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2021-02-24T13:44:53Z

MDExOlB1bGxSZXF1ZXN0NTc2NDY2ODQx

closed

Update: WMT - use mirror links

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2021-02-19T13:42:34Z

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MEMBER

As asked in #1892 I created mirrors of the data hosted on statmt.org and updated the wmt scripts. Now downloading the wmt datasets is blazing fast :) cc @stas00 @patrickvonplaten

2021-02-24T13:44:53Z

https://github.com/huggingface/datasets/pull/1912

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MDU6SXNzdWU4MTIwMDk5NTY=

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Saving processed dataset running infinitely

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2021-02-19T13:09:19Z

https://api.github.com/repos/huggingface/datasets/issues/1911

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NONE

I have a text dataset of size 220M. For pre-processing, I need to tokenize this and filter rows with the large sequence. My tokenization took roughly 3hrs. I used map() with batch size 1024 and multi-process with 96 processes. filter() function was way to slow, so I used a hack to use pyarrow filter table function, which is damm fast. Mentioned [here](https://github.com/huggingface/datasets/issues/1796) ```dataset._data = dataset._data.filter(...)``` It took 1 hr for the filter. Then i use `save_to_disk()` on processed dataset and it is running forever. I have been waiting since 8 hrs, it has not written a single byte. Infact it has actually read from disk more than 100GB, screenshot below shows the stats using `iotop`. Second process is the one. <img width="1672" alt="Screenshot 2021-02-19 at 6 36 53 PM" src="https://user-images.githubusercontent.com/20911334/108508197-7325d780-72e1-11eb-8369-7c057d137d81.png"> I am not able to figure out, whether this is some issue with dataset library or that it is due to my hack for filter() function.

2021-02-23T07:34:44Z

https://github.com/huggingface/datasets/issues/1911

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2021-03-04T22:02:47Z

MDExOlB1bGxSZXF1ZXN0NTc2MTg0MDQ3

closed

Adding CoNLLpp dataset.

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2021-02-19T05:12:30Z

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2021-03-04T22:02:47Z

https://github.com/huggingface/datasets/pull/1910

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2021-02-22T23:22:04Z

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DBPedia14 Dataset Checksum bug?

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2021-02-18T22:25:48Z

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CONTRIBUTOR

Hi there!!! I've been using successfully the DBPedia dataset (https://huggingface.co/datasets/dbpedia_14) with my codebase in the last couple of weeks, but in the last couple of days now I get this error: ``` Traceback (most recent call last): File "./conditional_classification/basic_pipeline.py", line 178, in <module> main() File "./conditional_classification/basic_pipeline.py", line 128, in main corpus.load_data(limit_train_examples_per_class=args.data_args.train_examples_per_class, File "/home/fp/dev/conditional_classification/conditional_classification/datasets_base.py", line 83, in load_data datasets = load_dataset(self.name, split=dataset_split) File "/home/fp/anaconda3/envs/conditional/lib/python3.8/site-packages/datasets/load.py", line 609, in load_dataset builder_instance.download_and_prepare( File "/home/fp/anaconda3/envs/conditional/lib/python3.8/site-packages/datasets/builder.py", line 526, in download_and_prepare self._download_and_prepare( File "/home/fp/anaconda3/envs/conditional/lib/python3.8/site-packages/datasets/builder.py", line 586, in _download_and_prepare verify_checksums( File "/home/fp/anaconda3/envs/conditional/lib/python3.8/site-packages/datasets/utils/info_utils.py", line 39, in verify_checksums raise NonMatchingChecksumError(error_msg + str(bad_urls)) datasets.utils.info_utils.NonMatchingChecksumError: Checksums didn't match for dataset source files: ['https://drive.google.com/uc?export=download&id=0Bz8a_Dbh9QhbQ2Vic1kxMmZZQ1k'] ``` I've seen this has happened before in other datasets as reported in #537. I've tried clearing my cache and call again `load_dataset` but still is not working. My same codebase is successfully downloading and using other datasets (e.g. AGNews) without any problem, so I guess something has happened specifically to the DBPedia dataset in the last few days. Can you please check if there's a problem with the checksums? Or this is related to any other stuff? I've seen that the path in the cache for the dataset is `/home/fp/.cache/huggingface/datasets/d_bpedia14/dbpedia_14/2.0.0/a70413e39e7a716afd0e90c9e53cb053691f56f9ef5fe317bd07f2c368e8e897...` and includes `d_bpedia14` instead maybe of `dbpedia_14`. Was this maybe a bug introduced recently? Thanks!

