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# coding=utf-8
# Copyright 2020 HuggingFace Datasets Authors.
# Modified by Vésteinn Snæbjarnarson 2021
#
# 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
LABELS = [
"O",
"B-EVN",
"B-GRO",
"B-LOC",
"B-MNT",
"B-PRS",
"B-SMP",
"B-TME",
"B-WRK",
"I-EVN",
"I-GRO",
"I-LOC",
"I-MNT",
"I-PRS",
"I-SMP",
"I-TME",
"I-WRK"
]
import datasets
logger = datasets.logging.get_logger(__name__)
_CITATION = """\
@misc{swe-nerc,
title = {Swe-NERC},
author = {Ahrenberg, Lars ; Frid, Johan and Olsson, Leif-Jöran},
url = {https://hdl.handle.net/10794/121},
year = {2020} }
"""
_DESCRIPTION = """\
The corpus consists of ca. 150.000 words of text.
"""
_URL = "https://huggingface.co/datasets/vesteinn/swe-nerc/raw/main/"
_TRAINING_FILE = "swe_nerc_v1.tsv"
class SweNERCConfig(datasets.BuilderConfig):
"""BuilderConfig for swe-nerc"""
def __init__(self, **kwargs):
"""BuilderConfig for swe-nerc.
Args:
**kwargs: keyword arguments forwarded to super.
"""
super(SweNERCConfig, self).__init__(**kwargs)
class SweNERC(datasets.GeneratorBasedBuilder):
"""sosialurin-faroese-ner dataset."""
BUILDER_CONFIGS = [
SweNERCConfig(name="swe-nerc", version=datasets.Version("1.0.0"), description="swedish ner 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=LABELS
)
),
}
),
supervised_keys=None,
homepage="",
citation=_CITATION,
)
def _split_generators(self, dl_manager):
"""Returns SplitGenerators."""
urls_to_download = {
"train": f"{_URL}{_TRAINING_FILE}",
}
downloaded_files = dl_manager.download_and_extract(urls_to_download)
return [
datasets.SplitGenerator(name=datasets.Split.TRAIN, gen_kwargs={"filepath": downloaded_files["train"]}),
]
def _generate_examples(self, filepath):
logger.info("⏳ Generating examples from = %s", filepath)
with open(filepath, encoding="utf-8") as f:
guid = 0
tokens = []
ner_tags = []
last_tag = None
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 = []
last_tag = None
else:
# tokens are tab separated
splits = line.split("\t")
tokens.append(splits[0])
try:
tag = splits[1].rstrip()
if tag == "O":
pass
elif tag == last_tag:
tag = "I-" + tag
else:
tag = "B-" + tag
ner_tags.append(tag)
last_tag = splits[1].rstrip()
except:
print(splits)
raise
# last example
yield guid, {
"id": str(guid),
"tokens": tokens,
"ner_tags": ner_tags,
} |