Datasets:
Tasks:
Token Classification
Modalities:
Text
Sub-tasks:
named-entity-recognition
Languages:
English
Size:
1K - 10K
License:
File size: 6,583 Bytes
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# 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
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