|
|
|
"""REBEL""" |
|
|
|
from __future__ import absolute_import, division, print_function |
|
|
|
import datasets |
|
|
|
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": data_dir + "/en_train.jsonl"}), |
|
datasets.SplitGenerator(name=datasets.Split.VALIDATION, gen_kwargs={"filepath": data_dir + "/en_val.jsonl"}), |
|
datasets.SplitGenerator(name=datasets.Split.TEST, gen_kwargs={"filepath": 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['uri'] + '-' + str(count), { |
|
"title": article['title'], |
|
"context": text, |
|
"id": article['uri'] + '-' + str(count), |
|
"triplets": decoder_output, |
|
} |
|
text = '' |
|
|
