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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
"""Introduction to the CoNLL-2003 Shared Task: Language-Independent Named Entity Recognition"""
import os
import json
import datasets
_CITATION = """\
"""
_DESCRIPTION = """\
"""
_URL = "https://huggingface.co/datasets/Red-8/NER_Gujarati_data/resolve/main/data/datas.zip"
_TRAINING_FILE = "train_data.json"
_DEV_FILE = "val_data.json"
_TEST_FILE = "test_data.json"
class RedConfig(datasets.BuilderConfig):
"""BuilderConfig for Red"""
def __init__(self, **kwargs):
"""BuilderConfig forRed.
Args:
**kwargs: keyword arguments forwarded to super.
"""
super(RedConfig, self).__init__(**kwargs)
class Red(datasets.GeneratorBasedBuilder):
"""Red dataset."""
BUILDER_CONFIGS = [
RedConfig(name="NER_Gujarati_data", version=datasets.Version("1.0.0"), description="Red dataset"),
]
def _info(self):
return datasets.DatasetInfo(
description=_DESCRIPTION,
features=datasets.Features(
{
"tokens": datasets.Sequence(datasets.Value("string")),
"ner_tags": datasets.Sequence(
datasets.features.ClassLabel(
names=[
"O",
"B-PERIOD",
"I-PERIOD",
"B-DURATION",
"I-DURATION",
"B-WEATHER",
"I-WEATHER",
"B-DIGIT",
"I-DIGIT",
"B-NUMINAL",
"I-NUMINAL",
]
)
),
}
),
supervised_keys=None,
citation=_CITATION,
)
def _split_generators(self, dl_manager):
"""Returns SplitGenerators."""
downloaded_file = dl_manager.download_and_extract(_URL)
data_files = {
"train": os.path.join(downloaded_file, _TRAINING_FILE),
"dev": os.path.join(downloaded_file, _DEV_FILE),
"test": os.path.join(downloaded_file, _TEST_FILE),
}
return [
datasets.SplitGenerator(name=datasets.Split.TRAIN, gen_kwargs={"filepath": data_files["train"]}),
datasets.SplitGenerator(name=datasets.Split.VALIDATION, gen_kwargs={"filepath": data_files["dev"]}),
datasets.SplitGenerator(name=datasets.Split.TEST, gen_kwargs={"filepath": data_files["test"]}),
]
def _generate_examples(self, filepath):
"""Yields examples as (key, example) tuples."""
with open(filepath,encoding="utf-8") as f:
for idx_, row in enumerate(f):
data = json.loads(row)
yield idx_, {"tokens": data["text"], "ner_tags": data["label"]} |