Datasets:
few_rel

	# 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": [
	"",
	],
	}