turku_ner_corpus / turku_ner_corpus.py
# 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
import datasets
_DESCRIPTION = """\
An open, broad-coverage corpus for Finnish named entity recognition presented in Luoma et al. (2020) A Broad-coverage Corpus for Finnish Named Entity Recognition.
"""
_HOMEPAGE_URL = "https://turkunlp.org/fin-ner.html"
_URL = "https://github.com/TurkuNLP/turku-ner-corpus/archive/v1.0.tar.gz"
_CITATION = """\
@inproceedings{luoma-etal-2020-broad,
title = "A Broad-coverage Corpus for {F}innish Named Entity Recognition",
author = {Luoma, Jouni and Oinonen, Miika and Pyyk{\"o}nen, Maria and Laippala, Veronika and Pyysalo, Sampo},
booktitle = "Proceedings of The 12th Language Resources and Evaluation Conference",
year = "2020",
url = "https://www.aclweb.org/anthology/2020.lrec-1.567",
pages = "4615--4624",
}
"""
class TurkuNERCorpus(datasets.GeneratorBasedBuilder):
VERSION = datasets.Version("1.0.0")
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=[
"B-DATE",
"B-EVENT",
"B-LOC",
"B-ORG",
"B-PER",
"B-PRO",
"I-DATE",
"I-EVENT",
"I-LOC",
"I-ORG",
"I-PER",
"I-PRO",
"O",
]
)
),
},
),
supervised_keys=None,
homepage=_HOMEPAGE_URL,
citation=_CITATION,
)
def _split_generators(self, dl_manager):
archive = dl_manager.download(_URL)
return [
datasets.SplitGenerator(
name=datasets.Split.TRAIN,
gen_kwargs={"files": dl_manager.iter_archive(archive), "data_type": "train"},
),
datasets.SplitGenerator(
name=datasets.Split.VALIDATION,
gen_kwargs={"files": dl_manager.iter_archive(archive), "data_type": "valid"},
),
datasets.SplitGenerator(
name=datasets.Split.TEST,
gen_kwargs={"files": dl_manager.iter_archive(archive), "data_type": "test"},
),
]
def _generate_examples(self, files, data_type):
if data_type == "train":
data_path = "turku-ner-corpus-1.0/data/conll/train.tsv"
elif data_type == "valid":
data_path = "turku-ner-corpus-1.0/data/conll/dev.tsv"
elif data_type == "test":
data_path = "turku-ner-corpus-1.0/data/conll/test.tsv"
else:
raise Exception("data_type not understood")
sentence_counter = 0
for path, f in files:
if path == data_path:
current_words = []
current_labels = []
for row in f:
row = row.decode("utf-8").rstrip()
row_split = row.split("\t")
if len(row_split) == 2:
token, label = row_split
current_words.append(token)
current_labels.append(label)
else:
if not current_words:
continue
assert len(current_words) == len(current_labels), "word len doesnt match label length"
sentence = (
sentence_counter,
{
"id": str(sentence_counter),
"tokens": current_words,
"ner_tags": current_labels,
},
)
sentence_counter += 1
current_words = []
current_labels = []
yield sentence
# if something remains:
if current_words:
sentence = (
sentence_counter,
{
"id": str(sentence_counter),
"tokens": current_words,
"ner_tags": current_labels,
},
)
yield sentence
break