531 Reusing dataset json ...\r\n```", "Do you confirm the extracted folder stays empty after reloading?", "> \r\n> \r\n> Do you confirm the extracted folder stays empty after reloading?\r\n\r\nYes, I have the above mentioned assertion on the emptiness of the extracted folder:\r\n```python\r\nassert sorted((cache_dir / \"downloads\" / \"extracted\").iterdir()) == []\r\n```\r\n" ]
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I'm loading a dataset constituted of 44 GB of compressed JSON files. When loading the dataset with the JSON script, extracting the files create about 200 GB of uncompressed files before creating the 180GB of arrow cache tables Having a simple way to delete the extracted files after usage (or even better, to stream extraction/delete) would be nice to avoid disk cluter. I can maybe tackle this one in the JSON script unless you want a more general solution.
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Fix DuplicatedKeysError in omp
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Close #2598.
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When pickling a tokenizer within multiprocessing, check that is instance of transformers PreTrainedTokenizerBase without importing transformers. Related to huggingface/transformers#12549 and #502.
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Fixes #2600 Also I moved the check for `num_proc` larger than dataset size added in #2566 up so that multiprocessing is not used with one process.
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Crash when using multiprocessing (`num_proc` > 1) on `filter` and all samples are discarded
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## Describe the bug If `filter` is applied to a dataset using multiprocessing (`num_proc` > 1) and all sharded datasets are empty afterwards (due to all samples being discarded), the program crashes. ## Steps to reproduce the bug ```python from datasets import Dataset data = Dataset.from_dict({'id': [0,1]}) data.filter(lambda x: False, num_proc=2) ``` ## Expected results An empty table should be returned without crashing. ## Actual results ``` Traceback (most recent call last): File "<stdin>", line 1, in <module> File "/home/user/venv/lib/python3.8/site-packages/datasets/arrow_dataset.py", line 185, in wrapper out: Union["Dataset", "DatasetDict"] = func(self, *args, **kwargs) File "/home/user/venv/lib/python3.8/site-packages/datasets/fingerprint.py", line 397, in wrapper out = func(self, *args, **kwargs) File "/home/user/venv/lib/python3.8/site-packages/datasets/arrow_dataset.py", line 2143, in filter return self.map( File "/home/user/venv/lib/python3.8/site-packages/datasets/arrow_dataset.py", line 1738, in map result = concatenate_datasets(transformed_shards) File "/home/user/venv/lib/python3.8/site-packages/datasets/arrow_dataset.py", line 3267, in concatenate_datasets table = concat_tables(tables_to_concat, axis=axis) File "/home/user/venv/lib/python3.8/site-packages/datasets/table.py", line 853, in concat_tables return ConcatenationTable.from_tables(tables, axis=axis) File "/home/user/venv/lib/python3.8/site-packages/datasets/table.py", line 713, in from_tables blocks = to_blocks(tables[0]) IndexError: list index out of range ``` ## Environment info <!-- You can run the command `datasets-cli env` and copy-and-paste its output below. --> - `datasets` version: 1.9.0 - Platform: Linux-5.12.11-300.fc34.x86_64-x86_64-with-glibc2.2.5 - Python version: 3.8.10 - PyArrow version: 3.0.0
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Update processing.rst with other export formats
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Add other supported export formats than CSV in the docs.
