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# 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 MMarcoDev(datasets.GeneratorBasedBuilder):
BUILDER_CONFIGS = (
[
datasets.BuilderConfig(
name=language,
description=f"{language.capitalize()} dev queries",
version=datasets.Version("2.0.0"),
)
for language in LANGUAGES
]
)
DEFAULT_CONFIG_NAME = "english"
def _info(self):
name = self.config.name
assert name in LANGUAGES, f"Does not support languge {name}. Must be one of {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')}
],
}
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},
"queries": {lang: _BASE_URLS["queries-dev"] + lang + "_queries.dev.small.tsv" for lang in languages},
}
dl_path = dl_manager.download_and_extract(urls)
return [
datasets.SplitGenerator(
name="dev",
gen_kwargs={
"files": dl_path["train"],
"args": {
"queries": dl_path["queries"],
},
},
)
]
def _generate_examples(self, files, args=None):
"""Yields examples."""
lang = self.config.name
assert lang in LANGUAGES
# loading
queries_path = args["queries"][lang]
with open(queries_path, encoding="utf-8") as f:
for line in f:
query_id, query = line.rstrip().split("\t")
features = {
"query_id": query_id,
"query": query,
"positive_passages": [],
"negative_passages": [],
}
yield f"{lang}-{query_id}", features
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