mmarco-corpus.py

# coding=utf-8
# Copyright 2020 The TensorFlow Datasets Authors and the HuggingFace Datasets Authors.
#
# 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.

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

from collections import defaultdict
from gc import collect
import datasets


_CITATION = """
@misc{bonifacio2021mmarco,
      title={mMARCO: A Multilingual Version of the MS MARCO Passage Ranking Dataset},
      author={Luiz Henrique Bonifacio and Israel Campiotti and Vitor Jeronymo and Hugo Queiroz Abonizio and Roberto Lotufo and Rodrigo Nogueira},
      year={2021},
      eprint={2108.13897},
      archivePrefix={arXiv},
      primaryClass={cs.CL}
}
"""

_URL = "https://github.com/unicamp-dl/mMARCO"

_DESCRIPTION = """
mMARCO translated datasets
"""


_BASE_URLS = {
    "collections": "https://huggingface.co/datasets/unicamp-dl/mmarco/resolve/main/data/google/collections/",
    "queries-train": "https://huggingface.co/datasets/unicamp-dl/mmarco/resolve/main/data/google/queries/train/",
    "queries-dev": "https://huggingface.co/datasets/unicamp-dl/mmarco/resolve/main/data/google/queries/dev/",
    "runs": "https://huggingface.co/datasets/unicamp-dl/mmarco/resolve/main/data/google/runs/",
    "train": "https://huggingface.co/datasets/unicamp-dl/mmarco/resolve/main/data/triples.train.ids.small.tsv",
}

LANGUAGES = [
    "arabic",
    "chinese",
    "dutch",
    "english",
    "french",
    "german",
    "hindi",
    "indonesian",
    "italian",
    "japanese",
    "portuguese",
    "russian",
    "spanish",
    "vietnamese",
]


class MMarco(datasets.GeneratorBasedBuilder):

    BUILDER_CONFIGS = [
        datasets.BuilderConfig(
            name=language,
            description=f"{language.capitalize()} triples",
            version=datasets.Version("2.0.0"),
        )
        for language in LANGUAGES
    ]

    
    # size_per_lang = {lang: 398792 for lang in LANGUAGES}
    # $ cat triples.train.ids.small.tsv  | cut -f 1  | sort | uniq | wc -l 
    # 398792

    DEFAULT_CONFIG_NAME = "english"

    def _info(self):
        name = self.config.name
        assert name in LANGUAGES

        # 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')}
        #     ],
        # }

        features = datasets.Features(
            {'docid': datasets.Value('string'), 'title': datasets.Value('string'), 'text': datasets.Value('string')}
        )
        return datasets.DatasetInfo(
            description=f"{_DESCRIPTION}\n{self.config.description}",
            features=datasets.Features(features),
            supervised_keys=None,
            homepage=_URL,
            citation=_CITATION,
        )

    def _split_generators(self, dl_manager):
        """Returns SplitGenerators."""
        languages = [self.config.name] if self.config.name in LANGUAGES else LANGUAGES
        urls = {
            "collection": {lang: _BASE_URLS["collections"] + lang + "_collection.tsv" for lang in languages},
        }
        dl_path = dl_manager.download_and_extract(urls)

        return [
            datasets.SplitGenerator(
                name=datasets.Split.TRAIN,
                gen_kwargs={
                    # "files": dl_path["train"],
                    'files': dl_path["collection"],
                },
            )
        ]

    def _generate_examples(self, files):
        """Yields examples."""

        # languages = [self.config.name] if self.config.name in LANGUAGES else LANGUAGES

        # loading
        collection_path = files[self.config.name] 

        with open(collection_path, encoding="utf-8") as f:
            for line in f:
                doc_id, doc = line.rstrip().split("\t")
                # collection[doc_id] = doc
                yield doc_id, {"docid": doc_id, "title": "", "text": doc}