Datasets:
amttl

Tasks: parsing
Task Categories: token-classification
Languages: Chinese
Multilinguality: monolingual
Size Categories: 1K<n<10K
Language Creators: found
Annotations Creators: crowdsourced
Source Datasets: original
Licenses: mit
amttl / amttl.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
"""Introduction to AMTTL CWS Dataset"""
import datasets
logger = datasets.logging.get_logger(__name__)
_CITATION = """\
@inproceedings{xing2018adaptive,
title={Adaptive multi-task transfer learning for Chinese word segmentation in medical text},
author={Xing, Junjie and Zhu, Kenny and Zhang, Shaodian},
booktitle={Proceedings of the 27th International Conference on Computational Linguistics},
pages={3619--3630},
year={2018}
}
"""
_DESCRIPTION = """\
Chinese word segmentation (CWS) trained from open source corpus faces dramatic performance drop
when dealing with domain text, especially for a domain with lots of special terms and diverse
writing styles, such as the biomedical domain. However, building domain-specific CWS requires
extremely high annotation cost. In this paper, we propose an approach by exploiting domain-invariant
knowledge from high resource to low resource domains. Extensive experiments show that our mode
achieves consistently higher accuracy than the single-task CWS and other transfer learning
baselines, especially when there is a large disparity between source and target domains.
This dataset is the accompanied medical Chinese word segmentation (CWS) dataset.
The tags are in BIES scheme.
For more details see https://www.aclweb.org/anthology/C18-1307/
"""
_URL = "https://raw.githubusercontent.com/adapt-sjtu/AMTTL/master/medical_data/"
_TRAINING_FILE = "forum_train.txt"
_DEV_FILE = "forum_dev.txt"
_TEST_FILE = "forum_test.txt"
class AmttlConfig(datasets.BuilderConfig):
"""BuilderConfig for AMTTL"""
def __init__(self, **kwargs):
"""BuilderConfig for AMTTL.
Args:
**kwargs: keyword arguments forwarded to super.
"""
super(AmttlConfig, self).__init__(**kwargs)
class Amttl(datasets.GeneratorBasedBuilder):
"""AMTTL Chinese Word Segmentation dataset."""
BUILDER_CONFIGS = [
AmttlConfig(
name="amttl",
version=datasets.Version("1.0.0"),
description="AMTTL medical Chinese word segmentation dataset",
),
]
def _info(self):
return datasets.DatasetInfo(
description=_DESCRIPTION,
features=datasets.Features(
{
"id": datasets.Value("string"),
"tokens": datasets.Sequence(datasets.Value("string")),
"tags": datasets.Sequence(
datasets.features.ClassLabel(
names=[
"B",
"I",
"E",
"S",
]
)
),
}
),
supervised_keys=None,
homepage="https://www.aclweb.org/anthology/C18-1307/",
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:
guid = 0
tokens = []
tags = []
for line in f:
line_stripped = line.strip()
if line_stripped == "":
if tokens:
yield guid, {
"id": str(guid),
"tokens": tokens,
"tags": tags,
}
guid += 1
tokens = []
tags = []
else:
splits = line_stripped.split("\t")
if len(splits) == 1:
splits.append("O")
tokens.append(splits[0])
tags.append(splits[1])
# last example
yield guid, {
"id": str(guid),
"tokens": tokens,
"tags": tags,
}