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https://api.github.com/repos/huggingface/datasets/issues/4932 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/4932/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/4932/comments | https://api.github.com/repos/huggingface/datasets/issues/4932/events | https://github.com/huggingface/datasets/issues/4932 | 1,362,522,423 | I_kwDODunzps5RNnE3 | 4,932 | Dataset Viewer issue for bigscience-biomedical/biosses | {
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https://huggingface.co/datasets/bigscience-biomedical/biosses
### Description
I've just been working on adding the dataset loader script to this dataset and working with the relative imports. I'm not sure how to interpret the error below (show where the dataset preview used to be) .
```
Status code: 400
Exception: ModuleNotFoundError
Message: No module named 'datasets_modules.datasets.bigscience-biomedical--biosses.ddbd5893bf6c2f4db06f407665eaeac619520ba41f69d94ead28f7cc5b674056.bigbiohub'
```
### Owner
Yes | {
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} | Fix missing tags in dataset cards:
- coqa
- hyperpartisan_news_detection
- opinosis
- scientific_papers
- scifact
- search_qa
- wiki_qa
- wiki_split
- wikisql
This PR partially fixes the missing tags in dataset cards. Subsequent PRs will follow to complete this task.
Related to:
- #4833
- #4891
- #4896
- #4908
- #4921 | {
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} | As show in the [documentation page](https://huggingface.co/docs/datasets/loading) here the `"tr"in` should be `"train`.
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} | Fixes #3094
Add ability to read/write to SQLite files and also read from any SQL database supported by SQLAlchemy.
I didn't add SQLAlchemy as a dependence as it is fairly big and it remains optional.
I also recorded a Loom to showcase the feature.
https://www.loom.com/share/f0e602c2de8a46f58bca4b43333d541f | {
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https://api.github.com/repos/huggingface/datasets/issues/4926 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/4926/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/4926/comments | https://api.github.com/repos/huggingface/datasets/issues/4926/events | https://github.com/huggingface/datasets/pull/4926 | 1,360,384,484 | PR_kwDODunzps4-Srm1 | 4,926 | Dataset infos in yaml | {
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] | closed | false | null | [] | null | 6 | 2022-09-02T16:10:05 | 2024-05-04T14:52:50 | 2022-10-03T09:11:12 | MEMBER | null | {
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} | To simplify the addition of new datasets, we'd like to have the dataset infos in the YAML and deprecate the dataset_infos.json file. YAML is readable and easy to edit, and the YAML metadata of the readme already contain dataset metadata so we would have everything in one place.
To be more specific, I moved these fields from DatasetInfo to the YAML:
- config_name (if there are several configs)
- download_size
- dataset_size
- features
- splits
Here is what I ended up with for `squad`:
```yaml
dataset_info:
features:
- name: id
dtype: string
- name: title
dtype: string
- name: context
dtype: string
- name: question
dtype: string
- name: answers
sequence:
- name: text
dtype: string
- name: answer_start
dtype: int32
splits:
- name: train
num_bytes: 79346360
num_examples: 87599
- name: validation
num_bytes: 10473040
num_examples: 10570
config_name: plain_text
download_size: 35142551
dataset_size: 89819400
```
and it can be a list if there are several configs
I already did the change for `conll2000` and `crime_and_punish` as an example.
## Implementation details
### Load/Read
This is done via `DatasetInfosDict.write_to_directory/from_directory`
I had to implement a custom the YAML export logic for `SplitDict`, `Version` and `Features`.
The first two are trivial, but the logic for `Features` is more complicated, because I added a simplification step (or the YAML would be too long and less readable): it's just a formatting step to remove unnecessary nesting of YAML data.
### Other changes
I had to update the DatasetModule factories to also download the README.md alongside the dataset scripts/data files, and not just the dataset_infos.json
## YAML validation
I removed the old validation code that was in metadata.py, now we can just use the Hub YAML validation
## Datasets-cli
The `datasets-cli test --save_infos` command now creates a README.md file with the dataset_infos in it, instead of a datasets_infos.json file
## Backward compatibility
`dataset_infos.json` files are still supported and loaded if they exist to have full backward compatibility.
Though I removed the unnecessary keys when the value is the default (like all the `id: null` from the Value feature types) to make them easier to read.
## TODO
- [x] add comments
- [x] tests
- [x] document the new YAML fields
- [x] try to reload the new dataset_infos.json file content with an old version of `datasets`
## EDITS
- removed "config_name" when there's only one config
- removed "version" for now (?), because it's not useful in general
- renamed the yaml field "dataset_info" instead of "dataset_infos", since it only has one by default (and because "infos" is not english)
Fix https://github.com/huggingface/datasets/issues/4876 and fix #2773 | {
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https://api.github.com/repos/huggingface/datasets/issues/4925 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/4925/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/4925/comments | https://api.github.com/repos/huggingface/datasets/issues/4925/events | https://github.com/huggingface/datasets/pull/4925 | 1,360,007,616 | PR_kwDODunzps4-RbP5 | 4,925 | Add note about loading image / audio files to docs | {
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} | This PR adds a small note about how to load image / audio datasets that have multiple splits in their dataset structure.
Related forum thread: https://discuss.huggingface.co/t/loading-train-and-test-splits-with-audiofolder/22447
cc @NielsRogge | {
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https://api.github.com/repos/huggingface/datasets/issues/4924 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/4924/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/4924/comments | https://api.github.com/repos/huggingface/datasets/issues/4924/events | https://github.com/huggingface/datasets/issues/4924 | 1,358,611,513 | I_kwDODunzps5Q-sQ5 | 4,924 | Concatenate_datasets loads everything into RAM | {
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} | null | null | null | ## Describe the bug
When loading the datasets seperately and saving them on disk, I want to concatenate them. But `concatenate_datasets` is filling up my RAM and the process gets killed. Is there a way to prevent this from happening or is this intended behaviour? Thanks in advance
## Steps to reproduce the bug
```python
gcs = gcsfs.GCSFileSystem(project='project')
datasets = [load_from_disk(f'path/to/slice/of/data/{i}', fs=gcs, keep_in_memory=False) for i in range(10)]
dataset = concatenate_datasets(datasets)
```
## Expected results
A concatenated dataset which is stored on my disk.
## Actual results
Concatenated dataset gets loaded into RAM and overflows it which gets the process killed.
## Environment info
<!-- You can run the command `datasets-cli env` and copy-and-paste its output below. -->
- `datasets` version: 2.4.0
- Platform: Linux-4.19.0-21-cloud-amd64-x86_64-with-glibc2.10
- Python version: 3.8.13
- PyArrow version: 8.0.1
- Pandas version: 1.4.3 | {
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https://api.github.com/repos/huggingface/datasets/issues/4923 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/4923/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/4923/comments | https://api.github.com/repos/huggingface/datasets/issues/4923/events | https://github.com/huggingface/datasets/pull/4923 | 1,357,735,287 | PR_kwDODunzps4-Jv7C | 4,923 | decode mp3 with librosa if torchaudio is > 0.12 as a temporary workaround | {
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} | `torchaudio>0.12` fails with decoding mp3 files if `ffmpeg<4`. currently we ask users to downgrade torchaudio, but sometimes it's not possible as torchaudio version is binded to torch version. as a temporary workaround we can decode mp3 with librosa (though it 60 times slower, at least it works)
another option would be to ask users to install the required version of `ffmpeg`, but is non-trivial on colab: it's not in apt packages in ubuntu 18 and `conda` is not preinstalled (with `conda` it would be easily installable)
- [x] decode with torchaudio anyway if the version of ffmpeg is correct? it's 60 times faster
- [x] tests
- [x] DO NOT FORGET to get back all the tests
see https://github.com/huggingface/datasets/issues/4776 and https://github.com/huggingface/datasets/issues/3663#issuecomment-1225797165 (there is a Colab notebook to reproduce the error) | {
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https://api.github.com/repos/huggingface/datasets/issues/4922 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/4922/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/4922/comments | https://api.github.com/repos/huggingface/datasets/issues/4922/events | https://github.com/huggingface/datasets/issues/4922 | 1,357,684,018 | I_kwDODunzps5Q7J0y | 4,922 | I/O error on Google Colab in streaming mode | {
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"total": 0,
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} | null | null | null | ## Describe the bug
When trying to load a streaming dataset in Google Colab the loading fails with an I/O error
## Steps to reproduce the bug
```python
import datasets
from datasets import load_dataset
hf_ds = load_dataset(path='wmt19', name='cs-en', streaming=True, split=datasets.Split.VALIDATION)
list(hf_ds.take(5))
```
## Expected results
It should load five data points
## Actual results
```
---------------------------------------------------------------------------
ValueError Traceback (most recent call last)
[<ipython-input-13-7b5b8b1e7e58>](https://localhost:8080/#) in <module>
2 from datasets import load_dataset
3 hf_ds = load_dataset(path='wmt19', name='cs-en', streaming=True, split=datasets.Split.VALIDATION)
----> 4 list(hf_ds.take(5))
6 frames
[/usr/local/lib/python3.7/dist-packages/datasets/iterable_dataset.py](https://localhost:8080/#) in __iter__(self)
716
717 def __iter__(self):
--> 718 for key, example in self._iter():
719 if self.features:
720 # `IterableDataset` automatically fills missing columns with None.
[/usr/local/lib/python3.7/dist-packages/datasets/iterable_dataset.py](https://localhost:8080/#) in _iter(self)
706 else:
707 ex_iterable = self._ex_iterable
--> 708 yield from ex_iterable
709
710 def _iter_shard(self, shard_idx: int):
[/usr/local/lib/python3.7/dist-packages/datasets/iterable_dataset.py](https://localhost:8080/#) in __iter__(self)
582
583 def __iter__(self):
--> 584 yield from islice(self.ex_iterable, self.n)
585
586 def shuffle_data_sources(self, generator: np.random.Generator) -> "TakeExamplesIterable":
[/usr/local/lib/python3.7/dist-packages/datasets/iterable_dataset.py](https://localhost:8080/#) in __iter__(self)
110
111 def __iter__(self):
--> 112 yield from self.generate_examples_fn(**self.kwargs)
113
114 def shuffle_data_sources(self, generator: np.random.Generator) -> "ExamplesIterable":
[~/.cache/huggingface/modules/datasets_modules/datasets/wmt19/aeadcbe9f1cbf9969e603239d33d3e43670cf250c1158edf74f5f6e74d4f21d0/wmt_utils.py](https://localhost:8080/#) in _generate_examples(self, split_subsets, extraction_map, with_translation)
845 raise ValueError("Invalid number of files: %d" % len(files))
846
--> 847 for sub_key, ex in sub_generator(*sub_generator_args):
848 if not all(ex.values()):
849 continue
[~/.cache/huggingface/modules/datasets_modules/datasets/wmt19/aeadcbe9f1cbf9969e603239d33d3e43670cf250c1158edf74f5f6e74d4f21d0/wmt_utils.py](https://localhost:8080/#) in _parse_parallel_sentences(f1, f2, filename1, filename2)
923 l2_sentences, l2 = parse_file(f2_i, filename2)
924
--> 925 for line_id, (s1, s2) in enumerate(zip(l1_sentences, l2_sentences)):
926 key = f"{f_id}/{line_id}"
927 yield key, {l1: s1, l2: s2}
[~/.cache/huggingface/modules/datasets_modules/datasets/wmt19/aeadcbe9f1cbf9969e603239d33d3e43670cf250c1158edf74f5f6e74d4f21d0/wmt_utils.py](https://localhost:8080/#) in gen()
895
896 def gen():
--> 897 with open(path, encoding="utf-8") as f:
898 for line in f:
899 seg_match = re.match(seg_re, line)
ValueError: I/O operation on closed file.
```
## Environment info
Copy-and-paste the text below in your GitHub issue.
- `datasets` version: 2.4.0
- Platform: Linux-5.4.188+-x86_64-with-Ubuntu-18.04-bionic
- Python version: 3.7.13
- PyArrow version: 9.0.0. (the same error happened with PyArrow version 6.0.0)
- Pandas version: 1.3.5
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https://api.github.com/repos/huggingface/datasets/issues/4921 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/4921/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/4921/comments | https://api.github.com/repos/huggingface/datasets/issues/4921/events | https://github.com/huggingface/datasets/pull/4921 | 1,357,609,003 | PR_kwDODunzps4-JVFV | 4,921 | Fix missing tags in dataset cards | {
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} | [] | closed | false | null | [] | null | 1 | 2022-08-31T16:52:27 | 2022-09-22T14:34:11 | 2022-09-01T05:04:53 | MEMBER | null | {
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"url": "https://api.github.com/repos/huggingface/datasets/pulls/4921",
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} | Fix missing tags in dataset cards:
- eraser_multi_rc
- hotpot_qa
- metooma
- movie_rationales
- qanta
- quora
- quoref
- race
- ted_hrlr
- ted_talks_iwslt
This PR partially fixes the missing tags in dataset cards. Subsequent PRs will follow to complete this task.
Related to:
- #4833
- #4891
- #4896
- #4908 | {
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https://api.github.com/repos/huggingface/datasets/issues/4920 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/4920/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/4920/comments | https://api.github.com/repos/huggingface/datasets/issues/4920/events | https://github.com/huggingface/datasets/issues/4920 | 1,357,564,589 | I_kwDODunzps5Q6sqt | 4,920 | Unable to load local tsv files through load_dataset method | {
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} | null | null | null | ## Describe the bug
Unable to load local tsv files through load_dataset method.
## Steps to reproduce the bug
```python
# Sample code to reproduce the bug
data_files = {
'train': 'train.tsv',
'test': 'test.tsv'
}
raw_datasets = load_dataset('tsv', data_files=data_files)
## Expected results
I am pretty sure the data files exist in the current directory. The above code should load them as Datasets, but threw exceptions.
## Actual results
---------------------------------------------------------------------------
FileNotFoundError Traceback (most recent call last)
[<ipython-input-9-24207899c1af>](https://localhost:8080/#) in <module>
----> 1 raw_datasets = load_dataset('tsv', data_files='train.tsv')
2 frames
[/usr/local/lib/python3.7/dist-packages/datasets/load.py](https://localhost:8080/#) in dataset_module_factory(path, revision, download_config, download_mode, dynamic_modules_path, data_dir, data_files, **download_kwargs)
1244 f"Couldn't find a dataset script at {relative_to_absolute_path(combined_path)} or any data file in the same directory. "
1245 f"Couldn't find '{path}' on the Hugging Face Hub either: {type(e1).__name__}: {e1}"
-> 1246 ) from None
1247 raise e1 from None
1248 else:
FileNotFoundError: Couldn't find a dataset script at /content/tsv/tsv.py or any data file in the same directory. Couldn't find 'tsv' on the Hugging Face Hub either: FileNotFoundError: Couldn't find file at https://raw.githubusercontent.com/huggingface/datasets/main/datasets/tsv/tsv.py
## Environment info
<!-- You can run the command `datasets-cli env` and copy-and-paste its output below. -->
- `datasets` version: 2.4.0
- Platform: Linux-5.4.188+-x86_64-with-Ubuntu-18.04-bionic
- Python version: 3.7.13
- PyArrow version: 6.0.1
- Pandas version: 1.3.5
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https://api.github.com/repos/huggingface/datasets/issues/4919 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/4919/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/4919/comments | https://api.github.com/repos/huggingface/datasets/issues/4919/events | https://github.com/huggingface/datasets/pull/4919 | 1,357,441,599 | PR_kwDODunzps4-IxDZ | 4,919 | feat: improve error message on Keys mismatch. closes #4917 | {
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} | [] | closed | false | null | [] | null | 2 | 2022-08-31T14:41:36 | 2022-09-05T08:46:01 | 2022-09-05T08:43:33 | CONTRIBUTOR | null | {
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} | Hi @lhoestq what do you think?
