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
•
58cebd9
1
Parent(s):
2b4c3f2
Delete legacy dataset_infos.json
Browse files- dataset_infos.json +0 -65
dataset_infos.json
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{
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"amttl": {
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"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",
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"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",
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"homepage": "https://www.aclweb.org/anthology/C18-1307/",
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"license": "",
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"features": {
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"id": {
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"dtype": "string",
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"_type": "Value"
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},
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"tokens": {
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"feature": {
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"dtype": "string",
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"_type": "Value"
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},
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"_type": "Sequence"
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},
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"tags": {
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"feature": {
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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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"_type": "ClassLabel"
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},
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"_type": "Sequence"
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}
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},
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"builder_name": "parquet",
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"dataset_name": "amttl",
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"config_name": "amttl",
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"version": {
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"version_str": "1.0.0",
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"major": 1,
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"minor": 0,
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"patch": 0
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},
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"splits": {
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"train": {
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"name": "train",
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"num_bytes": 1132196,
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"num_examples": 3063,
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"dataset_name": null
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},
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"validation": {
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"name": "validation",
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"num_bytes": 324358,
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"num_examples": 822,
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"dataset_name": null
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},
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"test": {
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"name": "test",
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"num_bytes": 328509,
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"num_examples": 908,
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"dataset_name": null
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}
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},
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"download_size": 274351,
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"dataset_size": 1785063,
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"size_in_bytes": 2059414
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}
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}
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