2021-02-22T23:22:05Z

https://github.com/huggingface/datasets/issues/1907

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MDU6SXNzdWU4MTE0MDUyNzQ=

open

Feature Request: Support for Pandas `Categorical`

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2021-02-18T19:46:05Z

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CONTRIBUTOR

``` from datasets import Dataset import pandas as pd import pyarrow df = pd.DataFrame(pd.Series(["a", "b", "c", "a"], dtype="category")) pyarrow.Table.from_pandas(df) Dataset.from_pandas(df) # Throws NotImplementedError # TODO(thom) this will need access to the dictionary as well (for labels). I.e. to the py_table ``` I'm curious if https://github.com/huggingface/datasets/blob/master/src/datasets/features.py#L796 could be built out in a way similar to `Sequence`? e.g. a `Map` class (or whatever name the maintainers might prefer) that can accept: ``` index_type = generate_from_arrow_type(pa_type.index_type) value_type = generate_from_arrow_type(pa_type.value_type) ``` and then additional code points to modify: - FeatureType: https://github.com/huggingface/datasets/blob/master/src/datasets/features.py#L694 - A branch to handle Map in get_nested_type: https://github.com/huggingface/datasets/blob/master/src/datasets/features.py#L719 - I don't quite understand what `encode_nested_example` does but perhaps a branch there? https://github.com/huggingface/datasets/blob/master/src/datasets/features.py#L755 - Similarly, I don't quite understand why `Sequence` is used this way in `generate_from_dict`, but perhaps a branch here? https://github.com/huggingface/datasets/blob/master/src/datasets/features.py#L775 I couldn't find other usages of `Sequence` outside of defining specific datasets, so I'm not sure if that's a comprehensive set of touchpoints.

2021-02-23T14:38:50Z

https://github.com/huggingface/datasets/issues/1906

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2021-02-20T22:01:30Z

MDExOlB1bGxSZXF1ZXN0NTc1OTIxMDk1

closed

Standardizing datasets.dtypes

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2021-02-18T19:15:31Z

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CONTRIBUTOR

This PR was further branched off of jdy-str-to-pyarrow-parsing, so it depends on https://github.com/huggingface/datasets/pull/1900 going first for the diff to be up-to-date (I'm not sure if there's a way for me to use jdy-str-to-pyarrow-parsing as a base branch while having it appear in the pull requests here). This moves away from `str(pyarrow.DataType)` as the method of choice for creating dtypes, favoring an explicit mapping to a list of supported Value dtypes. I believe in practice this should be backward compatible, since anyone previously using Value() would only have been able to use dtypes that had an identically named pyarrow factory function, which are all explicitly supported here.

2021-02-20T22:01:30Z

https://github.com/huggingface/datasets/pull/1905

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2021-02-18T17:10:01Z

MDExOlB1bGxSZXF1ZXN0NTc1ODE4MjA0

closed

Fix to_pandas for boolean ArrayXD

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2021-02-18T16:30:46Z

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MEMBER

As noticed in #1887 the conversion of a dataset with a boolean ArrayXD feature types fails because of the underlying ListArray conversion to numpy requires `zero_copy_only=False`. zero copy is available for all primitive types except booleans see https://arrow.apache.org/docs/python/generated/pyarrow.Array.html#pyarrow.Array.to_numpy and https://issues.apache.org/jira/browse/ARROW-2871?jql=text%20~%20%22boolean%20to_numpy%22 cc @SBrandeis

2021-02-18T17:10:03Z

https://github.com/huggingface/datasets/pull/1904

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2021-03-01T09:39:12Z

MDExOlB1bGxSZXF1ZXN0NTc1NzIwOTk2

closed

Initial commit for the addition of TIMIT dataset

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2021-02-18T14:23:12Z

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CONTRIBUTOR

Below points needs to be addressed: - Creation of dummy dataset is failing - Need to check on the data representation - License is not creative commons. Copyright: Portions © 1993 Trustees of the University of Pennsylvania Also the links (_except the download_) point to the ami corpus! ;-) @patrickvonplaten Requesting your comments, will be happy to address them!

2021-03-01T09:39:12Z

https://github.com/huggingface/datasets/pull/1903

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2021-02-18T09:55:41Z

MDExOlB1bGxSZXF1ZXN0NTc1NTQwMDM1

closed

Fix setimes_2 wmt urls

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2021-02-18T09:42:26Z

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Continuation of #1901 Some other urls were missing https

2021-02-18T09:55:41Z

https://github.com/huggingface/datasets/pull/1902

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2021-02-18T09:39:21Z

MDExOlB1bGxSZXF1ZXN0NTc1NDY5MDUy

closed

Fix OPUS dataset download errors

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2021-02-18T07:39:41Z

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CONTRIBUTOR

Replace http to https. https://github.com/huggingface/datasets/issues/854 https://discuss.huggingface.co/t/cannot-download-wmt16/2081

2021-02-18T15:07:20Z

https://github.com/huggingface/datasets/pull/1901

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2021-02-19T18:27:11Z

MDExOlB1bGxSZXF1ZXN0NTc1MTkxNTc3

closed

Issue #1895: Bugfix for string_to_arrow timestamp[ns] support

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2021-02-17T20:26:04Z

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CONTRIBUTOR

Should resolve https://github.com/huggingface/datasets/issues/1895 The main part of this PR adds additional parsing in `string_to_arrow` to convert the timestamp dtypes that result from `str(pa_type)` back into the pa.DataType TimestampType. While adding unit-testing, I noticed that support for the double/float types also don't invert correctly, so I added them, which I believe would hypothetically make this section of `Value` redundant: ``` def __post_init__(self): if self.dtype == "double": # fix inferred type self.dtype = "float64" if self.dtype == "float": # fix inferred type self.dtype = "float32" ``` However, since I think Value.dtype is part of the public interface, removing that would result in a backward-incompatible change, so I didn't muck with that. The rest of the PR consists of docstrings that I added while developing locally so I could keep track of which functions were supposed to be inverses of each other, and thought I'd include them initially in case you want to keep them around, but I'm happy to delete or remove any of them at your request!

2021-02-19T18:27:11Z

https://github.com/huggingface/datasets/pull/1900

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2021-02-17T17:20:49Z

MDExOlB1bGxSZXF1ZXN0NTc1MDIxMjc4

closed

Fix: ALT - fix duplicated examples in alt-parallel

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2021-02-17T15:53:56Z

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As noticed in #1898 by @10-zin the examples of the `alt-paralel` configurations have all the same values for the `translation` field. This was due to a bad copy of a python dict. This PR fixes that.

2021-02-17T17:20:49Z

https://github.com/huggingface/datasets/pull/1899

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2021-02-19T06:18:46Z

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ALT dataset has repeating instances in all splits

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2021-02-17T12:51:42Z

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The [ALT](https://huggingface.co/datasets/alt) dataset has all the same instances within each split :/ Seemed like a great dataset for some experiments I wanted to carry out, especially since its medium-sized, and has all splits. Would be great if this could be fixed :) Added a snapshot of the contents from `explore-datset` feature, for quick reference. 

2021-02-19T06:18:46Z

https://github.com/huggingface/datasets/issues/1898

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2021-02-17T13:15:15Z

MDExOlB1bGxSZXF1ZXN0NTc0ODU3MTIy

closed

Fix PandasArrayExtensionArray conversion to native type

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2021-02-17T11:48:24Z

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To make the conversion to csv work in #1887 , we need PandasArrayExtensionArray used for multidimensional numpy arrays to be converted to pandas native types. However previously pandas.core.internals.ExtensionBlock.to_native_types would fail with an PandasExtensionArray because 1. the PandasExtensionArray.isna method was wrong 2. the conversion of a PandasExtensionArray to a numpy array with dtype=object was returning a multidimensional array while pandas excepts a 1D array in this case (more info [here](https://pandas.pydata.org/pandas-docs/stable/reference/api/pandas.api.extensions.ExtensionArray.html#pandas.api.extensions.ExtensionArray)) I fixed these two issues and now the conversion to native types works, and so is the export to csv. cc @SBrandeis

2021-02-17T13:15:16Z

https://github.com/huggingface/datasets/pull/1897

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2021-02-19T18:27:11Z

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Bug Report: timestamp[ns] not recognized

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2021-02-16T20:38:04Z

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CONTRIBUTOR

