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--- |
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language: |
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- zh |
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license: apache-2.0 |
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tags: |
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- t5 |
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- text error correction |
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widget: |
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- text: "今天天气不太好，我的心情也不是很偷快" |
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example_title: "案例1" |
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- text: "听到这个消息，心情真的蓝瘦" |
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example_title: "案例2" |
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- text: "脑子有点胡涂了，这道题冥冥学过还没有做出来" |
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example_title: "案例3" |
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inference: |
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parameters: |
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max_length: 256 |
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num_beams: 10 |
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no_repeat_ngram_size: 5 |
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do_sample: True |
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early_stopping: True |
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--- |
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## 功能介绍 |
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T5Corrector：中文字音与字形纠错模型 |
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这个模型是基于mengzi-t5-base进行文本纠错训练，使用500w+句子，通过替换同音词、近音词和形近字来构造纠错平行语料，共计3kw+句对，累计训练45000步。 |
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<a href='https://github.com/Macielyoung/T5Corrector'>Github项目地址</a> |
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加载模型： |
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```python |
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# 加载模型 |
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from transformers import T5Tokenizer, T5ForConditionalGeneration |
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pretrained = "Maciel/T5Corrector-base-v1" |
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tokenizer = T5Tokenizer.from_pretrained(pretrained) |
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model = T5ForConditionalGeneration.from_pretrained(pretrained) |
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``` |
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使用模型进行预测推理方法： |
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```python |
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import torch |
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device = torch.device("cuda" if torch.cuda.is_available() else "cpu") |
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model.to(device) |
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def correct(text, max_length): |
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model_inputs = tokenizer(text, |
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max_length=max_length, |
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truncation=True, |
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return_tensors="pt").to(device) |
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output = model.generate(**model_inputs, |
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num_beams=5, |
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no_repeat_ngram_size=4, |
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do_sample=True, |
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early_stopping=True, |
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max_length=max_length, |
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return_dict_in_generate=True, |
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output_scores=True) |
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pred_output = tokenizer.batch_decode(output.sequences, skip_special_tokens=True)[0] |
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return pred_output |
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text = "听到这个消息，心情真的蓝瘦" |
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correction = correct(text, max_length=32) |
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print(correction) |
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``` |
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### 案例展示 |
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``` |
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示例1: |
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input: 听到这个消息，心情真的蓝瘦 |
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output: 听到这个消息,心情真的难受 |
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示例2: |
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input: 脑子有点胡涂了，这道题冥冥学过还没有做出来 |
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output: 脑子有点糊涂了,这道题明明学过还没有做出来 |
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示例3: |
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input: 今天天气不太好，我的心情也不是很偷快 |
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output: 今天天气不太好,我的心情也不是很愉快 |
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``` |
