shibing624_macbert4csc-base-chinese

---

language:

• zh tags:
• bert
• pytorch
• zh
• pycorrector license: apache-2.0 datasets:
• shibing624/CSC library_name: transformers pipeline_tag: text-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：

	Correct-Precision	Correct-Recall	Correct-F1
Chararcter-level	93.72	86.40	89.91
Sentence-level	82.64	73.66	77.89

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，可支持macbert4csc模型，通过如下命令调用：

from pycorrector.macbert.macbert_corrector import MacBertCorrector

m = MacBertCorrector("shibing624/macbert4csc-base-chinese")

i = m.correct('今天新情很好')
print(i)

当然，你也可使用transformers调用：

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:

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）	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。"
    },
]

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

About MacBERT

MacBERT is an improved BERT with novel MLM as correction 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

Citation

@software{pycorrector,
  author = {Xu Ming},
  title = {pycorrector: Text Error Correction Tool},
  year = {2021},
  url = {https://github.com/shibing624/pycorrector},
}