Repro: ``` from datasets import Dataset import pandas as pd import pyarrow df = pd.DataFrame(pd.date_range("2018-01-01", periods=3, freq="H")) pyarrow.Table.from_pandas(df) Dataset.from_pandas(df) # Throws ValueError: Neither timestamp[ns] nor timestamp[ns]_ seems to be a pyarrow data type. ``` The factory function seems to be just "timestamp": https://arrow.apache.org/docs/python/generated/pyarrow.timestamp.html#pyarrow.timestamp It seems like https://github.com/huggingface/datasets/blob/master/src/datasets/features.py#L36-L43 could have a little bit of additional structure for handling these cases? I'd be happy to take a shot at opening a PR if I could receive some guidance on whether parsing something like `timestamp[ns]` and resolving it to timestamp('ns') is the goal of this method. Alternatively, if I'm using this incorrectly (e.g. is the expectation that we always provide a schema when timestamps are involved?), that would be very helpful to know as well! ``` $ pip list # only the relevant libraries/versions datasets 1.2.1 pandas 1.0.3 pyarrow 3.0.0 ```

2021-02-19T18:27:11Z

https://github.com/huggingface/datasets/issues/1895

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benchmarking against MMapIndexedDataset

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2021-02-16T20:04:58Z

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CONTRIBUTOR

I am trying to benchmark my datasets based implementation against fairseq's [`MMapIndexedDataset`](https://github.com/pytorch/fairseq/blob/master/fairseq/data/indexed_dataset.py#L365) and finding that, according to psrecord, my `datasets` implem uses about 3% more CPU memory and runs 1% slower for `wikitext103` (~1GB of tokens). Questions: 1) Is this (basically identical) performance expected? 2) Is there a scenario where this library will outperform `MMapIndexedDataset`? (maybe more examples/larger examples?) 3) Should I be using different benchmarking tools than `psrecord`/how do you guys do benchmarks? Thanks in advance! Sam

2021-02-17T18:52:28Z

https://github.com/huggingface/datasets/issues/1894

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2021-03-03T17:42:02Z

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closed

wmt19 is broken

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2021-02-16T18:39:58Z

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CONTRIBUTOR

1. Check which lang pairs we have: `--dataset_name wmt19`: Please pick one among the available configs: ['cs-en', 'de-en', 'fi-en', 'gu-en', 'kk-en', 'lt-en', 'ru-en', 'zh-en', 'fr-de'] 2. OK, let's pick `ru-en`: `--dataset_name wmt19 --dataset_config "ru-en"` no cookies: ``` Traceback (most recent call last): File "./run_seq2seq.py", line 661, in <module> main() File "./run_seq2seq.py", line 317, in main datasets = load_dataset(data_args.dataset_name, data_args.dataset_config_name) File "/mnt/nvme1/code/huggingface/datasets-master/src/datasets/load.py", line 740, in load_dataset builder_instance.download_and_prepare( File "/mnt/nvme1/code/huggingface/datasets-master/src/datasets/builder.py", line 572, in download_and_prepare self._download_and_prepare( File "/mnt/nvme1/code/huggingface/datasets-master/src/datasets/builder.py", line 628, in _download_and_prepare split_generators = self._split_generators(dl_manager, **split_generators_kwargs) File "/home/stas/.cache/huggingface/modules/datasets_modules/datasets/wmt19/436092de5f3faaf0fc28bc84875475b384e90a5470fa6afaee11039ceddc5052/wmt_utils.py", line 755, in _split_generators downloaded_files = dl_manager.download_and_extract(urls_to_download) File "/mnt/nvme1/code/huggingface/datasets-master/src/datasets/utils/download_manager.py", line 276, in download_and_extract return self.extract(self.download(url_or_urls)) File "/mnt/nvme1/code/huggingface/datasets-master/src/datasets/utils/download_manager.py", line 191, in download downloaded_path_or_paths = map_nested( File "/mnt/nvme1/code/huggingface/datasets-master/src/datasets/utils/py_utils.py", line 233, in map_nested mapped = [ File "/mnt/nvme1/code/huggingface/datasets-master/src/datasets/utils/py_utils.py", line 234, in <listcomp> _single_map_nested((function, obj, types, None, True)) for obj in tqdm(iterable, disable=disable_tqdm) File "/mnt/nvme1/code/huggingface/datasets-master/src/datasets/utils/py_utils.py", line 190, in _single_map_nested mapped = [_single_map_nested((function, v, types, None, True)) for v in pbar] File "/mnt/nvme1/code/huggingface/datasets-master/src/datasets/utils/py_utils.py", line 190, in <listcomp> mapped = [_single_map_nested((function, v, types, None, True)) for v in pbar] File "/mnt/nvme1/code/huggingface/datasets-master/src/datasets/utils/py_utils.py", line 172, in _single_map_nested return function(data_struct) File "/mnt/nvme1/code/huggingface/datasets-master/src/datasets/utils/download_manager.py", line 211, in _download return cached_path(url_or_filename, download_config=download_config) File "/mnt/nvme1/code/huggingface/datasets-master/src/datasets/utils/file_utils.py", line 274, in cached_path output_path = get_from_cache( File "/mnt/nvme1/code/huggingface/datasets-master/src/datasets/utils/file_utils.py", line 584, in get_from_cache raise FileNotFoundError("Couldn't find file at {}".format(url)) FileNotFoundError: Couldn't find file at https://storage.googleapis.com/tfdataset-data/downloadataset/uncorpus/UNv1.0.en-ru.tar.gz ```

2021-03-03T17:42:02Z

https://github.com/huggingface/datasets/issues/1893

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2021-03-25T11:53:23Z

completed

MDU6SXNzdWU4MDk1NTQxNzQ=

closed

request to mirror wmt datasets, as they are really slow to download

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2021-02-16T18:36:11Z

https://api.github.com/repos/huggingface/datasets/issues/1892

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CONTRIBUTOR

Would it be possible to mirror the wmt data files under hf? Some of them take hours to download and not because of the local speed. They are all quite small datasets, just extremely slow to download. Thank you!

2021-10-26T06:55:42Z

https://github.com/huggingface/datasets/issues/1892

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2022-10-05T12:48:38Z

completed

MDU6SXNzdWU4MDk1NTAwMDE=

closed

suggestion to improve a missing dataset error

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2021-02-16T18:29:13Z

https://api.github.com/repos/huggingface/datasets/issues/1891

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CONTRIBUTOR

I was using `--dataset_name wmt19` all was good. Then thought perhaps wmt20 is out, so I tried to use `--dataset_name wmt20`, got 3 different errors (1 repeated twice), none telling me the real issue - that `wmt20` isn't in the `datasets`: ``` True, predict_with_generate=True) Traceback (most recent call last): File "/mnt/nvme1/code/huggingface/datasets-master/src/datasets/load.py", line 323, in prepare_module local_path = cached_path(file_path, download_config=download_config) File "/mnt/nvme1/code/huggingface/datasets-master/src/datasets/utils/file_utils.py", line 274, in cached_path output_path = get_from_cache( File "/mnt/nvme1/code/huggingface/datasets-master/src/datasets/utils/file_utils.py", line 584, in get_from_cache raise FileNotFoundError("Couldn't find file at {}".format(url)) FileNotFoundError: Couldn't find file at https://raw.githubusercontent.com/huggingface/datasets/master/datasets/wmt20/wmt20.py During handling of the above exception, another exception occurred: Traceback (most recent call last): File "/mnt/nvme1/code/huggingface/datasets-master/src/datasets/load.py", line 335, in prepare_module local_path = cached_path(file_path, download_config=download_config) File "/mnt/nvme1/code/huggingface/datasets-master/src/datasets/utils/file_utils.py", line 274, in cached_path output_path = get_from_cache( File "/mnt/nvme1/code/huggingface/datasets-master/src/datasets/utils/file_utils.py", line 584, in get_from_cache raise FileNotFoundError("Couldn't find file at {}".format(url)) FileNotFoundError: Couldn't find file at https://raw.githubusercontent.com/huggingface/datasets/master/datasets/wmt20/wmt20.py During handling of the above exception, another exception occurred: Traceback (most recent call last): File "./run_seq2seq.py", line 661, in <module> main() File "./run_seq2seq.py", line 317, in main datasets = load_dataset(data_args.dataset_name, data_args.dataset_config_name) File "/mnt/nvme1/code/huggingface/datasets-master/src/datasets/load.py", line 706, in load_dataset module_path, hash, resolved_file_path = prepare_module( File "/mnt/nvme1/code/huggingface/datasets-master/src/datasets/load.py", line 343, in prepare_module raise FileNotFoundError( FileNotFoundError: Couldn't find file locally at wmt20/wmt20.py, or remotely at https://raw.githubusercontent.com/huggingface/datasets/master/datasets/wmt20/wmt20.py. The file is also not present on the master branch on github. ``` Suggestion: if it is not in a local path, check that there is an actual `https://github.com/huggingface/datasets/tree/master/datasets/wmt20` first and assert "dataset `wmt20` doesn't exist in datasets", rather than trying to find a load script - since the whole repo is not there. The error occured when running: ``` cd examples/seq2seq export BS=16; rm -r output_dir; PYTHONPATH=../../src USE_TF=0 CUDA_VISIBLE_DEVICES=0 python ./run_seq2seq.py --model_name_or_path t5-small --output_dir output_dir --adam_eps 1e-06 --do_eval --evaluation_strategy=steps --label_smoothing 0.1 --learning_rate 3e-5 --logging_first_step --logging_steps 1000 --max_source_length 128 --max_target_length 128 --num_train_epochs 1 --overwrite_output_dir --per_device_eval_batch_size $BS --predict_with_generate --eval_steps 25000 --sortish_sampler --task translation_en_to_ro --val_max_target_length 128 --warmup_steps 500 --max_val_samples 500 --dataset_name wmt20 --dataset_config "ro-en" --source_prefix "translate English to Romanian: " ``` Thanks.

