xwinograd.py

"""XWinograd"""

import json

import datasets


logger = datasets.logging.get_logger(__name__)


_CITATION = """\
@article{muennighoff2022crosslingual,
  title={Crosslingual Generalization through Multitask Finetuning},
  author={Muennighoff, Niklas and Wang, Thomas and Sutawika, Lintang and Roberts, Adam and Biderman, Stella and Scao, Teven Le and Bari, M Saiful and Shen, Sheng and Yong, Zheng-Xin and Schoelkopf, Hailey and others},
  journal={arXiv preprint arXiv:2211.01786},
  year={2022}
}

@misc{tikhonov2021heads,
    title={It's All in the Heads: Using Attention Heads as a Baseline for Cross-Lingual Transfer in Commonsense Reasoning},
    author={Alexey Tikhonov and Max Ryabinin},
    year={2021},
    eprint={2106.12066},
    archivePrefix={arXiv},
    primaryClass={cs.CL}
}
"""

_DESCRIPTION = """\
A multilingual collection of Winograd Schemas in six languages \
that can be used for evaluation of cross-lingual commonsense reasoning capabilities.
"""
_LANG = ["en", "fr", "jp", "pt", "ru", "zh"]
_URL = "https://huggingface.co/datasets/Muennighoff/xwinograd/raw/main/test/{lang}.jsonl"
_VERSION = datasets.Version("1.1.0", "")


class XWinograd(datasets.GeneratorBasedBuilder):
    """XWinograd"""


    BUILDER_CONFIGS = [
        datasets.BuilderConfig(
            name=lang,
            description=f"XWinograd in {lang}",
            version=_VERSION,
        )
        for lang in _LANG
    ]

    def _info(self):
        return datasets.DatasetInfo(
            description=_DESCRIPTION,
            features=datasets.Features(
                {
                    "sentence": datasets.Value("string"),
                    "option1": datasets.Value("string"),
                    "option2": datasets.Value("string"),
                    "answer": datasets.Value("string")
                }
            ),
            supervised_keys=None,
            citation=_CITATION,
        )

    def _split_generators(self, dl_manager):

        downloaded_files = dl_manager.download(_URL.format(lang=self.config.name))
        return [
            datasets.SplitGenerator(
                name=datasets.Split.TEST,
                gen_kwargs={'filepath': downloaded_files}
            )
        ]

    def _generate_examples(self, filepath):
        """This function returns the examples in the raw (text) form."""
        logger.info("Generating examples from = %s", filepath)

        with open(filepath, encoding="utf-8") as f:
            for id_, row in enumerate(f):
                data = json.loads(row)

                yield id_, {
                "sentence": data["sentence"],
                "option1": data["option1"],
                "option2": data["option2"],
                "answer": data["answer"],
            }