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Bart for Chinese Spelling Correction(bart4csc) Model
BART中文拼写纠错模型
bart4csc-base-chinese evaluate SIGHAN2015 test data：
Sentence Level: acc:0.6845, precision:0.6984, recall:0.6354, f1:0.6654
case:
| input_text | pred |
|---|---|
| 辰导中引述她的话说：核子间题的解决之道系于克什米尔纷争。 | 报导中引述她的话说：核子问题的解决之道系于克什米尔纷争。 |
| 报导并末说明事故发生的原因。 | 报导并未说明事故发生的原因。 |
训练使用了SIGHAN+Wang271K中文纠错数据集，在SIGHAN2015的测试集上达到接近SOTA水平。
Usage
本项目开源在文本生成项目：textgen，可支持Bart模型，通过如下命令调用：
Install package:
pip install -U textgen
from transformers import BertTokenizerFast
from textgen import BartSeq2SeqModel
tokenizer = BertTokenizerFast.from_pretrained('shibing624/bart4csc-base-chinese')
model = BartSeq2SeqModel(
encoder_type='bart',
encoder_decoder_type='bart',
encoder_decoder_name='shibing624/bart4csc-base-chinese',
tokenizer=tokenizer,
args={"max_length": 128, "eval_batch_size": 128})
sentences = ["少先队员因该为老人让坐"]
print(model.predict(sentences))
# ['少先队员应该为老人让座']
模型文件组成：
bart4csc-base-chinese
├── config.json
├── model_args.json
├── pytorch_model.bin
├── special_tokens_map.json
├── tokenizer_config.json
├── spiece.model
└── vocab.txt
训练数据集
SIGHAN+Wang271K中文纠错数据集
| 数据集 | 语料 | 下载链接 | 压缩包大小 |
|---|---|---|---|
SIGHAN+Wang271K中文纠错数据集 |
SIGHAN+Wang271K(27万条) | 百度网盘（密码01b9） | 106M |
原始SIGHAN数据集 |
SIGHAN13 14 15 | 官方csc.html | 339K |
原始Wang271K数据集 |
Wang271K | Automatic-Corpus-Generation dimmywang提供 | 93M |
SIGHAN+Wang271K中文纠错数据集，数据格式：
[
{
"id": "B2-4029-3",
"original_text": "晚间会听到嗓音，白天的时候大家都不会太在意，但是在睡觉的时候这嗓音成为大家的恶梦。",
"wrong_ids": [
5,
31
],
"correct_text": "晚间会听到噪音，白天的时候大家都不会太在意，但是在睡觉的时候这噪音成为大家的恶梦。"
},
]
- 如果需要训练Bart模型，请参考https://github.com/shibing624/textgen/blob/main/examples/seq2seq/training_bartseq2seq_zh_demo.py
- 了解更多纠错模型，请移步：https://github.com/shibing624/pycorrector
Citation
@software{textgen,
author = {Xu Ming},
title = {textgen: Implementation of Text Generation models},
year = {2022},
url = {https://github.com/shibing624/textgen},
}
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