ner-tr.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
"""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,
 }