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Unable to download omp dataset
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[ "Hi @erikadistefano , thanks for reporting the issue.\r\n\r\nI have created a Pull Request that should fix it. \r\n\r\nOnce merged into master, feel free to update your installed `datasets` library (either by installing it from our GitHub master branch or waiting until our next release) to be able to load omp dataset." ]
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## Describe the bug The omp dataset cannot be downloaded because of a DuplicatedKeysError ## Steps to reproduce the bug from datasets import load_dataset omp = load_dataset('omp', 'posts_labeled') print(omp) ## Expected results This code should download the omp dataset and print the dictionary ## Actual results Downloading and preparing dataset omp/posts_labeled (download: 1.27 MiB, generated: 13.31 MiB, post-processed: Unknown size, total: 14.58 MiB) to /home/erika_distefano/.cache/huggingface/datasets/omp/posts_labeled/1.1.0/2fe5b067be3bff1d4588d5b0cbb9b5b22ae1b9d5b026a8ff572cd389f862735b... 0 examples [00:00, ? examples/s]2021-07-06 09:43:55.868815: I tensorflow/stream_executor/platform/default/dso_loader.cc:48] Successfully opened dynamic library libcudart.so.11.0 Traceback (most recent call last): File "/home/erika_distefano/.local/lib/python3.6/site-packages/datasets/builder.py", line 990, in _prepare_split writer.write(example, key) File "/home/erika_distefano/.local/lib/python3.6/site-packages/datasets/arrow_writer.py", line 338, in write self.check_duplicate_keys() File "/home/erika_distefano/.local/lib/python3.6/site-packages/datasets/arrow_writer.py", line 349, in check_duplicate_keys raise DuplicatedKeysError(key) datasets.keyhash.DuplicatedKeysError: FAILURE TO GENERATE DATASET ! Found duplicate Key: 3326 Keys should be unique and deterministic in nature During handling of the above exception, another exception occurred: Traceback (most recent call last): File "hf_datasets.py", line 32, in <module> omp = load_dataset('omp', 'posts_labeled') File "/home/erika_distefano/.local/lib/python3.6/site-packages/datasets/load.py", line 748, in load_dataset use_auth_token=use_auth_token, File "/home/erika_distefano/.local/lib/python3.6/site-packages/datasets/builder.py", line 575, in download_and_prepare dl_manager=dl_manager, verify_infos=verify_infos, **download_and_prepare_kwargs File "/home/erika_distefano/.local/lib/python3.6/site-packages/datasets/builder.py", line 652, in _download_and_prepare self._prepare_split(split_generator, **prepare_split_kwargs) File "/home/erika_distefano/.local/lib/python3.6/site-packages/datasets/builder.py", line 992, in _prepare_split num_examples, num_bytes = writer.finalize() File "/home/erika_distefano/.local/lib/python3.6/site-packages/datasets/arrow_writer.py", line 409, in finalize self.check_duplicate_keys() File "/home/erika_distefano/.local/lib/python3.6/site-packages/datasets/arrow_writer.py", line 349, in check_duplicate_keys raise DuplicatedKeysError(key) datasets.keyhash.DuplicatedKeysError: FAILURE TO GENERATE DATASET ! Found duplicate Key: 3326 Keys should be unique and deterministic in nature ## Environment info - `datasets` version: 1.8.0 - Platform: Ubuntu 18.04.4 LTS - Python version: 3.6.9 - PyArrow version: 3.0.0
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Remove redundant prepare_module
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I have noticed that after implementing `load_dataset_builder` (#2500), there is a redundant call to `prepare_module`.
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Transformer Class on dataset
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[ "Hi ! Do you have an example in mind that shows how this could be useful ?", "Example:\n\nMerge 2 datasets into one datasets\n\nLabel extraction from dataset\n\ndataset(text, label)\n β€”> dataset(text, newlabel)\n\nTextCleaning.\n\n\nFor image dataset, \nTransformation are easier (ie linear algebra).\n\n\n\n\n\n\n> On Jul 6, 2021, at 17:39, Quentin Lhoest ***@***.***> wrote:\n> \n> ο»Ώ\n> Hi ! Do you have an example in mind that shows how this could be useful ?\n> \n> β€”\n> You are receiving this because you authored the thread.\n> Reply to this email directly, view it on GitHub, or unsubscribe.\n", "There are already a few transformations that you can apply on a dataset using methods like `dataset.map()`.