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--- |
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license: mit |
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--- |
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## Usage (HuggingFace Transformers) |
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Without [ChineseErrorCorrector](https://github.com/TW-NLP/ChineseErrorCorrector), you can use the model like this: |
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First, you pass your input through the transformer model, then you get the generated sentence. |
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Install package: |
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``` |
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pip install transformers |
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``` |
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```python |
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# pip install transformers |
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from transformers import AutoModelForCausalLM, AutoTokenizer |
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checkpoint = "twnlp/ChineseErrorCorrector-7B" |
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device = "cuda" # for GPU usage or "cpu" for CPU usage |
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tokenizer = AutoTokenizer.from_pretrained(checkpoint) |
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model = AutoModelForCausalLM.from_pretrained(checkpoint).to(device) |
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input_content = "你是一个拼写纠错专家，对原文进行错别字纠正，不要更改原文字数，原文为：\n少先队员因该为老人让坐。" |
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messages = [{"role": "user", "content": input_content}] |
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input_text=tokenizer.apply_chat_template(messages, tokenize=False) |
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print(input_text) |
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inputs = tokenizer.encode(input_text, return_tensors="pt").to(device) |
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outputs = model.generate(inputs, max_new_tokens=1024, temperature=0, do_sample=False, repetition_penalty=1.08) |
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print(tokenizer.decode(outputs[0])) |
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``` |
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output: |
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```shell |
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少先队员应该为老人让座。 |
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``` |
