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---
language:
- zh
tags:
- t5
- pytorch
- prompt
- zh
- Text2Text-Generation
license: "apache-2.0"
widget:
- text: "宫颈癌的早期会有哪些危险信号"
- text: "夏季如何进行饮食调养养生？"
---
中文版对话机器人
在1000w+问答和对话数据上做有监督预训练
请使用下面方式调用模型输出结果，Hosted inference API的结果因为我无法修改后台推理程序，不能保证模型输出效果，只是举了两个例子展示。
Install package:
```
pip install transformers
```
```python
import os
os.environ["CUDA_VISIBLE_DEVICES"] = '-1'
import torch
from torch import cuda
from transformers import AutoTokenizer, AutoModelForSeq2SeqLM
tokenizer = AutoTokenizer.from_pretrained("mxmax/Chinese_Chat_T5_Base")
model = AutoModelForSeq2SeqLM.from_pretrained("mxmax/Chinese_Chat_T5_Base")
device = 'cuda' if cuda.is_available() else 'cpu'
model_trained.to(device)
def postprocess(text):
return text.replace(".", "").replace('</>','')
def answer_fn(text, sample=False, top_p=0.6):
encoding = tokenizer(text=[text], truncation=True, padding=True, max_length=256, return_tensors="pt").to(device)
out = model.generate(**encoding, return_dict_in_generate=True, output_scores=False, max_length=512,temperature=0.5,do_sample=True,repetition_penalty=6.0 ,top_p=top_p)
result = tokenizer.batch_decode(out["sequences"], skip_special_tokens=True)
return postprocess(result[0])
text="宫颈癌的早期会有哪些危险信号"
result=answer_fn(text, sample=True, top_p=0.6)
print('prompt:',text)
print("result:",result)
``` |