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---
license: mit
---
## Usage (HuggingFace Transformers)
Without [ChineseErrorCorrector](https://github.com/TW-NLP/ChineseErrorCorrector), you can use the model like this:
First, you pass your input through the transformer model, then you get the generated sentence.
Install package:
```
pip install transformers
```
```python
# pip install transformers
from transformers import AutoModelForCausalLM, AutoTokenizer
checkpoint = "twnlp/ChineseErrorCorrector-7B"
device = "cuda" # for GPU usage or "cpu" for CPU usage
tokenizer = AutoTokenizer.from_pretrained(checkpoint)
model = AutoModelForCausalLM.from_pretrained(checkpoint).to(device)
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。"
messages = [{"role": "user", "content": input_content}]
input_text=tokenizer.apply_chat_template(messages, tokenize=False)
print(input_text)
inputs = tokenizer.encode(input_text, return_tensors="pt").to(device)
outputs = model.generate(inputs, max_new_tokens=1024, temperature=0, do_sample=False, repetition_penalty=1.08)
print(tokenizer.decode(outputs[0]))
```
output:
```shell
少先队员应该为老人让座。
``` |