Let me give you a code sample:
```py
>>> import datasets
>>> foo = datasets.Dataset.from_dict({'foo':[0,1], 'bar':[2,3]})
>>> foo.save_to_disk('foo')
# edit foo/dataset_info.json e.g. rename the 'foo' feature to 'baz'
>>> datasets.load_from_disk('foo')
---------------------------------------------------------------------------
ValueError Traceback (most recent call last)
<ipython-input-2-4863e606b330> in <module>
----> 1 datasets.load_from_disk('foo')
~/code/datasets/src/datasets/load.py in load_from_disk(dataset_path, fs, keep_in_memory)
1851 raise FileNotFoundError(f"Directory {dataset_path} not found")
1852 if fs.isfile(Path(dest_dataset_path, config.DATASET_INFO_FILENAME).as_posix()):
-> 1853 return Dataset.load_from_disk(dataset_path, fs, keep_in_memory=keep_in_memory)
1854 elif fs.isfile(Path(dest_dataset_path, config.DATASETDICT_JSON_FILENAME).as_posix()):
1855 return DatasetDict.load_from_disk(dataset_path, fs, keep_in_memory=keep_in_memory)
~/code/datasets/src/datasets/arrow_dataset.py in load_from_disk(dataset_path, fs, keep_in_memory)
1230 info=dataset_info,
1231 split=split,
-> 1232 fingerprint=state["_fingerprint"],
1233 )
1234
~/code/datasets/src/datasets/arrow_dataset.py in __init__(self, arrow_table, info, split, indices_table, fingerprint)
687 self.info.features = inferred_features
688 else: # make sure the nested columns are in the right order
--> 689 self.info.features = self.info.features.reorder_fields_as(inferred_features)
690
691 # Infer fingerprint if None
~/code/datasets/src/datasets/features/features.py in reorder_fields_as(self, other)
1771 return source
1772
-> 1773 return Features(recursive_reorder(self, other))
1774
1775 def flatten(self, max_depth=16) -> "Features":
~/code/datasets/src/datasets/features/features.py in recursive_reorder(source, target, stack)
1760 f"{source.keys()-target.keys()} are missing from dataset.arrow "
1761 f"and {target.keys()-source.keys()} are missing from dataset_info.json"+stack_position)
-> 1762 raise ValueError(message)
1763 return {key: recursive_reorder(source[key], target[key], stack + f".{key}") for key in target}
1764 elif isinstance(source, list):
ValueError: Keys mismatch: between {'baz': Value(dtype='int64', id=None), 'bar': Value(dtype='int64', id=None)} (dataset_info.json) and {'foo': Value(dtype='int64', id=None), 'bar': Value(dtype='int64', id=None)} (inferred from dataset.arrow).
{'baz'} are missing from dataset.arrow and {'foo'} are missing from dataset_info.json
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} | null | null | null | ### Link
https://huggingface.co/datasets/pysentimiento/spanish-targeted-sentiment-headlines
### Description
After moving the dataset from my user (`finiteautomata`) to the `pysentimiento` organization, the dataset viewer says that it doesn't exist.
### Owner
_No response_ | {
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https://api.github.com/repos/huggingface/datasets/issues/4917 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/4917/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/4917/comments | https://api.github.com/repos/huggingface/datasets/issues/4917/events | https://github.com/huggingface/datasets/issues/4917 | 1,357,193,841 | I_kwDODunzps5Q5SJx | 4,917 | Keys mismatch: make error message more informative | {
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] | closed | false | null | [] | null | 4 | 2022-08-31T11:24:34 | 2022-09-05T08:43:38 | 2022-09-05T08:43:38 | CONTRIBUTOR | null | {
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} | null | null | null | **Is your feature request related to a problem? Please describe.**
When loading a dataset from disk with a defect in its `dataset_info.json` describing its features (I don’t know when/why/how this happens but it deserves its own issue), you will get an error message like:
`ValueError: Keys mismatch: between {'bar': Value(dtype='int64', id=None)} and {'foo': Value(dtype='int64', id=None)}`
Which is fine when you have only a few features like in the example but it gets very hard to read when you have a lot of features in your dataset.
**Describe the solution you'd like**
The error message should give the difference between the features (what keys are in A but missing in B and vice-versa). It should also tell which keys are inferred from `dataset.arrow` and which come from `dataset_info.json`.
Willing to help :)
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https://api.github.com/repos/huggingface/datasets/issues/4916 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/4916/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/4916/comments | https://api.github.com/repos/huggingface/datasets/issues/4916/events | https://github.com/huggingface/datasets/issues/4916 | 1,357,076,940 | I_kwDODunzps5Q41nM | 4,916 | Apache Beam unable to write the downloaded wikipedia dataset | {
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] | closed | false | null | [] | null | 1 | 2022-08-31T09:39:25 | 2022-08-31T10:53:19 | 2022-08-31T10:53:19 | NONE | null | {
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} | null | null | null | ## Describe the bug
Hi, I am currently trying to download wikipedia dataset using
load_dataset("wikipedia", language="aa", date="20220401", split="train",beam_runner='DirectRunner'). However, I end up in getting filenotfound error. I get this error for any language I try to download. It downloads the file but while saving it in hugging face cache it fails to write. This happens for any available date of any language in wikipedia dump. I had raised another issue earlier #4915 but probably was not that clear and the solution provider misunderstood my problem. Hence raising one more issue. Any help is appreciated.
## Steps to reproduce the bug
```python
from datasets import load_dataset
load_dataset("wikipedia", language="aa", date="20220401", split="train",beam_runner='DirectRunner')
```
## Expected results
to load the dataset
## Actual results
I am pasting the error trace here:
Downloading builder script: 35.9kB [00:00, ?B/s]
Downloading metadata: 30.4kB [00:00, 1.94MB/s]
Using custom data configuration 20220401.aa-date=20220401,language=aa
Downloading and preparing dataset wikipedia/20220401.aa to C:\Users\Shilpa.cache\huggingface\datasets\wikipedia\20220401.aa-date=20220401,language=aa\2.0.0\aa542ed919df55cc5d3347f42dd4521d05ca68751f50dbc32bae2a7f1e167559...
Downloading data: 100%|████████████████████████████████████████████████████████████| 11.1k/11.1k [00:00<00:00, 712kB/s]
Downloading data files: 100%|████████████████████████████████████████████████████████████| 1/1 [00:02<00:00, 2.82s/it]
Extracting data files: 100%|█████████████████████████████████████████████████████████████████████| 1/1 [00:00<?, ?it/s]
Downloading data: 100%|███████████████████████████████████████████████████████████| 35.6k/35.6k [00:00<00:00, 84.3kB/s]
Downloading data files: 100%|████████████████████████████████████████████████████████████| 1/1 [00:02<00:00, 2.93s/it]
Traceback (most recent call last):
File "apache_beam\runners\common.py", line 1417, in apache_beam.runners.common.DoFnRunner.process
File "apache_beam\runners\common.py", line 837, in apache_beam.runners.common.PerWindowInvoker.invoke_process
File "apache_beam\runners\common.py", line 981, in apache_beam.runners.common.PerWindowInvoker._invoke_process_per_window
File "apache_beam\runners\common.py", line 1571, in apache_beam.runners.common._OutputHandler.handle_process_outputs
File "G:\Python3.7\lib\site-packages\apache_beam\io\iobase.py", line 1193, in process
self.writer = self.sink.open_writer(init_result, str(uuid.uuid4()))
File "G:\Python3.7\lib\site-packages\apache_beam\options\value_provider.py", line 193, in _f
return fnc(self, *args, **kwargs)
File "G:\Python3.7\lib\site-packages\apache_beam\io\filebasedsink.py", line 202, in open_writer
return FileBasedSinkWriter(self, writer_path)
File "G:\Python3.7\lib\site-packages\apache_beam\io\filebasedsink.py", line 419, in init
self.temp_handle = self.sink.open(temp_shard_path)
File "G:\Python3.7\lib\site-packages\apache_beam\io\parquetio.py", line 553, in open
self._file_handle = super().open(temp_path)
File "G:\Python3.7\lib\site-packages\apache_beam\options\value_provider.py", line 193, in _f
return fnc(self, *args, **kwargs)
File "G:\Python3.7\lib\site-packages\apache_beam\io\filebasedsink.py", line 139, in open
temp_path, self.mime_type, self.compression_type)
File "G:\Python3.7\lib\site-packages\apache_beam\io\filesystems.py", line 224, in create
return filesystem.create(path, mime_type, compression_type)
File "G:\Python3.7\lib\site-packages\apache_beam\io\localfilesystem.py", line 163, in create
return self._path_open(path, 'wb', mime_type, compression_type)
File "G:\Python3.7\lib\site-packages\apache_beam\io\localfilesystem.py", line 140, in _path_open
raw_file = io.open(path, mode)
FileNotFoundError: [Errno 2] No such file or directory: 'C:\Users\Shilpa\.cache\huggingface\datasets\wikipedia\20220401.aa-date=20220401,language=aa\2.0.0\aa542ed919df55cc5d3347f42dd4521d05ca68751f50dbc32bae2a7f1e167559.incomplete\beam-temp-wikipedia-train-880233e8287e11edaf9d3ca067f2714e\20a05238-6106-4420-a713-4eca6dd5959a.wikipedia-train'
During handling of the above exception, another exception occurred:
Traceback (most recent call last):
File "G:/abc/temp.py", line 32, in
beam_runner='DirectRunner')
File "G:\Python3.7\lib\site-packages\datasets\load.py", line 1751, in load_dataset
use_auth_token=use_auth_token,
File "G:\Python3.7\lib\site-packages\datasets\builder.py", line 705, in download_and_prepare
dl_manager=dl_manager, verify_infos=verify_infos, **download_and_prepare_kwargs
File "G:\Python3.7\lib\site-packages\datasets\builder.py", line 1394, in _download_and_prepare
pipeline_results = pipeline.run()
File "G:\Python3.7\lib\site-packages\apache_beam\pipeline.py", line 574, in run
return self.runner.run_pipeline(self, self._options)
File "G:\Python3.7\lib\site-packages\apache_beam\runners\direct\direct_runner.py", line 131, in run_pipeline
return runner.run_pipeline(pipeline, options)
File "G:\Python3.7\lib\site-packages\apache_beam\runners\portability\fn_api_runner\fn_runner.py", line 201, in run_pipeline
options)
File "G:\Python3.7\lib\site-packages\apache_beam\runners\portability\fn_api_runner\fn_runner.py", line 212, in run_via_runner_api
return self.run_stages(stage_context, stages)
File "G:\Python3.7\lib\site-packages\apache_beam\runners\portability\fn_api_runner\fn_runner.py", line 443, in run_stages
runner_execution_context, bundle_context_manager, bundle_input)
File "G:\Python3.7\lib\site-packages\apache_beam\runners\portability\fn_api_runner\fn_runner.py", line 776, in _execute_bundle
bundle_manager))
File "G:\Python3.7\lib\site-packages\apache_beam\runners\portability\fn_api_runner\fn_runner.py", line 1000, in _run_bundle
data_input, data_output, input_timers, expected_timer_output)
File "G:\Python3.7\lib\site-packages\apache_beam\runners\portability\fn_api_runner\fn_runner.py", line 1309, in process_bundle
result_future = self._worker_handler.control_conn.push(process_bundle_req)
File "G:\Python3.7\lib\site-packages\apache_beam\runners\portability\fn_api_runner\worker_handlers.py", line 380, in push
response = self.worker.do_instruction(request)
File "G:\Python3.7\lib\site-packages\apache_beam\runners\worker\sdk_worker.py", line 598, in do_instruction
getattr(request, request_type), request.instruction_id)
File "G:\Python3.7\lib\site-packages\apache_beam\runners\worker\sdk_worker.py", line 635, in process_bundle
bundle_processor.process_bundle(instruction_id))
File "G:\Python3.7\lib\site-packages\apache_beam\runners\worker\bundle_processor.py", line 1004, in process_bundle
element.data)
File "G:\Python3.7\lib\site-packages\apache_beam\runners\worker\bundle_processor.py", line 227, in process_encoded
self.output(decoded_value)
File "apache_beam\runners\worker\operations.py", line 526, in apache_beam.runners.worker.operations.Operation.output
File "apache_beam\runners\worker\operations.py", line 528, in apache_beam.runners.worker.operations.Operation.output
File "apache_beam\runners\worker\operations.py", line 237, in apache_beam.runners.worker.operations.SingletonElementConsumerSet.receive
File "apache_beam\runners\worker\operations.py", line 240, in apache_beam.runners.worker.operations.SingletonElementConsumerSet.receive
File "apache_beam\runners\worker\operations.py", line 907, in apache_beam.runners.worker.operations.DoOperation.process
File "apache_beam\runners\worker\operations.py", line 908, in apache_beam.runners.worker.operations.DoOperation.process
File "apache_beam\runners\common.py", line 1419, in apache_beam.runners.common.DoFnRunner.process
File "apache_beam\runners\common.py", line 1491, in apache_beam.runners.common.DoFnRunner._reraise_augmented
File "apache_beam\runners\common.py", line 1417, in apache_beam.runners.common.DoFnRunner.process
File "apache_beam\runners\common.py", line 623, in apache_beam.runners.common.SimpleInvoker.invoke_process
File "apache_beam\runners\common.py", line 1581, in apache_beam.runners.common._OutputHandler.handle_process_outputs
File "apache_beam\runners\common.py", line 1694, in apache_beam.runners.common._OutputHandler._write_value_to_tag
File "apache_beam\runners\worker\operations.py", line 240, in apache_beam.runners.worker.operations.SingletonElementConsumerSet.receive
File "apache_beam\runners\worker\operations.py", line 907, in apache_beam.runners.worker.operations.DoOperation.process
File "apache_beam\runners\worker\operations.py", line 908, in apache_beam.runners.worker.operations.DoOperation.process
File "apache_beam\runners\common.py", line 1419, in apache_beam.runners.common.DoFnRunner.process
File "apache_beam\runners\common.py", line 1491, in apache_beam.runners.common.DoFnRunner._reraise_augmented
File "apache_beam\runners\common.py", line 1417, in apache_beam.runners.common.DoFnRunner.process
File "apache_beam\runners\common.py", line 623, in apache_beam.runners.common.SimpleInvoker.invoke_process
File "apache_beam\runners\common.py", line 1581, in apache_beam.runners.common._OutputHandler.handle_process_outputs
File "apache_beam\runners\common.py", line 1694, in apache_beam.runners.common._OutputHandler._write_value_to_tag
File "apache_beam\runners\worker\operations.py", line 240, in apache_beam.runners.worker.operations.SingletonElementConsumerSet.receive
File "apache_beam\runners\worker\operations.py", line 907, in apache_beam.runners.worker.operations.DoOperation.process
File "apache_beam\runners\worker\operations.py", line 908, in apache_beam.runners.worker.operations.DoOperation.process
File "apache_beam\runners\common.py", line 1419, in apache_beam.runners.common.DoFnRunner.process
File "apache_beam\runners\common.py", line 1491, in apache_beam.runners.common.DoFnRunner._reraise_augmented
File "apache_beam\runners\common.py", line 1417, in apache_beam.runners.common.DoFnRunner.process
File "apache_beam\runners\common.py", line 837, in apache_beam.runners.common.PerWindowInvoker.invoke_process
File "apache_beam\runners\common.py", line 981, in apache_beam.runners.common.PerWindowInvoker._invoke_process_per_window
File "apache_beam\runners\common.py", line 1581, in apache_beam.runners.common._OutputHandler.handle_process_outputs
File "apache_beam\runners\common.py", line 1694, in apache_beam.runners.common._OutputHandler._write_value_to_tag
File "apache_beam\runners\worker\operations.py", line 240, in apache_beam.runners.worker.operations.SingletonElementConsumerSet.receive
File "apache_beam\runners\worker\operations.py", line 907, in apache_beam.runners.worker.operations.DoOperation.process
File "apache_beam\runners\worker\operations.py", line 908, in apache_beam.runners.worker.operations.DoOperation.process
File "apache_beam\runners\common.py", line 1419, in apache_beam.runners.common.DoFnRunner.process
File "apache_beam\runners\common.py", line 1491, in apache_beam.runners.common.DoFnRunner._reraise_augmented
File "apache_beam\runners\common.py", line 1417, in apache_beam.runners.common.DoFnRunner.process
File "apache_beam\runners\common.py", line 623, in apache_beam.runners.common.SimpleInvoker.invoke_process