2022-10-05T12:48:38Z

https://github.com/huggingface/datasets/issues/1891

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2021-02-16T15:12:33Z

MDExOlB1bGxSZXF1ZXN0NTc0MjY0OTMx

closed

Reformat dataset cards section titles

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2021-02-16T15:11:47Z

https://api.github.com/repos/huggingface/datasets/issues/1890

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Titles are formatted like [Foo](#foo) instead of just Foo

2021-02-16T15:12:34Z

https://github.com/huggingface/datasets/pull/1890

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2021-02-18T18:42:34Z

MDExOlB1bGxSZXF1ZXN0NTc0MTY1NDAz

closed

Implement to_dict and to_pandas for Dataset

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2021-02-16T12:38:19Z

https://api.github.com/repos/huggingface/datasets/issues/1889

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CONTRIBUTOR

With options to return a generator or the full dataset

2021-02-18T18:42:37Z

https://github.com/huggingface/datasets/pull/1889

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2021-02-16T11:58:57Z

MDExOlB1bGxSZXF1ZXN0NTc0MTM2MDU4

closed

Docs for adding new column on formatted dataset

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2021-02-16T11:45:00Z

https://api.github.com/repos/huggingface/datasets/issues/1888

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As mentioned in #1872 we should add in the documentation how the format gets updated when new columns are added Close #1872

2021-03-30T14:01:03Z

https://github.com/huggingface/datasets/pull/1888

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2021-02-19T09:41:59Z

MDExOlB1bGxSZXF1ZXN0NTc0MTI2NTMy

closed

Implement to_csv for Dataset

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2021-02-16T11:27:29Z

https://api.github.com/repos/huggingface/datasets/issues/1887

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CONTRIBUTOR

cc @thomwolf `to_csv` supports passing either a file path or a *binary* file object The writing is batched to avoid loading the whole table in memory

2021-02-19T09:41:59Z

https://github.com/huggingface/datasets/pull/1887

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2021-03-09T18:51:31Z

MDExOlB1bGxSZXF1ZXN0NTc0MTE5ODcz

closed

Common voice

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2021-02-16T11:16:10Z

https://api.github.com/repos/huggingface/datasets/issues/1886

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CONTRIBUTOR

Started filling out information about the dataset and a dataset card. To do Create tagging file Update the common_voice.py file with more information

2021-03-09T18:51:31Z

https://github.com/huggingface/datasets/pull/1886

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2021-02-16T11:44:12Z

MDExOlB1bGxSZXF1ZXN0NTczODQyNzcz

closed

add missing info on how to add large files

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2021-02-15T23:46:39Z

https://api.github.com/repos/huggingface/datasets/issues/1885

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CONTRIBUTOR

Thanks to @lhoestq's instructions I was able to add data files to a custom dataset repo. This PR is attempting to tell others how to do the same if they need to. @lhoestq

2021-02-16T16:22:19Z

https://github.com/huggingface/datasets/pull/1885

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2021-07-30T11:01:18Z

MDExOlB1bGxSZXF1ZXN0NTczNzQwNzI5

closed

dtype fix when using numpy arrays

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2021-02-15T18:55:25Z

https://api.github.com/repos/huggingface/datasets/issues/1884

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CONTRIBUTOR

As discussed in #625 this fix lets the user preserve the dtype of numpy array to pyarrow array which was getting lost due to conversion of numpy array -> list -> pyarrow array

2021-07-30T11:01:18Z

https://github.com/huggingface/datasets/pull/1884

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2021-02-24T14:53:26Z

MDExOlB1bGxSZXF1ZXN0NTczNzM2NTIz

closed

Add not-in-place implementations for several dataset transforms

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2021-02-15T18:44:26Z

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Should we deprecate in-place versions of such methods?

2021-02-24T14:54:49Z

https://github.com/huggingface/datasets/pull/1883

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MDExOlB1bGxSZXF1ZXN0NTczNzA4OTEw

open

Create Remote Manager

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2021-02-15T17:36:24Z

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Refactoring to separate the concern of remote (HTTP/FTP requests) management.

2022-07-06T15:19:47Z

https://github.com/huggingface/datasets/pull/1882

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2021-02-15T15:09:48Z

MDExOlB1bGxSZXF1ZXN0NTczNTk1Nzkw

closed

`list_datasets()` returns a list of strings, not objects

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2021-02-15T14:20:15Z

https://api.github.com/repos/huggingface/datasets/issues/1881

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CONTRIBUTOR

Here and there in the docs there is still stuff like this: ```python >>> datasets_list = list_datasets() >>> print(', '.join(dataset.id for dataset in datasets_list)) ``` However, my understanding is that `list_datasets()` returns a list of strings rather than a list of objects.

2021-02-15T15:09:49Z

https://github.com/huggingface/datasets/pull/1881

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2021-02-15T14:18:18Z

MDExOlB1bGxSZXF1ZXN0NTczNTgzNjg0

closed

Update multi_woz_v22 checksums

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2021-02-15T14:00:18Z

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As noticed in #1876 the checksums of this dataset are outdated. I updated them in this PR

2021-02-15T14:18:19Z

https://github.com/huggingface/datasets/pull/1880

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2021-02-19T18:35:14Z

MDExOlB1bGxSZXF1ZXN0NTczNTY1NDAx

closed

Replace flatten_nested

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2021-02-15T13:29:40Z

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Replace `flatten_nested` with `NestedDataStructure.flatten`. This is a first step towards having all NestedDataStructure logic as a separated concern, independent of the caller/user of the data structure. Eventually, all checks (whether the underlying data is list, dict, etc.) will be only inside this class. I have also generalized the flattening, and now it handles multiple levels of nesting.

2021-02-19T18:35:14Z

https://github.com/huggingface/datasets/pull/1879

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2021-02-15T14:18:09Z

MDExOlB1bGxSZXF1ZXN0NTczNTUyODk3

closed

Add LJ Speech dataset

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2021-02-15T13:10:42Z

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This PR adds the LJ Speech dataset (https://keithito.com/LJ-Speech-Dataset/) As requested by #1841 The ASR format is based on #1767 There are a couple of quirks that should be addressed: - I tagged this dataset as `other-other-automatic-speech-recognition` and `other-other-text-to-speech` (as classified by paperswithcode). Since the number of speech datasets is about to grow, maybe these categories should be added to the main list? - Similarly to #1767 this dataset uses only a single dummy sample to reduce the zip size (`wav`s are quite heavy). Is there a plan to allow LFS or S3 usage for dummy data in the repo? - The dataset is distributed under the Public Domain license, which is not used anywhere else in the repo, AFAIK. Do you think Public Domain is worth adding to the tagger app as well? Pinging @patrickvonplaten to review

2021-02-15T19:39:41Z

https://github.com/huggingface/datasets/pull/1878

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2021-03-26T16:51:58Z

completed

MDU6SXNzdWU4MDg0NjIyNzI=

closed

Allow concatenation of both in-memory and on-disk datasets

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2021-02-15T11:39:46Z

https://api.github.com/repos/huggingface/datasets/issues/1877

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This is a prerequisite for the addition of the `add_item` feature (see #1870). Currently there is one assumption that we would need to change: a dataset is either fully in memory (dataset._data_files is empty), or the dataset can be reloaded from disk (using the dataset._data_files). This assumption is used for pickling for example: - in-memory dataset can just be pickled/unpickled in-memory - on-disk dataset can be unloaded to only keep the filepaths when pickling, and then reloaded from the disk when unpickling Maybe let's have a design that allows a Dataset to have a Table that can be rebuilt from heterogenous sources like in-memory tables or on-disk tables ? This could also be further extended in the future One idea would be to define a list of sources and each source implements a way to reload its corresponding pyarrow Table. Then the dataset would be the concatenation of all these tables. Depending on the source type, the serialization using pickle would be different. In-memory data would be copied while on-disk data would simply be replaced by the path to these data. If you have some ideas you would like to share about the design/API feel free to do so :) cc @albertvillanova

2021-03-26T16:51:58Z

https://github.com/huggingface/datasets/issues/1877

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2021-08-04T18:08:00Z

completed

MDU6SXNzdWU4MDgwMjU4NTk=

closed

load_dataset("multi_woz_v22") NonMatchingChecksumError

