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iioSnail/
ReaLiSe-for-csc

Name: ReaLiSe-for-csc
Availability: InStock

Chinese Spell Correction

csc

Chinese Spell Checking

custom_code

2105.12306

afl-3.0

text-embeddings-inference

endpoints_compatible

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ReaLiSe-for-csc

中文拼写纠错(Chinese Spell Checking, CSC)模型

该模型源于ReaLiSe源码提供的模型

原论文为：https://arxiv.org/abs/2105.12306

原论文官方代码为：https://github.com/DaDaMrX/ReaLiSe

本模型在SIGHAN2015上的表现如下：

	Detect-Acc	Detect-Precision	Detect-Recall	Detect-F1	Correct-Acc	Correct-Precision	Correct-Recall	Correct-F1
Sentence-level	84.7	77.3	81.3	79.3	84.0	75.9	79.9	77.8

模型使用方法

Open In Colab

安装依赖：

!pip install transformers
!pip install pypinyin
!pip install boto3

from transformers import AutoTokenizer, AutoModel

tokenizer = AutoTokenizer.from_pretrained("iioSnail/ReaLiSe-for-csc", trust_remote_code=True)
model = AutoModel.from_pretrained("iioSnail/ReaLiSe-for-csc", trust_remote_code=True)

inputs = tokenizer(["我是炼习时长两念半的个人练习生蔡徐坤"], return_tensors='pt')
output_hidden = model(**inputs).logits
print(''.join(tokenizer.convert_ids_to_tokens(output_hidden.argmax(-1)[0, 1:-1])))

输出：

我是练习时长两年半的个人练习生蔡徐坤

你也可以使用本模型封装的predict方法。

from transformers import AutoTokenizer, AutoModel

tokenizer = AutoTokenizer.from_pretrained("iioSnail/ReaLiSe-for-csc", trust_remote_code=True)
model = AutoModel.from_pretrained("iioSnail/ReaLiSe-for-csc", trust_remote_code=True)

model.set_tokenizer(tokenizer)  # 使用predict方法前，调用该方法
print(model.predict("我是练习时长两念半的鸽仁练习生蔡徐坤"))
print(model.predict(["我是练习时长两念半的鸽仁练习生蔡徐坤", "喜换唱跳、rap 和 蓝球"]))

输出：

我是练习时长两年半的各仁练习生蔡徐坤
['我是练习时长两年半的各仁练习生蔡徐坤', '喜欢唱跳、rap 和 蓝球']

模型训练

from transformers import AutoTokenizer, AutoModel

tokenizer = AutoTokenizer.from_pretrained("iioSnail/ReaLiSe-for-csc", trust_remote_code=True)
model = AutoModel.from_pretrained("iioSnail/ReaLiSe-for-csc", trust_remote_code=True)

inputs = tokenizer(["我是炼习时长两念半的个人练习生蔡徐坤", "喜换唱跳rap蓝球"],
                   text_target=["我是练习时长两年半的个人练习生蔡徐坤", "喜欢唱跳rap篮球"],
                   padding=True,
                   return_tensors='pt')
loss = model(**inputs).loss
print("loss:", loss)
loss.backward()

输出：

loss: tensor(0.6515, grad_fn=<NllLossBackward0>)

常见问题

网络问题，例如：Connection Error

解决方案：将模型下载到本地使用。批量下载方案可参考该博客

将模型下载到本地使用时出现报错：ModuleNotFoundError: No module named 'transformers_modules.iioSnail/ReaLiSe-for-csc'

解决方案：将 iioSnail/ChineseBERT-for-csc 改为 iioSnail\ChineseBERT-for-csc，或升级transformers