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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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@inproceedings{castro-ferreira20:bilin-bi-direc-webnl-shared, |
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title={The 2020 Bilingual, Bi-Directional WebNLG+ Shared Task Overview and Evaluation Results (WebNLG+ 2020)}, |
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author={Castro Ferreira, Thiago and |
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Gardent, Claire and |
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Ilinykh, Nikolai and |
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van der Lee, Chris and |
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Mille, Simon and |
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Moussallem, Diego and |
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Shimorina, Anastasia}, |
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booktitle = {Proceedings of the 3rd WebNLG Workshop on Natural Language Generation from the Semantic Web (WebNLG+ 2020)}, |
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pages = "55--76", |
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year = 2020, |
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address = {Dublin, Ireland (Virtual)}, |
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publisher = {Association for Computational Linguistics}} |
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""" |
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_DESCRIPTION = """\ |
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WebNLG is a bi-lingual dataset (English, Russian) of parallel DBpedia triple sets |
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and short texts that cover about 450 different DBpedia properties. The WebNLG data |
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was originally created to promote the development of RDF verbalisers able to |
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generate short text and to handle micro-planning (i.e., sentence segmentation and |
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ordering, referring expression generation, aggregation); the goal of the task is |
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to generate texts starting from 1 to 7 input triples which have entities in common |
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(so the input is actually a connected Knowledge Graph). The dataset contains about |
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17,000 triple sets and 45,000 crowdsourced texts in English, and 7,000 triples sets |
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and 19,000 crowdsourced texts in Russian. A challenging test set section with |
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entities and/or properties that have not been seen at training time is available. |
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""" |
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_LANG = ["en", "ru"] |
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_URLs = { |
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"en": { |
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"train": "https://storage.googleapis.com/huggingface-nlp/datasets/gem/gem_web_nlg/webnlg_en_train.json", |
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"validation": "https://storage.googleapis.com/huggingface-nlp/datasets/gem/gem_web_nlg/webnlg_en_val.json", |
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"test": "https://storage.googleapis.com/huggingface-nlp/datasets/gem/gem_web_nlg/webnlg_en_test.json", |
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"challenge_set": "https://storage.googleapis.com/huggingface-nlp/datasets/gem/gem_challenge_sets/web_nlg_en.zip", |
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}, |
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"ru": { |
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"train": "https://storage.googleapis.com/huggingface-nlp/datasets/gem/gem_web_nlg/webnlg_ru_train.json", |
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"validation": "https://storage.googleapis.com/huggingface-nlp/datasets/gem/gem_web_nlg/webnlg_ru_val.json", |
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"test": "https://storage.googleapis.com/huggingface-nlp/datasets/gem/gem_web_nlg/webnlg_ru_test.json", |
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"challenge_set": "https://storage.googleapis.com/huggingface-nlp/datasets/gem/gem_challenge_sets/web_nlg_ru.zip", |
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}, |
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} |
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class WebNLG(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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|
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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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"input": [datasets.Value("string")], |
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"target": datasets.Value("string"), |
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"references": [datasets.Value("string")], |
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"category": datasets.Value("string"), |
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"webnlg_id": datasets.Value("string"), |
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} |
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), |
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supervised_keys=None, |
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homepage="https://webnlg-challenge.loria.fr/challenge_2020/", |
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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_web_nlg_{lang}_RandomSample500.json"), |
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( |
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"challenge_validation_sample", |
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f"validation_web_nlg_{lang}_RandomSample500.json", |
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), |
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( |
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"challenge_test_scramble", |
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f"test_web_nlg_{lang}_ScrambleInputStructure500.json", |
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), |
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] |
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if lang == "en": |
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challenge_sets += [ |
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( |
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"challenge_test_numbers", |
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f"test_web_nlg_{lang}_replace_numbers_500.json", |
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) |
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] |
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return [ |
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datasets.SplitGenerator( |
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name=spl, gen_kwargs={"filepath": dl_dir[spl], "split": spl} |
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) |
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for spl in ["train", "validation", "test"] |
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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( |
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dl_dir["challenge_set"], self.config.name, filename |
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), |
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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 "challenge" in split: |
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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"{self.config.name}-{split}-{id_}" |
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yield id_, exple |
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else: |
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with open(filepath, encoding="utf-8") as f: |
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examples = json.load(f) |
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id_ = -1 |
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for example in examples["values"]: |
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if split == "train": |
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for target in example["target"]: |
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id_ += 1 |
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yield id_, { |
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"gem_id": f"{self.config.name}-{split}-{id_}", |
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"gem_parent_id": f"{self.config.name}-{split}-{id_}", |
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"input": example["input"], |
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"target": target, |
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"references": [] |
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if split == "train" |
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else example["target"], |
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"category": example["category"], |
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"webnlg_id": example["webnlg-id"], |
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} |
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else: |
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id_ += 1 |
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yield id_, { |
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"gem_id": f"{self.config.name}-{split}-{id_}", |
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"gem_parent_id": f"{self.config.name}-{split}-{id_}", |
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"input": example["input"], |
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"target": example["target"][0] |
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if len(example["target"]) > 0 |
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else "", |
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"references": example["target"], |
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"category": example["category"], |
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"webnlg_id": example["webnlg-id"], |
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} |
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