\r\nYou can find examples in the documentation here:\r\nhttps://huggingface.co/docs/datasets/processing.html\r\n\r\nYou can merge two datasets with `concatenate_datasets()` or do label extraction with `dataset.map()` for example", "Ok, sure.\n\nThanks for pointing on functional part.\nMy question is more\nβ€œPhilosophical”/Design perspective.\n\nThere are 2 perspetive:\n Add transformation methods to \n Dataset Class\n\n\n OR Create a Transformer Class\n which operates on Dataset Class.\n\nT(Dataset) β€”> Dataset\n\ndatasetnew = MyTransform.transform(dataset)\ndatasetNew.save(path)\n\n\nWhat would be the difficulty\nof implementing a Transformer Class\noperating at dataset level ?\n\n\nthanks\n\n\n\n\n\n\n\n\n\n> On Jul 6, 2021, at 22:00, Quentin Lhoest ***@***.***> wrote:\n> \n> ο»Ώ\n> There are already a few transformations that you can apply on a dataset using methods like dataset.map().\n> You can find examples in the documentation here:\n> https://huggingface.co/docs/datasets/processing.html\n> \n> You can merge two datasets with concatenate_datasets() or do label extraction with dataset.map() for example\n> \n> β€”\n> You are receiving this because you authored the thread.\n> Reply to this email directly, view it on GitHub, or unsubscribe.\n", "I can imagine that this would be a useful API to implement processing pipelines as transforms. They could be used to perform higher level transforms compared to the atomic transforms allowed by methods like map, filter, etc.\r\n\r\nI guess if you find any transform that could be useful for text dataset processing, image dataset processing etc. we could definitely start having such transforms :)", "Thanks for reply.\n\nWhat would be the constraints\nto have\nDataset β€”> Dataset consistency ?\n\nMain issue would be\nlarger than memory dataset and\nserialization on disk.\n\nTechnically,\none still process at atomic level\nand try to wrap the full results\ninto Dataset…. (!)\n\nWhat would you think ?\n\n\n\n\n\n\n\n\n> On Jul 7, 2021, at 16:51, Quentin Lhoest ***@***.***> wrote:\n> \n> ο»Ώ\n> I can imagine that this would be a useful API to implement processing pipelines as transforms. They could be used to perform higher level transforms compared to the atomic transforms allowed by methods like map, filter, etc.\n> \n> I guess if you find any transform that could be useful for text dataset processing, image dataset processing etc. we could definitely start having such transforms :)\n> \n> β€”\n> You are receiving this because you authored the thread.\n> Reply to this email directly, view it on GitHub, or unsubscribe.\n", "We can be pretty flexible and not impose any constraints for transforms.\r\n\r\nMoreover, this library is designed to support datasets bigger than memory. The datasets are loaded from the disk via memory mapping, without filling up RAM. Even processing functions like `map` work in a batched fashion to not fill up your RAM. So this shouldn't be an issue", "Ok thanks.\n\nBut, Dataset has various flavors.\nIn current design of Dataset,\n how the serialization on disk is done (?)\n\n\nThe main issue is serialization \nof newdataset= Transform(Dataset)\n (ie thats why am referring to Out Of memory dataset…):\n\n Should be part of Transform or part of dataset ?\n\n\n\n\nMaybe, not, since the output is aimed to feed model in memory (?)\n\n\n\n\n\n\n\n\n> On Jul 7, 2021, at 18:04, Quentin Lhoest ***@***.***> wrote:\n> \n> ο»Ώ\n> We can be pretty flexible and not impose any constraints for transforms.\n> \n> Moreover, this library is designed to support datasets bigger than memory. The datasets are loaded from the disk via memory mapping, without filling up RAM. Even processing functions like map work in a batched fashion to not fill up your RAM. So this shouldn't be an issue\n> \n> β€”\n> You are receiving this because you authored the thread.\n> Reply to this email directly, view it on GitHub, or unsubscribe.\n", "I'm not sure I understand, could you elaborate a bit more please ?\r\n\r\nEach dataset is a wrapper of a PyArrow Table that contains all the data. The table is loaded from an arrow file on the disk.\r\nWe have an ArrowWriter and ArrowReader class to write/read arrow tables on disk or in in-memory buffers." ]
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Just wondering if you have intenttion to create TransformerClass : dataset --> dataset and make determnistic transformation (ie not fit).
