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"""mMARCO dataset.""" |
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import datasets |
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_CITATION = """ |
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@misc{bonifacio2021mmarco, |
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title={mMARCO: A Multilingual Version of the MS MARCO Passage Ranking Dataset}, |
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author={Luiz Henrique Bonifacio and Israel Campiotti and Vitor Jeronymo and Hugo Queiroz Abonizio and Roberto Lotufo and Rodrigo Nogueira}, |
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year={2021}, |
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eprint={2108.13897}, |
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archivePrefix={arXiv}, |
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primaryClass={cs.CL} |
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} |
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""" |
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_URL = "https://github.com/unicamp-dl/mMARCO" |
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_DESCRIPTION = """ |
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mMARCO translated datasets |
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""" |
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def generate_examples_triples(filepath, collection_path, queries_path): |
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collection = {} |
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with open(collection_path, encoding="utf-8") as f: |
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for line in f: |
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doc_id, doc = line.rstrip().split("\t") |
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collection[doc_id] = doc |
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queries = {} |
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with open(queries_path, encoding="utf-8") as f: |
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for line in f: |
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query_id, query = line.rstrip().split("\t") |
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queries[query_id] = query |
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with open(filepath, encoding="utf-8") as f: |
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for (idx, line) in enumerate(f): |
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query_id, pos_id, neg_id = line.rstrip().split("\t") |
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features = { |
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"query": queries[query_id], |
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"positive": collection[pos_id], |
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"negative": collection[neg_id], |
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} |
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yield idx, features |
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def generate_examples_tuples(filepath): |
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with open(filepath, encoding="utf-8") as f: |
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for (idx, line) in enumerate(f): |
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idx, text = line.rstrip().split("\t") |
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features = { |
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"id": idx, |
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"text": text, |
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} |
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yield idx, features |
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def generate_examples_runs(filepath, collection_path, queries_path): |
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collection = {} |
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with open(collection_path, encoding="utf-8") as f: |
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for line in f: |
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doc_id, doc = line.rstrip().split("\t") |
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collection[doc_id] = doc |
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queries = {} |
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with open(queries_path, encoding="utf-8") as f: |
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for line in f: |
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query_id, query = line.rstrip().split("\t") |
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queries[query_id] = query |
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qid_to_ranked_candidate_passages = {} |
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with open(filepath, encoding="utf-8") as f: |
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for line in f: |
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qid, pid, rank = line.rstrip().split("\t") |
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if qid not in qid_to_ranked_candidate_passages: |
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qid_to_ranked_candidate_passages[qid] = [] |
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qid_to_ranked_candidate_passages[qid].append(pid) |
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for (idx, qid) in enumerate(qid_to_ranked_candidate_passages): |
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features = { |
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"id": qid, |
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"query": queries[qid], |
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"passages": [ |
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{ |
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"id": pid, |
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"passage": collection[pid], |
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} |
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for pid in qid_to_ranked_candidate_passages[qid] |
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], |
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} |
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yield idx, features |
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_BASE_URLS = { |
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"collections": "https://huggingface.co/datasets/unicamp-dl/mmarco/resolve/main/data/google/collections/", |
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"queries-train": "https://huggingface.co/datasets/unicamp-dl/mmarco/resolve/main/data/google/queries/train/", |
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"queries-dev": "https://huggingface.co/datasets/unicamp-dl/mmarco/resolve/main/data/google/queries/dev/", |
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"runs": "https://huggingface.co/datasets/unicamp-dl/mmarco/resolve/main/data/google/runs/", |
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"train": "https://huggingface.co/datasets/unicamp-dl/mmarco/resolve/main/data/triples.train.ids.small.tsv", |
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} |
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LANGUAGES = [ |
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"arabic", |
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"chinese", |
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"dutch", |
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"english", |
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"french", |
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"german", |
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"hindi", |
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"indonesian", |
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"italian", |
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"japanese", |
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"portuguese", |
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"russian", |
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"spanish", |
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"vietnamese", |
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] |
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class MMarco(datasets.GeneratorBasedBuilder): |
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BUILDER_CONFIGS = ( |
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[ |
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datasets.BuilderConfig( |
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name=language, |
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description=f"{language.capitalize()} triples", |
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version=datasets.Version("2.0.0"), |
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) |
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for language in LANGUAGES |
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] |
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+ [ |
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datasets.BuilderConfig( |
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name=f"collection-{language}", |
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description=f"{language.capitalize()} collection version v2", |
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version=datasets.Version("2.0.0"), |
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) |
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for language in LANGUAGES |
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] |
