Datasets:

eriktks
/

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"),
	]

	def _info(self):
	return datasets.DatasetInfo(
	description=_DESCRIPTION,
	features=datasets.Features(
	{
	"id": datasets.Value("string"),
	"tokens": datasets.Sequence(datasets.Value("string")),
	"pos_tags": datasets.Sequence(
	datasets.features.ClassLabel(
	names=[
	'"',
	"''",
	"#",
	"$",
	"(",
	")",
	",",
	".",
	":",
	"``",
	"CC",
	"CD",
	"DT",
	"EX",
	"FW",
	"IN",
	"JJ",
	"JJR",
	"JJS",
	"LS",
	"MD",
	"NN",
	"NNP",
	"NNPS",
	"NNS",
	"NN\|SYM",
	"PDT",
	"POS",
	"PRP",
	"PRP$",
	"RB",
	"RBR",
	"RBS",
	"RP",
	"SYM",
	"TO",
	"UH",
	"VB",
	"VBD",
	"VBG",
	"VBN",
	"VBP",
	"VBZ",
	"WDT",
	"WP",
	"WP$",
	"WRB",
	]
	)
	),
	"chunk_tags": datasets.Sequence(
	datasets.features.ClassLabel(
	names=[
	"O",
	"B-ADJP",
	"I-ADJP",
	"B-ADVP",
	"I-ADVP",
	"B-CONJP",
	"I-CONJP",
	"B-INTJ",
	"I-INTJ",
	"B-LST",
	"I-LST",
	"B-NP",
	"I-NP",
	"B-PP",
	"I-PP",
	"B-PRT",
	"I-PRT",
	"B-SBAR",
	"I-SBAR",
	"B-UCP",
	"I-UCP",
	"B-VP",
	"I-VP",
	]
	)
	),
	"ner_tags": datasets.Sequence(
	datasets.features.ClassLabel(
	names=[
	"O",
	"B-PER",
	"I-PER",
	"B-ORG",
	"I-ORG",
	"B-LOC",
	"I-LOC",
	"B-MISC",
	"I-MISC",
	]
	)
	),
	}
	),
	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),
	}

	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,
	"pos_tags": pos_tags,
	"chunk_tags": chunk_tags,
	"ner_tags": ner_tags,
	}
	guid += 1
	tokens = []
	pos_tags = []
	chunk_tags = []
	ner_tags = []
	else:
	# conll2003 tokens are space separated
	splits = line.split(" ")
	tokens.append(splits[0])
	pos_tags.append(splits[1])
	chunk_tags.append(splits[2])
	ner_tags.append(splits[3].rstrip())
	# last example
	if tokens:
	yield guid, {
	"id": str(guid),
	"tokens": tokens,
	"pos_tags": pos_tags,
	"chunk_tags": chunk_tags,
	"ner_tags": ner_tags,
	}