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ModuleNotFoundError: No module named 'datasets.tasks' while importing common voice datasets
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[ "Hi @profsatwinder.\r\n\r\nIt looks like you are using an old version of `datasets`. Please update it with `pip install -U datasets` and indicate if the problem persists.", "@albertvillanova Thanks for the information. I updated it to 1.9.0 and the issue is resolved. Thanks again. " ]
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Error traceback: --------------------------------------------------------------------------- ModuleNotFoundError Traceback (most recent call last) <ipython-input-8-a7b592d3bca0> in <module>() 1 from datasets import load_dataset, load_metric 2 ----> 3 common_voice_train = load_dataset("common_voice", "pa-IN", split="train+validation") 4 common_voice_test = load_dataset("common_voice", "pa-IN", split="test") 9 frames /root/.cache/huggingface/modules/datasets_modules/datasets/common_voice/078d412587e9efeb0ae2e574da99c31e18844c496008d53dc5c60f4159ed639b/common_voice.py in <module>() 19 20 import datasets ---> 21 from datasets.tasks import AutomaticSpeechRecognition 22 23 ModuleNotFoundError: No module named 'datasets.tasks'
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Fix BibTeX entry
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Fix BibTeX entry.
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Support pandas 1.3.0 read_csv
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Workaround for this issue in pandas 1.3.0 : https://github.com/pandas-dev/pandas/issues/42387 The csv reader raises an error: ```python /usr/local/lib/python3.7/dist-packages/pandas/io/parsers/readers.py in _refine_defaults_read(dialect, delimiter, delim_whitespace, engine, sep, error_bad_lines, warn_bad_lines, on_bad_lines, names, prefix, defaults) 1304 1305 if names is not lib.no_default and prefix is not lib.no_default: -> 1306 raise ValueError("Specified named and prefix; you can only specify one.") 1307 1308 kwds["names"] = None if names is lib.no_default else names ValueError: Specified named and prefix; you can only specify one. ```
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Add c4.noclean infos
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Adding the data files checksums and the dataset size of the c4.noclean configuration of the C4 dataset
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Cached dataset overflowing disk space
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[ "Hi! I'm transferring this issue over to `datasets`", "I'm using the datasets concatenate dataset to combine the datasets and then train.\r\ntrain_dataset = concatenate_datasets([dataset1, dataset2, common_voice_train])\r\n\r\n", "Hi @BirgerMoell.\r\n\r\nYou have several options:\r\n- to set caching to be stored on a different path location, other than the default one (`~/.cache/huggingface/datasets`):\r\n - either setting the environment variable `HF_DATASETS_CACHE` with the path to the new cache location\r\n - or by passing it with the parameter `cache_dir` when loading each of the datasets: `dataset = load_dataset(..., cache_dir=your_new_location)`\r\n\r\n You can get all the information in the docs: https://huggingface.co/docs/datasets/loading_datasets.html#cache-directory\r\n- I wouldn't recommend disabling caching, because current implementation generates cache files anyway, although in a temporary directory and they are deleted when the session closes. See details here: https://huggingface.co/docs/datasets/processing.html#enable-or-disable-caching\r\n- You could alternatively load the datasets in streaming mode. This is a new feature which allows loading the datasets without downloading the entire files. More information here: https://huggingface.co/docs/datasets/dataset_streaming.html", "Hi @BirgerMoell,\r\n\r\nWe are planning to add a new feature to datasets, which could be interesting in your case: Add the option to delete temporary files (decompressed files) from the cache directory (see: #2481, #2604).\r\n\r\nWe will ping you once this feature is implemented, so that the size of your cache directory will be considerably reduced." ]
