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# I am trying to understand to the following code. Do not use this for any purpose as I do not support this.
# Use the original source from https://huggingface.co/datasets/DFKI-SLT/science_ie/raw/main/science_ie.py
# 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.
"""DWIE is conceived as an entity-centric dataset that describes interactions and properties of conceptual entities on the level of the complete document."""
import datasets
from datasets import DownloadManager
import os
import json
import requests
from typing import Optional, List, Union
import argparse
import hashlib
from collections import OrderedDict
from time import sleep
#from dataset.utils.tokenizer import TokenizerCPN
# Find for instance the citation on arxiv or on the dataset repo/website
_CITATION = """\
@article{ZAPOROJETS2021102563,
title = {{DWIE}: An entity-centric dataset for multi-task document-level information extraction},
journal = {Information Processing & Management},
volume = {58},
number = {4},
pages = {102563},
year = {2021},
issn = {0306-4573},
doi = {https://doi.org/10.1016/j.ipm.2021.102563},
url = {https://www.sciencedirect.com/science/article/pii/S0306457321000662},
author = {Klim Zaporojets and Johannes Deleu and Chris Develder and Thomas Demeester}
}
"""
# You can copy an official description
_DESCRIPTION = """\
DWIE is conceived as an entity-centric dataset that describes interactions and properties of conceptual entities
on the level of the complete document. This contrasts with currently dominant mention-driven approaches that start
from the detection and classification of named entity mentions in individual sentences. Also, the dataset was
randomly sampled from a news platform (English online content from Deutsche Welle), and the annotation scheme
was generated to cover that content. This makes the setting more realistic than in datasets with pre-determined
annotation schemes, and non-uniform sampling of content to obtain balanced annotations."""
# Add a link to an official homepage for the dataset here
_HOMEPAGE = "https://github.com/klimzaporojets/DWIE"
# Add the licence for the dataset here if you can find it
_LICENSE = ""
# Add link to the official dataset URLs here
# The HuggingFace Datasets library doesn't host the datasets but only points to the original files.
# This can be an arbitrary nested dict/list of URLs (see below in `_split_generators` method)
_URLS = {"Task_1":
{
"url":"https://github.com/klimzaporojets/DWIE/archive/refs/heads/master.zip"
}
}
class DWIE(datasets.GeneratorBasedBuilder):
"""
DWIE is conceived as an entity-centric dataset that describes interactions and properties of conceptual entities on the level of the complete document.
"""
VERSION = datasets.Version("1.1.0")
BUILDER_CONFIGS = [
datasets.BuilderConfig(name="Task_1", version=VERSION,
description="Relation classification"),
]
DEFAULT_CONFIG_NAME = "Task_1"
def _info(self):
features = datasets.Features(
{
"id": datasets.Value("string"),
"content": datasets.Value("string"),
"tags": datasets.Value("string"),
"mentions": [
{
"begin": datasets.Value("int32"),
"end": datasets.Value("int32"),
"text": datasets.Value("string"),
"concept": datasets.Value("int32"),
"candidates" : datasets.Sequence(datasets.Value("string")),
"scores": datasets.Sequence(datasets.Value("float32"))
}
],
"concepts": [
{
"concept": datasets.Value("int32"),
"text": datasets.Value("string"),
"keyword": datasets.Value("bool"),
"count": datasets.Value("int32"),
"link": datasets.Value("string"),
"tags": datasets.Sequence(datasets.Value("string")),
}
],
"relations": [
{
"s": datasets.Value("int32"),
"p": datasets.Value("string"),
"o": datasets.Value("int32"),
}
],
"frames": [
{
"type": datasets.Value("string"),
"slots": [{
"name": datasets.Value("string"),
"value":datasets.Value("int32")
}]
}
],
"iptc": datasets.Sequence(datasets.Value("string"))
}
)
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, uncomment supervised_keys line below and
# specify them. They'll be used if as_supervised=True in builder.as_dataset.
# supervised_keys=("sentence", "label"),
# 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):
# If several configurations are possible (listed in BUILDER_CONFIGS), the configuration selected by the user is in self.config.name
# dl_manager is a datasets.download.DownloadManager that can be used to download and extract URLS
# It can accept any type or nested list/dict and will give back the same structure with the url replaced with path to local files.
