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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."""
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/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
]
+ [
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",
"validation": _BASE_URLS["queries-dev"] + self.config.name[8:] + "_queries.dev.tsv",
"dev": _BASE_URLS["queries-dev"] + self.config.name[8:] + "_queries.dev.small.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="dev.full", gen_kwargs={"filepath": dl_path["validation"]}),
datasets.SplitGenerator(name="dev", 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:] + "-msmarco.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"])
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