File "apache_beam\runners\common.py", line 1581, in apache_beam.runners.common._OutputHandler.handle_process_outputs
File "apache_beam\runners\common.py", line 1694, in apache_beam.runners.common._OutputHandler._write_value_to_tag
File "apache_beam\runners\worker\operations.py", line 324, in apache_beam.runners.worker.operations.GeneralPurposeConsumerSet.receive
File "apache_beam\runners\worker\operations.py", line 905, in apache_beam.runners.worker.operations.DoOperation.process
File "apache_beam\runners\worker\operations.py", line 907, in apache_beam.runners.worker.operations.DoOperation.process
File "apache_beam\runners\worker\operations.py", line 908, in apache_beam.runners.worker.operations.DoOperation.process
File "apache_beam\runners\common.py", line 1419, in apache_beam.runners.common.DoFnRunner.process
File "apache_beam\runners\common.py", line 1491, in apache_beam.runners.common.DoFnRunner._reraise_augmented
File "apache_beam\runners\common.py", line 1417, in apache_beam.runners.common.DoFnRunner.process
File "apache_beam\runners\common.py", line 623, in apache_beam.runners.common.SimpleInvoker.invoke_process
File "apache_beam\runners\common.py", line 1581, in apache_beam.runners.common._OutputHandler.handle_process_outputs
File "apache_beam\runners\common.py", line 1694, in apache_beam.runners.common._OutputHandler._write_value_to_tag
File "apache_beam\runners\worker\operations.py", line 240, in apache_beam.runners.worker.operations.SingletonElementConsumerSet.receive
File "apache_beam\runners\worker\operations.py", line 907, in apache_beam.runners.worker.operations.DoOperation.process
File "apache_beam\runners\worker\operations.py", line 908, in apache_beam.runners.worker.operations.DoOperation.process
File "apache_beam\runners\common.py", line 1419, in apache_beam.runners.common.DoFnRunner.process
File "apache_beam\runners\common.py", line 1491, in apache_beam.runners.common.DoFnRunner._reraise_augmented
File "apache_beam\runners\common.py", line 1417, in apache_beam.runners.common.DoFnRunner.process
File "apache_beam\runners\common.py", line 837, in apache_beam.runners.common.PerWindowInvoker.invoke_process
File "apache_beam\runners\common.py", line 981, in apache_beam.runners.common.PerWindowInvoker._invoke_process_per_window
File "apache_beam\runners\common.py", line 1581, in apache_beam.runners.common._OutputHandler.handle_process_outputs
File "apache_beam\runners\common.py", line 1694, in apache_beam.runners.common._OutputHandler._write_value_to_tag
File "apache_beam\runners\worker\operations.py", line 240, in apache_beam.runners.worker.operations.SingletonElementConsumerSet.receive
File "apache_beam\runners\worker\operations.py", line 907, in apache_beam.runners.worker.operations.DoOperation.process
File "apache_beam\runners\worker\operations.py", line 908, in apache_beam.runners.worker.operations.DoOperation.process
File "apache_beam\runners\common.py", line 1419, in apache_beam.runners.common.DoFnRunner.process
File "apache_beam\runners\common.py", line 1507, in apache_beam.runners.common.DoFnRunner._reraise_augmented
File "apache_beam\runners\common.py", line 1417, in apache_beam.runners.common.DoFnRunner.process
File "apache_beam\runners\common.py", line 837, in apache_beam.runners.common.PerWindowInvoker.invoke_process
File "apache_beam\runners\common.py", line 981, in apache_beam.runners.common.PerWindowInvoker._invoke_process_per_window
File "apache_beam\runners\common.py", line 1571, in apache_beam.runners.common._OutputHandler.handle_process_outputs
File "G:\Python3.7\lib\site-packages\apache_beam\io\iobase.py", line 1193, in process
self.writer = self.sink.open_writer(init_result, str(uuid.uuid4()))
File "G:\Python3.7\lib\site-packages\apache_beam\options\value_provider.py", line 193, in _f
return fnc(self, *args, **kwargs)
File "G:\Python3.7\lib\site-packages\apache_beam\io\filebasedsink.py", line 202, in open_writer
return FileBasedSinkWriter(self, writer_path)
File "G:\Python3.7\lib\site-packages\apache_beam\io\filebasedsink.py", line 419, in init
self.temp_handle = self.sink.open(temp_shard_path)
File "G:\Python3.7\lib\site-packages\apache_beam\io\parquetio.py", line 553, in open
self._file_handle = super().open(temp_path)
File "G:\Python3.7\lib\site-packages\apache_beam\options\value_provider.py", line 193, in _f
return fnc(self, *args, **kwargs)
File "G:\Python3.7\lib\site-packages\apache_beam\io\filebasedsink.py", line 139, in open
temp_path, self.mime_type, self.compression_type)
File "G:\Python3.7\lib\site-packages\apache_beam\io\filesystems.py", line 224, in create
return filesystem.create(path, mime_type, compression_type)
File "G:\Python3.7\lib\site-packages\apache_beam\io\localfilesystem.py", line 163, in create
return self._path_open(path, 'wb', mime_type, compression_type)
File "G:\Python3.7\lib\site-packages\apache_beam\io\localfilesystem.py", line 140, in _path_open
raw_file = io.open(path, mode)
RuntimeError: FileNotFoundError: [Errno 2] No such file or directory: 'C:\Users\Shilpa\.cache\huggingface\datasets\wikipedia\20220401.aa-date=20220401,language=aa\2.0.0\aa542ed919df55cc5d3347f42dd4521d05ca68751f50dbc32bae2a7f1e167559.incomplete\beam-temp-wikipedia-train-880233e8287e11edaf9d3ca067f2714e\20a05238-6106-4420-a713-4eca6dd5959a.wikipedia-train' [while running 'train/Save to parquet/Write/WriteImpl/WriteBundles']
## Environment info
Python: 3.7.6
Windows 10 Pro
datasets :2.4.0
apache_beam: 2.41.0
mwparserfromhell: 0.6.4 | {
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https://api.github.com/repos/huggingface/datasets/issues/4915 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/4915/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/4915/comments | https://api.github.com/repos/huggingface/datasets/issues/4915/events | https://github.com/huggingface/datasets/issues/4915 | 1,356,009,042 | I_kwDODunzps5Q0w5S | 4,915 | FileNotFoundError while downloading wikipedia dataset for any language | {
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} | null | null | null | ## Describe the bug
Hi, I am currently trying to download wikipedia dataset using
load_dataset("wikipedia", language="aa", date="20220401", split="train",beam_runner='DirectRunner'). However, I end up in getting filenotfound error. I get this error for any language I try to download.
Environment:
## Steps to reproduce the bug
```python
from datasets import load_dataset
load_dataset("wikipedia", language="aa", date="20220401", split="train",beam_runner='DirectRunner')
```
## Expected results
to load the dataset
## Actual results
I am pasting the error trace here:
Downloading builder script: 35.9kB [00:00, ?B/s]
Downloading metadata: 30.4kB [00:00, 1.94MB/s]
Using custom data configuration 20220401.aa-date=20220401,language=aa
Downloading and preparing dataset wikipedia/20220401.aa to C:\Users\Shilpa\.cache\huggingface\datasets\wikipedia\20220401.aa-date=20220401,language=aa\2.0.0\aa542ed919df55cc5d3347f42dd4521d05ca68751f50dbc32bae2a7f1e167559...
Downloading data: 100%|████████████████████████████████████████████████████████████| 11.1k/11.1k [00:00<00:00, 712kB/s]
Downloading data files: 100%|████████████████████████████████████████████████████████████| 1/1 [00:02<00:00, 2.82s/it]
Extracting data files: 100%|█████████████████████████████████████████████████████████████████████| 1/1 [00:00<?, ?it/s]
Downloading data: 100%|███████████████████████████████████████████████████████████| 35.6k/35.6k [00:00<00:00, 84.3kB/s]
Downloading data files: 100%|████████████████████████████████████████████████████████████| 1/1 [00:02<00:00, 2.93s/it]
Traceback (most recent call last):
File "apache_beam\runners\common.py", line 1417, in apache_beam.runners.common.DoFnRunner.process
File "apache_beam\runners\common.py", line 837, in apache_beam.runners.common.PerWindowInvoker.invoke_process
File "apache_beam\runners\common.py", line 981, in apache_beam.runners.common.PerWindowInvoker._invoke_process_per_window
File "apache_beam\runners\common.py", line 1571, in apache_beam.runners.common._OutputHandler.handle_process_outputs
File "G:\Python3.7\lib\site-packages\apache_beam\io\iobase.py", line 1193, in process
self.writer = self.sink.open_writer(init_result, str(uuid.uuid4()))
File "G:\Python3.7\lib\site-packages\apache_beam\options\value_provider.py", line 193, in _f
return fnc(self, *args, **kwargs)
File "G:\Python3.7\lib\site-packages\apache_beam\io\filebasedsink.py", line 202, in open_writer
return FileBasedSinkWriter(self, writer_path)
File "G:\Python3.7\lib\site-packages\apache_beam\io\filebasedsink.py", line 419, in __init__
self.temp_handle = self.sink.open(temp_shard_path)
File "G:\Python3.7\lib\site-packages\apache_beam\io\parquetio.py", line 553, in open
self._file_handle = super().open(temp_path)
File "G:\Python3.7\lib\site-packages\apache_beam\options\value_provider.py", line 193, in _f
return fnc(self, *args, **kwargs)
File "G:\Python3.7\lib\site-packages\apache_beam\io\filebasedsink.py", line 139, in open
temp_path, self.mime_type, self.compression_type)
File "G:\Python3.7\lib\site-packages\apache_beam\io\filesystems.py", line 224, in create
return filesystem.create(path, mime_type, compression_type)
File "G:\Python3.7\lib\site-packages\apache_beam\io\localfilesystem.py", line 163, in create
return self._path_open(path, 'wb', mime_type, compression_type)
File "G:\Python3.7\lib\site-packages\apache_beam\io\localfilesystem.py", line 140, in _path_open
raw_file = io.open(path, mode)
FileNotFoundError: [Errno 2] No such file or directory: 'C:\\Users\\Shilpa\\.cache\\huggingface\\datasets\\wikipedia\\20220401.aa-date=20220401,language=aa\\2.0.0\\aa542ed919df55cc5d3347f42dd4521d05ca68751f50dbc32bae2a7f1e167559.incomplete\\beam-temp-wikipedia-train-880233e8287e11edaf9d3ca067f2714e\\20a05238-6106-4420-a713-4eca6dd5959a.wikipedia-train'
During handling of the above exception, another exception occurred:
Traceback (most recent call last):
File "G:/abc/temp.py", line 32, in <module>
beam_runner='DirectRunner')
File "G:\Python3.7\lib\site-packages\datasets\load.py", line 1751, in load_dataset
use_auth_token=use_auth_token,
File "G:\Python3.7\lib\site-packages\datasets\builder.py", line 705, in download_and_prepare
dl_manager=dl_manager, verify_infos=verify_infos, **download_and_prepare_kwargs
File "G:\Python3.7\lib\site-packages\datasets\builder.py", line 1394, in _download_and_prepare
pipeline_results = pipeline.run()
File "G:\Python3.7\lib\site-packages\apache_beam\pipeline.py", line 574, in run
return self.runner.run_pipeline(self, self._options)
File "G:\Python3.7\lib\site-packages\apache_beam\runners\direct\direct_runner.py", line 131, in run_pipeline
return runner.run_pipeline(pipeline, options)
File "G:\Python3.7\lib\site-packages\apache_beam\runners\portability\fn_api_runner\fn_runner.py", line 201, in run_pipeline
options)
File "G:\Python3.7\lib\site-packages\apache_beam\runners\portability\fn_api_runner\fn_runner.py", line 212, in run_via_runner_api
return self.run_stages(stage_context, stages)
File "G:\Python3.7\lib\site-packages\apache_beam\runners\portability\fn_api_runner\fn_runner.py", line 443, in run_stages
runner_execution_context, bundle_context_manager, bundle_input)
File "G:\Python3.7\lib\site-packages\apache_beam\runners\portability\fn_api_runner\fn_runner.py", line 776, in _execute_bundle
bundle_manager))
File "G:\Python3.7\lib\site-packages\apache_beam\runners\portability\fn_api_runner\fn_runner.py", line 1000, in _run_bundle
data_input, data_output, input_timers, expected_timer_output)
File "G:\Python3.7\lib\site-packages\apache_beam\runners\portability\fn_api_runner\fn_runner.py", line 1309, in process_bundle
result_future = self._worker_handler.control_conn.push(process_bundle_req)
File "G:\Python3.7\lib\site-packages\apache_beam\runners\portability\fn_api_runner\worker_handlers.py", line 380, in push
response = self.worker.do_instruction(request)
File "G:\Python3.7\lib\site-packages\apache_beam\runners\worker\sdk_worker.py", line 598, in do_instruction
getattr(request, request_type), request.instruction_id)
File "G:\Python3.7\lib\site-packages\apache_beam\runners\worker\sdk_worker.py", line 635, in process_bundle
bundle_processor.process_bundle(instruction_id))
File "G:\Python3.7\lib\site-packages\apache_beam\runners\worker\bundle_processor.py", line 1004, in process_bundle
element.data)
File "G:\Python3.7\lib\site-packages\apache_beam\runners\worker\bundle_processor.py", line 227, in process_encoded
self.output(decoded_value)
File "apache_beam\runners\worker\operations.py", line 526, in apache_beam.runners.worker.operations.Operation.output
File "apache_beam\runners\worker\operations.py", line 528, in apache_beam.runners.worker.operations.Operation.output
File "apache_beam\runners\worker\operations.py", line 237, in apache_beam.runners.worker.operations.SingletonElementConsumerSet.receive
File "apache_beam\runners\worker\operations.py", line 240, in apache_beam.runners.worker.operations.SingletonElementConsumerSet.receive
File "apache_beam\runners\worker\operations.py", line 907, in apache_beam.runners.worker.operations.DoOperation.process
File "apache_beam\runners\worker\operations.py", line 908, in apache_beam.runners.worker.operations.DoOperation.process
File "apache_beam\runners\common.py", line 1419, in apache_beam.runners.common.DoFnRunner.process
File "apache_beam\runners\common.py", line 1491, in apache_beam.runners.common.DoFnRunner._reraise_augmented
File "apache_beam\runners\common.py", line 1417, in apache_beam.runners.common.DoFnRunner.process
File "apache_beam\runners\common.py", line 623, in apache_beam.runners.common.SimpleInvoker.invoke_process
File "apache_beam\runners\common.py", line 1581, in apache_beam.runners.common._OutputHandler.handle_process_outputs
File "apache_beam\runners\common.py", line 1694, in apache_beam.runners.common._OutputHandler._write_value_to_tag
File "apache_beam\runners\worker\operations.py", line 240, in apache_beam.runners.worker.operations.SingletonElementConsumerSet.receive
File "apache_beam\runners\worker\operations.py", line 907, in apache_beam.runners.worker.operations.DoOperation.process
File "apache_beam\runners\worker\operations.py", line 908, in apache_beam.runners.worker.operations.DoOperation.process
File "apache_beam\runners\common.py", line 1419, in apache_beam.runners.common.DoFnRunner.process
File "apache_beam\runners\common.py", line 1491, in apache_beam.runners.common.DoFnRunner._reraise_augmented
File "apache_beam\runners\common.py", line 1417, in apache_beam.runners.common.DoFnRunner.process
File "apache_beam\runners\common.py", line 623, in apache_beam.runners.common.SimpleInvoker.invoke_process
File "apache_beam\runners\common.py", line 1581, in apache_beam.runners.common._OutputHandler.handle_process_outputs
File "apache_beam\runners\common.py", line 1694, in apache_beam.runners.common._OutputHandler._write_value_to_tag
File "apache_beam\runners\worker\operations.py", line 240, in apache_beam.runners.worker.operations.SingletonElementConsumerSet.receive
File "apache_beam\runners\worker\operations.py", line 907, in apache_beam.runners.worker.operations.DoOperation.process
File "apache_beam\runners\worker\operations.py", line 908, in apache_beam.runners.worker.operations.DoOperation.process
File "apache_beam\runners\common.py", line 1419, in apache_beam.runners.common.DoFnRunner.process
File "apache_beam\runners\common.py", line 1491, in apache_beam.runners.common.DoFnRunner._reraise_augmented
File "apache_beam\runners\common.py", line 1417, in apache_beam.runners.common.DoFnRunner.process
File "apache_beam\runners\common.py", line 837, in apache_beam.runners.common.PerWindowInvoker.invoke_process
File "apache_beam\runners\common.py", line 981, in apache_beam.runners.common.PerWindowInvoker._invoke_process_per_window
File "apache_beam\runners\common.py", line 1581, in apache_beam.runners.common._OutputHandler.handle_process_outputs
File "apache_beam\runners\common.py", line 1694, in apache_beam.runners.common._OutputHandler._write_value_to_tag
File "apache_beam\runners\worker\operations.py", line 240, in apache_beam.runners.worker.operations.SingletonElementConsumerSet.receive
