|
--- |
|
language: |
|
- zh |
|
tags: |
|
- bert |
|
- pytorch |
|
- zh |
|
- pycorrector |
|
license: apache-2.0 |
|
datasets: |
|
- shibing624/CSC |
|
library_name: transformers |
|
pipeline_tag: text2text-generation |
|
widget: |
|
- text: 少先队员因该为老人让坐 |
|
--- |
|
|
|
# MacBERT for Chinese Spelling Correction(macbert4csc) Model |
|
中文拼写纠错模型 |
|
|
|
`macbert4csc-base-chinese` evaluate SIGHAN2015 test data： |
|
|
|
- Char Level: precision:0.9372, recall:0.8640, f1:0.8991 |
|
- Sentence Level: precision:0.8264, recall:0.7366, f1:0.7789 |
|
|
|
由于训练使用的数据使用了SIGHAN2015的训练集（复现paper），在SIGHAN2015的测试集上达到SOTA水平。 |
|
|
|
模型结构，魔改于softmaskedbert： |
|
|
|
 |
|
|
|
## Usage |
|
|
|
本项目开源在中文文本纠错项目：[pycorrector](https://github.com/shibing624/pycorrector)，可支持macbert4csc模型，通过如下命令调用： |
|
|
|
```python |
|
from pycorrector.macbert.macbert_corrector import MacBertCorrector |
|
|
|
nlp = MacBertCorrector("shibing624/macbert4csc-base-chinese").macbert_correct |
|
|
|
i = nlp('今天新情很好') |
|
print(i) |
|
``` |
|
|
|
当然，你也可使用官方的huggingface/transformers调用： |
|
|
|
*Please use 'Bert' related functions to load this model!* |
|
|
|
```python |
|
import operator |
|
import torch |
|
from transformers import BertTokenizer, BertForMaskedLM |
|
device = torch.device("cuda" if torch.cuda.is_available() else "cpu") |
|
|
|
tokenizer = BertTokenizer.from_pretrained("shibing624/macbert4csc-base-chinese") |
|
model = BertForMaskedLM.from_pretrained("shibing624/macbert4csc-base-chinese") |
|
model.to(device) |
|
|
|
texts = ["今天新情很好", "你找到你最喜欢的工作，我也很高心。"] |
|
with torch.no_grad(): |
|
outputs = model(**tokenizer(texts, padding=True, return_tensors='pt').to(device)) |
|
|
|
def get_errors(corrected_text, origin_text): |
|
sub_details = [] |
|
for i, ori_char in enumerate(origin_text): |
|
if ori_char in [' ', '“', '”', '‘', '’', '琊', '\n', '…', '—', '擤']: |
|
# add unk word |
|
corrected_text = corrected_text[:i] + ori_char + corrected_text[i:] |
|
continue |
|
if i >= len(corrected_text): |
|
continue |
|
if ori_char != corrected_text[i]: |
|
if ori_char.lower() == corrected_text[i]: |
|
# pass english upper char |
|
corrected_text = corrected_text[:i] + ori_char + corrected_text[i + 1:] |
|
continue |
|
sub_details.append((ori_char, corrected_text[i], i, i + 1)) |
|
sub_details = sorted(sub_details, key=operator.itemgetter(2)) |
|
return corrected_text, sub_details |
|
|
|
result = [] |
|
for ids, text in zip(outputs.logits, texts): |
|
_text = tokenizer.decode(torch.argmax(ids, dim=-1), skip_special_tokens=True).replace(' ', '') |
|
corrected_text = _text[:len(text)] |
|
corrected_text, details = get_errors(corrected_text, text) |
|
print(text, ' => ', corrected_text, details) |
|
result.append((corrected_text, details)) |
|
print(result) |
|
``` |
|
|
|
output: |
|
```shell |
|
今天新情很好 => 今天心情很好 [('新', '心', 2, 3)] |
|
你找到你最喜欢的工作，我也很高心。 => 你找到你最喜欢的工作，我也很高兴。 [('心', '兴', 15, 16)] |
|
``` |
|
|
|
模型文件组成： |
|
``` |
|
macbert4csc-base-chinese |
|
├── config.json |
|
├── added_tokens.json |
|
├── pytorch_model.bin |
|
├── special_tokens_map.json |
|
├── tokenizer_config.json |
|
└── 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）](https://pan.baidu.com/s/1BV5tr9eONZCI0wERFvr0gQ)| 106M | |
|
| **`原始SIGHAN数据集`** | SIGHAN13 14 15 | [官方csc.html](http://nlp.ee.ncu.edu.tw/resource/csc.html)| 339K | |
|
| **`原始Wang271K数据集`** | Wang271K | [Automatic-Corpus-Generation dimmywang提供](https://github.com/wdimmy/Automatic-Corpus-Generation/blob/master/corpus/train.sgml)| 93M | |
|
|
|
|
|
SIGHAN+Wang271K中文纠错数据集，数据格式： |
|
```json |
|
[ |
|
{ |
|
"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。" |
|
}, |
|
] |
|
``` |
|
|
|
```shell |
|
macbert4csc |
|
├── config.json |
|
├── pytorch_model.bin |
|
├── special_tokens_map.json |
|
├── tokenizer_config.json |
|
└── vocab.txt |
|
``` |
|
|
|
如果需要训练macbert4csc，请参考[https://github.com/shibing624/pycorrector/tree/master/pycorrector/macbert](https://github.com/shibing624/pycorrector/tree/master/pycorrector/macbert) |
|
|
|
|
|
### About MacBERT |
|
**MacBERT** is an improved BERT with novel **M**LM **a**s **c**orrection pre-training task, which mitigates the discrepancy of pre-training and fine-tuning. |
|
|
|
Here is an example of our pre-training task. |
|
|
|
| task | Example | |
|
| -------------- | ----------------- | |
|
| **Original Sentence** | we use a language model to predict the probability of the next word. | |
|
| **MLM** | we use a language [M] to [M] ##di ##ct the pro [M] ##bility of the next word . | |
|
| **Whole word masking** | we use a language [M] to [M] [M] [M] the [M] [M] [M] of the next word . | |
|
| **N-gram masking** | we use a [M] [M] to [M] [M] [M] the [M] [M] [M] [M] [M] next word . | |
|
| **MLM as correction** | we use a text system to ca ##lc ##ulate the po ##si ##bility of the next word . | |
|
|
|
Except for the new pre-training task, we also incorporate the following techniques. |
|
|
|
- Whole Word Masking (WWM) |
|
- N-gram masking |
|
- Sentence-Order Prediction (SOP) |
|
|
|
**Note that our MacBERT can be directly replaced with the original BERT as there is no differences in the main neural architecture.** |
|
|
|
For more technical details, please check our paper: [Revisiting Pre-trained Models for Chinese Natural Language Processing](https://arxiv.org/abs/2004.13922) |
|
|
|
|
|
## Citation |
|
|
|
```latex |
|
@software{pycorrector, |
|
author = {Xu Ming}, |
|
title = {pycorrector: Text Error Correction Tool}, |
|
year = {2021}, |
|
url = {https://github.com/shibing624/pycorrector}, |
|
} |
|
``` |
