Datasets:
Tasks:
Other
Languages:
English
Multilinguality:
monolingual
Language Creators:
found
Source Datasets:
original
ArXiv:
Tags:
relation-extraction
License:
# 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": [ | |
"", | |
], | |
} | |