# 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"])