tevatron-mmarco-passage.py

# coding=utf-8
# Copyright 2020 The HuggingFace Datasets Authors and the current dataset script contributor.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
#     http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.Wikipedia

# Lint as: python3
"""mMARCO Passage dataset."""

import json

import datasets

_CITATION = """
"""

_DESCRIPTION = "dataset load script for mMARCO Passage following tevatron training data format"

languages = [
    "arabic",
    "hindi",
    "korean",
    "japanese",
    "spanish",
]
_DATASET_URLS = {
    lang: {
        'train': f"https://huggingface.co./datasets/jonghwi/tevatron-mmarco-passage/resolve/main/{lang}-train.jsonl.gz",
    } for lang in languages
}

class MMarcoPassage(datasets.GeneratorBasedBuilder):
    BUILDER_CONFIGS = [datasets.BuilderConfig(
            version=datasets.Version("0.0.1"),
            name=lang, 
            description=f"mMARCO passage train datasets for {lang} following tevatron training data format"
        ) for lang in languages
    ]
   
    def _info(self):
        features = datasets.Features({
            'query_id': datasets.Value('string'),
            'query': datasets.Value('string'),
            'positive_passages': [
                {'docid': datasets.Value('string'), 'text': datasets.Value('string')}
            ],
            'negative_passages': [
                {'docid': datasets.Value('string'), 'text': datasets.Value('string')}
            ],
        })
        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="",
            # License for the dataset if available
            license="",
            # Citation for the dataset
            citation=_CITATION,
        )

    def _split_generators(self, dl_manager):
        lang = self.config.name
        downloaded_files = dl_manager.download_and_extract(_DATASET_URLS[lang])
        '''
        if self.config.data_files:
            downloaded_files = self.config.data_files
        else:
            downloaded_files = dl_manager.download_and_extract(_DATASET_URLS)
        '''
        splits = [
            datasets.SplitGenerator(
                name=split,
                gen_kwargs={
                    "files": [downloaded_files[split]] if isinstance(downloaded_files[split], str) else downloaded_files[split],
                },
            ) for split in downloaded_files
        ]
        return splits
        
    def _generate_examples(self, files):
        """Yields examples."""
        for filepath in files:
            with open(filepath, encoding="utf-8") as f:
                for line in f:
                    data = json.loads(line)
                    if data.get('negative_passages') is None:
                        data['negative_passages'] = []
                    if data.get('positive_passages') is None:
                        data['positive_passages'] = []
                    yield data['query_id'], data