File "apache_beam\runners\worker\operations.py", line 907, in apache_beam.runners.worker.operations.DoOperation.process
File "apache_beam\runners\worker\operations.py", line 908, in apache_beam.runners.worker.operations.DoOperation.process
File "apache_beam\runners\common.py", line 1419, in apache_beam.runners.common.DoFnRunner.process
File "apache_beam\runners\common.py", line 1491, in apache_beam.runners.common.DoFnRunner._reraise_augmented
File "apache_beam\runners\common.py", line 1417, in apache_beam.runners.common.DoFnRunner.process
File "apache_beam\runners\common.py", line 623, in apache_beam.runners.common.SimpleInvoker.invoke_process
File "apache_beam\runners\common.py", line 1581, in apache_beam.runners.common._OutputHandler.handle_process_outputs
File "apache_beam\runners\common.py", line 1694, in apache_beam.runners.common._OutputHandler._write_value_to_tag
File "apache_beam\runners\worker\operations.py", line 324, in apache_beam.runners.worker.operations.GeneralPurposeConsumerSet.receive
File "apache_beam\runners\worker\operations.py", line 905, in apache_beam.runners.worker.operations.DoOperation.process
File "apache_beam\runners\worker\operations.py", line 907, in apache_beam.runners.worker.operations.DoOperation.process
File "apache_beam\runners\worker\operations.py", line 908, in apache_beam.runners.worker.operations.DoOperation.process
File "apache_beam\runners\common.py", line 1419, in apache_beam.runners.common.DoFnRunner.process
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File "apache_beam\runners\common.py", line 1417, in apache_beam.runners.common.DoFnRunner.process
File "apache_beam\runners\common.py", line 623, in apache_beam.runners.common.SimpleInvoker.invoke_process
File "apache_beam\runners\common.py", line 1581, in apache_beam.runners.common._OutputHandler.handle_process_outputs
File "apache_beam\runners\common.py", line 1694, in apache_beam.runners.common._OutputHandler._write_value_to_tag
File "apache_beam\runners\worker\operations.py", line 240, in apache_beam.runners.worker.operations.SingletonElementConsumerSet.receive
File "apache_beam\runners\worker\operations.py", line 907, in apache_beam.runners.worker.operations.DoOperation.process
File "apache_beam\runners\worker\operations.py", line 908, in apache_beam.runners.worker.operations.DoOperation.process
File "apache_beam\runners\common.py", line 1419, in apache_beam.runners.common.DoFnRunner.process
File "apache_beam\runners\common.py", line 1491, in apache_beam.runners.common.DoFnRunner._reraise_augmented
File "apache_beam\runners\common.py", line 1417, in apache_beam.runners.common.DoFnRunner.process
File "apache_beam\runners\common.py", line 837, in apache_beam.runners.common.PerWindowInvoker.invoke_process
File "apache_beam\runners\common.py", line 981, in apache_beam.runners.common.PerWindowInvoker._invoke_process_per_window
File "apache_beam\runners\common.py", line 1581, in apache_beam.runners.common._OutputHandler.handle_process_outputs
File "apache_beam\runners\common.py", line 1694, in apache_beam.runners.common._OutputHandler._write_value_to_tag
File "apache_beam\runners\worker\operations.py", line 240, in apache_beam.runners.worker.operations.SingletonElementConsumerSet.receive
File "apache_beam\runners\worker\operations.py", line 907, in apache_beam.runners.worker.operations.DoOperation.process
File "apache_beam\runners\worker\operations.py", line 908, in apache_beam.runners.worker.operations.DoOperation.process
File "apache_beam\runners\common.py", line 1419, in apache_beam.runners.common.DoFnRunner.process
File "apache_beam\runners\common.py", line 1507, in apache_beam.runners.common.DoFnRunner._reraise_augmented
File "apache_beam\runners\common.py", line 1417, in apache_beam.runners.common.DoFnRunner.process
File "apache_beam\runners\common.py", line 837, in apache_beam.runners.common.PerWindowInvoker.invoke_process
File "apache_beam\runners\common.py", line 981, in apache_beam.runners.common.PerWindowInvoker._invoke_process_per_window
File "apache_beam\runners\common.py", line 1571, in apache_beam.runners.common._OutputHandler.handle_process_outputs
File "G:\Python3.7\lib\site-packages\apache_beam\io\iobase.py", line 1193, in process
self.writer = self.sink.open_writer(init_result, str(uuid.uuid4()))
File "G:\Python3.7\lib\site-packages\apache_beam\options\value_provider.py", line 193, in _f
return fnc(self, *args, **kwargs)
File "G:\Python3.7\lib\site-packages\apache_beam\io\filebasedsink.py", line 202, in open_writer
return FileBasedSinkWriter(self, writer_path)
File "G:\Python3.7\lib\site-packages\apache_beam\io\filebasedsink.py", line 419, in __init__
self.temp_handle = self.sink.open(temp_shard_path)
File "G:\Python3.7\lib\site-packages\apache_beam\io\parquetio.py", line 553, in open
self._file_handle = super().open(temp_path)
File "G:\Python3.7\lib\site-packages\apache_beam\options\value_provider.py", line 193, in _f
return fnc(self, *args, **kwargs)
File "G:\Python3.7\lib\site-packages\apache_beam\io\filebasedsink.py", line 139, in open
temp_path, self.mime_type, self.compression_type)
File "G:\Python3.7\lib\site-packages\apache_beam\io\filesystems.py", line 224, in create
return filesystem.create(path, mime_type, compression_type)
File "G:\Python3.7\lib\site-packages\apache_beam\io\localfilesystem.py", line 163, in create
return self._path_open(path, 'wb', mime_type, compression_type)
File "G:\Python3.7\lib\site-packages\apache_beam\io\localfilesystem.py", line 140, in _path_open
raw_file = io.open(path, mode)
RuntimeError: FileNotFoundError: [Errno 2] No such file or directory: 'C:\\Users\\Shilpa\\.cache\\huggingface\\datasets\\wikipedia\\20220401.aa-date=20220401,language=aa\\2.0.0\\aa542ed919df55cc5d3347f42dd4521d05ca68751f50dbc32bae2a7f1e167559.incomplete\\beam-temp-wikipedia-train-880233e8287e11edaf9d3ca067f2714e\\20a05238-6106-4420-a713-4eca6dd5959a.wikipedia-train' [while running 'train/Save to parquet/Write/WriteImpl/WriteBundles']
## Environment info
Python: 3.7.6
Windows 10 Pro
datasets :2.4.0
apache_beam: 2.41.0
mwparserfromhell: 0.6.4
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https://api.github.com/repos/huggingface/datasets/issues/4914 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/4914/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/4914/comments | https://api.github.com/repos/huggingface/datasets/issues/4914/events | https://github.com/huggingface/datasets/pull/4914 | 1,355,482,624 | PR_kwDODunzps4-CFyN | 4,914 | Support streaming swda dataset | {
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https://api.github.com/repos/huggingface/datasets/issues/4913 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/4913/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/4913/comments | https://api.github.com/repos/huggingface/datasets/issues/4913/events | https://github.com/huggingface/datasets/pull/4913 | 1,355,232,007 | PR_kwDODunzps4-BP00 | 4,913 | Add license and citation information to cosmos_qa dataset | {
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} | This PR adds the license information to `cosmos_qa` dataset, once reported via email by Yejin Choi, the dataset is licensed under CC BY 4.0.
This PR also updates the citation information. | {
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https://api.github.com/repos/huggingface/datasets/issues/4912 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/4912/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/4912/comments | https://api.github.com/repos/huggingface/datasets/issues/4912/events | https://github.com/huggingface/datasets/issues/4912 | 1,355,078,864 | I_kwDODunzps5QxNzQ | 4,912 | datasets map() handles all data at a stroke and takes long time | {
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} | null | null | null | **1. Background**
Huggingface datasets package advises using `map()` to process data in batches. In the example code on pretraining masked language model, they use `map()` to tokenize all data at a stroke before the train loop.
The corresponding code:
```
with accelerator.main_process_first():
tokenized_datasets = raw_datasets.map(
tokenize_function,
batched=True,
num_proc=args.preprocessing_num_workers,
remove_columns=column_names,
load_from_cache_file=not args.overwrite_cache,
desc="Running tokenizer on every text in dataset"
)
```
**2. The problem**
Thus, when I try the same pertaining code with a much larger corpus, it takes quite a long time to tokenize.
Also, we can choose to tokenize data in `data-collator`. In this way, the program only tokenizes one batch in the next training step and avoids getting stuck in tokenization.
**3. My question**
As described above, my questions are:
* **Which is better? Process in `map()` or in `data-collator`**
* **Why huggingface advises `map()` function?** There should be some advantages to using `map()`
Thanks for your answers! | {
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} | null | null | null | On https://hf.co/datasets you can rename a dataset (or sometimes move it to another user/org). The website handles redirections correctly and AFAIK `datasets` does as well.
However it would be nice to have an integration test to make sure we don't break support for renamed datasets.
To implement this we can use the /api/repos/move endpoint on hub-ci to rename/move a repo (it is documented at https://huggingface.co/docs/hub/api) | null | {
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https://api.github.com/repos/huggingface/datasets/issues/4910 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/4910/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/4910/comments | https://api.github.com/repos/huggingface/datasets/issues/4910/events | https://github.com/huggingface/datasets/issues/4910 | 1,354,374,328 | I_kwDODunzps5Quhy4 | 4,910 | Identical keywords in build_kwargs and config_kwargs lead to TypeError in load_dataset_builder() | {
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} | null | null | null | ## Describe the bug
In `load_dataset_builder()`, `build_kwargs` and `config_kwargs` can contain the same keywords leading to a TypeError("type object got multiple values for keyword argument "xyz").
I ran into this problem with the keyword: `base_path`. It might happen with other kwargs as well. I think a quickfix would be
```python
builder_cls = import_main_class(dataset_module.module_path)
builder_kwargs = dataset_module.builder_kwargs
data_files = builder_kwargs.pop("data_files", data_files)
config_name = builder_kwargs.pop("config_name", name)
hash = builder_kwargs.pop("hash")
base_path = builder_kwargs.pop("base_path")
```
and then pass base_path into `builder_cls`.
## Steps to reproduce the bug
```python
from datasets import load_dataset
load_dataset("rotten_tomatoes", base_path="./sample_data")
```
## Expected results
The docs state: `**config_kwargs` — Keyword arguments to be passed to the [BuilderConfig](https://huggingface.co/docs/datasets/v2.4.0/en/package_reference/builder_classes#datasets.BuilderConfig) and used in the [DatasetBuilder](https://huggingface.co/docs/datasets/v2.4.0/en/package_reference/builder_classes#datasets.DatasetBuilder).
So I would expect to be able to pass the base_path into `load_dataset()`.
## Actual results
TypeError("type object got multiple values for keyword argument "base_path").
## Environment info
<!-- You can run the command `datasets-cli env` and copy-and-paste its output below. -->
- `datasets` version: 2.4.0
- Platform: macOS-12.5-arm64-arm-64bit
- Python version: 3.8.9
- PyArrow version: 9.0.0
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https://api.github.com/repos/huggingface/datasets/issues/4909 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/4909/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/4909/comments | https://api.github.com/repos/huggingface/datasets/issues/4909/events | https://github.com/huggingface/datasets/pull/4909 | 1,353,997,788 | PR_kwDODunzps499Fhe | 4,909 | Update GLUE evaluation metadata | {
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} | This PR updates the evaluation metadata for GLUE to:
* Include defaults for all configs except `ax` (which only has a `test` split with no known labels)
* Fix the default split from `test` to `validation` since `test` splits in GLUE have no labels (they're private)
* Fix the `task_id` for some existing defaults
cc @sashavor @douwekiela | {
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https://api.github.com/repos/huggingface/datasets/issues/4908 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/4908/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/4908/comments | https://api.github.com/repos/huggingface/datasets/issues/4908/events | https://github.com/huggingface/datasets/pull/4908 | 1,353,995,574 | PR_kwDODunzps499FDS | 4,908 | Fix missing tags in dataset cards | {
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} | Fix missing tags in dataset cards:
- asnq
- clue
- common_gen
- cosmos_qa
- guardian_authorship
- hindi_discourse
- py_ast
- x_stance
This PR partially fixes the missing tags in dataset cards. Subsequent PRs will follow to complete this task.
Related to:
- #4833
- #4891
- #4896 | {
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I got `'NoneType' object is not callable` error while calling the swda datasets.
## Steps to reproduce the bug
```python
from datasets import load_dataset
dataset = load_dataset("swda")
```
## Expected results
Run without error
## Environment info
<!-- You can run the command `datasets-cli env` and copy-and-paste its output below. -->
- `datasets` version: 2.4.0
- Python version: 3.8.10
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} | null | null | null | ## Describe the bug
A clear and concise description of what the bug is.
Not able to import datasets
## Steps to reproduce the bug
```python
# Sample code to reproduce the bug
import os
os.environ["WANDB_API_KEY"] = "0" ## to silence warning
import numpy as np
import random
import sklearn
import matplotlib.pyplot as plt
import pandas as pd
import sys
import tensorflow as tf
import plotly.express as px
import transformers
import tokenizers
import nlp as nlp
import utils
import datasets
```
## Expected results
A clear and concise description of the expected results.
import should work normal
## Actual results
Specify the actual results or traceback.
---------------------------------------------------------------------------
AttributeError Traceback (most recent call last)
<ipython-input-21-b3b5b0b62103> in <module>
13 import nlp as nlp
14 import utils
---> 15 import datasets
~\anaconda3\lib\site-packages\datasets\__init__.py in <module>
44 from .fingerprint import disable_caching, enable_caching, is_caching_enabled, set_caching_enabled
45 from .info import DatasetInfo, MetricInfo
---> 46 from .inspect import (
47 get_dataset_config_info,
48 get_dataset_config_names,
~\anaconda3\lib\site-packages\datasets\inspect.py in <module>
28 from .download.streaming_download_manager import StreamingDownloadManager
29 from .info import DatasetInfo
---> 30 from .load import dataset_module_factory, import_main_class, load_dataset_builder, metric_module_factory
31 from .utils.file_utils import relative_to_absolute_path
32 from .utils.logging import get_logger
~\anaconda3\lib\site-packages\datasets\load.py in <module>
53 from .iterable_dataset import IterableDataset
54 from .metric import Metric
---> 55 from .packaged_modules import (
56 _EXTENSION_TO_MODULE,
57 _MODULE_SUPPORTS_METADATA,
~\anaconda3\lib\site-packages\datasets\packaged_modules\__init__.py in <module>
4 from typing import List
5
----> 6 from .csv import csv
7 from .imagefolder import imagefolder
8 from .json import json
~\anaconda3\lib\site-packages\datasets\packaged_modules\csv\csv.py in <module>
13
14
---> 15 logger = datasets.utils.logging.get_logger(__name__)
16
17 _PANDAS_READ_CSV_NO_DEFAULT_PARAMETERS = ["names", "prefix"]
AttributeError: partially initialized module 'datasets' has no attribute 'utils' (most likely due to a circular import)
## Environment info
<!-- You can run the command `datasets-cli env` and copy-and-paste its output below. -->
Copy-and-paste the text below in your GitHub issue.
