Datasets:
Tasks:
Token Classification
Modalities:
Text
Sub-tasks:
named-entity-recognition
Languages:
English
Size:
10K - 100K
File size: 4,257 Bytes
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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
import os
import datasets
logger = datasets.logging.get_logger(__name__)
_CITATION = ""
_DESCRIPTION = ""
#_URL = "."
_TRAINING_FILE = "train.txt"
_DEV_FILE = "validation.txt"
_TEST_FILE = "test.txt"
class UBBDemoConfig(datasets.BuilderConfig):
"""BuilderConfig for UBBDemo"""
def __init__(self, **kwargs):
"""BuilderConfig for UBBDemo.
Args:
**kwargs: keyword arguments forwarded to super.
"""
super(UBBDemoConfig, self).__init__(**kwargs)
class UBBDemo(datasets.GeneratorBasedBuilder):
"""UBBDemo dataset."""
BUILDER_CONFIGS = [
UBBDemoConfig(name="UBBDemo", version=datasets.Version("1.0.0"), description="UBBDemo 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-PER",
"I-PER",
"B-ORG",
"I-ORG",
"B-LOC",
"I-LOC",
"B-MISC",
"I-MISC",
]
)
),
}
),
supervised_keys=None,
homepage="",
citation=_CITATION,
)
def _split_generators(self, dl_manager):
"""Returns SplitGenerators."""
path = "./"
data_files = {
"train": os.path.join(path, _TRAINING_FILE),
"validation": os.path.join(path, _DEV_FILE),
"test": os.path.join(path, _TEST_FILE),
}
downloaded_file = dl_manager.download_and_extract(data_files)
return [
datasets.SplitGenerator(name=datasets.Split.TRAIN, gen_kwargs={"filepath": downloaded_file ["train"]}),
datasets.SplitGenerator(name=datasets.Split.VALIDATION, gen_kwargs={"filepath": downloaded_file ["validation"]}),
datasets.SplitGenerator(name=datasets.Split.TEST, gen_kwargs={"filepath": downloaded_file ["test"]}),
]
def _generate_examples(self, filepath):
print("I am here" + filepath)
logger.info("⏳ Generating examples from = %s", filepath)
with open(filepath, encoding="utf-8") as f:
guid = 0
tokens = []
ner_tags = []
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 = []
else:
# UBBDemo tokens are space separated
splits = line.split(" ")
tokens.append(splits[0])
ner_tags.append(splits[3].rstrip())
# last example
yield guid, {
"id": str(guid),
"tokens": tokens,
"ner_tags": ner_tags,
} |