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ner-tr.py

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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,
+ }