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
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Update files from the datasets library (from 1.2.0)

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Release notes: https://github.com/huggingface/datasets/releases/tag/1.2.0

Files changed (5) hide show
  1. .gitattributes +27 -0
  2. README.md +144 -0
  3. amttl.py +146 -0
  4. dataset_infos.json +1 -0
  5. dummy/amttl/1.0.0/dummy_data.zip +3 -0
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+ *.arrow filter=lfs diff=lfs merge=lfs -text
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README.md ADDED
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+ ---
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+ annotations_creators:
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+ - crowdsourced
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+ language_creators:
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+ - found
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+ languages:
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+ - zh
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+ licenses:
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+ - mit
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+ multilinguality:
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+ - monolingual
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+ size_categories:
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+ - 1K<n<10K
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+ source_datasets:
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+ - original
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+ task_categories:
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+ - structure-prediction
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+ task_ids:
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+ - parsing
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+ ---
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+
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+ # Dataset Card for AMTTL
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+
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+ ## Table of Contents
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+ - [Dataset Description](#dataset-description)
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+ - [Dataset Summary](#dataset-summary)
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+ - [Supported Tasks](#supported-tasks-and-leaderboards)
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+ - [Languages](#languages)
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+ - [Dataset Structure](#dataset-structure)
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+ - [Data Instances](#data-instances)
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+ - [Data Fields](#data-instances)
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+ - [Data Splits](#data-instances)
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+ - [Dataset Creation](#dataset-creation)
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+ - [Curation Rationale](#curation-rationale)
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+ - [Source Data](#source-data)
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+ - [Annotations](#annotations)
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+ - [Personal and Sensitive Information](#personal-and-sensitive-information)
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+ - [Considerations for Using the Data](#considerations-for-using-the-data)
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+ - [Social Impact of Dataset](#social-impact-of-dataset)
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+ - [Discussion of Biases](#discussion-of-biases)
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+ - [Other Known Limitations](#other-known-limitations)
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+ - [Additional Information](#additional-information)
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+ - [Dataset Curators](#dataset-curators)
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+ - [Licensing Information](#licensing-information)
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+ - [Citation Information](#citation-information)
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+
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+ ## Dataset Description
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+
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+ - **Homepage:** [Github](https://github.com/adapt-sjtu/AMTTL/tree/master/medical_data)
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+ - **Repository:** [Github](https://github.com/adapt-sjtu/AMTTL/tree/master/medical_data)
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+ - **Paper:** [Aclweb](http://aclweb.org/anthology/C18-1307)
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+ - **Leaderboard:**
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+ - **Point of Contact:**
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+
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+ ### Dataset Summary
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+
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+ [More Information Needed]
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+
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+ ### Supported Tasks and Leaderboards
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+
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+ [More Information Needed]
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+
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+ ### Languages
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+
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+ [More Information Needed]
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+
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+ ## Dataset Structure
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+
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+ ### Data Instances
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+
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+ [More Information Needed]
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+
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+ ### Data Fields
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+
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+ [More Information Needed]
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+
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+ ### Data Splits
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+
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+ [More Information Needed]
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+
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+ ## Dataset Creation
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+
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+ ### Curation Rationale
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+
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+ [More Information Needed]
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+
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+ ### Source Data
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+
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+ #### Initial Data Collection and Normalization
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+
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+ [More Information Needed]
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+
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+ #### Who are the source language producers?
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+
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+ [More Information Needed]
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+
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+ ### Annotations
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+
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+ #### Annotation process
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+
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+ [More Information Needed]
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+
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+ #### Who are the annotators?
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+
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+ [More Information Needed]
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+
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+ ### Personal and Sensitive Information
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+
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+ [More Information Needed]
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+
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+ ## Considerations for Using the Data
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+
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+ ### Social Impact of Dataset
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+
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+ [More Information Needed]
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+
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+ ### Discussion of Biases
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+
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+ [More Information Needed]
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+
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+ ### Other Known Limitations
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+
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+ [More Information Needed]
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+
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+ ## Additional Information
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+
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+ ### Dataset Curators
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+
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+ [More Information Needed]
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+
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+ ### Licensing Information
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+
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+ [More Information Needed]
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+
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+ ### Citation Information
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+ ```bibtex
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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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+ ```
amttl.py ADDED
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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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+
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+ # Lint as: python3
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+ """Introduction to AMTTL CWS Dataset"""
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+
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+ import logging
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+
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+ import datasets
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+
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+
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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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+
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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
39
+ knowledge from high resource to low resource domains. Extensive experiments show that our mode
40
+ 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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+
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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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+
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+ For more details see https://www.aclweb.org/anthology/C18-1307/
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+ """
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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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+
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+
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+ class AmttlConfig(datasets.BuilderConfig):
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+ """BuilderConfig for AMTTL"""
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+
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+ def __init__(self, **kwargs):
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+ """BuilderConfig for AMTTL.