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2021-02-14T19:14:48Z

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Hi, it seems that loading the multi_woz_v22 dataset gives a NonMatchingChecksumError. To reproduce: `dataset = load_dataset('multi_woz_v22','v2.2_active_only',split='train')` This will give the following error: ``` raise NonMatchingChecksumError(error_msg + str(bad_urls)) datasets.utils.info_utils.NonMatchingChecksumError: Checksums didn't match for dataset source files: ['https://github.com/budzianowski/multiwoz/raw/master/data/MultiWOZ_2.2/dialog_acts.json', 'https://github.com/budzianowski/multiwoz/raw/master/data/MultiWOZ_2.2/train/dialogues_001.json', 'https://github.com/budzianowski/multiwoz/raw/master/data/MultiWOZ_2.2/train/dialogues_003.json', 'https://github.com/budzianowski/multiwoz/raw/master/data/MultiWOZ_2.2/train/dialogues_004.json', 'https://github.com/budzianowski/multiwoz/raw/master/data/MultiWOZ_2.2/train/dialogues_005.json', 'https://github.com/budzianowski/multiwoz/raw/master/data/MultiWOZ_2.2/train/dialogues_006.json', 'https://github.com/budzianowski/multiwoz/raw/master/data/MultiWOZ_2.2/train/dialogues_007.json', 'https://github.com/budzianowski/multiwoz/raw/master/data/MultiWOZ_2.2/train/dialogues_008.json', 'https://github.com/budzianowski/multiwoz/raw/master/data/MultiWOZ_2.2/train/dialogues_009.json', 'https://github.com/budzianowski/multiwoz/raw/master/data/MultiWOZ_2.2/train/dialogues_010.json', 'https://github.com/budzianowski/multiwoz/raw/master/data/MultiWOZ_2.2/train/dialogues_012.json', 'https://github.com/budzianowski/multiwoz/raw/master/data/MultiWOZ_2.2/train/dialogues_013.json', 'https://github.com/budzianowski/multiwoz/raw/master/data/MultiWOZ_2.2/train/dialogues_014.json', 'https://github.com/budzianowski/multiwoz/raw/master/data/MultiWOZ_2.2/train/dialogues_015.json', 'https://github.com/budzianowski/multiwoz/raw/master/data/MultiWOZ_2.2/train/dialogues_016.json', 'https://github.com/budzianowski/multiwoz/raw/master/data/MultiWOZ_2.2/train/dialogues_017.json', 'https://github.com/budzianowski/multiwoz/raw/master/data/MultiWOZ_2.2/dev/dialogues_001.json', 'https://github.com/budzianowski/multiwoz/raw/master/data/MultiWOZ_2.2/dev/dialogues_002.json', 'https://github.com/budzianowski/multiwoz/raw/master/data/MultiWOZ_2.2/test/dialogues_001.json', 'https://github.com/budzianowski/multiwoz/raw/master/data/MultiWOZ_2.2/test/dialogues_002.json'] ```

2021-08-04T18:08:00Z

https://github.com/huggingface/datasets/issues/1876

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2021-02-17T15:56:27Z

MDExOlB1bGxSZXF1ZXN0NTczMDM2NzE0

closed

Adding sari metric

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2021-02-14T04:38:35Z

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Adding SARI metric that is used in evaluation of text simplification. This is required as part of the GEM benchmark.

2021-02-17T15:56:27Z

https://github.com/huggingface/datasets/pull/1875

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2021-03-04T10:38:22Z

MDExOlB1bGxSZXF1ZXN0NTcyOTYzMjAy

closed

Adding Europarl Bilingual dataset

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2021-02-13T17:02:04Z

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Implementation of Europarl bilingual dataset from described [here](https://opus.nlpl.eu/Europarl.php). This dataset allows to use every language pair detailed in the original dataset. The loading script manages also the small errors contained in the original dataset (in very rare cases (1 over 10M) there are some keys that references to inexistent sentences). I chose to follow the the style of a similar dataset available in this repository: `multi_para_crawl`.

2021-03-04T10:38:22Z

https://github.com/huggingface/datasets/pull/1874

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2021-02-16T14:21:58Z

MDExOlB1bGxSZXF1ZXN0NTcyOTM4MTYy

closed

add iapp_wiki_qa_squad

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2021-02-13T13:34:27Z

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`iapp_wiki_qa_squad` is an extractive question answering dataset from Thai Wikipedia articles. It is adapted from [the original iapp-wiki-qa-dataset](https://github.com/iapp-technology/iapp-wiki-qa-dataset) to [SQuAD](https://rajpurkar.github.io/SQuAD-explorer/) format, resulting in 5761/742/739 questions from 1529/191/192 articles.

2021-02-16T14:21:58Z

https://github.com/huggingface/datasets/pull/1873

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2021-03-30T14:01:45Z

completed

MDU6SXNzdWU4MDc3MTE5MzU=

closed

Adding a new column to the dataset after set_format was called

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2021-02-13T09:14:35Z

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Hi, thanks for the nice library. I'm in the process of creating a custom dataset, which has a mix of tensors and lists of strings. I stumbled upon an error and want to know if its a problem on my side. I load some lists of strings and integers, then call `data.set_format("torch", columns=["some_integer_column1", "some_integer_column2"], output_all_columns=True)`. This converts the integer columns into tensors, but keeps the lists of strings as they are. I then call `map` to add a new column to my dataset, which is a **list of strings**. Once I iterate through my dataset, I get an error that the new column can't be converted into a tensor (which is probably caused by `set_format`). Below some pseudo code: ```python def augment_func(sample: Dict) -> Dict: # do something return { "some_integer_column1" : augmented_data["some_integer_column1"], # <-- tensor "some_integer_column2" : augmented_data["some_integer_column2"], # <-- tensor "NEW_COLUMN": targets, # <-- list of strings } data = datasets.load_dataset(__file__, data_dir="...", split="train") data.set_format("torch", columns=["some_integer_column1", "some_integer_column2"], output_all_columns=True) augmented_dataset = data.map(augment_func, batched=False) for sample in augmented_dataset: print(sample) # fails ``` and the exception: ```python Traceback (most recent call last): File "dataset.py", line 487, in <module> main() File "dataset.py", line 471, in main for sample in augmented_dataset: File "lib/python3.8/site-packages/datasets/arrow_dataset.py", line 697, in __iter__ yield self._getitem( File "lib/python3.8/site-packages/datasets/arrow_dataset.py", line 1069, in _getitem outputs = self._convert_outputs( File "lib/python3.8/site-packages/datasets/arrow_dataset.py", line 890, in _convert_outputs v = map_nested(command, v, **map_nested_kwargs) File "lib/python3.8/site-packages/datasets/utils/py_utils.py", line 225, in map_nested return function(data_struct) File "lib/python3.8/site-packages/datasets/arrow_dataset.py", line 850, in command return [map_nested(command, i, **map_nested_kwargs) for i in x] File "lib/python3.8/site-packages/datasets/arrow_dataset.py", line 850, in <listcomp> return [map_nested(command, i, **map_nested_kwargs) for i in x] File "lib/python3.8/site-packages/datasets/utils/py_utils.py", line 225, in map_nested return function(data_struct) File "lib/python3.8/site-packages/datasets/arrow_dataset.py", line 850, in command return [map_nested(command, i, **map_nested_kwargs) for i in x] File "lib/python3.8/site-packages/datasets/arrow_dataset.py", line 850, in <listcomp> return [map_nested(command, i, **map_nested_kwargs) for i in x] File "lib/python3.8/site-packages/datasets/utils/py_utils.py", line 225, in map_nested return function(data_struct) File "lib/python3.8/site-packages/datasets/arrow_dataset.py", line 851, in command return torch.tensor(x, **format_kwargs) TypeError: new(): invalid data type 'str' ``` Thanks!

2021-03-30T14:01:45Z

https://github.com/huggingface/datasets/issues/1872

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2021-03-08T10:12:45Z

MDExOlB1bGxSZXF1ZXN0NTcyODk5Nzgz

closed

Add newspop dataset

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2021-02-13T07:31:23Z

https://api.github.com/repos/huggingface/datasets/issues/1871

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2021-03-08T10:12:45Z

https://github.com/huggingface/datasets/pull/1871

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2021-04-23T10:01:31Z

MDExOlB1bGxSZXF1ZXN0NTcyNTc4Mjc4

closed

Implement Dataset add_item

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2021-02-12T15:03:46Z

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Implement `Dataset.add_item`. Close #1854.

2021-04-23T10:01:31Z

https://github.com/huggingface/datasets/pull/1870

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2021-02-12T16:13:08Z

MDExOlB1bGxSZXF1ZXN0NTcyNDU0NTMy

closed

Remove outdated commands in favor of huggingface-cli

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2021-02-12T11:28:10Z

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Removing the old user commands since `huggingface_hub` is going to be used instead. cc @julien-c

2021-02-12T16:13:09Z

https://github.com/huggingface/datasets/pull/1869

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2021-02-12T11:03:06Z

MDExOlB1bGxSZXF1ZXN0NTcyNDM2MjA0

closed

Update oscar sizes

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2021-02-12T10:55:35Z

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This commit https://github.com/huggingface/datasets/commit/837a152e4724adc5308e2c4481908c00a8d93383 removed empty lines from the oscar deduplicated datasets. This PR updates the size of each deduplicated dataset to fix possible `NonMatchingSplitsSizesError` errors. cc @cahya-wirawan

2021-02-12T11:03:07Z

https://github.com/huggingface/datasets/pull/1868

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2021-02-24T12:00:43Z

completed