- `datasets` version: 2.4.0
- Platform: Windows-10-10.0.22000-SP0
- Python version: 3.8.8
- PyArrow version: 9.0.0
- Pandas version: 1.2.4
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https://api.github.com/repos/huggingface/datasets/issues/4904 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/4904/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/4904/comments | https://api.github.com/repos/huggingface/datasets/issues/4904/events | https://github.com/huggingface/datasets/pull/4904 | 1,353,002,837 | PR_kwDODunzps4959Ad | 4,904 | [LibriSpeech] Fix dev split local_extracted_archive for 'all' config | {
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} | We define the keys for the `_DL_URLS` of the dev split as `dev.clean` and `dev.other`:
https://github.com/huggingface/datasets/blob/2e7142a3c6500b560da45e8d5128e320a09fcbd4/datasets/librispeech_asr/librispeech_asr.py#L60-L61
These keys get forwarded to the `dl_manager` and thus the `local_extracted_archive`.
However, when calling `SplitGenerator` for the dev sets, we query the `local_extracted_archive` keys `validation.clean` and `validation.other`:
https://github.com/huggingface/datasets/blob/2e7142a3c6500b560da45e8d5128e320a09fcbd4/datasets/librispeech_asr/librispeech_asr.py#L212
https://github.com/huggingface/datasets/blob/2e7142a3c6500b560da45e8d5128e320a09fcbd4/datasets/librispeech_asr/librispeech_asr.py#L219
The consequence of this is that the `local_extracted_archive` arg passed to `_generate_examples` is always `None`, as the keys `validation.clean` and `validation.other` do not exists in the `local_extracted_archive`.
When defining the `audio_file` in `_generate_examples`, since `local_extracted_archive` is always `None`, we always omit the `local_extracted_archive` path from the `audio_file` path, **even** if in non-streaming mode:
https://github.com/huggingface/datasets/blob/2e7142a3c6500b560da45e8d5128e320a09fcbd4/datasets/librispeech_asr/librispeech_asr.py#L259-L263
Thus, `audio_file` will only ever be the streaming path (`audio_file`, not `os.path.join(local_extracted_archive, audio_file)`).
This PR fixes the `.get()` keys for the `local_extracted_archive` for the dev splits.
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https://api.github.com/repos/huggingface/datasets/issues/4903 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/4903/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/4903/comments | https://api.github.com/repos/huggingface/datasets/issues/4903/events | https://github.com/huggingface/datasets/pull/4903 | 1,352,539,075 | PR_kwDODunzps494aud | 4,903 | Fix CI reporting | {
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} | Fix CI so that it reports defaults (failed and error) besides the custom (xfailed and xpassed) in the test summary.
This PR fixes a regression introduced by:
- #4845
This introduced the reporting of xfailed and xpassed, but wrongly removed the reporting of the defaults failed and error. | {
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https://api.github.com/repos/huggingface/datasets/issues/4902 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/4902/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/4902/comments | https://api.github.com/repos/huggingface/datasets/issues/4902/events | https://github.com/huggingface/datasets/issues/4902 | 1,352,469,196 | I_kwDODunzps5QnQrM | 4,902 | Name the default config `default` | {
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For example, for a dataset loaded from the hub repository, such as https://huggingface.co/datasets/user/dataset (repo id is `user/dataset`), the default configuration will be `user--dataset`.
It might be easier to handle to set it to `default`, or another reserved word. | {
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https://api.github.com/repos/huggingface/datasets/issues/4901 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/4901/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/4901/comments | https://api.github.com/repos/huggingface/datasets/issues/4901/events | https://github.com/huggingface/datasets/pull/4901 | 1,352,438,915 | PR_kwDODunzps494FNX | 4,901 | Raise ManualDownloadError from get_dataset_config_info | {
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} | This PRs raises a specific `ManualDownloadError` when `get_dataset_config_info` is called for a dataset that requires manual download.
Related to:
- #4898
CC: @severo | {
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} | This PR fixes the removal of 2 language tags done by:
- #4882
The tags are:
- "code": this is not a IANA tag but needed
- "und": this is one of the special scoped tags removed by 0d53202b9abce6fd0358cb00d06fcfd904b875af
- used in "mc4" and "udhr" datasets | {
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As we know, the size of cifar10 dataset is ~130MB, but the cache file has almost 30GB size, there may be some problems here. | {
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} | Fix missing tags in dataset cards:
- anli
- coarse_discourse
- commonsense_qa
- cos_e
- ilist
- lc_quad
- web_questions
- xsum
This PR partially fixes the missing tags in dataset cards. Subsequent PRs will follow to complete this task.
Related to:
- #4833
- #4891 | {
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} | null | null | null | ## Describe the bug
The `datasets.load_dataset` returns a `ValueError: Unknown split "validation". Should be one of ['train', 'test'].` when running `load_dataset(local_data_dir_path, split="validation")` even if the `validation` sub-directory exists in the local data path.
The data directories are as follows and attached to this issue:
```
test_data1
|_ train
|_ 1012.png
|_ metadata.jsonl
...
|_ test
...
|_ validation
|_ 234.png
|_ metadata.jsonl
...
test_data2
|_ train
|_ train_1012.png
|_ metadata.jsonl
...
|_ test
...
|_ validation
|_ val_234.png
|_ metadata.jsonl
...
```
They contain the same image files and `metadata.jsonl` but the images in `test_data2` have the split names prepended i.e.
`train_1012.png, val_234.png` and the images in `test_data1` do not have the split names prepended to the image names i.e. `1012.png, 234.png`
I actually saw in another issue `val` was not recognized as a split name but here I would expect the files to take the split from the parent directory name i.e. val should become part of the validation split?
## Steps to reproduce the bug
```python
import datasets
datasets.logging.set_verbosity_error()
from datasets import load_dataset, get_dataset_split_names
# the following only finds train, validation and test splits correctly
path = "./test_data1"
print("######################", get_dataset_split_names(path), "######################")
dataset_list = []
for spt in ["train", "test", "validation"]:
dataset = load_dataset(path, split=spt)
dataset_list.append(dataset)
# the following only finds train and test splits
path = "./test_data2"
print("######################", get_dataset_split_names(path), "######################")
dataset_list = []
for spt in ["train", "test", "validation"]:
dataset = load_dataset(path, split=spt)
dataset_list.append(dataset)
```
## Expected results
```
###################### ['train', 'test', 'validation'] ######################
###################### ['train', 'test', 'validation'] ######################
```
## Actual results
```
Traceback (most recent call last):
File "test_data_loader.py", line 11, in <module>
dataset = load_dataset(path, split=spt)
File "/home/venv/lib/python3.8/site-packages/datasets/load.py", line 1758, in load_dataset
ds = builder_instance.as_dataset(split=split, ignore_verifications=ignore_verifications, in_memory=keep_in_memory)
File "/home/venv/lib/python3.8/site-packages/datasets/builder.py", line 893, in as_dataset
datasets = map_nested(
File "/home/venv/lib/python3.8/site-packages/datasets/utils/py_utils.py", line 385, in map_nested
return function(data_struct)
File "/home/venv/lib/python3.8/site-packages/datasets/builder.py", line 924, in _build_single_dataset
ds = self._as_dataset(
File "/home/venv/lib/python3.8/site-packages/datasets/builder.py", line 993, in _as_dataset
dataset_kwargs = ArrowReader(self._cache_dir, self.info).read(
File "/home/venv/lib/python3.8/site-packages/datasets/arrow_reader.py", line 211, in read
files = self.get_file_instructions(name, instructions, split_infos)
File "/home/venv/lib/python3.8/site-packages/datasets/arrow_reader.py", line 184, in get_file_instructions
file_instructions = make_file_instructions(
File "/home/venv/lib/python3.8/site-packages/datasets/arrow_reader.py", line 107, in make_file_instructions
absolute_instructions = instruction.to_absolute(name2len)
File "/home/venv/lib/python3.8/site-packages/datasets/arrow_reader.py", line 616, in to_absolute
return [_rel_to_abs_instr(rel_instr, name2len) for rel_instr in self._relative_instructions]
File "/home/venv/lib/python3.8/site-packages/datasets/arrow_reader.py", line 616, in <listcomp>
return [_rel_to_abs_instr(rel_instr, name2len) for rel_instr in self._relative_instructions]
File "/home/venv/lib/python3.8/site-packages/datasets/arrow_reader.py", line 433, in _rel_to_abs_instr
raise ValueError(f'Unknown split "{split}". Should be one of {list(name2len)}.')
ValueError: Unknown split "validation". Should be one of ['train', 'test'].
```
## Environment info
<!-- You can run the command `datasets-cli env` and copy-and-paste its output below. -->
- `datasets` version:
- Platform: Linux Ubuntu 18.04
- Python version: 3.8.12
- PyArrow version: 9.0.0
Data files
[test_data1.zip](https://github.com/huggingface/datasets/files/9424463/test_data1.zip)
[test_data2.zip](https://github.com/huggingface/datasets/files/9424468/test_data2.zip)
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- https://github.com/zeerakahmed/makhzan/issues/43 | {
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It would be nice to expand `interleave_datasets` for iterable datasets as well to support this oversampling strategy
```python
>>> from datasets.iterable_dataset import IterableDataset, ExamplesIterable
>>> d1 = IterableDataset(ExamplesIterable(lambda: [(yield i, {"a": i}) for i in [0, 1, 2]], {}))
>>> d2 = IterableDataset(ExamplesIterable(lambda: [(yield i, {"a": i}) for i in [10, 11, 12, 13]], {}))
>>> d3 = IterableDataset(ExamplesIterable(lambda: [(yield i, {"a": i}) for i in [20, 21, 22, 23, 24]], {}))
>>> dataset = interleave_datasets([d1, d2, d3]) # is supported
>>> [x["a"] for x in dataset]
[0, 10, 20, 1, 11, 21, 2, 12, 22]
>>> dataset = interleave_datasets([d1, d2, d3], stopping_strategy="all_exhausted") # is not supported yet
>>> [x["a"] for x in dataset]
[0, 10, 20, 1, 11, 21, 2, 12, 22, 0, 13, 23, 1, 0, 24]
```
This can be implemented by adding the strategy to both `CyclingMultiSourcesExamplesIterable` and `RandomlyCyclingMultiSourcesExamplesIterable` used in `_interleave_iterable_datasets` in `iterable_dataset.py`
I would be happy to share some guidance if anyone would like to give it a shot :) | {
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- https://github.com/dumitrescustefan/RO-STS/issues/4 | {
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} | Fix missing tags in dataset cards:
- aslg_pc12
- librispeech_lm
- mwsc
- opus100
- qasc
- quail
- squadshifts
- winograd_wsc
This PR partially fixes the missing tags in dataset cards. Subsequent PRs will follow to complete this task.
Related to:
- #4833
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} | As discussed in #4885
I initially added this bit at the end, thinking filling this field was necessary as it is done in from_dict.
However, it seems the constructor takes care of filling info when it is empty.
```
if info.features is None:
info.features = Features(
{
col: generate_from_arrow_type(coldata.type)
for col, coldata in zip(pa_table.column_names, pa_table.columns)
}
)
``` | {
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"percent_completed": 0
} | null | null | null | ## Describe the bug
When loading Common Voice with torchaudio 0.11.0 the results are different to 0.12.1 which leads to problems in transformers see: https://github.com/huggingface/transformers/pull/18749
## Steps to reproduce the bug
If you run the following code once with `torchaudio==0.11.0+cu102` and `torchaudio==0.12.1+cu102` you can see that the tensors differ. This is a pretty big breaking change and makes some integration tests fail in Transformers.
```python
#!/usr/bin/env python3
from datasets import load_dataset
import datasets
import numpy as np
import torch
import torchaudio
print("torch vesion", torch.__version__)
print("torchaudio vesion", torchaudio.__version__)
save_audio = True
load_audios = False
if save_audio:
ds = load_dataset("common_voice", "en", split="train", streaming=True)
ds = ds.cast_column("audio", datasets.Audio(sampling_rate=16_000))
ds_iter = iter(ds)
sample = next(ds_iter)
np.save(f"audio_sample_{torch.__version__}", sample["audio"]["array"])
print(sample["audio"]["array"])
if load_audios:
array_torch_11 = np.load("/home/patrick/audio_sample_1.11.0+cu102.npy")
print("Array 11 Shape", array_torch_11.shape)
print("Array 11 abs sum", np.sum(np.abs(array_torch_11)))
array_torch_12 = np.load("/home/patrick/audio_sample_1.12.1+cu102.npy")
print("Array 12 Shape", array_torch_12.shape)
print("Array 12 abs sum", np.sum(np.abs(array_torch_12)))
```
Having saved the tensors the print output yields:
```
torch vesion 1.12.1+cu102
torchaudio vesion 0.12.1+cu102
Array 11 Shape (122880,)
Array 11 abs sum 1396.4988
Array 12 Shape (123264,)
Array 12 abs sum 1396.5193
```
## Expected results
torchaudio 11.0 and 12.1 should yield same results.
## Actual results
See above.
## Environment info
<!-- You can run the command `datasets-cli env` and copy-and-paste its output below. -->
- `datasets` version: 2.1.1.dev0
- Platform: Linux-5.18.10-76051810-generic-x86_64-with-glibc2.34
- Python version: 3.9.7
- PyArrow version: 6.0.1
- Pandas version: 1.4.2
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} | null | null | null | ### Link
https://huggingface.co/datasets/subjqa
### Description
Getting the following error for this dataset:
```
Status code: 500
Exception: Status500Error
Message: 2 or more items returned, instead of 1
```
Not sure what's causing it though 🤔
### Owner
Yes | {
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https://api.github.com/repos/huggingface/datasets/issues/4886 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/4886/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/4886/comments | https://api.github.com/repos/huggingface/datasets/issues/4886/events | https://github.com/huggingface/datasets/issues/4886 | 1,349,285,569 | I_kwDODunzps5QbHbB | 4,886 | Loading huggan/CelebA-HQ throws pyarrow.lib.ArrowInvalid | {
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"name": "bug",
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"description": "Something isn't working"
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] | open | false | null | [] | null | 9 | 2022-08-24T11:24:21 | 2023-02-02T02:40:53 | null | NONE | null | {
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} | null | null | null | ## Describe the bug
Loading huggan/CelebA-HQ throws pyarrow.lib.ArrowInvalid
## Steps to reproduce the bug
```python
from datasets import load_dataset
dataset = load_dataset('huggan/CelebA-HQ')
```
## Expected results
See https://colab.research.google.com/drive/141LJCcM2XyqprPY83nIQ-Zk3BbxWeahq?usp=sharing#scrollTo=N3ml_7f8kzDd
## Actual results
```
File "/home/jean/projects/cold_diffusion/celebA.py", line 4, in <module>
dataset = load_dataset('huggan/CelebA-HQ')
File "/home/jean/miniconda3/envs/seq/lib/python3.10/site-packages/datasets/load.py", line 1793, in load_dataset
builder_instance.download_and_prepare(
File "/home/jean/miniconda3/envs/seq/lib/python3.10/site-packages/datasets/builder.py", line 704, in download_and_prepare
self._download_and_prepare(
File "/home/jean/miniconda3/envs/seq/lib/python3.10/site-packages/datasets/builder.py", line 793, in _download_and_prepare
self._prepare_split(split_generator, **prepare_split_kwargs)
File "/home/jean/miniconda3/envs/seq/lib/python3.10/site-packages/datasets/builder.py", line 1274, in _prepare_split
for key, table in logging.tqdm(
File "/home/jean/miniconda3/envs/seq/lib/python3.10/site-packages/tqdm/std.py", line 1195, in __iter__
for obj in iterable:
File "/home/jean/miniconda3/envs/seq/lib/python3.10/site-packages/datasets/packaged_modules/parquet/parquet.py", line 67, in _generate_tables
parquet_file = pq.ParquetFile(f)
File "/home/jean/miniconda3/envs/seq/lib/python3.10/site-packages/pyarrow/parquet/__init__.py", line 286, in __init__
self.reader.open(
File "pyarrow/_parquet.pyx", line 1227, in pyarrow._parquet.ParquetReader.open
File "pyarrow/error.pxi", line 100, in pyarrow.lib.check_status
pyarrow.lib.ArrowInvalid: Parquet magic bytes not found in footer. Either the file is corrupted or this is not a parquet file.
