ChineseErrorCorrector-1.5B

Usage (HuggingFace Transformers)

Without 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 

# pip install transformers
from transformers import AutoModelForCausalLM, AutoTokenizer
checkpoint = "twnlp/ChineseErrorCorrector-1.5B"

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:

少先队员应该为老人让座。