MDU6SXNzdWU4MDcxMjcxODE=

closed

ERROR WHEN USING SET_TRANSFORM()

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2021-02-12T10:38:31Z

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Hi, I'm trying to use dataset.set_transform(encode) as @lhoestq told me in this issue: https://github.com/huggingface/datasets/issues/1825#issuecomment-774202797 However, when I try to use Trainer from transformers with such dataset, it throws an error: ``` TypeError: __init__() missing 1 required positional argument: 'transform' [INFO|trainer.py:357] 2021-02-12 10:18:09,893 >> The following columns in the training set don't have a corresponding argument in `AlbertForMaskedLM.forward` and have been ignored: text. Exception in device=TPU:0: __init__() missing 1 required positional argument: 'transform' Traceback (most recent call last): File "/anaconda3/envs/torch-xla-1.7/lib/python3.6/site-packages/torch_xla/distributed/xla_multiprocessing.py", line 330, in _mp_start_fn _start_fn(index, pf_cfg, fn, args) File "/anaconda3/envs/torch-xla-1.7/lib/python3.6/site-packages/torch_xla/distributed/xla_multiprocessing.py", line 324, in _start_fn fn(gindex, *args) File "/home/alejandro_vaca/transformers/examples/language-modeling/run_mlm_wwm.py", line 368, in _mp_fn main() File "/home/alejandro_vaca/transformers/examples/language-modeling/run_mlm_wwm.py", line 332, in main data_collator=data_collator, File "/anaconda3/envs/torch-xla-1.7/lib/python3.6/site-packages/transformers/trainer.py", line 286, in __init__ self._remove_unused_columns(self.train_dataset, description="training") File "/anaconda3/envs/torch-xla-1.7/lib/python3.6/site-packages/transformers/trainer.py", line 359, in _remove_unused_columns dataset.set_format(type=dataset.format["type"], columns=columns) File "/home/alejandro_vaca/datasets/src/datasets/fingerprint.py", line 312, in wrapper out = func(self, *args, **kwargs) File "/home/alejandro_vaca/datasets/src/datasets/arrow_dataset.py", line 818, in set_format _ = get_formatter(type, **format_kwargs) File "/home/alejandro_vaca/datasets/src/datasets/formatting/__init__.py", line 112, in get_formatter return _FORMAT_TYPES[format_type](**format_kwargs) TypeError: __init__() missing 1 required positional argument: 'transform' ``` The code I'm using: ```{python} def tokenize_function(examples): # Remove empty lines examples["text"] = [line for line in examples["text"] if len(line) > 0 and not line.isspace()] return tokenizer(examples["text"], padding=padding, truncation=True, max_length=data_args.max_seq_length) datasets.set_transform(tokenize_function) data_collator = DataCollatorForWholeWordMask(tokenizer=tokenizer, mlm_probability=data_args.mlm_probability) # Initialize our Trainer trainer = Trainer( model=model, args=training_args, train_dataset=datasets["train"] if training_args.do_train else None, eval_dataset=datasets["val"] if training_args.do_eval else None, tokenizer=tokenizer, data_collator=data_collator, ) ``` I've installed from source, master branch.

2021-03-01T14:04:24Z

https://github.com/huggingface/datasets/issues/1867

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2021-02-17T14:22:36Z

MDExOlB1bGxSZXF1ZXN0NTcyMzM3NDQ1

closed

Add dataset for Financial PhraseBank

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2021-02-12T07:30:56Z

https://api.github.com/repos/huggingface/datasets/issues/1866

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2021-02-17T14:22:36Z

https://github.com/huggingface/datasets/pull/1866

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2021-02-12T16:59:44Z

MDExOlB1bGxSZXF1ZXN0NTcxODE2ODI2

closed

Updated OPUS Open Subtitles Dataset with metadata information

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2021-02-11T13:26:26Z

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Close #1844 Problems: - I ran `python datasets-cli test datasets/open_subtitles --save_infos --all_configs`, hence the change in `dataset_infos.json`, but it appears that the metadata features have not been added for all pairs. Any idea why that might be? - Possibly related to the above, I tried doing `pip uninstall datasets && pip install -e ".[dev]"` after the changes, and loading the dataset via `load_dataset("open_subtitles", lang1='hi', lang2='it')` to check if the update worked, but the loaded dataset did not contain the metadata fields (neither in the features nor doing `next(iter(dataset['train']))`). What step(s) did I miss? Questions: - Is it ok to have a `classmethod` in there? I have not seen any in the few other datasets I have checked. I could make it a local method of the `_generate_examples` method, but I'd rather not duplicate the logic...

2021-02-19T12:38:09Z

https://github.com/huggingface/datasets/pull/1865

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2021-02-11T08:19:51Z

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closed

Add Winogender Schemas

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2021-02-11T08:18:38Z

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## Adding a Dataset - **Name:** Winogender Schemas - **Description:** Winogender Schemas (inspired by Winograd Schemas) are minimal pairs of sentences that differ only by the gender of one pronoun in the sentence, designed to test for the presence of gender bias in automated coreference resolution systems. - **Paper:** https://arxiv.org/abs/1804.09301 - **Data:** https://github.com/rudinger/winogender-schemas (see data directory) - **Motivation:** Testing gender bias in automated coreference resolution systems, improve coreference resolution in general. Instructions to add a new dataset can be found [here](https://github.com/huggingface/datasets/blob/master/ADD_NEW_DATASET.md).

2021-02-11T08:19:51Z

https://github.com/huggingface/datasets/issues/1864

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open

Add WikiCREM

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2021-02-11T08:16:00Z

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## Adding a Dataset - **Name:** WikiCREM - **Description:** A large unsupervised corpus for coreference resolution. - **Paper:** https://arxiv.org/abs/1905.06290 - **Github repo:**: https://github.com/vid-koci/bert-commonsense - **Data:** https://ora.ox.ac.uk/objects/uuid:c83e94bb-7584-41a1-aef9-85b0e764d9e3 - **Motivation:** Coreference resolution, common sense reasoning Instructions to add a new dataset can be found [here](https://github.com/huggingface/datasets/blob/master/ADD_NEW_DATASET.md).

2021-03-07T07:27:13Z

https://github.com/huggingface/datasets/issues/1863

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2021-02-10T18:17:47Z

MDExOlB1bGxSZXF1ZXN0NTcxMjc2ODAx

closed

Fix writing GPU Faiss index

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2021-02-10T17:32:03Z

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As reported in by @corticalstack there is currently an error when we try to save a faiss index on GPU. I fixed that by checking the index `getDevice()` method before calling `index_gpu_to_cpu` Close #1859

2021-02-10T18:17:48Z

https://github.com/huggingface/datasets/pull/1862

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2021-02-10T16:14:59Z

MDExOlB1bGxSZXF1ZXN0NTcxMjAwNjA1

closed

Fix Limit url

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2021-02-10T15:44:56Z

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The test.json file of the Literal-Motion-in-Text (LiMiT) dataset was removed recently on the master branch of the repo at https://github.com/ilmgut/limit_dataset This PR uses the previous commit sha to download the file instead, as suggested by @Paethon Close #1836

2021-02-10T16:15:00Z

https://github.com/huggingface/datasets/pull/1861

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2021-02-12T19:13:29Z

MDExOlB1bGxSZXF1ZXN0NTcxMDk4OTIz

closed

Add loading from the Datasets Hub + add relative paths in download manager

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2021-02-10T13:24:11Z

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With the new Datasets Hub on huggingface.co it's now possible to have a dataset repo with your own script and data. For example: https://huggingface.co/datasets/lhoestq/custom_squad/tree/main contains one script and two json files. You can load it using ```python from datasets import load_dataset d = load_dataset("lhoestq/custom_squad") ``` To be able to use the data files that live right next to the dataset script on the repo in the hub, I added relative paths support for the DownloadManager. For example in the repo mentioned above, there are two json files that can be downloaded via ```python _URLS = { "train": "train-v1.1.json", "dev": "dev-v1.1.json", } downloaded_files = dl_manager.download_and_extract(_URLS) ``` To make it work, I set the `base_path` of the DownloadManager to be the parent path of the dataset script (which comes from either a local path or a remote url). I also had to add the auth header of the requests to huggingface.co for private datasets repos. The token is fetched from [huggingface_hub](https://github.com/huggingface/huggingface_hub).

2021-02-12T19:13:30Z

https://github.com/huggingface/datasets/pull/1860

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2021-02-10T18:17:47Z

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Error "in void don't know how to serialize this type of index" when saving index to disk when device=0 (GPU)

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2021-02-10T12:41:00Z