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+ [ |
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datasets.BuilderConfig( |
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name=f"queries-{language}", |
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description=f"{language.capitalize()} queries version v2", |
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version=datasets.Version("2.0.0"), |
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) |
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for language in LANGUAGES |
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] |
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+ [ |
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datasets.BuilderConfig( |
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name=f"runs-{language}", |
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description=f"{language.capitalize()} runs version v2", |
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version=datasets.Version("2.0.0"), |
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) |
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for language in LANGUAGES |
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] |
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) |
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DEFAULT_CONFIG_NAME = "english" |
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def _info(self): |
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name = self.config.name |
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if name.startswith("collection") or name.startswith("queries"): |
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features = { |
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"id": datasets.Value("int32"), |
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"text": datasets.Value("string"), |
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} |
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elif name.startswith("runs"): |
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features = { |
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"id": datasets.Value("int32"), |
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"query": datasets.Value("string"), |
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"passages": datasets.Sequence( |
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{ |
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"id": datasets.Value("int32"), |
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"passage": datasets.Value("string"), |
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} |
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), |
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} |
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else: |
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features = { |
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"query": datasets.Value("string"), |
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"positive": datasets.Value("string"), |
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"negative": datasets.Value("string"), |
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} |
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return datasets.DatasetInfo( |
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description=f"{_DESCRIPTION}\n{self.config.description}", |
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features=datasets.Features(features), |
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supervised_keys=None, |
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homepage=_URL, |
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citation=_CITATION, |
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) |
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def _split_generators(self, dl_manager): |
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"""Returns SplitGenerators.""" |
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if self.config.name.startswith("collection"): |
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url = _BASE_URLS["collections"] + self.config.name[11:] + "_collection.tsv" |
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dl_path = dl_manager.download_and_extract(url) |
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return (datasets.SplitGenerator(name="collection", gen_kwargs={"filepath": dl_path}),) |
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elif self.config.name.startswith("queries"): |
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urls = { |
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"train": _BASE_URLS["queries-train"] + self.config.name[8:] + "_queries.train.tsv", |
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"validation": _BASE_URLS["queries-dev"] + self.config.name[8:] + "_queries.dev.tsv", |
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"dev": _BASE_URLS["queries-dev"] + self.config.name[8:] + "_queries.dev.small.tsv", |
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} |
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dl_path = dl_manager.download_and_extract(urls) |
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return [ |
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datasets.SplitGenerator(name=datasets.Split.TRAIN, gen_kwargs={"filepath": dl_path["train"]}), |
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datasets.SplitGenerator(name="dev.full", gen_kwargs={"filepath": dl_path["validation"]}), |
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datasets.SplitGenerator(name="dev", gen_kwargs={"filepath": dl_path["dev"]}), |
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] |
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elif self.config.name.startswith("runs"): |
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urls = { |
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"collection": _BASE_URLS["collections"] + self.config.name[5:] + "_collection.tsv", |
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"queries": _BASE_URLS["queries-dev"] + self.config.name[5:] + "_queries.dev.tsv", |
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"run": _BASE_URLS["runs"] + "run.bm25_" + self.config.name[5:] + "-msmarco.txt", |
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} |
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dl_path = dl_manager.download_and_extract(urls) |
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return ( |
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datasets.SplitGenerator( |
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name="bm25", |
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gen_kwargs={ |
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"filepath": dl_path["run"], |
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"args": { |
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"collection": dl_path["collection"], |
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"queries": dl_path["queries"], |
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}, |
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}, |
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), |
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) |
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else: |
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urls = { |
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"collection": _BASE_URLS["collections"] + self.config.name + "_collection.tsv", |
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"queries": _BASE_URLS["queries-train"] + self.config.name + "_queries.train.tsv", |
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"train": _BASE_URLS["train"], |
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} |
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dl_path = dl_manager.download_and_extract(urls) |
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return [ |
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datasets.SplitGenerator( |
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name=datasets.Split.TRAIN, |
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gen_kwargs={ |
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"filepath": dl_path["train"], |
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"args": { |
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"collection": dl_path["collection"], |
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"queries": dl_path["queries"], |
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}, |
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}, |
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) |
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] |
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def _generate_examples(self, filepath, args=None): |
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"""Yields examples.""" |
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if self.config.name.startswith("collection") or self.config.name.startswith("queries"): |
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return generate_examples_tuples(filepath) |
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if self.config.name.startswith("runs"): |
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return generate_examples_runs(filepath, args["collection"], args["queries"]) |
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else: |
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return generate_examples_triples(filepath, args["collection"], args["queries"]) |
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