```
## Environment info
<!-- You can run the command `datasets-cli env` and copy-and-paste its output below. -->
- `datasets` version: datasets-2.4.1.dev0
- Platform: Ubuntu 18.04
- Python version: 3.10
- PyArrow version: pyarrow 9.0.0
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https://api.github.com/repos/huggingface/datasets/issues/4885 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/4885/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/4885/comments | https://api.github.com/repos/huggingface/datasets/issues/4885/events | https://github.com/huggingface/datasets/issues/4885 | 1,349,181,448 | I_kwDODunzps5QauAI | 4,885 | Create dataset from list of dicts | {
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] | closed | false | null | [] | null | 3 | 2022-08-24T10:01:24 | 2022-09-08T16:02:52 | 2022-09-08T16:02:52 | CONTRIBUTOR | null | {
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} | null | null | null | I often find myself with data from a variety of sources, and a list of dicts is very common among these.
However, converting this to a Dataset is a little awkward, requiring either
```Dataset.from_pandas(pd.DataFrame(formatted_training_data))```
Which can error out on some more exotic values as 2-d arrays for reasons that are not entirely clear
> ArrowInvalid: ('Can only convert 1-dimensional array values', 'Conversion failed for column labels with type object')
Alternatively:
```Dataset.from_dict({k: [s[k] for s in formatted_training_data] for k in formatted_training_data[0].keys()})```
Which works, but is a little ugly.
**Describe the solution you'd like**
Either `.from_dict` accepting a list of dicts, or a `.from_records` function accepting such.
I am happy to PR this, just wanted to check you are happy to accept this I haven't missed something obvious, and which of the solutions would be prefered.
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https://api.github.com/repos/huggingface/datasets/issues/4884 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/4884/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/4884/comments | https://api.github.com/repos/huggingface/datasets/issues/4884/events | https://github.com/huggingface/datasets/pull/4884 | 1,349,105,946 | PR_kwDODunzps49s6Aj | 4,884 | Fix documentation card of math_qa dataset | {
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} | [] | closed | false | null | [] | null | 1 | 2022-08-24T09:00:56 | 2022-08-24T11:33:17 | 2022-08-24T11:33:16 | MEMBER | null | {
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https://api.github.com/repos/huggingface/datasets/issues/4883 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/4883/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/4883/comments | https://api.github.com/repos/huggingface/datasets/issues/4883/events | https://github.com/huggingface/datasets/issues/4883 | 1,349,083,235 | I_kwDODunzps5QaWBj | 4,883 | With dataloader RSS memory consumed by HF datasets monotonically increases | {
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] | open | false | null | [] | null | 44 | 2022-08-24T08:42:54 | 2024-01-23T12:42:40 | null | CONTRIBUTOR | null | {
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} | null | null | null | ## Describe the bug
When the HF datasets is used in conjunction with PyTorch Dataloader, the RSS memory of the process keeps on increasing when it should stay constant.
## Steps to reproduce the bug
Run and observe the output of this snippet which logs RSS memory.
```python
import psutil
import os
from transformers import BertTokenizer
from datasets import load_dataset
from torch.utils.data import DataLoader
BATCH_SIZE = 32
NUM_TRIES = 10
tokenizer = BertTokenizer.from_pretrained("bert-base-uncased")
def transform(x):
x.update(tokenizer(x["text"], return_tensors="pt", max_length=64, padding="max_length", truncation=True))
x.pop("text")
x.pop("label")
return x
dataset = load_dataset("imdb", split="train")
dataset.set_transform(transform)
train_loader = DataLoader(dataset, batch_size=BATCH_SIZE, shuffle=True, num_workers=4)
mem_before = psutil.Process(os.getpid()).memory_info().rss / (1024 * 1024)
count = 0
while count < NUM_TRIES:
for idx, batch in enumerate(train_loader):
mem_after = psutil.Process(os.getpid()).memory_info().rss / (1024 * 1024)
print(count, idx, mem_after - mem_before)
count += 1
```
## Expected results
Memory should not increase after initial setup and loading of the dataset
## Actual results
Memory continuously increases as can be seen in the log.
## Environment info
<!-- You can run the command `datasets-cli env` and copy-and-paste its output below. -->
- `datasets` version: 2.3.2
- Platform: Linux-4.19.0-21-cloud-amd64-x86_64-with-glibc2.10
- Python version: 3.8.13
- PyArrow version: 7.0.0
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https://api.github.com/repos/huggingface/datasets/issues/4882 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/4882/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/4882/comments | https://api.github.com/repos/huggingface/datasets/issues/4882/events | https://github.com/huggingface/datasets/pull/4882 | 1,348,913,665 | PR_kwDODunzps49sRtv | 4,882 | Fix language tags resource file | {
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} | This PR fixes/updates/adds ALL language tags from IANA (as of 2022-08-08).
This PR also removes all BCP47 suffixes (the languages file only contains language subtags, i.e. ISO 639 1 or 2 codes; no script/region/variant suffixes). See:
- #4753 | {
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} | null | null | null | **The problem:**
Language diversity is an important dimension of the diversity of datasets. To find one's way around datasets, being able to search by language name and by standardized codes appears crucial.
Currently the list of language codes is [here](https://github.com/huggingface/datasets/blob/main/src/datasets/utils/resources/languages.json), right? At about 1,500 entries, it is roughly at 1/4th of the world's diversity of extant languages. (Probably less, as the list of 1,418 contains variants that are linguistically very close: 108 varieties of English, for instance.)
Looking forward to ever increasing coverage, how will the list of language names and language codes improve over time?
Enrichment of the custom list by HFT contributors (like [here](https://github.com/huggingface/datasets/pull/4880)) has several issues:
* progress is likely to be slow:
(input required from reviewers, etc.)
* the more contributors, the less consistency can be expected among contributions. No need to elaborate on how much confusion is likely to ensue as datasets accumulate.
* there is no information on which language relates with which: no encoding of the special closeness between the languages of the Northwestern Germanic branch (English+Dutch+German etc.), for instance. Information on phylogenetic closeness can be relevant to run experiments on transfer of technology from one language to its close relatives.
**A solution that seems desirable:**
Connecting to an established database that (i) aims at full coverage of the world's languages and (ii) has information on higher-level groupings, alternative names, etc.
It takes a lot of hard work to do such databases. Two important initiatives are [Ethnologue](https://www.ethnologue.com/) (ISO standard) and [Glottolog](https://glottolog.org/). Both have pros and cons. Glottolog contains references to Ethnologue identifiers, so adopting Glottolog entails getting the advantages of both sets of language codes.
Both seem technically accessible & 'developer-friendly'. Glottolog has a [GitHub repo](https://github.com/glottolog/glottolog). For Ethnologue, harvesting tools have been devised (see [here](https://github.com/lyy1994/ethnologue); I did not try it out).
In case a conversation with linguists seemed in order here, I'd be happy to participate ('pro bono', of course), & to rustle up more colleagues as useful, to help this useful development happen.
With appreciation of HFT, | null | {
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} | Fix Citation Information section in dataset cards:
- cc_news
- conllpp
- datacommons_factcheck
- gnad10
- id_panl_bppt
- jigsaw_toxicity_pred
- kinnews_kirnews
- kor_sarcasm
- makhzan
- reasoning_bg
- ro_sts
- ro_sts_parallel
- sanskrit_classic
- telugu_news
- thaiqa_squad
- wiki_movies
This PR partially fixes the Citation Information section in dataset cards. Subsequent PRs will follow to complete this task. | {
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See
https://github.com/huggingface/huggingface_hub/blob/43499582b19df1ed081a5b2bd7a364e9cacdc91d/src/huggingface_hub/hf_api.py#L2164-L2169
It's used here:
https://github.com/huggingface/datasets/blob/fcfcc951a73efbc677f9def9a8707d0af93d5890/src/datasets/dataset_dict.py#L1373-L1381
https://github.com/huggingface/datasets/blob/fdcb8b144ce3ef241410281e125bd03e87b8caa1/src/datasets/arrow_dataset.py#L4354-L4362
https://github.com/huggingface/datasets/blob/fdcb8b144ce3ef241410281e125bd03e87b8caa1/src/datasets/arrow_dataset.py#L4197-L4213
We should remove it.
Maybe the third code sample has an unexpected behavior since it uses the non-default value `identical_ok = False`, but the argument is ignored. | {
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} | null | null | null | Currently there are two places to find metadata for datasets:
- datasets_infos.json, which contains **per dataset config**
- description
- citation
- license
- splits and sizes
- checksums of the data files
- feature types
- and more
- YAML tags, which contain
- license
- language
- train-eval-index
- and more
It would be nice to have a single place instead. We can rely on the YAML tags more than the JSON file for consistency with models. And it would all be indexed by our back-end directly, which is nice to have.
One way would be to move everything to the YAML tags except the checksums (there can be tens of thousands of them). The description/citation is already in the dataset card so we probably don't need to have them in the YAML card, it would be redundant.
Here is an example for SQuAD
```yaml
download_size: 35142551
dataset_size: 89789763
version: 1.0.0
splits:
- name: train
num_examples: 87599
num_bytes: 79317110
- name: validation
num_examples: 10570
num_bytes: 10472653
features:
- name: id
dtype: string
- name: title
dtype: string
- name: context
dtype: string
- name: question
dtype: string
- name: answers
struct:
- name: text
list:
dtype: string
- name: answer_start
list:
dtype: int32
```
Since there is only one configuration for SQuAD, this structure is ok. For datasets with several configs we can see in a second step, but IMO it would be ok to have these fields per config using another syntax
```yaml
configs:
- config: unlabeled
splits:
- name: train
num_examples: 10000
features:
- name: text
dtype: string
- config: labeled
splits:
- name: train
num_examples: 100
features:
- name: text
dtype: string
- name: label
dtype: ClassLabel
names:
- negative
- positive
```
So in the end you could specify a YAML tag either at the top level (for all configs) or per config in the `configs` field
Alternatively we could keep config specific stuff in the `dataset_infos.json` as it it today
Not sure yet what's the best approach here but cc @julien-c @mariosasko @albertvillanova @polinaeterna for feedback :) | {
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https://api.github.com/repos/huggingface/datasets/issues/4875 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/4875/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/4875/comments | https://api.github.com/repos/huggingface/datasets/issues/4875/events | https://github.com/huggingface/datasets/issues/4875 | 1,348,095,686 | I_kwDODunzps5QWk7G | 4,875 | `_resolve_features` ignores the token | {
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} | null | null | null | ## Describe the bug
When calling [`_resolve_features()`](https://github.com/huggingface/datasets/blob/54b532a8a2f5353fdb0207578162153f7b2da2ec/src/datasets/iterable_dataset.py#L1255) on a gated dataset, ie. a dataset which requires a token to be loaded, the token seems to be ignored even if it has been provided to `load_dataset` before.
## Steps to reproduce the bug
```python
import os
os.environ["HF_ENDPOINT"] = "https://hub-ci.huggingface.co/"
hf_token = "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"
from datasets import load_dataset
# public
dataset_name = "__DUMMY_DATASETS_SERVER_USER__/repo_csv_data-16612654226756"
config_name = "__DUMMY_DATASETS_SERVER_USER__--repo_csv_data-16612654226756"
split_name = "train"
iterable_dataset = load_dataset(
dataset_name,
name=config_name,
split=split_name,
streaming=True,
use_auth_token=hf_token,
)
iterable_dataset = iterable_dataset._resolve_features()
print(iterable_dataset.features)
# gated
dataset_name = "__DUMMY_DATASETS_SERVER_USER__/repo_csv_data-16612654317644"
config_name = "__DUMMY_DATASETS_SERVER_USER__--repo_csv_data-16612654317644"
split_name = "train"
iterable_dataset = load_dataset(
dataset_name,
name=config_name,
split=split_name,
streaming=True,
use_auth_token=hf_token,
)
try:
iterable_dataset = iterable_dataset._resolve_features()
except FileNotFoundError as e:
print("FAILS")
```
## Expected results
I expect to have the same result on a public dataset and on a gated (or private) dataset, if the token has been provided.
## Actual results
An exception is thrown on gated datasets.
## Environment info
- `datasets` version: 2.4.0
- Platform: Linux-5.15.0-1017-aws-x86_64-with-glibc2.35
- Python version: 3.9.6
- PyArrow version: 7.0.0
- Pandas version: 1.4.2 | {
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} | null | null | null | For the use of multiple datasets and tasks, we use around more than 200+ dataloaders, then pass it into `dataloader1, dataloader2, ..., dataloader200=accelerate.prepare(dataloader1, dataloader2, ..., dataloader200)`
It causes the memory error when generating batches. Any solutions to it?
```bash
File "/home/xxx/my_code/src/utils/data_utils.py", line 54, in generate_batch
x = next(iterator)
File "/home/xxx/anaconda3/envs/pt1.7/lib/python3.7/site-packages/accelerate/data_loader.py", line 301, in __iter__
for batch in super().__iter__():
File "/home/xxx/anaconda3/envs/pt1.7/lib/python3.7/site-packages/torch/utils/data/dataloader.py", line 435, in __next__
data = self._next_data()
File "/home/xxx/anaconda3/envs/pt1.7/lib/python3.7/site-packages/torch/utils/data/dataloader.py", line 475, in _next_data
data = self._dataset_fetcher.fetch(index) # may raise StopIteration
File "/home/xxx/anaconda3/envs/pt1.7/lib/python3.7/site-packages/torch/utils/data/_utils/fetch.py", line 28, in fetch
data.append(next(self.dataset_iter))
File "/home/xxx/anaconda3/envs/pt1.7/lib/python3.7/site-packages/accelerate/data_loader.py", line 249, in __iter__
for element in self.dataset:
File "/home/xxx/anaconda3/envs/pt1.7/lib/python3.7/site-packages/datasets/iterable_dataset.py", line 503, in __iter__
for key, example in self._iter():
File "/home/xxx/anaconda3/envs/pt1.7/lib/python3.7/site-packages/datasets/iterable_dataset.py", line 500, in _iter
yield from ex_iterable
File "/home/xxx/anaconda3/envs/pt1.7/lib/python3.7/site-packages/datasets/iterable_dataset.py", line 231, in __iter__
new_key = "_".join(str(key) for key in keys)
MemoryError
``` | null | {
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TODO: run `datasets-cli test`:
- [x] wmt17
- [x] wmt18
- [x] wmt19 | {
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} | I'm addding the MAFAND dataset by Masakhane based on the paper/repository below:
Paper: https://aclanthology.org/2022.naacl-main.223/
Code: https://github.com/masakhane-io/lafand-mt
Please, help merge this
Everything works except for creating dummy data file | {
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_No response_
### Description
I've just uploaded a new dataset to the hub and the viewer does not work for some reason, see here: https://huggingface.co/datasets/MoritzLaurer/multilingual_nli
It displays the error:
```
Status code: 400
Exception: Status400Error
Message: The dataset does not exist.
```
Weirdly enough the dataviewer works for an earlier version of the same dataset. The only difference is that it is smaller, but I'm not aware of other changes I have made: https://huggingface.co/datasets/MoritzLaurer/multilingual_nli_test
Do you know why the dataviewer is not working?