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I'm training a Swedish Wav2vec2 model on a Linux GPU and having issues that the huggingface cached dataset folder is completely filling up my disk space (I'm training on a dataset of around 500 gb). The cache folder is 500gb (and now my disk space is full). Is there a way to toggle caching or set the caching to be stored on a different device (I have another drive with 4 tb that could hold the caching files). This might not technically be a bug, but I was unsure and I felt that the bug was the closest one. Traceback (most recent call last): File "/home/birger/miniconda3/envs/wav2vec2/lib/python3.7/site-packages/multiprocess/pool.py", line 121, in worker result = (True, func(*args, **kwds)) File "/home/birger/miniconda3/envs/wav2vec2/lib/python3.7/site-packages/datasets/arrow_dataset.py", line 186, in wrapper out: Union["Dataset", "DatasetDict"] = func(self, *args, **kwargs) File "/home/birger/miniconda3/envs/wav2vec2/lib/python3.7/site-packages/datasets/fingerprint.py", line 397, in wrapper out = func(self, *args, **kwargs) File "/home/birger/miniconda3/envs/wav2vec2/lib/python3.7/site-packages/datasets/arrow_dataset.py", line 1983, in _map_single writer.finalize() File "/home/birger/miniconda3/envs/wav2vec2/lib/python3.7/site-packages/datasets/arrow_writer.py", line 418, in finalize self.pa_writer.close() File "pyarrow/ipc.pxi", line 402, in pyarrow.lib._CRecordBatchWriter.close File "pyarrow/error.pxi", line 97, in pyarrow.lib.check_status OSError: [Errno 28] Error writing bytes to file. Detail: [errno 28] No space left on device """ The above exception was the direct cause of the following exception:
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Add language tags
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This PR adds some missing language tags needed for ASR datasets in #2565
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Support multilabel metrics
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[ "Hi ! Thanks for the fix :)\r\n\r\nIf I understand correctly, `OptionalSequence` doesn't have an associated arrow type that we know in advance unlike the other feature types, because it depends on the type of the examples.\r\n\r\nFor example, I tested this and it raises an error:\r\n```python\r\nimport datasets as ds\r\nimport pyarrow as pa\r\n\r\nfeatures = ds.Features({\"a\": ds.features.OptionalSequence(ds.Value(\"int32\"))})\r\nbatch = {\"a\": [[0]]}\r\n\r\nwriter = ds.ArrowWriter(features=features, stream=pa.BufferOutputStream())\r\nwriter.write_batch(batch)\r\n# ArrowInvalid: Could not convert [0] with type list: tried to convert to int\r\n```\r\nThis error happens because `features.type` is `StructType(struct<a: int32>)`.\r\n\r\nAnother way to add support for multilabel would be to have several configurations for these metrics. By default it would set the features without sequences, and for the multi label configuration it would use features with sequences. Let me know what you think", "Hi @lhoestq, thanks for your feedback :)\r\n\r\nDefinitely, your suggested approach is simpler. I am going to refactor all my PR unless we could envision some other use cases where an OptionalSequence might be convenient, but for now I can't think of any..." ]
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Currently, multilabel metrics are not supported because `predictions` and `references` are defined as `Value("int32")`. This PR creates a new feature type `OptionalSequence` which can act as either `Value("int32")` or `Sequence(Value("int32"))`, depending on the data passed. Close #2554.
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Fix test_is_small_dataset
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Remove environment variable fixture `env_max_in_memory_dataset_size`. This fixture does not work because env variable is read in datasets.config when first loading datasets, and it is never reread during tests.
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Add aiohttp to tests extras require
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Currently, none of the streaming tests are runned within our CI test suite, because the streaming tests require aiohttp and this is missing from our tests extras require dependencies. Our CI test suite should be exhaustive and test all the library functionalities.
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Fix misalignment in SQuAD
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Fix misalignment between: - the answer text and - the answer_start within the context by keeping original leading blank spaces in the context. Fix #2585.
