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Cannot load the dataset split (in streaming mode) to extract the first rows.
Error code:   StreamingRowsError
Exception:    FileNotFoundError
Message:      https://dl.fbaipublicfiles.com/XNLI/XNLI-MT-1.0.zip
Traceback:    Traceback (most recent call last):
                File "/src/services/worker/.venv/lib/python3.9/site-packages/fsspec/implementations/http.py", line 391, in _info
                  await _file_info(
                File "/src/services/worker/.venv/lib/python3.9/site-packages/fsspec/implementations/http.py", line 772, in _file_info
                  r.raise_for_status()
                File "/src/services/worker/.venv/lib/python3.9/site-packages/aiohttp/client_reqrep.py", line 1004, in raise_for_status
                  raise ClientResponseError(
              aiohttp.client_exceptions.ClientResponseError: 429, message='Too Many Requests', url=URL('https://dl.fbaipublicfiles.com/XNLI/XNLI-MT-1.0.zip')
              
              The above exception was the direct cause of the following exception:
              
              Traceback (most recent call last):
                File "/src/services/worker/src/worker/responses/first_rows.py", line 337, in get_first_rows_response
                  rows = get_rows(dataset, config, split, streaming=True, rows_max_number=rows_max_number, hf_token=hf_token)
                File "/src/services/worker/src/worker/utils.py", line 123, in decorator
                  return func(*args, **kwargs)
                File "/src/services/worker/src/worker/responses/first_rows.py", line 77, in get_rows
                  rows_plus_one = list(itertools.islice(ds, rows_max_number + 1))
                File "/src/services/worker/.venv/lib/python3.9/site-packages/datasets/iterable_dataset.py", line 718, in __iter__
                  for key, example in self._iter():
                File "/src/services/worker/.venv/lib/python3.9/site-packages/datasets/iterable_dataset.py", line 708, in _iter
                  yield from ex_iterable
                File "/src/services/worker/.venv/lib/python3.9/site-packages/datasets/iterable_dataset.py", line 112, in __iter__
                  yield from self.generate_examples_fn(**self.kwargs)
                File "/tmp/modules-cache/datasets_modules/datasets/xnli/818164464f9c9fd15776ca8a00423b074344c3e929d00a2c1a84aa5a50c928bd/xnli.py", line 192, in _generate_examples
                  file = open(filepath, encoding="utf-8")
                File "/src/services/worker/.venv/lib/python3.9/site-packages/datasets/streaming.py", line 67, in wrapper
                  return function(*args, use_auth_token=use_auth_token, **kwargs)
                File "/src/services/worker/.venv/lib/python3.9/site-packages/datasets/download/streaming_download_manager.py", line 453, in xopen
                  file_obj = fsspec.open(file, mode=mode, *args, **kwargs).open()
                File "/src/services/worker/.venv/lib/python3.9/site-packages/fsspec/core.py", line 441, in open
                  return open_files(
                File "/src/services/worker/.venv/lib/python3.9/site-packages/fsspec/core.py", line 273, in open_files
                  fs, fs_token, paths = get_fs_token_paths(
                File "/src/services/worker/.venv/lib/python3.9/site-packages/fsspec/core.py", line 606, in get_fs_token_paths
                  fs = filesystem(protocol, **inkwargs)
                File "/src/services/worker/.venv/lib/python3.9/site-packages/fsspec/registry.py", line 268, in filesystem
                  return cls(**storage_options)
                File "/src/services/worker/.venv/lib/python3.9/site-packages/fsspec/spec.py", line 76, in __call__
                  obj = super().__call__(*args, **kwargs)
                File "/src/services/worker/.venv/lib/python3.9/site-packages/fsspec/implementations/zip.py", line 59, in __init__
                  self.fo = fo.__enter__()  # the whole instance is a context
                File "/src/services/worker/.venv/lib/python3.9/site-packages/fsspec/core.py", line 103, in __enter__
                  f = self.fs.open(self.path, mode=mode)
                File "/src/services/worker/.venv/lib/python3.9/site-packages/fsspec/spec.py", line 1034, in open
                  f = self._open(
                File "/src/services/worker/.venv/lib/python3.9/site-packages/fsspec/implementations/http.py", line 340, in _open
                  size = size or self.info(path, **kwargs)["size"]
                File "/src/services/worker/.venv/lib/python3.9/site-packages/fsspec/asyn.py", line 111, in wrapper
                  return sync(self.loop, func, *args, **kwargs)
                File "/src/services/worker/.venv/lib/python3.9/site-packages/fsspec/asyn.py", line 96, in sync
                  raise return_result
                File "/src/services/worker/.venv/lib/python3.9/site-packages/fsspec/asyn.py", line 53, in _runner
                  result[0] = await coro
                File "/src/services/worker/.venv/lib/python3.9/site-packages/fsspec/implementations/http.py", line 404, in _info
                  raise FileNotFoundError(url) from exc
              FileNotFoundError: https://dl.fbaipublicfiles.com/XNLI/XNLI-MT-1.0.zip

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Dataset Card for "xnli"

Dataset Summary

XNLI is a subset of a few thousand examples from MNLI which has been translated into a 14 different languages (some low-ish resource). As with MNLI, the goal is to predict textual entailment (does sentence A imply/contradict/neither sentence B) and is a classification task (given two sentences, predict one of three labels).

