Commit
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53b5b42
1
Parent(s):
2c63d54
Convert dataset to Parquet
Browse filesConvert dataset to Parquet.
- README.md +16 -6
- amttl/test-00000-of-00001.parquet +3 -0
- amttl/train-00000-of-00001.parquet +3 -0
- amttl/validation-00000-of-00001.parquet +3 -0
- dataset_infos.json +65 -1
README.md
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@@ -19,6 +19,7 @@ task_ids:
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- parsing
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pretty_name: AMTTL
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dataset_info:
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features:
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- name: id
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dtype: string
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'1': I
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'2': E
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'3': S
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config_name: amttl
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splits:
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- name: train
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num_bytes:
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num_examples: 3063
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- name: validation
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num_bytes:
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num_examples: 822
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- name: test
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num_bytes:
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num_examples: 908
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download_size:
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dataset_size:
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---
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# Dataset Card for AMTTL
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- parsing
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pretty_name: AMTTL
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dataset_info:
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config_name: amttl
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features:
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- name: id
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dtype: string
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'1': I
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'2': E
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'3': S
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splits:
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- name: train
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num_bytes: 1132196
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num_examples: 3063
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- name: validation
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num_bytes: 324358
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num_examples: 822
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- name: test
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num_bytes: 328509
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num_examples: 908
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download_size: 274351
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dataset_size: 1785063
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configs:
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- config_name: amttl
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data_files:
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- split: train
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path: amttl/train-*
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- split: validation
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path: amttl/validation-*
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- split: test
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path: amttl/test-*
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default: true
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---
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# Dataset Card for AMTTL
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amttl/test-00000-of-00001.parquet
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version https://git-lfs.github.com/spec/v1
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oid sha256:e162219c3d2e9a4b234407072169e58475c70f69a1118c4c92c1cc8bdb7fddcf
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size 51311
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amttl/train-00000-of-00001.parquet
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version https://git-lfs.github.com/spec/v1
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oid sha256:93ff0e728fa5bf6cf4c32805ac01529c1b022f29b39f28406a5e7fd28b9b6342
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size 172615
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amttl/validation-00000-of-00001.parquet
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version https://git-lfs.github.com/spec/v1
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oid sha256:e7992b50bd6d87521937260ed7ebce5a986b8eb52ad0905373fe94d6b155c53e
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size 50425
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dataset_infos.json
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{
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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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