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Error serializing faiss index. Error as follows: `Error in void faiss::write_index(const faiss::Index*, faiss::IOWriter*) at /home/conda/feedstock_root/build_artifacts/faiss-split_1612472484670/work/faiss/impl/index_write.cpp:453: don't know how to serialize this type of index` Note: `torch.cuda.is_available()` reports: ``` Cuda is available cuda:0 ``` Adding index, device=0 for GPU. `dataset.add_faiss_index(column='embeddings', index_name='idx_embeddings', device=0)` However, during a quick debug, self.faiss_index has no attr "device" when checked in` search.py, method save`, so fails to transform gpu index to cpu index. If I add index without device, index is saved OK. ``` def save(self, file: str): """Serialize the FaissIndex on disk""" import faiss # noqa: F811 if ( hasattr(self.faiss_index, "device") and self.faiss_index.device is not None and self.faiss_index.device > -1 ): index = faiss.index_gpu_to_cpu(self.faiss_index) else: index = self.faiss_index faiss.write_index(index, file) ```

2021-02-10T18:32:12Z

https://github.com/huggingface/datasets/issues/1859

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2021-02-10T15:52:29Z

MDExOlB1bGxSZXF1ZXN0NTcxMDcxNzIx

closed

Clean config getenvs

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2021-02-10T12:39:14Z

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Following #1848 Remove double getenv calls and fix one issue with rarfile cc @albertvillanova

2021-02-10T15:52:30Z

https://github.com/huggingface/datasets/pull/1858

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2021-08-03T05:06:13Z

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Unable to upload "community provided" dataset - 400 Client Error

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2021-02-10T10:39:01Z

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Hi, i'm trying to a upload a dataset as described [here](https://huggingface.co/docs/datasets/v1.2.0/share_dataset.html#sharing-a-community-provided-dataset). This is what happens: ``` $ datasets-cli login $ datasets-cli upload_dataset my_dataset About to upload file /path/to/my_dataset/dataset_infos.json to S3 under filename my_dataset/dataset_infos.json and namespace username About to upload file /path/to/my_dataset/my_dataset.py to S3 under filename my_dataset/my_dataset.py and namespace username Proceed? [Y/n] Y Uploading... This might take a while if files are large 400 Client Error: Bad Request for url: https://huggingface.co/api/datasets/presign huggingface.co migrated to a new model hosting system. You need to upgrade to transformers v3.5+ to upload new models. More info at https://discuss.hugginface.co or https://twitter.com/julien_c. Thank you! ``` I'm using the latest releases of datasets and transformers.

2021-08-03T05:06:13Z

https://github.com/huggingface/datasets/issues/1857

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2022-03-15T13:55:23Z

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load_dataset("amazon_polarity") NonMatchingChecksumError

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2021-02-10T10:00:56Z

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Hi, it seems that loading the amazon_polarity dataset gives a NonMatchingChecksumError. To reproduce: ``` load_dataset("amazon_polarity") ``` This will give the following error: ``` --------------------------------------------------------------------------- NonMatchingChecksumError Traceback (most recent call last) <ipython-input-3-8559a03fe0f8> in <module>() ----> 1 dataset = load_dataset("amazon_polarity") 3 frames /usr/local/lib/python3.6/dist-packages/datasets/utils/info_utils.py in verify_checksums(expected_checksums, recorded_checksums, verification_name) 37 if len(bad_urls) > 0: 38 error_msg = "Checksums didn't match" + for_verification_name + ":\n" ---> 39 raise NonMatchingChecksumError(error_msg + str(bad_urls)) 40 logger.info("All the checksums matched successfully" + for_verification_name) 41 NonMatchingChecksumError: Checksums didn't match for dataset source files: ['https://drive.google.com/u/0/uc?id=0Bz8a_Dbh9QhbaW12WVVZS2drcnM&export=download'] ```

2022-03-15T13:55:24Z

https://github.com/huggingface/datasets/issues/1856

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2021-02-10T12:33:09Z

MDExOlB1bGxSZXF1ZXN0NTcwODkzNDY3

closed

Minor fix in the docs

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2021-02-10T07:27:43Z

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2021-02-10T12:33:09Z

https://github.com/huggingface/datasets/pull/1855

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2021-04-23T10:01:30Z

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closed

Feature Request: Dataset.add_item

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2021-02-10T06:06:00Z

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I'm trying to integrate `huggingface/datasets` functionality into `fairseq`, which requires (afaict) being able to build a dataset through an `add_item` method, such as https://github.com/pytorch/fairseq/blob/master/fairseq/data/indexed_dataset.py#L318, as opposed to loading all the text into arrow, and then `dataset.map(binarizer)`. Is this possible at the moment? Is there an example? I'm happy to use raw `pa.Table` but not sure whether it will support uneven length entries. ### Desired API ```python import numpy as np tokenized: List[np.NDArray[np.int64]] = [np.array([4,4,2]), np.array([8,6,5,5,2]), np.array([3,3,31,5]) def build_dataset_from_tokenized(tokenized: List[np.NDArray[int]]) -> Dataset: """FIXME""" dataset = EmptyDataset() for t in tokenized: dataset.append(t) return dataset ds = build_dataset_from_tokenized(tokenized) assert (ds[0] == np.array([4,4,2])).all() ``` ### What I tried grep, google for "add one entry at a time", "datasets.append" ### Current Code This code achieves the same result but doesn't fit into the `add_item` abstraction. ```python dataset = load_dataset('text', data_files={'train': 'train.txt'}) tokenizer = RobertaTokenizerFast.from_pretrained('roberta-base', max_length=4096) def tokenize_function(examples): ids = tokenizer(examples['text'], return_attention_mask=False)['input_ids'] return {'input_ids': [x[1:] for x in ids]} ds = dataset.map(tokenize_function, batched=True, num_proc=4, remove_columns=['text'], load_from_cache_file=not overwrite_cache) print(ds['train'][0]) => np array ``` Thanks in advance!

2021-04-23T10:01:30Z

https://github.com/huggingface/datasets/issues/1854

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2021-02-10T12:32:34Z

MDExOlB1bGxSZXF1ZXN0NTcwNTAwMjc4

closed

Configure library root logger at the module level

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2021-02-09T18:11:12Z

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Configure library root logger at the datasets.logging module level (singleton-like). By doing it this way: - we are sure configuration is done only once: module level code is only runned once - no need of global variable - no need of threading lock

2021-02-10T12:32:34Z

https://github.com/huggingface/datasets/pull/1853

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2021-02-11T10:18:55Z

MDExOlB1bGxSZXF1ZXN0NTcwMzY3NTU1

closed

Add Arabic Speech Corpus

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2021-02-09T15:02:26Z

https://api.github.com/repos/huggingface/datasets/issues/1852

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2021-02-11T10:18:55Z

https://github.com/huggingface/datasets/pull/1852

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2021-02-09T14:21:48Z

MDExOlB1bGxSZXF1ZXN0NTcwMjc2MTk5

closed

set bert_score version dependency

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2021-02-09T12:51:07Z

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CONTRIBUTOR

Set the bert_score version in requirements since previous versions of bert_score will fail with datasets (closes #843)

2021-02-09T14:21:48Z

https://github.com/huggingface/datasets/pull/1851

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2021-02-09T15:16:26Z

MDExOlB1bGxSZXF1ZXN0NTcwMTg0MDAx

closed

Add cord 19 dataset

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2021-02-09T10:22:08Z

https://api.github.com/repos/huggingface/datasets/issues/1850

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CONTRIBUTOR

Initial version only reading the metadata in CSV. ### Checklist: - [x] Create the dataset script /datasets/my_dataset/my_dataset.py using the template - [x] Fill the _DESCRIPTION and _CITATION variables - [x] Implement _infos(), _split_generators() and _generate_examples() - [x] Make sure that the BUILDER_CONFIGS class attribute is filled with the different configurations of the dataset and that the BUILDER_CONFIG_CLASS is specified if there is a custom config class. - [x] Generate the metadata file dataset_infos.json for all configurations - [x] Generate the dummy data dummy_data.zip files to have the dataset script tested and that they don't weigh too much (<50KB) - [x] Add the dataset card README.md using the template and at least fill the tags - [x] Both tests for the real data and the dummy data pass. ### Extras: - [x] add more metadata - [x] add full text - [x] add pre-computed document embedding

2021-02-09T15:16:26Z

https://github.com/huggingface/datasets/pull/1850

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2021-03-15T05:59:37Z

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closed

Add TIMIT

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2021-02-09T07:29:41Z

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CONTRIBUTOR

## Adding a Dataset - **Name:** *TIMIT* - **Description:** *The TIMIT corpus of read speech has been designed to provide speech data for the acquisition of acoustic-phonetic knowledge and for the development and evaluation of automatic speech recognition systems* - **Paper:** *Homepage*: http://groups.inf.ed.ac.uk/ami/corpus/ / *Wikipedia*: https://en.wikipedia.org/wiki/TIMIT - **Data:** *https://deepai.org/dataset/timit* - **Motivation:** Important speech dataset If interested in tackling this issue, feel free to tag @patrickvonplaten Instructions to add a new dataset can be found [here](https://github.com/huggingface/datasets/blob/master/ADD_NEW_DATASET.md).

2021-03-15T05:59:37Z

