Datasets:

Babelscape
/

rebel-dataset

	# Lint as: python3
	"""REBEL"""

	from __future__ import absolute_import, division, print_function

	import datasets
	import os
	import re
	import json
	import logging

	_DESCRIPTION = """\
	REBEL is a silver dataset created for the paper REBEL: Relation Extraction By End-to-end Language generation
	"""

	_URL = "https://huggingface.co/datasets/Babelscape/rebel-dataset/resolve/main/rebel_dataset.zip"
	_URLS = {
	"train": _URL + "en_train.jsonl",
	"dev": _URL + "en_val.jsonl",
	"test": _URL + "en_test.jsonl",
	}
	_LICENSE = "Creative Commons Attribution-NonCommercial-ShareAlike 4.0 International License (CC BY-NC-SA 4.0)"
	_CITATION = """\
	@inproceedings{huguet-cabot-navigli-2021-rebel,
	title = "REBEL: Relation Extraction By End-to-end Language generation",
	author = "Huguet Cabot, Pere-Llu{\'\i}s and
	Navigli, Roberto",
	booktitle = "Findings of the Association for Computational Linguistics: EMNLP 2021",
	month = nov,
	year = "2021",
	address = "Online and in the Barceló Bávaro Convention Centre, Punta Cana, Dominican Republic",
	publisher = "Association for Computational Linguistics",
	url = "https://github.com/Babelscape/rebel/blob/main/docs/EMNLP_2021_REBEL__Camera_Ready_.pdf",
	}
	"""
	_HOMEPAGE = "https://github.com/Babelscape/rebel"


	class RebelConfig(datasets.BuilderConfig):
	"""BuilderConfig for REBEL."""

	def __init__(self, **kwargs):
	"""BuilderConfig for REBEL.
	Args:
	**kwargs: keyword arguments forwarded to super.
	"""
	super(RebelConfig, self).__init__(**kwargs)


	class Rebel(datasets.GeneratorBasedBuilder):
	"""Rebel 1.0"""

	BUILDER_CONFIGS = [
	RebelConfig(
	name="REBEL",
	version=datasets.Version("1.0.0"),
	description=_DESCRIPTION,
	),
	]

	def _info(self):
	return datasets.DatasetInfo(
	description=_DESCRIPTION,
	features=datasets.Features(
	{
	"id": datasets.Value("string"),
	"title": datasets.Value("string"),
	"context": datasets.Value("string"),
	"triplets": datasets.Value("string"),
	}
	),
	supervised_keys=None,
	homepage=_HOMEPAGE,
	citation=_CITATION,
	license=_LICENSE,
	)

	def _split_generators(self, dl_manager):
	if self.config.data_dir:
	data_dir = self.config.data_dir
	else:
	data_dir = dl_manager.download_and_extract(_URL)

	return [
	datasets.SplitGenerator(name=datasets.Split.TRAIN, gen_kwargs={"filepath": os.path.join(data_dir, "en_train.jsonl")}),
	datasets.SplitGenerator(name=datasets.Split.VALIDATION, gen_kwargs={"filepath": os.path.join(data_dir,"en_val.jsonl")}),
	datasets.SplitGenerator(name=datasets.Split.TEST, gen_kwargs={"filepath": os.path.join(data_dir,"en_test.jsonl")}),
	]

	def _generate_examples(self, filepath):
	"""This function returns the examples in the raw (text) form."""
	logging.info("generating examples from = %s", filepath)

	with open(filepath, encoding="utf-8") as f:
	for id_, row in enumerate(f):
	article = json.loads(row)
	prev_len = 0
	if len(article['triples']) == 0:
	continue
	count = 0
	for text_paragraph in article['text'].split('\n'):
	if len(text_paragraph) == 0:
	continue
	sentences = re.split(r'(?<=[.])\s', text_paragraph)
	text = ''
	for sentence in sentences:
	text += sentence + ' '
	if any([entity['boundaries'][0] < len(text) + prev_len < entity['boundaries'][1] for entity in article['entities']]):
	continue
	entities = sorted([entity for entity in article['entities'] if prev_len < entity['boundaries'][1] <= len(text)+prev_len], key=lambda tup: tup['boundaries'][0])
	decoder_output = '<triplet> '
	for int_ent, entity in enumerate(entities):
	triplets = sorted([triplet for triplet in article['triples'] if triplet['subject'] == entity and prev_len< triplet['subject']['boundaries'][1]<=len(text) + prev_len and prev_len< triplet['object']['boundaries'][1]<=len(text)+ prev_len], key=lambda tup: tup['object']['boundaries'][0])
	if len(triplets) == 0:
	continue
	decoder_output += entity['surfaceform'] + ' <subj> '
	for triplet in triplets:
	decoder_output += triplet['object']['surfaceform'] + ' <obj> ' + triplet['predicate']['surfaceform'] + ' <subj> '
	decoder_output = decoder_output[:-len(' <subj> ')]
	decoder_output += ' <triplet> '
	decoder_output = decoder_output[:-len(' <triplet> ')]
	count += 1
	prev_len += len(text)

	if len(decoder_output) == 0:
	text = ''
	continue

	text = re.sub('([\[\].,!?()])', r' \1 ', text.replace('()', ''))
	text = re.sub('\s{2,}', ' ', text)

	yield article['docid'] + '-' + str(count), {
	"title": article['title'],
	"context": text,
	"id": article['uri'] + '-' + str(count),
	"triplets": decoder_output,
	}
	text = ''