File size: 4,584 Bytes
f50aca7 bffafcb f50aca7 bffafcb f50aca7 a0e07ab f50aca7 a0e07ab f50aca7 8305e3a f50aca7 8305e3a f50aca7 73aa0c3 |
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143 |
# 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}
|