### Owner
_No response_ | {
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```
from pathlib import Path
from datasets import Dataset
ds = Dataset.read_json(Path('data.json'))
```
causes an error
```
AttributeError: 'PosixPath' object has no attribute 'decode'
```
**Describe the solution you'd like**
It should be able to accept PosixPath and read the json from inside. | null | {
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- **Name:** *Wiki_dialog*
- **Description: https://github.com/google-research/dialog-inpainting#:~:text=JSON%20object%2C%20for-,example,-%3A
- **Paper: https://arxiv.org/abs/2205.09073
- **Data: https://github.com/google-research/dialog-inpainting
- **Motivation:** *Research and Development on biggest corpus of dialog data*
Instructions to add a new dataset can be found [here](https://github.com/huggingface/datasets/blob/main/ADD_NEW_DATASET.md).
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A clear and concise description of what the bug is.
## Steps to reproduce the bug
```python
# Sample code to reproduce the bug
# The dataset function is as follows：
from pathlib import Path
from typing import Dict, List, Tuple
import datasets
import pandas as pd
_CITATION = """\
"""
_DATASETNAME = "jadi_ide"
_DESCRIPTION = """\
"""
_HOMEPAGE = ""
_LICENSE = "Unknown"
_URLS = {
_DATASETNAME: "https://github.com/fathanick/Javanese-Dialect-Identification-from-Twitter-Data/raw/main/Update 16K_Dataset.xlsx",
}
_SOURCE_VERSION = "1.0.0"
class JaDi_Ide(datasets.GeneratorBasedBuilder):
SOURCE_VERSION = datasets.Version(_SOURCE_VERSION)
BUILDER_CONFIGS = [
NusantaraConfig(
name="jadi_ide_source",
version=SOURCE_VERSION,
description="JaDi-Ide source schema",
schema="source",
subset_id="jadi_ide",
),
]
DEFAULT_CONFIG_NAME = "source"
def _info(self) -> datasets.DatasetInfo:
if self.config.schema == "source":
features = datasets.Features(
{
"id": datasets.Value("string"),
"text": datasets.Value("string"),
"label": datasets.Value("string")
}
)
return datasets.DatasetInfo(
description=_DESCRIPTION,
features=features,
homepage=_HOMEPAGE,
license=_LICENSE,
citation=_CITATION,
)
def _split_generators(self, dl_manager: datasets.DownloadManager) -> List[datasets.SplitGenerator]:
"""Returns SplitGenerators."""
# Dataset does not have predetermined split, putting all as TRAIN
urls = _URLS[_DATASETNAME]
base_dir = Path(dl_manager.download_and_extract(urls))
data_files = {"train": base_dir}
return [
datasets.SplitGenerator(
name=datasets.Split.TRAIN,
gen_kwargs={
"filepath": data_files["train"],
"split": "train",
},
),
]
def _generate_examples(self, filepath: Path, split: str) -> Tuple[int, Dict]:
"""Yields examples as (key, example) tuples."""
df = pd.read_excel(filepath, engine='openpyxl')
df.columns = ["id", "text", "label"]
if self.config.schema == "source":
for row in df.itertuples():
ex = {
"id": str(row.id),
"text": row.text,
"label": row.label,
}
yield row.id, ex
```
## Expected results
Expecting to load the dataset smoothly.
## Actual results
File "/home/xuyan/anaconda3/lib/python3.7/site-packages/datasets/load.py", line 1751, in load_dataset
use_auth_token=use_auth_token,
File "/home/xuyan/anaconda3/lib/python3.7/site-packages/datasets/builder.py", line 705, in download_and_prepare
dl_manager=dl_manager, verify_infos=verify_infos, **download_and_prepare_kwargs
File "/home/xuyan/anaconda3/lib/python3.7/site-packages/datasets/builder.py", line 1227, in _download_and_prepare
super()._download_and_prepare(dl_manager, verify_infos, check_duplicate_keys=verify_infos)
File "/home/xuyan/anaconda3/lib/python3.7/site-packages/datasets/builder.py", line 793, in _download_and_prepare
self._prepare_split(split_generator, **prepare_split_kwargs)
File "/home/xuyan/anaconda3/lib/python3.7/site-packages/datasets/builder.py", line 1216, in _prepare_split
desc=f"Generating {split_info.name} split",
File "/home/xuyan/anaconda3/lib/python3.7/site-packages/tqdm/std.py", line 1195, in __iter__
for obj in iterable:
File "/home/xuyan/.cache/huggingface/modules/datasets_modules/datasets/jadi_ide/7a539f2b6f726defea8fbe36ceda17bae66c370f6d6c418e3a08d760ebef7519/jadi_ide.py", line 107, in _generate_examples
df = pd.read_excel(filepath, engine='openpyxl')
File "/home/xuyan/anaconda3/lib/python3.7/site-packages/datasets/download/streaming_download_manager.py", line 701, in xpandas_read_excel
return pd.read_excel(BytesIO(filepath_or_buffer.read()), **kwargs)
AttributeError: 'xPath' object has no attribute 'read'
## Environment info
<!-- You can run the command `datasets-cli env` and copy-and-paste its output below. -->
- `datasets` version: 2.4.0
- Platform: Linux-4.15.0-142-generic-x86_64-with-debian-stretch-sid
- Python version: 3.7.4
- PyArrow version: 9.0.0
- Pandas version: 0.25.1
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"name": "enhancement",
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"default": true,
"description": "New feature or request"
}
] | open | false | null | [] | null | 1 | 2022-08-18T15:18:18 | 2023-01-26T18:36:28 | null | NONE | null | {
"total": 0,
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} | null | null | null | **Is your feature request related to a problem? Please describe.**
I start with an empty dataset. In a loop, at each iteration, I create a new dataset with the method `from_dict` (based on some data I load) and I concatenate this new dataset with the one at the previous iteration. After some iterations, I have an OOM error.
**Describe the solution you'd like**
The method `from_dict` loads the data in RAM. It could be good to add an option to use the disk instead.
**Describe alternatives you've considered**
To solve the problem, I have to do an intermediate step where I save the new datasets at each iteration with `save_to_disk`. Once it's done, I open them all and concatenate them.
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https://api.github.com/repos/huggingface/datasets/issues/4860 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/4860/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/4860/comments | https://api.github.com/repos/huggingface/datasets/issues/4860/events | https://github.com/huggingface/datasets/pull/4860 | 1,342,311,540 | PR_kwDODunzps49WjEu | 4,860 | Add collection3 dataset | {
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https://api.github.com/repos/huggingface/datasets/issues/4859 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/4859/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/4859/comments | https://api.github.com/repos/huggingface/datasets/issues/4859/events | https://github.com/huggingface/datasets/issues/4859 | 1,342,231,016 | I_kwDODunzps5QANHo | 4,859 | can't install using conda on Windows 10 | {
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] | open | false | null | [] | null | 0 | 2022-08-17T19:57:37 | 2022-08-17T19:57:37 | null | NONE | null | {
"total": 0,
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} | null | null | null | ## Describe the bug
I wanted to install using conda or Anaconda navigator. That didn't work, so I had to install using pip.
## Steps to reproduce the bug
conda install -c huggingface -c conda-forge datasets
## Expected results
Should have indicated successful installation.
## Actual results
Solving environment: failed with initial frozen solve. Retrying with flexible solve.
Solving environment: failed with repodata from current_repodata.json, will retry with next repodata source.
... took forever, so I cancelled it with ctrl-c
## Environment info
- `datasets` version: 2.4.0 # after installing with pip
- Platform: Windows-10-10.0.19044-SP0
- Python version: 3.9.12
- PyArrow version: 9.0.0
- Pandas version: 1.4.2
- conda version: 4.13.0
conda info
active environment : base
active env location : G:\anaconda2022
shell level : 1
user config file : C:\Users\michael\.condarc
populated config files : C:\Users\michael\.condarc
conda version : 4.13.0
conda-build version : 3.21.8
python version : 3.9.12.final.0
virtual packages : __cuda=11.1=0
__win=0=0
__archspec=1=x86_64
base environment : G:\anaconda2022 (writable)
conda av data dir : G:\anaconda2022\etc\conda
conda av metadata url : None
channel URLs : https://conda.anaconda.org/pytorch/win-64
https://conda.anaconda.org/pytorch/noarch
https://conda.anaconda.org/huggingface/win-64
https://conda.anaconda.org/huggingface/noarch
https://conda.anaconda.org/conda-forge/win-64
https://conda.anaconda.org/conda-forge/noarch
https://conda.anaconda.org/anaconda-fusion/win-64
https://conda.anaconda.org/anaconda-fusion/noarch
https://repo.anaconda.com/pkgs/main/win-64
https://repo.anaconda.com/pkgs/main/noarch
https://repo.anaconda.com/pkgs/r/win-64
https://repo.anaconda.com/pkgs/r/noarch
https://repo.anaconda.com/pkgs/msys2/win-64
https://repo.anaconda.com/pkgs/msys2/noarch
package cache : G:\anaconda2022\pkgs
C:\Users\michael\.conda\pkgs
C:\Users\michael\AppData\Local\conda\conda\pkgs
envs directories : G:\anaconda2022\envs
C:\Users\michael\.conda\envs
C:\Users\michael\AppData\Local\conda\conda\envs
platform : win-64
user-agent : conda/4.13.0 requests/2.27.1 CPython/3.9.12 Windows/10 Windows/10.0.19044
administrator : False
netrc file : None
offline mode : False
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} | null | null | null | ## Describe the bug
The map function, removes features from the dataset that are not present in the _input_columns_ list of columns, despite the columns being removed not mentioned in the _remove_columns_ argument.
## Steps to reproduce the bug
```python
from datasets import Dataset
ds = Dataset.from_dict({"a" : [1,2,3],"b" : [0,1,0], "c" : [2,4,5]})
def double(x,y):
x = x*2
y = y*2
return {"d" : x, "e" : y}
ds.map(double, input_columns=["a","c"])
```
## Expected results
```
Dataset({
features: ['a', 'b', 'c', 'd', 'e'],
num_rows: 3
})
```
## Actual results
```
Dataset({
features: ['a', 'c', 'd', 'e'],
num_rows: 3
})
```
In this specific example feature **b** should not be removed.
## Environment info
<!-- You can run the command `datasets-cli env` and copy-and-paste its output below. -->
- `datasets` version: 2.4.0
- Platform: linux (colab)
- Python version: 3.7.13
- PyArrow version: 6.0.1
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} | null | null | null | ## Describe the bug
20220301 wikipedia dump has been deprecated, so now there is no working wikipedia dump on huggingface
https://huggingface.co/datasets/wikipedia
https://dumps.wikimedia.org/enwiki/
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"total": 0,
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} | null | null | null | ## Describe the bug
0015896-b1054262f7da52a0518521e29c8e352c.txt is missing when I run run_mlm.py with openwebtext. I check the cache_path and can not find 0015896-b1054262f7da52a0518521e29c8e352c.txt. but I can find this file in the 17ecf461bfccd469a1fbc264ccb03731f8606eea7b3e2e8b86e13d18040bf5b3/urlsf_subset00-16_data.xz with 7-zip.
## Steps to reproduce the bug
```sh
python run_mlm.py --model_type roberta --tokenizer_name roberta-base --dataset_name openwebtext --per_device_train_batch_size 8 --per_device_eval_batch_size 8 --do_train --do_eval --output_dir F:/model/roberta-base
```
or
```python
from datasets import load_dataset
load_dataset("openwebtext", None, cache_dir=None, use_auth_token=None)
```
## Expected results
Loading is successful
## Actual results
Traceback (most recent call last):
File "D:\Python\v3.8.5\lib\site-packages\datasets\builder.py", line 704, in download_and_prepare
self._download_and_prepare(
File "D:\Python\v3.8.5\lib\site-packages\datasets\builder.py", line 1227, in _download_and_prepare
super()._download_and_prepare(dl_manager, verify_infos, check_duplicate_keys=verify_infos)
File "D:\Python\v3.8.5\lib\site-packages\datasets\builder.py", line 795, in _download_and_prepare
raise OSError(
OSError: Cannot find data file.
Original error:
[Errno 22] Invalid argument: 'F://huggingface/datasets/downloads/extracted/0901d27f43b7e9ac0577da0d0061c8c632ba0b70ecd1b4bfb21562d9b7486faa/0015896-b1054262f7da52a0518521e29c8e352c.txt'
## Environment info
<!-- You can run the command `datasets-cli env` and copy-and-paste its output below. -->
- `datasets` version: 2.4.0
- Platform: windows
- Python version: 3.8.5
- PyArrow version: 9.0.0
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https://huggingface.co/datasets/super_glue
### Description
can't view super_glue dataset on the web page
### Owner
_No response_ | {
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} | null | null | null | ## Describe the bug
There is a bug for "multilingual_with_para" config in exams dataset:
```python
ds = load_dataset("./datasets/exams", split="train")
```
raises:
```
KeyError: 'choices'
```
Moreover, there is a NonMatchingChecksumError:
```
NonMatchingChecksumError: Checksums didn't match for dataset source files:
['https://github.com/mhardalov/exams-qa/raw/main/data/exams/multilingual/with_paragraphs/train_with_para.jsonl.tar.gz', 'https://github.com/mhardalov/exams-qa/raw/main/data/exams/multilingual/with_paragraphs/dev_with_para.jsonl.tar.gz', 'https://github.com/mhardalov/exams-qa/raw/main/data/exams/multilingual/with_paragraphs/test_with_para.jsonl.tar.gz', 'https://github.com/mhardalov/exams-qa/raw/main/data/exams/cross-lingual/with_paragraphs/test_with_para.jsonl.tar.gz', 'https://github.com/mhardalov/exams-qa/raw/main/data/exams/cross-lingual/with_paragraphs/train_bg_with_para.jsonl.tar.gz', 'https://github.com/mhardalov/exams-qa/raw/main/data/exams/cross-lingual/with_paragraphs/dev_bg_with_para.jsonl.tar.gz', 'https://github.com/mhardalov/exams-qa/raw/main/data/exams/cross-lingual/with_paragraphs/train_hr_with_para.jsonl.tar.gz', 'https://github.com/mhardalov/exams-qa/raw/main/data/exams/cross-lingual/with_paragraphs/dev_hr_with_para.jsonl.tar.gz', 'https://github.com/mhardalov/exams-qa/raw/main/data/exams/cross-lingual/with_paragraphs/train_hu_with_para.jsonl.tar.gz', 'https://github.com/mhardalov/exams-qa/raw/main/data/exams/cross-lingual/with_paragraphs/dev_hu_with_para.jsonl.tar.gz', 'https://github.com/mhardalov/exams-qa/raw/main/data/exams/cross-lingual/with_paragraphs/train_it_with_para.jsonl.tar.gz', 'https://github.com/mhardalov/exams-qa/raw/main/data/exams/cross-lingual/with_paragraphs/dev_it_with_para.jsonl.tar.gz', 'https://github.com/mhardalov/exams-qa/raw/main/data/exams/cross-lingual/with_paragraphs/train_mk_with_para.jsonl.tar.gz', 'https://github.com/mhardalov/exams-qa/raw/main/data/exams/cross-lingual/with_paragraphs/dev_mk_with_para.jsonl.tar.gz', 'https://github.com/mhardalov/exams-qa/raw/main/data/exams/cross-lingual/with_paragraphs/train_pl_with_para.jsonl.tar.gz', 'https://github.com/mhardalov/exams-qa/raw/main/data/exams/cross-lingual/with_paragraphs/dev_pl_with_para.jsonl.tar.gz', 'https://github.com/mhardalov/exams-qa/raw/main/data/exams/cross-lingual/with_paragraphs/train_pt_with_para.jsonl.tar.gz', 'https://github.com/mhardalov/exams-qa/raw/main/data/exams/cross-lingual/with_paragraphs/dev_pt_with_para.jsonl.tar.gz', 'https://github.com/mhardalov/exams-qa/raw/main/data/exams/cross-lingual/with_paragraphs/train_sq_with_para.jsonl.tar.gz', 'https://github.com/mhardalov/exams-qa/raw/main/data/exams/cross-lingual/with_paragraphs/dev_sq_with_para.jsonl.tar.gz', 'https://github.com/mhardalov/exams-qa/raw/main/data/exams/cross-lingual/with_paragraphs/train_sr_with_para.jsonl.tar.gz', 'https://github.com/mhardalov/exams-qa/raw/main/data/exams/cross-lingual/with_paragraphs/dev_sr_with_para.jsonl.tar.gz', 'https://github.com/mhardalov/exams-qa/raw/main/data/exams/cross-lingual/with_paragraphs/train_tr_with_para.jsonl.tar.gz', 'https://github.com/mhardalov/exams-qa/raw/main/data/exams/cross-lingual/with_paragraphs/dev_tr_with_para.jsonl.tar.gz', 'https://github.com/mhardalov/exams-qa/raw/main/data/exams/cross-lingual/with_paragraphs/train_vi_with_para.jsonl.tar.gz', 'https://github.com/mhardalov/exams-qa/raw/main/data/exams/cross-lingual/with_paragraphs/dev_vi_with_para.jsonl.tar.gz']
```
CC: @thesofakillers | {
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https://api.github.com/repos/huggingface/datasets/issues/4850 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/4850/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/4850/comments | https://api.github.com/repos/huggingface/datasets/issues/4850/events | https://github.com/huggingface/datasets/pull/4850 | 1,338,702,306 | PR_kwDODunzps49KnZ8 | 4,850 | Fix test of _get_extraction_protocol for TAR files | {
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} | While working in another PR, I discovered an xpass test (a test that is supposed to xfail but nevertheless passes) when testing `_get_extraction_protocol`: https://github.com/huggingface/datasets/runs/7818845285?check_suite_focus=true
```
XPASS tests/test_streaming_download_manager.py::test_streaming_dl_manager_get_extraction_protocol_throws[https://foo.bar/train.tar]
```
This PR:
- refactors the test so that it tests the raise of the exceptions instead of xfailing
- fixes the test for TAR files: it does not raise an exception, but returns "tar"
- fixes some tests wrongly named: exchange `test_streaming_dl_manager_get_extraction_protocol` with `test_streaming_dl_manager_get_extraction_protocol_gg_drive` | {
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} | Hi !