https://github.com/huggingface/datasets/issues/1849

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2021-02-10T12:29:35Z

MDExOlB1bGxSZXF1ZXN0NTY5Njg5ODU1

closed

Refactoring: Create config module

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2021-02-08T18:43:51Z

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Refactorize configuration settings into their own module. This could be seen as a Pythonic singleton-like approach. Eventually a config instance class might be created.

2021-02-10T12:29:35Z

https://github.com/huggingface/datasets/pull/1848

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2021-02-09T17:53:21Z

MDExOlB1bGxSZXF1ZXN0NTY5Njg4NDY0

closed

[Metrics] Add word error metric metric

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2021-02-08T18:41:15Z

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This PR adds the word error rate metric to datasets. WER: https://en.wikipedia.org/wiki/Word_error_rate for speech recognition. WER is the main metric used in ASR. `jiwer` seems to be a solid library (see https://github.com/asteroid-team/asteroid/pull/329#discussion_r525158939)

2021-02-09T17:53:21Z

https://github.com/huggingface/datasets/pull/1847

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2021-02-25T14:10:18Z

MDExOlB1bGxSZXF1ZXN0NTY5NjczMzcy

closed

Make DownloadManager downloaded/extracted paths accessible

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2021-02-08T18:14:42Z

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Make accessible the file paths downloaded/extracted by DownloadManager. Close #1831. The approach: - I set these paths as DownloadManager attributes: these are DownloadManager's concerns - To access to these from DatasetBuilder, I set the DownloadManager instance as DatasetBuilder attribute: object composition

2021-02-25T14:10:18Z

https://github.com/huggingface/datasets/pull/1846

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2021-02-09T14:22:37Z

MDExOlB1bGxSZXF1ZXN0NTY5NTk2MTIz

closed

Enable logging propagation and remove logging handler

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2021-02-08T16:22:13Z

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We used to have logging propagation disabled because of this issue: https://github.com/tensorflow/tensorflow/issues/26691 But since it's now fixed we should re-enable it. This is important to keep the default logging behavior for users, and propagation is also needed for pytest fixtures as asked in #1826 I also removed the handler that was added since, according to the logging [documentation](https://docs.python.org/3/howto/logging.html#configuring-logging-for-a-library): > It is strongly advised that you do not add any handlers other than NullHandler to your library’s loggers. This is because the configuration of handlers is the prerogative of the application developer who uses your library. The application developer knows their target audience and what handlers are most appropriate for their application: if you add handlers ‘under the hood’, you might well interfere with their ability to carry out unit tests and deliver logs which suit their requirements. It could have been useful if we wanted to have a custom formatter for the logging but I think it's more important to keep the logging as default to not interfere with the users' logging management. Therefore I also removed the two methods `datasets.logging.enable_default_handler` and `datasets.logging.disable_default_handler`. cc @albertvillanova this should let you use capsys/caplog in pytest cc @LysandreJik @sgugger if you want to do the same in `transformers`

2021-02-09T14:22:38Z

https://github.com/huggingface/datasets/pull/1845

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2021-02-12T17:38:58Z

completed

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closed

Update Open Subtitles corpus with original sentence IDs

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2021-02-08T13:55:13Z

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Hi! It would be great if you could add the original sentence ids to [Open Subtitles](https://huggingface.co/datasets/open_subtitles). I can think of two reasons: first, it's possible to gather sentences for an entire document (the original ids contain media id, subtitle file id and sentence id), therefore somewhat allowing for document-level machine translation (and other document-level stuff which could be cool to have); second, it's possible to have parallel sentences in multiple languages, as they share the same ids across bitexts. I think I should tag @abhishekkrthakur as he's the one who added it in the first place. Thanks!

2021-02-12T17:38:58Z

https://github.com/huggingface/datasets/issues/1844

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open

MustC Speech Translation

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2021-02-08T13:27:45Z

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CONTRIBUTOR

## Adding a Dataset - **Name:** *IWSLT19* - **Description:** *The Speech Translation Task addresses the translation of English audio into German and Portuguese text.* - **Hompage:** *https://sites.google.com/view/iwslt-evaluation-2019/speech-translation* - **Data:** *https://sites.google.com/view/iwslt-evaluation-2019/speech-translation* - all data under "Allowed Training Data" and "Development and Evalutaion Data for TED/How2" - **Motivation:** Important speech dataset If interested in tackling this issue, feel free to tag @patrickvonplaten Instructions to add a new dataset can be found [here](https://github.com/huggingface/datasets/blob/master/ADD_NEW_DATASET.md).

2021-05-14T14:53:34Z

https://github.com/huggingface/datasets/issues/1843

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2023-02-28T16:29:22Z

completed

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closed

Add AMI Corpus

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2021-02-08T13:25:00Z

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CONTRIBUTOR

## Adding a Dataset - **Name:** *AMI* - **Description:** *The AMI Meeting Corpus is a multi-modal data set consisting of 100 hours of meeting recordings. For a gentle introduction to the corpus, see the corpus overview. To access the data, follow the directions given there. Around two-thirds of the data has been elicited using a scenario in which the participants play different roles in a design team, taking a design project from kick-off to completion over the course of a day. The rest consists of naturally occurring meetings in a range of domains. Detailed information can be found in the documentation section.* - **Paper:** *Homepage*: http://groups.inf.ed.ac.uk/ami/corpus/ - **Data:** *http://groups.inf.ed.ac.uk/ami/download/* - Select all cases in 1) and select "Individual Headsets" & "Microphone array" for 2) - **Motivation:** Important speech dataset If interested in tackling this issue, feel free to tag @patrickvonplaten Instructions to add a new dataset can be found [here](https://github.com/huggingface/datasets/blob/master/ADD_NEW_DATASET.md).

2023-02-28T16:29:22Z

https://github.com/huggingface/datasets/issues/1842

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2021-03-15T05:59:02Z

completed

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closed

Add ljspeech

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2021-02-08T13:22:26Z

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## Adding a Dataset - **Name:** *ljspeech* - **Description:** *This is a public domain speech dataset consisting of 13,100 short audio clips of a single speaker reading passages from 7 non-fiction books. A transcription is provided for each clip. Clips vary in length from 1 to 10 seconds and have a total length of approximately 24 hours. The texts were published between 1884 and 1964, and are in the public domain. The audio was recorded in 2016-17 by the LibriVox project and is also in the public domain.)* - **Paper:** *Homepage*: https://keithito.com/LJ-Speech-Dataset/ - **Data:** *https://keithito.com/LJ-Speech-Dataset/* - **Motivation:** Important speech dataset - **TFDatasets Implementation**: https://www.tensorflow.org/datasets/catalog/ljspeech If interested in tackling this issue, feel free to tag @patrickvonplaten Instructions to add a new dataset can be found [here](https://github.com/huggingface/datasets/blob/master/ADD_NEW_DATASET.md).

2021-03-15T05:59:02Z

https://github.com/huggingface/datasets/issues/1841

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2021-03-15T05:56:21Z

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Add common voice

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2021-02-08T13:21:05Z

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CONTRIBUTOR

## Adding a Dataset - **Name:** *common voice* - **Description:** *Mozilla Common Voice Dataset* - **Paper:** Homepage: https://voice.mozilla.org/en/datasets - **Data:** https://voice.mozilla.org/en/datasets - **Motivation:** Important speech dataset - **TFDatasets Implementation**: https://www.tensorflow.org/datasets/catalog/common_voice If interested in tackling this issue, feel free to tag @patrickvonplaten Instructions to add a new dataset can be found [here](https://github.com/huggingface/datasets/blob/master/ADD_NEW_DATASET.md).

2022-03-20T15:23:40Z

https://github.com/huggingface/datasets/issues/1840

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