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+
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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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+
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+
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+ class Amttl(datasets.GeneratorBasedBuilder):
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+ """AMTTL Chinese Word Segmentation dataset."""
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+
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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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+
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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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+
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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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+
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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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+
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+ def _generate_examples(self, filepath):
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+ logging.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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+ }
dataset_infos.json ADDED
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+ {"amttl": {"description": "Chinese word segmentation (CWS) trained from open source corpus faces dramatic performance drop\nwhen dealing with domain text, especially for a domain with lots of special terms and diverse\nwriting styles, such as the biomedical domain. However, building domain-specific CWS requires\nextremely high annotation cost. In this paper, we propose an approach by exploiting domain-invariant\nknowledge from high resource to low resource domains. Extensive experiments show that our mode\nachieves consistently higher accuracy than the single-task CWS and other transfer learning\nbaselines, especially when there is a large disparity between source and target domains.\n\nThis dataset is the accompanied medical Chinese word segmentation (CWS) dataset.\nThe tags are in BIES scheme.\n\nFor more details see https://www.aclweb.org/anthology/C18-1307/\n", "citation": "@inproceedings{xing2018adaptive,\n title={Adaptive multi-task transfer learning for Chinese word segmentation in medical text},\n author={Xing, Junjie and Zhu, Kenny and Zhang, Shaodian},\n booktitle={Proceedings of the 27th International Conference on Computational Linguistics},\n pages={3619--3630},\n year={2018}\n}\n", "homepage": "https://www.aclweb.org/anthology/C18-1307/", "license": "", "features": {"id": {"dtype": "string", "id": null, "_type": "Value"}, "tokens": {"feature": {"dtype": "string", "id": null, "_type": "Value"}, "length": -1, "id": null, "_type": "Sequence"}, "tags": {"feature": {"num_classes": 4, "names": ["B", "I", "E", "S"], "names_file": null, "id": null, "_type": "ClassLabel"}, "length": -1, "id": null, "_type": "Sequence"}}, "post_processed": null, "supervised_keys": null, "builder_name": "amttl", "config_name": "amttl", "version": {"version_str": "1.0.0", "description": null, "major": 1, "minor": 0, "patch": 0}, "splits": {"train": {"name": "train", "num_bytes": 1132212, "num_examples": 3063, "dataset_name": "amttl"}, "validation": {"name": "validation", "num_bytes": 324374, "num_examples": 822, "dataset_name": "amttl"}, "test": {"name": "test", "num_bytes": 328525, "num_examples": 908, "dataset_name": "amttl"}}, "download_checksums": {"https://raw.githubusercontent.com/adapt-sjtu/AMTTL/master/medical_data/forum_train.txt": {"num_bytes": 434357, "checksum": "9819373963ea04d1d28844d5bc83b6b0332fad8b5f2e73092bcfc58dc6d6292a"}, "https://raw.githubusercontent.com/adapt-sjtu/AMTTL/master/medical_data/forum_dev.txt": {"num_bytes": 124973, "checksum": "1a2eb461b98d2a9160baad7f76d003cc0917b998e8283bcffa52b71224dd9d17"}, "https://raw.githubusercontent.com/adapt-sjtu/AMTTL/master/medical_data/forum_test.txt": {"num_bytes": 126204, "checksum": "aea1a8cf244cd565e94bd193a1eef7a10b16eeb0b6fbb6ed1d2fefbd55360dd6"}}, "download_size": 685534, "post_processing_size": null, "dataset_size": 1785111, "size_in_bytes": 2470645}}
dummy/amttl/1.0.0/dummy_data.zip ADDED
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