Paper has been published at EMNLP. | {
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} | In order to make testing more robust (and avoid merges to master with red tests), we could mark tests as xfailed (instead of failed) when the Hub raises some temporary HTTP errors.
This PR:
- marks tests as xfailed only if the Hub raises a 500 error for:
- test_upstream_hub
- makes pytest report the xfailed/xpassed tests.
More tests could also be marked if needed.
Examples of CI failures due to temporary Hub HTTP errors:
- FAILED tests/test_upstream_hub.py::TestPushToHub::test_push_dataset_dict_to_hub_multiple_files
- https://github.com/huggingface/datasets/runs/7806855399?check_suite_focus=true
`requests.exceptions.HTTPError: 500 Server Error: Internal Server Error for url: https://hub-ci.huggingface.co/api/datasets/__DUMMY_TRANSFORMERS_USER__/test-16603108028233/commit/main (Request ID: aZeAQ5yLktoGHQYBcJ3zo)`
- FAILED tests/test_upstream_hub.py::TestPushToHub::test_push_dataset_dict_to_hub_no_token
- https://github.com/huggingface/datasets/runs/7840022996?check_suite_focus=true
`requests.exceptions.HTTPError: 500 Server Error: Internal Server Error for url: https://s3.us-east-1.amazonaws.com/lfs-staging.huggingface.co/repos/81/e3/81e3b831fa9bf23190ec041f26ef7ff6d6b71c1a937b8ec1ef1f1f05b508c089/caae596caa179cf45e7c9ac0c6d9a9cb0fe2d305291bfbb2d8b648ae26ed38b6?X-Amz-Algorithm=AWS4-HMAC-SHA256&X-Amz-Content-Sha256=UNSIGNED-PAYLOAD&X-Amz-Credential=AKIA4N7VTDGOZQA2IKWK%2F20220815%2Fus-east-1%2Fs3%2Faws4_request&X-Amz-Date=20220815T144713Z&X-Amz-Expires=900&X-Amz-Signature=5ddddfe8ef2b0601e80ab41c78a4d77d921942b0d8160bcab40ff894095e6823&X-Amz-SignedHeaders=host&x-id=PutObject`
- FAILED tests/test_upstream_hub.py::TestPushToHub::test_push_dataset_dict_to_hub_private
- https://github.com/huggingface/datasets/runs/7835921082?check_suite_focus=true
`requests.exceptions.HTTPError: 500 Server Error: Internal Server Error for url: https://hub-ci.huggingface.co/api/repos/create (Request ID: gL_1I7i2dii9leBhlZen-) - Internal Error - We're working hard to fix that as soon as possible!`
- FAILED tests/test_upstream_hub.py::TestPushToHub::test_push_dataset_to_hub_custom_features_image_list
- https://github.com/huggingface/datasets/runs/7835920900?check_suite_focus=true
- This is not 500, but 404:
`requests.exceptions.HTTPError: 404 Client Error: Not Found for url: [https://hub-ci.huggingface.co/datasets/__DUMMY_TRANSFORMERS_USER__/test-16605586458339.git/info/lfs/objects](https://hub-ci.huggingface.co/datasets/__DUMMY_TRANSFORMERS_USER__/test-16605586458339.git/info/lfs/objects/batch)`
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} | This PR fixes #4839 by adding the word `"val"` to the `VALIDATION_KEYWORDS` so that the `load_dataset()` method with `imagefolder` (and probably, some other directives as well) reads folders named `"val"` as well.
I think the supported keywords have to be mentioned in the documentation as well, but I couldn't think of a proper place to add that. | {
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} | Excerpt from the paper's abstract: "Aside from its cultural and social relevance, this content, which is published under the Creative Commons BY-NC-ND license, also represents a precious language resource for the machine translation research community" | {
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https://api.github.com/repos/huggingface/datasets/issues/4840 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/4840/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/4840/comments | https://api.github.com/repos/huggingface/datasets/issues/4840/events | https://github.com/huggingface/datasets/issues/4840 | 1,337,342,672 | I_kwDODunzps5PtjrQ | 4,840 | Dataset Viewer issue for darragh/demo_data_raw3 | {
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https://huggingface.co/datasets/darragh/demo_data_raw3
### Description
```
Exception: ValueError
Message: Arrow type extension<arrow.py_extension_type<pyarrow.lib.UnknownExtensionType>> does not have a datasets dtype equivalent.
```
reported by @NielsRogge
### Owner
No | null | {
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https://api.github.com/repos/huggingface/datasets/issues/4839 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/4839/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/4839/comments | https://api.github.com/repos/huggingface/datasets/issues/4839/events | https://github.com/huggingface/datasets/issues/4839 | 1,337,206,377 | I_kwDODunzps5PtCZp | 4,839 | ImageFolder dataset builder does not read the validation data set if it is named as "val" | {
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} | null | null | null | **Is your feature request related to a problem? Please describe.**
Currently, the `'imagefolder'` data set builder in [`load_dataset()`](https://github.com/huggingface/datasets/blob/2.4.0/src/datasets/load.py#L1541] ) only [supports](https://github.com/huggingface/datasets/blob/6c609a322da994de149b2c938f19439bca99408e/src/datasets/data_files.py#L31) the following names as the validation data set directory name: `["validation", "valid", "dev"]`. When the validation directory is named as `'val'`, the Data set will not have a validation split. I expected this to be a trivial task but ended up spending a lot of time before knowing that only the above names are supported.
Here's a minimal example of `val` not being recognized:
```python
import os
import numpy as np
import cv2
from datasets import load_dataset
# creating a dummy data set with the following structure:
# ROOT
# | -- train
# | ---- class_1
# | ---- class_2
# | -- val
# | ---- class_1
# | ---- class_2
ROOT = "data"
for which in ["train", "val"]:
for class_name in ["class_1", "class_2"]:
dir_name = os.path.join(ROOT, which, class_name)
if not os.path.exists(dir_name):
os.makedirs(dir_name)
for i in range(10):
cv2.imwrite(
os.path.join(dir_name, f"{i}.png"),
np.random.random((224, 224))
)
# trying to create a data set
dataset = load_dataset(
"imagefolder",
data_dir=ROOT
)
>> dataset
DatasetDict({
train: Dataset({
features: ['image', 'label'],
num_rows: 20
})
})
# ^ note how the dataset only has a 'train' subset
```
**Describe the solution you'd like**
The suggestion is to include `"val"` to [that list ](https://github.com/huggingface/datasets/blob/6c609a322da994de149b2c938f19439bca99408e/src/datasets/data_files.py#L31) as that's a commonly used phrase to name the validation directory.
Also, In the documentation, explicitly mention that only such directory names are supported as train/val/test directories to avoid confusion.
**Describe alternatives you've considered**
In the documentation, explicitly mention that only such directory names are supported as train/val/test directories without adding `val` to the above list.
**Additional context**
A question asked in the forum: [
Loading an imagenet-style image dataset with train/val directories](https://discuss.huggingface.co/t/loading-an-imagenet-style-image-dataset-with-train-val-directories/21554) | {
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] | open | false | null | [] | null | 0 | 2022-08-12T11:06:11 | 2022-08-12T11:06:11 | null | NONE | null | {
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} | null | null | null | **Is your feature request related to a problem? Please describe.**
I wanted to use a python loading script in hugging face datasets that use different sources of text (it's somehow a compilation of multiple datasets + my own dataset) based on how `load_dataset` [works](https://huggingface.co/docs/datasets/loading) I assumed I could do something like bellow in my loading script:
```python
...
_URL = "MY_DATASET_URL/resolve/main/data/"
_URLS = {
"train": [
"FIRST_URL_TO.txt",
_URL + "train-00000-of-00001-676bfebbc8742592.parquet"
]
}
...
```
but when loading the dataset it raises the following error:
```python
File ~/.local/lib/python3.8/site-packages/datasets/builder.py:704, in DatasetBuilder.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)
702 logger.warning("HF google storage unreachable. Downloading and preparing it from source")
703 if not downloaded_from_gcs:
--> 704 self._download_and_prepare(
705 dl_manager=dl_manager, verify_infos=verify_infos, **download_and_prepare_kwargs
...
668 if isinstance(a, str):
669 # Force-cast str subclasses to str (issue #21127)
670 parts.append(str(a))
TypeError: expected str, bytes or os.PathLike object, not list
```
**Describe the solution you'd like**
I believe since it's possible for `load_dataset` to get list of URLs instead of just a URL for `train` split it can be possible here too.
**Describe alternatives you've considered**
An alternative solution would be to download all needed datasets locally and `push_to_hub` them all, but since the datasets I'm talking about are huge it's not among my options.
**Additional context**
I think loading `text` beside the `parquet` is completely a different issue but I believe I can figure it out by proposing a config for my dataset to load each entry of `_URLS['train']` separately either by `load_dataset("text", ...` or `load_dataset("parquet", ...`.
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} | Fix missing tags in dataset cards:
- boolq
- break_data
- definite_pronoun_resolution
- emo
- kor_nli
- pg19
- quartz
- sciq
- squad_es
- wmt14
- wmt15
- wmt16
- wmt17
- wmt18
- wmt19
- wmt_t2t
This PR partially fixes the missing tags in dataset cards. Subsequent PRs will follow to complete this task. | {
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} | Hello everyone,
Here is a proposal to improve `interleave_datasets` function.
Following Issue #3064, and @lhoestq [comment](https://github.com/huggingface/datasets/issues/3064#issuecomment-1022333385), I propose here a code that performs oversampling when interleaving a `Dataset` list.
I have myself encountered this problem while trying to implement training on a multilingual dataset following a training strategy similar to that of [XLSUM paper](https://arxiv.org/pdf/2106.13822.pdf), a multilingual abstract summary dataset where the multilingual training dataset is created by sampling from the languages following a smoothing strategy. The main idea is to sample languages that have a low number of samples more frequently than other languages.
As in Issue #3064, the current default strategy is a undersampling strategy, which stops as soon as a dataset runs out of samples. The new `all_exhausted` strategy stops building the new dataset as soon as all samples in each dataset have been added at least once.
How does it work in practice:
- if ``probabilities`` is `None` and the strategy is `all_exhausted`, it simply performs a round robin interleaving that stops when the longest dataset is out of samples. Here the new dataset length will be $maxLengthDataset*nbDataset$.
- if ``probabilities`` is not `None` and the strategy is `all_exhausted`, it keeps trace of the datasets which were out of samples but continues to add them to the new dataset, and stops as soons as every dataset runs out of samples at least once.
- In the other cases, it is supposed to keep the same behaviour as before. Except that this time, when probabilities are precised, it really stops AS SOON AS a dataset is out of samples.
More on the last sentence:
The previous example of `interleave_datasets` was:
>>> from datasets import Dataset, interleave_datasets
>>> d1 = Dataset.from_dict({"a": [0, 1, 2]})
>>> d2 = Dataset.from_dict({"a": [10, 11, 12]})
>>> d3 = Dataset.from_dict({"a": [20, 21, 22]})
>>> dataset = interleave_datasets([d1, d2, d3])
>>> dataset["a"]
[0, 10, 20, 1, 11, 21, 2, 12, 22]
>>> dataset = interleave_datasets([d1, d2, d3], probabilities=[0.7, 0.2, 0.1], seed=42)
>>> dataset["a"]
[10, 0, 11, 1, 2, 20, 12]
With my implementation, `dataset = interleave_datasets([d1, d2, d3], probabilities=[0.7, 0.2, 0.1], seed=42)` gives:
>>> dataset["a"]
[10, 0, 11, 1, 2]
because `d1` is already out of samples just after `2` is added.
Example of the results of applying the different strategies:
>>> from datasets import Dataset, interleave_datasets
>>> d1 = Dataset.from_dict({"a": [0, 1, 2]})
>>> d2 = Dataset.from_dict({"a": [10, 11, 12]})
>>> d3 = Dataset.from_dict({"a": [20, 21, 22]})
>>> dataset = interleave_datasets([d1, d2, d3], probabilities=[0.7, 0.2, 0.1], seed=42, stopping_strategy="all_exhausted")
>>> dataset["a"]
[10, 0, 11, 1, 2, 20, 12, 10, 0, 1, 2, 21, 0, 11, 1, 2, 0, 1, 12, 2, 10, 0, 22]
>>> dataset = interleave_datasets([d1, d2, d3], probabilities=[0.7, 0.2, 0.1], seed=42)
>>> dataset["a"]
[10, 0, 11, 1, 2]
>>> dataset = interleave_datasets([d1, d2, d3])
>>> dataset["a"]
[0, 10, 20, 1, 11, 21, 2, 12, 22]
>>> dataset = interleave_datasets([d1, d2, d3], stopping_strategy="all_exhausted")
>>> dataset["a"]
[0, 10, 20, 1, 11, 21, 2, 12, 22]
>>> d1 = Dataset.from_dict({"a": [0, 1, 2]})
>>> d2 = Dataset.from_dict({"a": [10, 11, 12, 13]})
>>> d3 = Dataset.from_dict({"a": [20, 21, 22, 23, 24]})
>>> dataset = interleave_datasets([d1, d2, d3])
>>> dataset["a"]
[0, 10, 20, 1, 11, 21, 2, 12, 22]
>>> dataset = interleave_datasets([d1, d2, d3], stopping_strategy="all_exhausted")
>>> dataset["a"]
[0, 10, 20, 1, 11, 21, 2, 12, 22, 0, 13, 23, 1, 0, 24]
>>> dataset = interleave_datasets([d1, d2, d3], probabilities=[0.7, 0.2, 0.1], seed=42)
>>> dataset["a"]
[10, 0, 11, 1, 2]
>>> dataset = interleave_datasets([d1, d2, d3], probabilities=[0.7, 0.2, 0.1], seed=42, stopping_strategy="all_exhausted")
>>> dataset["a"]
[10, 0, 11, 1, 2, 20, 12, 13, ..., 0, 1, 2, 0, 24]
**Final note:** I've been using that code for a research project involving a large-scale multilingual dataset. One should be careful when using oversampling to avoid to avoid exploding the size of the dataset. For example, if a very large data set has a low probability of being sampled, the final dataset may be several times the size of that large data set.
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