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sqaud_v2 dataset contains misalignment between the answer text and the context value at the answer index
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[ "Hi @mmajurski, thanks for reporting this issue.\r\n\r\nIndeed this misalignment arises because the source dataset context field contains leading blank spaces (and these are counted within the answer_start), while our datasets loading script removes these leading blank spaces.\r\n\r\nI'm going to fix our script so that all leading blank spaces in the source dataset are kept, and there is no misalignment between the answer text and the answer_start within the context.", "If you are going to be altering the data cleaning from the source Squad dataset, here is one thing to consider.\r\nThere are occasional double spaces separating words which it might be nice to get rid of. \r\n\r\nEither way, thank you." ]
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## Describe the bug The built in huggingface squad_v2 dataset that you can access via datasets.load_dataset contains mis-alignment between the answers['text'] and the characters in the context at the location specified by answers['answer_start']. For example: id = '56d1f453e7d4791d009025bd' answers = {'text': ['Pure Land'], 'answer_start': [146]} However the actual text in context at location 146 is 'ure Land,' Which is an off-by-one error from the correct answer. ## Steps to reproduce the bug ```python import datasets def check_context_answer_alignment(example): for a_idx in range(len(example['answers']['text'])): # check raw dataset for answer consistency between context and answer answer_text = example['answers']['text'][a_idx] a_st_idx = example['answers']['answer_start'][a_idx] a_end_idx = a_st_idx + len(example['answers']['text'][a_idx]) answer_text_from_context = example['context'][a_st_idx:a_end_idx] if answer_text != answer_text_from_context: #print(example['id']) return False return True dataset = datasets.load_dataset('squad_v2', split='train', keep_in_memory=True) start_len = len(dataset) dataset = dataset.filter(check_context_answer_alignment, num_proc=1, keep_in_memory=True) end_len = len(dataset) print('{} instances contain mis-alignment between the answer text and answer index.'.format(start_len - end_len)) ``` ## Expected results This code should result in 0 rows being filtered out from the dataset. ## Actual results This filter command results in 258 rows being flagged as containing a discrepancy between the text contained within answers['text'] and the text in example['context'] at the answers['answer_start'] location. This code will reproduce the problem and produce the following count: "258 instances contain mis-alignment between the answer text and answer index." ## Environment info Steps to rebuilt the Conda environment: ``` # create a virtual environment to stuff all these packages into conda create -n round8 python=3.8 -y # activate the virtual environment conda activate round8 # install pytorch (best done through conda to handle cuda dependencies) conda install pytorch torchvision torchtext cudatoolkit=11.1 -c pytorch-lts -c nvidia pip install jsonpickle transformers datasets matplotlib ``` OS: Ubuntu 20.04 Python 3.8 Result of `conda env export`: ``` name: round8 channels: - pytorch-lts - nvidia - defaults dependencies: - _libgcc_mutex=0.1=main - _openmp_mutex=4.5=1_gnu - blas=1.0=mkl - brotlipy=0.7.0=py38h27cfd23_1003 - bzip2=1.0.8=h7b6447c_0 - ca-certificates=2021.5.25=h06a4308_1 - certifi=2021.5.30=py38h06a4308_0 - cffi=1.14.5=py38h261ae71_0 - chardet=4.0.0=py38h06a4308_1003 - cryptography=3.4.7=py38hd23ed53_0 - cudatoolkit=11.1.74=h6bb024c_0 - ffmpeg=4.2.2=h20bf706_0 - freetype=2.10.4=h5ab3b9f_0 - gmp=6.2.1=h2531618_2 - gnutls=3.6.15=he1e5248_0 - idna=2.10=pyhd3eb1b0_0 - intel-openmp=2021.2.0=h06a4308_610 - jpeg=9b=h024ee3a_2 - lame=3.100=h7b6447c_0 - lcms2=2.12=h3be6417_0 - ld_impl_linux-64=2.35.1=h7274673_9 - libffi=3.3=he6710b0_2 - libgcc-ng=9.3.0=h5101ec6_17 - libgomp=9.3.0=h5101ec6_17 - libidn2=2.3.1=h27cfd23_0 - libopus=1.3.1=h7b6447c_0 - libpng=1.6.37=hbc83047_0 - libstdcxx-ng=9.3.0=hd4cf53a_17 - libtasn1=4.16.0=h27cfd23_0 - libtiff=4.2.0=h85742a9_0 - libunistring=0.9.10=h27cfd23_0 - libuv=1.40.0=h7b6447c_0 - libvpx=1.7.0=h439df22_0 - libwebp-base=1.2.0=h27cfd23_0 - lz4-c=1.9.3=h2531618_0 - mkl=2021.2.0=h06a4308_296 - mkl-service=2.3.0=py38h27cfd23_1 - mkl_fft=1.3.0=py38h42c9631_2 - mkl_random=1.2.1=py38ha9443f7_2 - ncurses=6.2=he6710b0_1 - nettle=3.7.3=hbbd107a_1 - ninja=1.10.2=hff7bd54_1 - numpy=1.20.2=py38h2d18471_0 - numpy-base=1.20.2=py38hfae3a4d_0 - olefile=0.46=py_0 - openh264=2.1.0=hd408876_0 - openssl=1.1.1k=h27cfd23_0 - pillow=8.2.0=py38he98fc37_0 - pip=21.1.2=py38h06a4308_0 - pycparser=2.20=py_2 - pyopenssl=20.0.1=pyhd3eb1b0_1 - pysocks=1.7.1=py38h06a4308_0 - python=3.8.10=h12debd9_8 - pytorch=1.8.1=py3.8_cuda11.1_cudnn8.0.5_0 - readline=8.1=h27cfd23_0 - requests=2.25.1=pyhd3eb1b0_0 - setuptools=52.0.0=py38h06a4308_0 - six=1.16.0=pyhd3eb1b0_0 - sqlite=3.35.4=hdfb4753_0 - tk=8.6.10=hbc83047_0 - torchtext=0.9.1=py38 - torchvision=0.9.1=py38_cu111 - typing_extensions=3.7.4.3=pyha847dfd_0 - urllib3=1.26.4=pyhd3eb1b0_0 - wheel=0.36.2=pyhd3eb1b0_0 - x264=1!157.20191217=h7b6447c_0 - xz=5.2.5=h7b6447c_0 - zlib=1.2.11=h7b6447c_3 - zstd=1.4.9=haebb681_0 - pip: - click==8.0.1 - cycler==0.10.0 - datasets==1.8.0 - dill==0.3.4 - filelock==3.0.12 - fsspec==2021.6.0 - huggingface-hub==0.0.8 - joblib==1.0.1 - jsonpickle==2.0.0 - kiwisolver==1.3.1 - matplotlib==3.4.2 - multiprocess==0.70.12.2 - packaging==20.9 - pandas==1.2.4 - pyarrow==3.0.0 - pyparsing==2.4.7 - python-dateutil==2.8.1 - pytz==2021.1 - regex==2021.4.4 - sacremoses==0.0.45 - tokenizers==0.10.3 - tqdm==4.49.0 - transformers==4.6.1 - xxhash==2.0.2 prefix: /home/mmajurski/anaconda3/envs/round8 ```
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wi_locness: reference latest leaderboard on codalab
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The dataset's author asked me to put this codalab link into the dataset's README.
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Error iteration over IterableDataset using Torch DataLoader
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[ "Hi ! This is because you first need to format the dataset for pytorch:\r\n\r\n```python\r\n>>> import torch\r\n>>> from datasets import load_dataset\r\n>>> dataset = load_dataset('oscar', \"unshuffled_deduplicated_en\", split='train', streaming=True)\r\n>>> torch_iterable_dataset = dataset.with_format(\"torch\")\r\n>>> assert isinstance(torch_iterable_dataset, torch.utils.data.IterableDataset)\r\n>>> dataloader = torch.utils.data.DataLoader(torch_iterable_dataset, batch_size=4)\r\n>>> next(iter(dataloader))\r\n{'id': tensor([0, 1, 2, 3]), 'text': ['Mtendere Village was inspired...]}\r\n```\r\n\r\nThis is because the pytorch dataloader expects a subclass of `torch.utils.data.IterableDataset`. Since you can't pass an arbitrary iterable to a pytorch dataloader, you first need to build an object that inherits from `torch.utils.data.IterableDataset` using `with_format(\"torch\")` for example.\r\n", "Thank you for that and the example! \r\n\r\nWhat you said makes total sense; I just somehow missed that and assumed HF IterableDataset was a subclass of Torch IterableDataset. " ]
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## Describe the bug I have an IterableDataset (created using streaming=True) and I am trying to create batches using Torch DataLoader class by passing this IterableDataset to it. This throws error which is pasted below. I can do the same by using Torch IterableDataset. One thing I noticed is that in the former case when I look at the dataloader.sampler class I get torch.utils.data.sampler.SequentialSampler while the latter one gives torch.utils.data.dataloader._InfiniteConstantSampler. I am not sure if this is how it is meant to be used, but that's what seemed reasonable to me. ## Steps to reproduce the bug 1. Does not work. ```python >>> from datasets import load_dataset >>> dataset = load_dataset('oscar', "unshuffled_deduplicated_en", split='train', streaming=True) >>> dataloader = torch.utils.data.DataLoader(dataset, batch_size=4) >>> dataloader.sampler <torch.utils.data.sampler.SequentialSampler object at 0x7f245a510208> >>> for batch in dataloader: ... print(batch) ``` 