Supported Tasks and Leaderboards

More Information Needed

Languages

More Information Needed

Dataset Structure

Data Instances

all_languages

  • Size of downloaded dataset files: 461.54 MB
  • Size of the generated dataset: 1535.82 MB
  • Total amount of disk used: 1997.37 MB

An example of 'train' looks as follows.

This example was too long and was cropped:

{
    "hypothesis": "{\"language\": [\"ar\", \"bg\", \"de\", \"el\", \"en\", \"es\", \"fr\", \"hi\", \"ru\", \"sw\", \"th\", \"tr\", \"ur\", \"vi\", \"zh\"], \"translation\": [\"احد اع...",
    "label": 0,
    "premise": "{\"ar\": \"واحدة من رقابنا ستقوم بتنفيذ تعليماتك كلها بكل دقة\", \"bg\": \"един от нашите номера ще ви даде инструкции .\", \"de\": \"Eine ..."
}

ar

  • Size of downloaded dataset files: 461.54 MB
  • Size of the generated dataset: 104.26 MB
  • Total amount of disk used: 565.81 MB

An example of 'validation' looks as follows.

{
    "hypothesis": "اتصل بأمه حالما أوصلته حافلة المدرسية.",
    "label": 1,
    "premise": "وقال، ماما، لقد عدت للمنزل."
}

bg

  • Size of downloaded dataset files: 461.54 MB
  • Size of the generated dataset: 122.38 MB
  • Total amount of disk used: 583.92 MB

An example of 'train' looks as follows.

This example was too long and was cropped:

{
    "hypothesis": "\"губиш нещата на следното ниво , ако хората си припомнят .\"...",
    "label": 0,
    "premise": "\"по време на сезона и предполагам , че на твоето ниво ще ги загубиш на следващото ниво , ако те решат да си припомнят отбора на ..."
}

de

  • Size of downloaded dataset files: 461.54 MB
  • Size of the generated dataset: 82.18 MB
  • Total amount of disk used: 543.73 MB

An example of 'train' looks as follows.

This example was too long and was cropped:

{
    "hypothesis": "Man verliert die Dinge auf die folgende Ebene , wenn sich die Leute erinnern .",
    "label": 0,
    "premise": "\"Du weißt , während der Saison und ich schätze , auf deiner Ebene verlierst du sie auf die nächste Ebene , wenn sie sich entschl..."
}

el

  • Size of downloaded dataset files: 461.54 MB
  • Size of the generated dataset: 135.71 MB
  • Total amount of disk used: 597.25 MB

An example of 'validation' looks as follows.

This example was too long and was cropped:

{
    "hypothesis": "\"Τηλεφώνησε στη μαμά του μόλις το σχολικό λεωφορείο τον άφησε.\"...",
    "label": 1,
    "premise": "Και είπε, Μαμά, έφτασα στο σπίτι."
}

Data Fields

The data fields are the same among all splits.

all_languages

  • premise: a multilingual string variable, with possible languages including ar, bg, de, el, en.
  • hypothesis: a multilingual string variable, with possible languages including ar, bg, de, el, en.
  • label: a classification label, with possible values including entailment (0), neutral (1), contradiction (2).

ar

  • premise: a string feature.
  • hypothesis: a string feature.
  • label: a classification label, with possible values including entailment (0), neutral (1), contradiction (2).

bg

  • premise: a string feature.
  • hypothesis: a string feature.
  • label: a classification label, with possible values including entailment (0), neutral (1), contradiction (2).

de

  • premise: a string feature.
  • hypothesis: a string feature.
  • label: a classification label, with possible values including entailment (0), neutral (1), contradiction (2).

el

  • premise: a string feature.
  • hypothesis: a string feature.
  • label: a classification label, with possible values including entailment (0), neutral (1), contradiction (2).

Data Splits

name train validation test
all_languages 392702 2490 5010
ar 392702 2490 5010
bg 392702 2490 5010
de 392702 2490 5010
el 392702 2490 5010

Dataset Creation

Curation Rationale

More Information Needed

Source Data

Initial Data Collection and Normalization

More Information Needed

Who are the source language producers?

More Information Needed

Annotations

Annotation process

More Information Needed

Who are the annotators?

More Information Needed

Personal and Sensitive Information

More Information Needed

Considerations for Using the Data

Social Impact of Dataset

More Information Needed

Discussion of Biases

More Information Needed

Other Known Limitations

More Information Needed

Additional Information

Dataset Curators

More Information Needed

Licensing Information

More Information Needed

Citation Information

@InProceedings{conneau2018xnli,
  author = {Conneau, Alexis
                 and Rinott, Ruty
                 and Lample, Guillaume
                 and Williams, Adina
                 and Bowman, Samuel R.
                 and Schwenk, Holger
                 and Stoyanov, Veselin},
  title = {XNLI: Evaluating Cross-lingual Sentence Representations},
  booktitle = {Proceedings of the 2018 Conference on Empirical Methods
               in Natural Language Processing},
  year = {2018},
  publisher = {Association for Computational Linguistics},
  location = {Brussels, Belgium},
}

Contributions

Thanks to @lewtun, @mariamabarham, @thomwolf, @lhoestq, @patrickvonplaten for adding this dataset.

Models trained or fine-tuned on xnli