# coding=utf-8 # Copyright 2020 The HuggingFace Datasets Authors and the current dataset script contributor. # # 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. """FewRel Dataset.""" import json import datasets _CITATION = """@inproceedings{han-etal-2018-fewrel, title = "{F}ew{R}el: A Large-Scale Supervised Few-Shot Relation Classification Dataset with State-of-the-Art Evaluation", author = "Han, Xu and Zhu, Hao and Yu, Pengfei and Wang, Ziyun and Yao, Yuan and Liu, Zhiyuan and Sun, Maosong", booktitle = "Proceedings of the 2018 Conference on Empirical Methods in Natural Language Processing", month = oct # "-" # nov, year = "2018", address = "Brussels, Belgium", publisher = "Association for Computational Linguistics", url = "https://www.aclweb.org/anthology/D18-1514", doi = "10.18653/v1/D18-1514", pages = "4803--4809" } @inproceedings{gao-etal-2019-fewrel, title = "{F}ew{R}el 2.0: Towards More Challenging Few-Shot Relation Classification", author = "Gao, Tianyu and Han, Xu and Zhu, Hao and Liu, Zhiyuan and Li, Peng and Sun, Maosong and Zhou, Jie", booktitle = "Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP)", month = nov, year = "2019", address = "Hong Kong, China", publisher = "Association for Computational Linguistics", url = "https://www.aclweb.org/anthology/D19-1649", doi = "10.18653/v1/D19-1649", pages = "6251--6256" } """ _DESCRIPTION = """\ FewRel is a large-scale few-shot relation extraction dataset, which contains more than one hundred relations and tens of thousands of annotated instances cross different domains. """ _HOMEPAGE = "https://thunlp.github.io/" _LICENSE = "https://raw.githubusercontent.com/thunlp/FewRel/master/LICENSE" DATA_URL = "https://raw.githubusercontent.com/thunlp/FewRel/master/data/" _URLs = { "train_wiki": DATA_URL + "train_wiki.json", "val_nyt": DATA_URL + "val_nyt.json", "val_pubmed": DATA_URL + "val_pubmed.json", "val_semeval": DATA_URL + "val_semeval.json", "val_wiki": DATA_URL + "val_wiki.json", "pid2name": DATA_URL + "pid2name.json", "pubmed_unsupervised": DATA_URL + "pubmed_unsupervised.json", } class FewRel(datasets.GeneratorBasedBuilder): """The FewRelDataset.""" VERSION = datasets.Version("1.0.0") BUILDER_CONFIGS = [ datasets.BuilderConfig(name="default", version=VERSION, description="This covers the entire FewRel dataset."), ] def _info(self): features = datasets.Features( { "relation": datasets.Value("string"), "tokens": datasets.Sequence(datasets.Value("string")), "head": { "text": datasets.Value("string"), "type": datasets.Value("string"), "indices": datasets.Sequence(datasets.Sequence(datasets.Value("int64"))), }, "tail": { "text": datasets.Value("string"), "type": datasets.Value("string"), "indices": datasets.Sequence(datasets.Sequence(datasets.Value("int64"))), }, "names": datasets.Sequence(datasets.Value("string")) # These are the features of your dataset like images, labels ... } ) return datasets.DatasetInfo( # This is the description that will appear on the datasets page. description=_DESCRIPTION, # This defines the different columns of the dataset and their types features=features, # Here we define them above because they are different between the two configurations # If there's a common (input, target) tuple from the features, # specify them here. They'll be used if as_supervised=True in # builder.as_dataset. supervised_keys=None, # Homepage of the dataset for documentation homepage=_HOMEPAGE, # License for the dataset if available license=_LICENSE, # Citation for the dataset citation=_CITATION, ) def _split_generators(self, dl_manager): """Returns SplitGenerators.""" data_dir = dl_manager.download_and_extract(_URLs) return [ datasets.SplitGenerator( name=datasets.Split(key), # These kwargs will be passed to _generate_examples gen_kwargs={ "filepath": data_dir[key], "pid2name": data_dir["pid2name"], "return_names": key in ["train_wiki", "val_wiki", "val_nyt"], }, ) for key in data_dir.keys() if key != "pid2name" ] def _generate_examples(self, filepath, pid2name, return_names): """Yields examples.""" pid2name_dict = {} with open(pid2name, encoding="utf-8") as f: data = json.load(f) for key in list(data.keys()): name_1 = data[key][0] name_2 = data[key][1] pid2name_dict[key] = [name_1, name_2] with open(filepath, encoding="utf-8") as f: data = json.load(f) if isinstance(data, dict): id = 0 for key in list(data.keys()): for items in data[key]: tokens = items["tokens"] h_0 = items["h"][0] h_1 = items["h"][1] h_2 = items["h"][2] t_0 = items["t"][0] t_1 = items["t"][1] t_2 = items["t"][2] id += 1 yield id, { "relation": key, "tokens": tokens, "head": {"text": h_0, "type": h_1, "indices": h_2}, "tail": {"text": t_0, "type": t_1, "indices": t_2}, "names": pid2name_dict[key] if return_names else [ key, ], } else: # For `pubmed_unsupervised.json` id = 0 for items in data: tokens = items["tokens"] h_0 = items["h"][0] h_1 = items["h"][1] h_2 = items["h"][2] t_0 = items["t"][0] t_1 = items["t"][1] t_2 = items["t"][2] id += 1 yield id, { "relation": "", "tokens": tokens, "head": {"text": h_0, "type": h_1, "indices": h_2}, "tail": {"text": t_0, "type": t_1, "indices": t_2}, "names": [ "", ], }