2. Works. ```python import torch from torch.utils.data import Dataset, IterableDataset, DataLoader class CustomIterableDataset(IterableDataset): 'Characterizes a dataset for PyTorch' def __init__(self, data): 'Initialization' self.data = data def __iter__(self): return iter(self.data) data = list(range(12)) dataset = CustomIterableDataset(data) dataloader = DataLoader(dataset, batch_size=4) print("dataloader: ", dataloader.sampler) for batch in dataloader: print(batch) ``` ## Expected results To get batches of data with the batch size as 4. Output from the latter one (2) though Datasource is different here so actual data is different. dataloader: <torch.utils.data.dataloader._InfiniteConstantSampler object at 0x7f1cc29e2c50> tensor([0, 1, 2, 3]) tensor([4, 5, 6, 7]) tensor([ 8, 9, 10, 11]) ## Actual results <torch.utils.data.sampler.SequentialSampler object at 0x7f245a510208> ... Traceback (most recent call last): File "<stdin>", line 1, in <module> File "/data/leshekha/lib/HFDatasets/lib/python3.6/site-packages/torch/utils/data/dataloader.py", line 435, in __next__ data = self._next_data() File "/data/leshekha/lib/HFDatasets/lib/python3.6/site-packages/torch/utils/data/dataloader.py", line 474, in _next_data index = self._next_index() # may raise StopIteration File "/data/leshekha/lib/HFDatasets/lib/python3.6/site-packages/torch/utils/data/dataloader.py", line 427, in _next_index return next(self._sampler_iter) # may raise StopIteration File "/data/leshekha/lib/HFDatasets/lib/python3.6/site-packages/torch/utils/data/sampler.py", line 227, in __iter__ for idx in self.sampler: File "/data/leshekha/lib/HFDatasets/lib/python3.6/site-packages/torch/utils/data/sampler.py", line 67, in __iter__ return iter(range(len(self.data_source))) TypeError: object of type 'IterableDataset' has no len() ## Environment info <!-- You can run the command `datasets-cli env` and copy-and-paste its output below. --> - `datasets` version: '1.8.1.dev0' - Platform: Linux - Python version: Python 3.6.8 - PyArrow version: '3.0.0'
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Add skip and take
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[ "@lhoestq looks good. I tried with https://huggingface.co/datasets/vblagoje/wikipedia_snippets_streamed and it worked nicely. I would add more unit tests for edge cases. What happens if the n is larger than the total number of samples? Just to make sure these cases are handled properly. ", "Yup I'll add the tests thanks ;)\r\n\r\nMoreover, I just noticed something in your wiki snippets code. FYI you're using `++passage_counter ` at https://huggingface.co/datasets/vblagoje/wikipedia_snippets_streamed/blob/main/wikipedia_snippets_streamed.py#L102 but in python this doesn't increment the value @vblagoje ", "Thanks @lhoestq - not easy to convert after 10+ years of Java" ]
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As discussed in https://github.com/huggingface/datasets/pull/2375#discussion_r657084544 I added the `IterableDataset.skip` and `IterableDataset.take` methods that allows to do basic splitting of iterable datasets. You can create new dataset with the first `n` examples using `IterableDataset.take()`, or you can get a dataset with the rest of the examples by skipping the first `n` examples with `IterableDataset.skip()` One implementation detail: Using `take` (or `skip`) prevents future dataset shuffling from shuffling the dataset shards, otherwise the taken examples could come from other shards. In this case it only uses the shuffle buffer. I would have loved to allow the shards of the taken examples to be shuffled anyway, but since we don't know in advance the length of each shard we don't know what shards to take or skip. I think this is ok though since users can shuffle before doing take or skip. I mentioned this in the documentation cc @vblagoje @lewtun
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Faster search_batch for ElasticsearchIndex due to threading
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Hey, I think it makes sense to perform search_batch threaded, so ES can perform search in parallel. Cheers!
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