Datasets:
Tasks:
Token Classification
Sub-tasks:
parsing
Languages:
Chinese
Multilinguality:
monolingual
Size Categories:
1K<n<10K
Language Creators:
found
Annotations Creators:
crowdsourced
Source Datasets:
original
Tags:
License:
Commit
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Parent(s):
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Delete loading script
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amttl.py
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# coding=utf-8
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# Copyright 2020 HuggingFace Datasets Authors.
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#
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# Licensed under the Apache License, Version 2.0 (the "License");
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# you may not use this file except in compliance with the License.
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# You may obtain a copy of the License at
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#
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# http://www.apache.org/licenses/LICENSE-2.0
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#
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# Unless required by applicable law or agreed to in writing, software
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# distributed under the License is distributed on an "AS IS" BASIS,
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# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
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# See the License for the specific language governing permissions and
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# limitations under the License.
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# Lint as: python3
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"""Introduction to AMTTL CWS Dataset"""
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import datasets
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logger = datasets.logging.get_logger(__name__)
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_CITATION = """\
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@inproceedings{xing2018adaptive,
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title={Adaptive multi-task transfer learning for Chinese word segmentation in medical text},
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author={Xing, Junjie and Zhu, Kenny and Zhang, Shaodian},
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booktitle={Proceedings of the 27th International Conference on Computational Linguistics},
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pages={3619--3630},
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year={2018}
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}
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"""
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_DESCRIPTION = """\
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Chinese word segmentation (CWS) trained from open source corpus faces dramatic performance drop
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when dealing with domain text, especially for a domain with lots of special terms and diverse
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writing styles, such as the biomedical domain. However, building domain-specific CWS requires
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extremely high annotation cost. In this paper, we propose an approach by exploiting domain-invariant
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knowledge from high resource to low resource domains. Extensive experiments show that our mode
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achieves consistently higher accuracy than the single-task CWS and other transfer learning
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baselines, especially when there is a large disparity between source and target domains.
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This dataset is the accompanied medical Chinese word segmentation (CWS) dataset.
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The tags are in BIES scheme.
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For more details see https://www.aclweb.org/anthology/C18-1307/
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"""
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_URL = "https://raw.githubusercontent.com/adapt-sjtu/AMTTL/master/medical_data/"
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_TRAINING_FILE = "forum_train.txt"
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_DEV_FILE = "forum_dev.txt"
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_TEST_FILE = "forum_test.txt"
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class AmttlConfig(datasets.BuilderConfig):
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"""BuilderConfig for AMTTL"""
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def __init__(self, **kwargs):
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"""BuilderConfig for AMTTL.
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Args:
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**kwargs: keyword arguments forwarded to super.
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"""
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super(AmttlConfig, self).__init__(**kwargs)
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class Amttl(datasets.GeneratorBasedBuilder):
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"""AMTTL Chinese Word Segmentation dataset."""
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BUILDER_CONFIGS = [
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AmttlConfig(
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name="amttl",
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version=datasets.Version("1.0.0"),
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description="AMTTL medical Chinese word segmentation dataset",
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),
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]
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def _info(self):
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return datasets.DatasetInfo(
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description=_DESCRIPTION,
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features=datasets.Features(
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{
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"id": datasets.Value("string"),
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"tokens": datasets.Sequence(datasets.Value("string")),
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"tags": datasets.Sequence(
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datasets.features.ClassLabel(
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names=[
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"B",
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"I",
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"E",
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"S",
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]
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)
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),
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}
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),
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supervised_keys=None,
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homepage="https://www.aclweb.org/anthology/C18-1307/",
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citation=_CITATION,
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)
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def _split_generators(self, dl_manager):
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"""Returns SplitGenerators."""
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urls_to_download = {
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"train": f"{_URL}{_TRAINING_FILE}",
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"dev": f"{_URL}{_DEV_FILE}",
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"test": f"{_URL}{_TEST_FILE}",
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}
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downloaded_files = dl_manager.download_and_extract(urls_to_download)
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return [
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datasets.SplitGenerator(name=datasets.Split.TRAIN, gen_kwargs={"filepath": downloaded_files["train"]}),
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datasets.SplitGenerator(name=datasets.Split.VALIDATION, gen_kwargs={"filepath": downloaded_files["dev"]}),
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datasets.SplitGenerator(name=datasets.Split.TEST, gen_kwargs={"filepath": downloaded_files["test"]}),
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]
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def _generate_examples(self, filepath):
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logger.info("⏳ Generating examples from = %s", filepath)
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with open(filepath, encoding="utf-8") as f:
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guid = 0
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tokens = []
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tags = []
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for line in f:
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line_stripped = line.strip()
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if line_stripped == "":
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if tokens:
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yield guid, {
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"id": str(guid),
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"tokens": tokens,
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"tags": tags,
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}
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guid += 1
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tokens = []
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tags = []
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else:
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splits = line_stripped.split("\t")
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if len(splits) == 1:
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splits.append("O")
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tokens.append(splits[0])
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tags.append(splits[1])
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# last example
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yield guid, {
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"id": str(guid),
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"tokens": tokens,
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"tags": tags,
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}
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