Datasets:
Tasks:
Token Classification
Sub-tasks:
named-entity-recognition
Languages:
English
Multilinguality:
monolingual
Size Categories:
10K<n<100K
Language Creators:
found
Annotations Creators:
expert-generated
Source Datasets:
extended|other-reuters-corpus
License:
File size: 7,231 Bytes
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# Inspired by conll2003 dataset
# https://huggingface.co/datasets/conll2003
# 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{tjong-kim-sang-de-meulder-2003-introduction,
title = "Introduction to the {C}o{NLL}-2003 Shared Task: Language-Independent Named Entity Recognition",
author = "Tjong Kim Sang, Erik F. and
De Meulder, Fien",
booktitle = "Proceedings of the Seventh Conference on Natural Language Learning at {HLT}-{NAACL} 2003",
year = "2003",
url = "https://www.aclweb.org/anthology/W03-0419",
pages = "142--147",
}
"""
_DESCRIPTION = """\
The shared task of CoNLL-2003 concerns language-independent named entity recognition. We will concentrate on
four types of named entities: persons, locations, organizations and names of miscellaneous entities that do
not belong to the previous three groups.
The CoNLL-2003 shared task data files contain four columns separated by a single space. Each word has been put on
a separate line and there is an empty line after each sentence. The first item on each line is a word, the second
a part-of-speech (POS) tag, the third a syntactic chunk tag and the fourth the named entity tag. The chunk tags
and the named entity tags have the format I-TYPE which means that the word is inside a phrase of type TYPE. Only
if two phrases of the same type immediately follow each other, the first word of the second phrase will have tag
B-TYPE to show that it starts a new phrase. A word with tag O is not part of a phrase. Note the dataset uses IOB2
tagging scheme, whereas the original dataset uses IOB1.
For more details see https://www.clips.uantwerpen.be/conll2003/ner/ and https://www.aclweb.org/anthology/W03-0419
"""
_URL = "https://data.deepai.org/conll2003.zip"
_TRAINING_FILE = "train.txt"
_DEV_FILE = "valid.txt"
_TEST_FILE = "test.txt"
# class Conll2003Config(datasets.BuilderConfig):
# """BuilderConfig for Conll2003"""
# def __init__(self, **kwargs):
# """BuilderConfig forConll2003.
# Args:
# **kwargs: keyword arguments forwarded to super.
# """
# super(Conll2003Config, self).__init__(**kwargs)
class Conll2003(datasets.GeneratorBasedBuilder):
# """Conll2003 dataset."""
# BUILDER_CONFIGS = [
# Conll2003Config(name="conll2003", version=datasets.Version(
# "1.0.0"), description="Conll2003 dataset"),
# ]
VERSION = datasets.Version("1.1.0")
DEFAULT_CONFIG_NAME = "first_domain"
BUILDER_CONFIGS = [
# datasets.BuilderConfig(name="first_domain", version=VERSION,
# description="This part of my dataset covers a first domain"),
# datasets.BuilderConfig(name="second_domain", version=VERSION,
# description="This part of my dataset covers a second domain"),
]
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-TAG",
"I-TAG",
"B-LOC",
"I-LOC",
"B-TIME",
"I-TIME",
"B-SORT",
"I-SORT"
]
)
),
}
),
supervised_keys=None,
homepage="https://www.aclweb.org/anthology/W03-0419/",
citation=_CITATION,
)
def _split_generators(self, dl_manager):
"""Returns SplitGenerators."""
# downloaded_file = dl_manager.download_and_extract(_URL)
# data_files = {
# "train": os.path.join(downloaded_file, _TRAINING_FILE),
# "dev": os.path.join(downloaded_file, _DEV_FILE),
# "test": os.path.join(downloaded_file, _TEST_FILE),
# }
# data_files = {
# "train": os.path.join(downloaded_file, _TRAINING_FILE),
# "dev": os.path.join(downloaded_file, _DEV_FILE),
# "test": os.path.join(downloaded_file, _TEST_FILE),
# }
url = "https://pastebin.pl/view/raw/f1bffd94"
text_file = dl_manager.download(url)
data_files = {
"train": text_file,
"dev": text_file,
"test": text_file,
}
return [
datasets.SplitGenerator(name=datasets.Split.TRAIN, gen_kwargs={
"filepath": data_files["train"]}),
datasets.SplitGenerator(name=datasets.Split.VALIDATION, gen_kwargs={
"filepath": data_files["dev"]}),
datasets.SplitGenerator(name=datasets.Split.TEST, gen_kwargs={
"filepath": data_files["test"]}),
]
def _generate_examples(self, filepath):
logger.info("⏳ Generating examples from = %s", filepath)
with open(filepath, encoding="utf-8") as f:
guid = 0
tokens = []
pos_tags = []
chunk_tags = []
ner_tags = []
for line in f:
if line.startswith("-DOCSTART-") or line == "" or line == "\n":
if tokens:
yield guid, {
"id": str(guid),
"tokens": tokens,
"ner_tags": ner_tags,
}
guid += 1
tokens = []
ner_tags = []
else:
# conll2003 tokens are space separated
splits = line.split(" ")
tokens.append(splits[0])
ner_tags.append(splits[1].rstrip())
# last example
if tokens:
yield guid, {
"id": str(guid),
"tokens": tokens,
"ner_tags": ner_tags,
}
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