Datasets:

GateNLP
/

broad_twitter_corpus

	# coding=utf-8
	# Copyright 2020 HuggingFace Datasets Authors.
	#
	# 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.

	# Lint as: python3
	"""Introduction to the CoNLL-2003 Shared Task: Language-Independent Named Entity Recognition"""

	import os

	import datasets


	logger = datasets.logging.get_logger(__name__)


	_CITATION = """\
	@inproceedings{derczynski2016broad,
	title={Broad twitter corpus: A diverse named entity recognition resource},
	author={Derczynski, Leon and Bontcheva, Kalina and Roberts, Ian},
	booktitle={Proceedings of COLING 2016, the 26th International Conference on Computational Linguistics: Technical Papers},
	pages={1169--1179},
	year={2016}
	}
	"""

	_DESCRIPTION = """\
	This is the Broad Twitter corpus, a dataset of tweets collected over stratified times, places and social uses.
	The goal is to represent a broad range of activities, giving a dataset more representative of the language used
	in this hardest of social media formats to process. Further, the BTC is annotated for named entities.

	For more details see [https://aclanthology.org/C16-1111/](https://aclanthology.org/C16-1111/)
	"""

	_URL = "https://github.com/GateNLP/broad_twitter_corpus/archive/refs/heads/master.zip"
	_subpath = "broad_twitter_corpus-master/"
	_A_FILE = _subpath + "a.conll"
	_B_FILE = _subpath + "b.conll"
	_E_FILE = _subpath + "e.conll"
	_F_FILE = _subpath + "f.conll"
	_G_FILE = _subpath + "g.conll"
	_H_FILE = _subpath + "h.conll"

	# _TRAINING_FILE = "train.txt"
	_DEV_FILE = _H_FILE
	_TEST_FILE = _F_FILE


	class BroadTwitterCorpusConfig(datasets.BuilderConfig):
	"""BuilderConfig for BroadTwitterCorpus"""

	def __init__(self, **kwargs):
	"""BuilderConfig for BroadTwitterCorpus.

	Args:
	**kwargs: keyword arguments forwarded to super.
	"""
	super(BroadTwitterCorpusConfig, self).__init__(**kwargs)


	class BroadTwitterCorpus(datasets.GeneratorBasedBuilder):
	"""BroadTwitterCorpus dataset."""

	BUILDER_CONFIGS = [
	BroadTwitterCorpusConfig(name="broad-twitter-corpus", version=datasets.Version("1.0.0"), description="Broad Twitter Corpus"),
	]

	def _info(self):
	return datasets.DatasetInfo(
	description=_DESCRIPTION,
	features=datasets.Features(
	{
	"id": datasets.Value("string"),
	"tokens": datasets.Sequence(datasets.Value("string")),
	"ner_tags": datasets.Sequence(
	datasets.features.ClassLabel(
	names=[
	"O",
	"B-PER",
	"I-PER",
	"B-ORG",
	"I-ORG",
	"B-LOC",
	"I-LOC",
	]
	)
	),
	}
	),
	supervised_keys=None,
	homepage="https://aclanthology.org/C16-1111/",
	citation=_CITATION,
	)

	def _split_generators(self, dl_manager):
	"""Returns SplitGenerators."""
	downloaded_file = dl_manager.download_and_extract(_URL)

	data_files = {
	"a": os.path.join(downloaded_file, _A_FILE),
	"b": os.path.join(downloaded_file, _B_FILE),
	"e": os.path.join(downloaded_file, _E_FILE),
	"f": os.path.join(downloaded_file, _F_FILE),
	"g": os.path.join(downloaded_file, _G_FILE),
	"h": os.path.join(downloaded_file, _H_FILE),
	"dev": os.path.join(downloaded_file, _DEV_FILE),
	"test": os.path.join(downloaded_file, _TEST_FILE),
	}

	"""
	btc_section_a = datasets.SplitGenerator(name="BTC_A", gen_kwargs={"filepath": data_files["a"]})
	btc_section_b = datasets.SplitGenerator(name="BTC_B", gen_kwargs={"filepath": data_files["b"]})
	btc_section_e = datasets.SplitGenerator(name="BTC_E", gen_kwargs={"filepath": data_files["e"]})
	btc_section_f = datasets.SplitGenerator(name="BTC_F", gen_kwargs={"filepath": data_files["f"]})
	btc_section_g = datasets.SplitGenerator(name="BTC_G", gen_kwargs={"filepath": data_files["g"]})
	btc_section_h = datasets.SplitGenerator(name="BTC_H", gen_kwargs={"filepath": data_files["h"]})
	"""
	return [
	datasets.SplitGenerator(name=datasets.Split.TRAIN,
	gen_kwargs={"filepaths": [data_files['a'], data_files['b'], data_files['e'], data_files['g']]}
	),
	datasets.SplitGenerator(name=datasets.Split.VALIDATION, gen_kwargs={"filepaths": [data_files["dev"]]}),
	datasets.SplitGenerator(name=datasets.Split.TEST, gen_kwargs={"filepaths": [data_files["test"]]}),
	]

	def _generate_examples(self, filepaths):
	guid = 0
	for filepath in filepaths:
	with open(filepath, encoding="utf-8") as f:
	logger.info("⏳ Generating examples from = %s", filepath)
	tokens = []
	ner_tags = []
	for line in f:
	if line.startswith("-DOCSTART-") or line.strip() == "" or line == "\n":
	if tokens:
	yield guid, {
	"id": str(guid),
	"tokens": tokens,
	"ner_tags": ner_tags,
	}
	guid += 1
	tokens = []
	ner_tags = []
	else:
	# btc entries are tab separated
	fields = line.split("\t")
	tokens.append(fields[0])
	ner_tags.append(fields[1].rstrip())
	# last example
	yield guid, {
	"id": str(guid),
	"tokens": tokens,
	"ner_tags": ner_tags,
	}
	guid += 1 # for when files roll over