neuclir-2022-fast.py

import json

import datasets

_CITATION = '''
@article{lawrie2023overview,
  title={Overview of the TREC 2022 NeuCLIR track},
  author={Lawrie, Dawn and MacAvaney, Sean and Mayfield, James and McNamee, Paul and Oard, Douglas W and Soldaini, Luca and Yang, Eugene},
  journal={arXiv preprint arXiv:2304.12367},
  year={2023}
}
'''

_LANGUAGES = [
    'rus',
    'fas',
    'zho',
]

_DESCRIPTION = 'dataset load script for NeuCLIR 2022'

_DATASET_URLS = {
    lang: {
        'test': f'https://huggingface.co/datasets/MTEB/neuclir-2022-fast/resolve/main/neuclir-{lang}/test-00000-of-00001.parquet',
    } for lang in _LANGUAGES
}

_DATASET_CORPUS_URLS = {
    f'corpus-{lang}': {
        'corpus': f'https://huggingface.co/datasets/MTEB/neuclir-2022-fast/resolve/main/neuclir-{lang}/corpus-00000-of-00001.parquet'
    } for lang in _LANGUAGES
}

_DATASET_QUERIES_URLS = {
    f'queries-{lang}': {
        'queries': f'https://huggingface.co/datasets/MTEB/neuclir-2022-fast/resolve/main/neuclir-{lang}/queries-00000-of-00001.parquet'
    } for lang in _LANGUAGES
}


class MLDR(datasets.GeneratorBasedBuilder):
    BUILDER_CONFIGS = [datasets.BuilderConfig(
            version=datasets.Version('1.0.0'),
            name=lang, description=f'NeuCLIR dataset in language {lang}.'
        ) for lang in _LANGUAGES
    ] + [
        datasets.BuilderConfig(
            version=datasets.Version('1.0.0'),
            name=f'corpus-{lang}', description=f'corpus of NeuCLIR dataset in language {lang}.'
        ) for lang in _LANGUAGES
    ] + [ 
        datasets.BuilderConfig(
            version=datasets.Version('1.0.0'),
            name=f'queries-{lang}', description=f'queries of NeuCLIR dataset in language {lang}.'
        ) for lang in _LANGUAGES
    ]

    def _info(self):
        name = self.config.name
        if name.startswith('corpus-'):
            features = datasets.Features({
                '_id': datasets.Value('string'),
                'text': datasets.Value('string'),
                'title': datasets.Value('string'),
            })
        elif name.startswith("queries-"):
            features = datasets.Features({
                '_id': datasets.Value('string'),
                'text': datasets.Value('string'),
            })
        else:
            features = datasets.Features({
                'query-id': datasets.Value('string'),
                'corpus-id': datasets.Value('string'),
                'score': datasets.Value('int32'),
            })

        return datasets.DatasetInfo(
            # This is the description that will appear on the datasets page.
            description=_DESCRIPTION,
            # This defines the different columns of the dataset and their types
            features=features,  # Here we define them above because they are different between the two configurations
            supervised_keys=None,
            # Homepage of the dataset for documentation
            homepage='https://arxiv.org/abs/2304.12367',
            # License for the dataset if available
            license=None,
            # Citation for the dataset
            citation=_CITATION,
        )

    def _split_generators(self, dl_manager):
        name = self.config.name
        if name.startswith('corpus-'):
            downloaded_files = dl_manager.download_and_extract(_DATASET_CORPUS_URLS[name])
            splits = [
                datasets.SplitGenerator(
                    name='corpus',
                    gen_kwargs={
                        'filepath': downloaded_files['corpus'],
                    },
                ),
            ]
        elif name.startswith("queries-"):
            downloaded_files = dl_manager.download_and_extract(_DATASET_QUERIES_URLS[name])
            splits = [
                datasets.SplitGenerator(
                    name='queries',
                    gen_kwargs={
                        'filepath': downloaded_files['queries'],
                    },
                ),
            ]
        else:
            downloaded_files = dl_manager.download_and_extract(_DATASET_URLS[name])
            splits = [
                datasets.SplitGenerator(
                    name='test',
                    gen_kwargs={
                        'filepath': downloaded_files['test'],
                    },
                ),
            ]
        return splits

    def _generate_examples(self, filepath):
        import pandas as pd
        
        name = self.config.name
        df = pd.read_parquet(filepath)
        
        if name.startswith('corpus-'):
            for index, row in df.iterrows():
                yield row['_id'], {
                    '_id': row['_id'],
                    'text': row['text'],
                    'title': row['title']
                }
        elif name.startswith("queries-"):
            for index, row in df.iterrows():
                yield row['_id'], {
                    '_id': row['_id'],
                    'text': row['text']
                }
        else:
            for index, row in df.iterrows():
                yield f"{row['query-id']}-----{row['corpus-id']}", {
                    'query-id': row['query-id'],
                    'corpus-id': row['corpus-id'],
                    'score': row['score']
                }