File size: 9,747 Bytes
94ff39a 63de5fa 94ff39a 63de5fa 94ff39a 63de5fa 94ff39a 63de5fa 94ff39a 63de5fa 94ff39a |
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 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143 144 145 146 147 148 149 150 151 152 153 154 155 156 157 158 159 160 161 162 163 164 165 166 167 168 169 170 171 172 173 174 175 176 177 178 179 180 181 182 183 184 185 186 187 188 189 190 191 192 193 194 195 196 197 198 199 200 201 202 203 204 205 206 207 208 209 210 211 212 213 214 215 216 217 218 219 220 221 222 223 224 225 226 227 228 229 230 231 232 233 234 235 236 237 238 239 240 241 242 243 244 245 246 247 248 249 250 251 252 253 254 255 256 257 258 259 260 261 262 263 264 265 266 267 268 269 270 271 272 273 274 275 276 |
# 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."""
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
"""
def generate_examples_triples(filepath, collection_path, queries_path):
collection = {}
with open(collection_path, encoding="utf-8") as f:
for line in f:
doc_id, doc = line.rstrip().split("\t")
collection[doc_id] = doc
queries = {}
with open(queries_path, encoding="utf-8") as f:
for line in f:
query_id, query = line.rstrip().split("\t")
queries[query_id] = query
with open(filepath, encoding="utf-8") as f:
for (idx, line) in enumerate(f):
query_id, pos_id, neg_id = line.rstrip().split("\t")
features = {
"query": queries[query_id],
"positive": collection[pos_id],
"negative": collection[neg_id],
}
yield idx, features
def generate_examples_tuples(filepath):
with open(filepath, encoding="utf-8") as f:
for (idx, line) in enumerate(f):
idx, text = line.rstrip().split("\t")
features = {
"id": idx,
"text": text,
}
yield idx, features
def generate_examples_runs(filepath, collection_path, queries_path):
collection = {}
with open(collection_path, encoding="utf-8") as f:
for line in f:
doc_id, doc = line.rstrip().split("\t")
collection[doc_id] = doc
queries = {}
with open(queries_path, encoding="utf-8") as f:
for line in f:
query_id, query = line.rstrip().split("\t")
queries[query_id] = query
qid_to_ranked_candidate_passages = {}
with open(filepath, encoding="utf-8") as f:
for line in f:
qid, pid, rank = line.rstrip().split("\t")
if qid not in qid_to_ranked_candidate_passages:
qid_to_ranked_candidate_passages[qid] = []
qid_to_ranked_candidate_passages[qid].append(pid)
for (idx, qid) in enumerate(qid_to_ranked_candidate_passages):
features = {
"id": qid,
"query": queries[qid],
"passages": [
{
"id": pid,
"passage": collection[pid],
}
for pid in qid_to_ranked_candidate_passages[qid]
],
}
yield idx, features
_BASE_URLS = {
"collections": "https://huggingface.co/datasets/unicamp-dl/mmarco/resolve/main/data/v2/collections/",
"queries-train": "https://huggingface.co/datasets/unicamp-dl/mmarco/resolve/main/data/v2/queries/train/",
"queries-dev": "https://huggingface.co/datasets/unicamp-dl/mmarco/resolve/main/data/v2/queries/dev/",
"runs": "https://huggingface.co/datasets/unicamp-dl/mmarco/resolve/main/data/v2/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()} version v2",
version=datasets.Version("2.0.0"),
)
for language in LANGUAGES
]
+ [
datasets.BuilderConfig(
name=f"collection-{language}",
description=f"{language.capitalize()} collection version v2",
version=datasets.Version("2.0.0"),
)
for language in LANGUAGES
]
+ [
datasets.BuilderConfig(
name=f"queries-{language}",
description=f"{language.capitalize()} queries version v2",
version=datasets.Version("2.0.0"),
)
for language in LANGUAGES
]
+ [
datasets.BuilderConfig(
name=f"runs-{language}",
description=f"{language.capitalize()} runs version v2",
version=datasets.Version("2.0.0"),
)
for language in LANGUAGES
]
)
DEFAULT_CONFIG_NAME = "english"
def _info(self):
name = self.config.name
if name.startswith("collection") or name.startswith("queries"):
features = {
"id": datasets.Value("int32"),
"text": datasets.Value("string"),
}
elif name.startswith("runs"):
features = {
"id": datasets.Value("int32"),
"query": datasets.Value("string"),
"passages": datasets.Sequence(
{
"id": datasets.Value("int32"),
"passage": datasets.Value("string"),
}
),
}
else:
features = {
"query": datasets.Value("string"),
"positive": datasets.Value("string"),
"negative": 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."""
if self.config.name.startswith("collection"):
url = _BASE_URLS["collections"] + self.config.name[11:] + "_collection.tsv"
dl_path = dl_manager.download_and_extract(url)
return (datasets.SplitGenerator(name="collection", gen_kwargs={"filepath": dl_path}),)
elif self.config.name.startswith("queries"):
urls = {
"train": _BASE_URLS["queries-train"] + self.config.name[8:] + "_queries.train.tsv",
"dev": _BASE_URLS["queries-dev"] + self.config.name[8:] + "_queries.dev.tsv",
}
dl_path = dl_manager.download_and_extract(urls)
return [
datasets.SplitGenerator(name=datasets.Split.TRAIN, gen_kwargs={"filepath": dl_path["train"]}),
datasets.SplitGenerator(name=datasets.Split.VALIDATION, gen_kwargs={"filepath": dl_path["dev"]}),
]
elif self.config.name.startswith("runs"):
urls = {
"collection": _BASE_URLS["collections"] + self.config.name[5:] + "_collection.tsv",
"queries": _BASE_URLS["queries-dev"] + self.config.name[5:] + "_queries.dev.tsv",
"run": _BASE_URLS["runs"] + "run.bm25_" + self.config.name[5:] + ".txt",
}
dl_path = dl_manager.download_and_extract(urls)
return (
datasets.SplitGenerator(
name="bm25",
gen_kwargs={
"filepath": dl_path["run"],
"args": {
"collection": dl_path["collection"],
"queries": dl_path["queries"],
},
},
),
)
else:
urls = {
"collection": _BASE_URLS["collections"] + self.config.name + "_collection.tsv",
"queries": _BASE_URLS["queries-train"] + self.config.name + "_queries.train.tsv",
"train": _BASE_URLS["train"],
}
dl_path = dl_manager.download_and_extract(urls)
return [
datasets.SplitGenerator(
name=datasets.Split.TRAIN,
gen_kwargs={
"filepath": dl_path["train"],
"args": {
"collection": dl_path["collection"],
"queries": dl_path["queries"],
},
},
)
]
def _generate_examples(self, filepath, args=None):
"""Yields examples."""
if self.config.name.startswith("collection") or self.config.name.startswith("queries"):
return generate_examples_tuples(filepath)
if self.config.name.startswith("runs"):
return generate_examples_runs(filepath, args["collection"], args["queries"])
else:
return generate_examples_triples(filepath, args["collection"], args["queries"])
|