# By default the archives will be extracted and a path to a cached folder where they are extracted is returned instead of the archive
urls = _URLS[self.config.name]
downloaded = dl_manager.download_and_extract(_URLS)
article_id_to_url_json= json.load(open(downloaded['Task_1']['url'] + '/DWIE-master/data/article_id_to_url.json'))
ids_to_new_ids = dict()
# some ids seem to be different, for now only this one:
ids_to_new_ids[18525950] = 19026607
should_tokenize = False
content_to_new_content = {'DW_40663341': [('starting with Sunday\'s', 'starting Sunday\'s'),
('$1 million (€840,000)', 'one million dollars (840,000 euros)'),
('who kneel in protest during', 'to kneel in protest during')]}
articles_done = 0
total_articles = len(article_id_to_url_json)
problematic_articles = set()
problematic_hash_articles = set()
all_annos = []
for curr_article in article_id_to_url_json:
article_id = curr_article['id']
article_url = curr_article['url']
article_id_nr = int(article_id[3:])
if article_id_nr in ids_to_new_ids:
article_url = article_url.replace(str(article_id_nr), str(ids_to_new_ids[article_id_nr]))
article_hash = curr_article['hash']
#print('fetching {} out of {} articles -'.format(articles_done, total_articles), curr_article)
annos_only_art_path = downloaded['Task_1']['url'] + '/DWIE-master/data/annos/' + curr_article['id'] + '.json'
annos_only_json = json.load(open(annos_only_art_path))
done = False
attempts = 0
while not done and attempts <= 3:
# try:
a = requests.get(article_url, allow_redirects=True).json()
if 'name' in a:
article_title = a['name']
else:
print('WARNING: no name detected for ', article_id)
article_title = ''
if 'teaser' in a:
article_teaser = a['teaser']
else:
print('WARNING: no teaser detected for ', article_id)
article_teaser = ''
if 'text' in a:
article_text = a['text']
else:
print('WARNING: no text detected for ', article_id)
article_text = ''
article_content_no_strip = '{}\n{}\n{}'.format(article_title, article_teaser, article_text)
article_content = article_content_no_strip
if article_id in content_to_new_content:
for str_dw, str_dwie in content_to_new_content[article_id]:
article_content = article_content.replace(str_dw, str_dwie)
if 'mentions' in annos_only_json:
for idx_mention, curr_mention in enumerate(annos_only_json['mentions']):
curr_mention_text = curr_mention['text'].replace(' ', ' ')
curr_mention_text = curr_mention_text.replace('', '')
solved = False
if "begin" not in curr_mention:
curr_mention["begin"] = 0
if "end" not in curr_mention:
curr_mention["end"] = 0
if "text" not in curr_mention:
curr_mention["text"] = ""
if "concept" not in curr_mention:
curr_mention["concept"] = 0
if "candidates" not in curr_mention:
curr_mention["candidates"] = []
if "scores" not in curr_mention:
curr_mention["scores"] = []
if article_content[curr_mention['begin']:curr_mention['end']] != curr_mention_text:
curr_mention_begin = curr_mention['begin']
curr_mention_end = curr_mention['end']
offset = 0
if not solved:
print('--------------------------------')
print('ERROR ALIGNMENT: texts don\'t match for {}: "{}" vs "{}", the textual content of '
'the files won\'t be complete '
.format(article_id, article_content[curr_mention['begin']:curr_mention['end']],
curr_mention_text))
print('--------------------------------')
problematic_articles.add(article_id)
else:
if "candidates" not in curr_mention:
curr_mention["candidates"] = []
curr_mention['begin'] = curr_mention_begin - offset
curr_mention['end'] = curr_mention_end - offset
if 'concepts' in annos_only_json:
for idx_concept, curr_concept in enumerate(annos_only_json['concepts']):
if "concept" not in curr_concept:
curr_concept["concept"] = 0
if "text" not in curr_concept:
curr_concept["text"] = ""
if "count" not in curr_concept:
curr_concept["count"] = 0
if "link" not in curr_concept:
curr_concept["link"] = ""
if "tags" not in curr_concept:
curr_concept["tags"] = []
if not should_tokenize:
annos_json = {'id': annos_only_json['id'],
'content': article_content,
'tags': annos_only_json['tags'],
'mentions': annos_only_json['mentions'],
'concepts': annos_only_json['concepts'],
'relations': annos_only_json['relations'],
'frames': annos_only_json['frames'],
'iptc': annos_only_json['iptc']}
all_annos.append(annos_json)
#print("annos_json",annos_json)
else:
tokenized = tokenizer.tokenize(article_content)
tokens = list()
begin = list()
end = list()
for curr_token in tokenized:
tokens.append(curr_token['token'])
begin.append(curr_token['offset'])
end.append(curr_token['offset'] + curr_token['length'])
annos_json = OrderedDict({'id': annos_only_json['id'],
'content': article_content,
'tokenization': OrderedDict({'tokens': tokens, 'begin': begin, 'end': end}),
'tags': annos_only_json['tags'],
'mentions': annos_only_json['mentions'],
'concepts': annos_only_json['concepts'],
'relations': annos_only_json['relations'],
'frames': annos_only_json['frames'],
'iptc': annos_only_json['iptc']})
hash_content = hashlib.sha1(article_content.encode("UTF-8")).hexdigest()
if hash_content != article_hash:
print('!!ERROR - hash doesn\'t match for ', article_id)
problematic_hash_articles.add(article_id)
attempts += 1
sleep(.1)
done = True
if done:
articles_done += 1
return[
datasets.SplitGenerator(
name=datasets.Split.TRAIN,
# These kwargs will be passed to _generate_examples
gen_kwargs={
"all_annos" : all_annos,
}
)
]
# method parameters are unpacked from `gen_kwargs` as given in `_split_generators`
def _generate_examples(self, all_annos):
# TODO: This method handles input defined in _split_generators to yield (key, example) tuples from the dataset.
# The `key` is for legacy reasons (tfds) and is not important in itself, but must be unique for each example.
for data in all_annos:
yield data['id'], {
"id": data['id'],
"content":data['content'],
"tags": data['tags'],
"mentions": data['mentions'],
"concepts": data['concepts'],
"relations": data['relations'],
"frames": data['frames'],
"iptc": data['iptc']
}
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