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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
"""The ner-tr Entities Dataset."""
import datasets
logger = datasets.logging.get_logger(__name__)
_CITATION = """\
aa
}
"""
_DESCRIPTION = """\
aa
"""
_URL = "https://raw.githubusercontent.com/BihterDass/named/main/"
_TRAINING_FILE = "train.conll"
_DEV_FILE = "train.conll"
_TEST_FILE = "train.conll"
class NERTRConfig(datasets.BuilderConfig):
"""The NERTRConfig Entities Dataset."""
def __init__(self, **kwargs):
"""BuilderConfig for NERTRConfig.
Args:
**kwargs: keyword arguments forwarded to super.
"""
super(NERTRConfig, self).__init__(**kwargs)
class NERTR(datasets.GeneratorBasedBuilder):
"""The NERTR Entities Dataset."""
BUILDER_CONFIGS = [
NERTRConfig(
name="NERTR", version=datasets.Version("1.0.0"), description="The NERTR Entities Dataset"
),
]
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-DepositProduct",
"I-DepositProduct",
"B-Product",
"I-Product",
"B-ProductProblemInfo",
"I-ProductProblemInfo",
"B-ServiceInformation",
"I-ServiceInformation",
"B-ServiceClosest",
"I-ServiceClosest",
"B-Location",
"I-Location",
"B-ServiceNumber",
"I-ServiceNumber",
"B-Brand",
"I-Brand",
"B-Campaign",
"I-Campaign",
"B-ProductSelector",
"I-ProductSelector",
"B-SpecialCampaign",
"I-SpecialCampaign",
]
)
),
}
),
supervised_keys=None,
homepage="https://github.com/BihterDass/named",
citation=_CITATION,
)
def _split_generators(self, dl_manager):
"""Returns SplitGenerators."""
urls_to_download = {
"train": f"{_URL}{_TRAINING_FILE}",
"dev": f"{_URL}{_DEV_FILE}",
"test": f"{_URL}{_TEST_FILE}",
}
downloaded_files = dl_manager.download_and_extract(urls_to_download)
return [
datasets.SplitGenerator(name=datasets.Split.TRAIN, gen_kwargs={"filepath": downloaded_files["train"]}),
datasets.SplitGenerator(name=datasets.Split.VALIDATION, gen_kwargs={"filepath": downloaded_files["dev"]}),
datasets.SplitGenerator(name=datasets.Split.TEST, gen_kwargs={"filepath": downloaded_files["test"]}),
]
def _generate_examples(self, filepath):
logger.info("⏳ Generating examples from = %s", filepath)
with open(filepath, encoding="utf-8") as f:
current_tokens = []
current_labels = []
sentence_counter = 0
for row in f:
row = row.rstrip()
if row:
token, label = row.split("\t")
current_tokens.append(token)
current_labels.append(label)
else:
# New sentence
if not current_tokens:
# Consecutive empty lines will cause empty sentences
continue
assert len(current_tokens) == len(current_labels), "💔 between len of tokens & labels"
sentence = (
sentence_counter,
{
"id": str(sentence_counter),
"tokens": current_tokens,
"ner_tags": current_labels,
},
)
sentence_counter += 1
current_tokens = []
current_labels = []
yield sentence
# Don't forget last sentence in dataset 🧐
if current_tokens:
yield sentence_counter, {
"id": str(sentence_counter),
"tokens": current_tokens,
"ner_tags": current_labels,
} |