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import json |
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import os |
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import datasets |
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_CITATION = """\ |
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@article{scialom2020mlsum, |
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title={MLSUM: The Multilingual Summarization Corpus}, |
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author={Scialom, Thomas and Dray, Paul-Alexis and Lamprier, Sylvain and Piwowarski, Benjamin and Staiano, Jacopo}, |
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journal={arXiv preprint arXiv:2004.14900}, |
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year={2020} |
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} |
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""" |
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_DESCRIPTION = """\ |
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This is the MLSUM subset of the GEM benchmark. MLSUM is the first large-scale MultiLingual SUMmarization dataset. |
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Obtained from online newspapers, it contains 1.5M+ article/summary pairs in five different languages -- namely, French, German, Spanish, Russian, Turkish. |
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Together with English newspapers from the popular CNN/Daily mail dataset, the collected data form a large scale multilingual dataset which can enable new research directions for the text summarization community. |
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We report cross-lingual comparative analyses based on state-of-the-art systems. |
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These highlight existing biases which motivate the use of a multi-lingual dataset. |
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""" |
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_URL = "https://gitlab.lip6.fr/scialom/mlsum_data/-/raw/master/MLSUM/" |
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_LANG = ["de", "es"] |
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_URLs = { |
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"de": { |
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"train": "https://gitlab.lip6.fr/scialom/mlsum_data/-/raw/master/MLSUM/de_train.zip", |
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"validation": "https://gitlab.lip6.fr/scialom/mlsum_data/-/raw/master/MLSUM/de_val.zip", |
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"test": "https://gitlab.lip6.fr/scialom/mlsum_data/-/raw/master/MLSUM/de_test.zip", |
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"bad_ids": "https://storage.googleapis.com/huggingface-nlp/datasets/gem/gem_mlsum_bad_ids_fixed.json", |
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"challenge_set": "https://storage.googleapis.com/huggingface-nlp/datasets/gem/gem_challenge_sets/mlsum_de.zip", |
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}, |
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"es": { |
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"train": "https://gitlab.lip6.fr/scialom/mlsum_data/-/raw/master/MLSUM/es_train.zip", |
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"validation": "https://gitlab.lip6.fr/scialom/mlsum_data/-/raw/master/MLSUM/es_val.zip", |
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"test": "https://gitlab.lip6.fr/scialom/mlsum_data/-/raw/master/MLSUM/es_test.zip", |
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"bad_ids": "https://storage.googleapis.com/huggingface-nlp/datasets/gem/gem_mlsum_bad_ids_fixed.json", |
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"challenge_set": "https://storage.googleapis.com/huggingface-nlp/datasets/gem/gem_challenge_sets/mlsum_es.zip", |
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}, |
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} |
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class Mlsum(datasets.GeneratorBasedBuilder): |
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BUILDER_CONFIGS = [ |
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datasets.BuilderConfig( |
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name=lang, |
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version=datasets.Version("1.0.0"), |
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description="", |
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) |
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for lang in _LANG |
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] |
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def _info(self): |
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return datasets.DatasetInfo( |
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description=_DESCRIPTION, |
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features=datasets.Features( |
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{ |
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"gem_id": datasets.Value("string"), |
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"gem_parent_id": datasets.Value("string"), |
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"text": datasets.Value("string"), |
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"topic": datasets.Value("string"), |
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"url": datasets.Value("string"), |
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"title": datasets.Value("string"), |
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"date": datasets.Value("string"), |
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"target": datasets.Value("string"), |
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"references": [datasets.Value("string")], |
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} |
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), |
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supervised_keys=None, |
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homepage="", |
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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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dl_dir = dl_manager.download_and_extract(_URLs[self.config.name]) |
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lang = str(self.config.name) |
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challenge_sets = [ |
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("challenge_train_sample", f"train_mlsum_{lang}_RandomSample500.json"), |
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("challenge_validation_sample", f"validation_mlsum_{lang}_RandomSample500.json"), |
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("challenge_test_covid", f"{lang}_test_covid19_cleaned.jsonl"), |
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] |
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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": os.path.join(dl_dir["train"], lang + "_train.jsonl"), |
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"split": "train", |
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"lang": lang, |
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"filepaths": dl_dir["bad_ids"], |
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}, |
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), |
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datasets.SplitGenerator( |
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name=datasets.Split.VALIDATION, |
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gen_kwargs={ |
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"filepath": os.path.join(dl_dir["validation"], lang + "_val.jsonl"), |
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"split": "validation", |
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"lang": lang, |
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"filepaths": dl_dir["bad_ids"], |
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}, |
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), |
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datasets.SplitGenerator( |
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name=datasets.Split.TEST, |
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gen_kwargs={ |
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"filepath": os.path.join(dl_dir["test"], lang + "_test.jsonl"), |
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"split": "test", |
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"lang": lang, |
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"filepaths": dl_dir["bad_ids"], |
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}, |
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), |
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] + [ |
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datasets.SplitGenerator( |
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name=challenge_split, |
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gen_kwargs={ |
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"filepath": os.path.join(dl_dir["challenge_set"], f"mlsum_{self.config.name}", filename), |
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"split": challenge_split, |
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}, |
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) |
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for challenge_split, filename in challenge_sets |
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] |
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def _generate_examples(self, filepath, split, filepaths=None, lang=None): |
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"""Yields examples.""" |
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if split in ["train", "validation", "test", "challenge_test_covid"]: |
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if split == "challenge_test_covid": |
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bad_ids = {} |
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else: |
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bad_ids_dct = json.load(open(filepaths, encoding="utf-8")) |
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bad_ids = dict((bad_url, True) for _, bad_url in bad_ids_dct[f"{lang}-{split}"]) |
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with open(filepath, encoding="utf-8") as f: |
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id_ = -1 |
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for line in f: |
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data = json.loads(line) |
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if data["url"] in bad_ids: |
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continue |
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else: |
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id_ += 1 |
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yield id_, { |
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"gem_id": f"mlsum_{self.config.name}-{split}-{id_}", |
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"gem_parent_id": f"mlsum_{self.config.name}-{split}-{id_}", |
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"text": data["text"], |
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"target": data["summary"], |
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"references": [] if split == "train" else [data["summary"]], |
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"topic": data["topic"], |
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"url": data["url"], |
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"title": data["title"], |
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"date": data["date"], |
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} |
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else: |
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exples = json.load(open(filepath, encoding="utf-8")) |
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if isinstance(exples, dict): |
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assert len(exples) == 1, "multiple entries found" |
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exples = list(exples.values())[0] |
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for id_, exple in enumerate(exples): |
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if len(exple) == 0: |
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continue |
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exple["gem_parent_id"] = exple["gem_id"] |
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exple["gem_id"] = f"mlsum_{self.config.name}-{split}-{id_}" |
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yield id_, exple |
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