# 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 from tqdm import tqdm import random _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 ] + [ 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 ] + [ datasets.BuilderConfig( name=f"all", description=f"All training data version v2", version=datasets.Version("2.0.0"), ) ] ) 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 + ["all"], f"Does not support languge {name}. Must be one of {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')} ], } 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}, "queries": {lang: _BASE_URLS["queries-train"] + lang + "_queries.train.tsv" for lang in languages}, "train": _BASE_URLS["train"], } dl_path = dl_manager.download_and_extract(urls) return [ datasets.SplitGenerator( name=datasets.Split.TRAIN, gen_kwargs={ "files": dl_path["train"], "args": { "collection": dl_path["collection"], "queries": dl_path["queries"], }, }, ) ] def _generate_examples(self, files, args=None): """Yields examples.""" languages = [self.config.name] if self.config.name in LANGUAGES else LANGUAGES # loading runs = dict() # each query: [set(pos_passages), set(neg_passages)] with open(files, encoding="utf-8") as f: for (idx, line) in enumerate(f): query_id, pos_id, neg_id = line.rstrip().split("\t") if query_id not in runs: runs[query_id] = [set(pos_id), set(neg_id)] else: runs[query_id][0].add(pos_id) runs[query_id][1].add(neg_id) # it would generate language by language so that it would be easier to constrain that each batch only contain one language; for lang in tqdm(languages, desc=f"Preparing training example for {len(languages)} languages."): n_missed_q = 0 n_missed_d = 0 collection_path, queries_path = args["collection"][lang], args["queries"][lang] collection = {} with open(collection_path, encoding="utf-8") as f: collection = dict(line.rstrip().split("\t") for line in f) queries = {} with open(queries_path, encoding="utf-8") as f: for line in f: queries = dict(line.rstrip().split("\t") for line in f) assert len(runs) == self.size_per_lang[lang] for query_id, (pos_ids, neg_ids) in runs.items(): if query_id not in queries: n_missed_q += 1 continue pos_ids, neg_ids = list(pos_ids), list(neg_ids) pos_ids = [d for d in pos_ids if d in collection] neg_ids = [d for d in neg_ids if d in collection] if len(neg_ids) == 0 or len(pos_ids) == 0: n_missed_d += 1 continue NNEG = min(10, len(neg_ids)) neg_ids = random.choices(neg_ids, k=NNEG) features = { "query_id": query_id, "query": queries[query_id], "positive_passages": [{ "docid": pos_id, "text": collection[pos_id], } for pos_id in pos_ids], "negative_passages": [{ "docid": neg_id, "text": collection[neg_id], } for neg_id in neg_ids], } yield f"{lang}-{query_id}-{idx}", features print(f'Number of missed Q: {n_missed_q}. Number of missed D: {n_missed_d}')