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---
language:
- zh
- en
license: mit
datasets:
- TigerResearch/tigerbot-zhihu-zh-10k
- TigerResearch/tigerbot-book-qa-1k
- TigerResearch/sft_zh
pipeline_tag: text-generation
---
# 中文文本生成
## 1 Usage
### 1.1 Initalization 初始化
!pip install transformers[torch]
```
from transformers import GPT2Tokenizer, GPT2LMHeadModel
import torch
device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
tokenizer = GPT2Tokenizer.from_pretrained('Hollway/gpt2_finetune')
model = GPT2LMHeadModel.from_pretrained('Hollway/gpt2_finetune').to(device)
```
### 1.2 Inference 基本推理任务
```
def generate(text): # 基本的下文预测任务
inputs = tokenizer(text, return_tensors="pt").to(device)
with torch.no_grad():
tokens = model.generate(
**inputs,
max_new_tokens=512,
do_sample=True,
pad_token_id=tokenizer.pad_token_id,
)
return tokenizer.decode(tokens[0], skip_special_tokens=True)
generate("派蒙是应急食品，但是不能吃派蒙，请分析不能吃的原因。")
```
### 1.3 Chatbot 聊天模式
```
def chat(turns=5): # 多轮对话模式，通过字符串拼接实现。
for step in range(turns):
query = input(">> 用户:")
new_user_input_ids = tokenizer.encode(
f"用户: {query}\n\n系统: ", return_tensors='pt').to(device)
bot_input_ids = torch.cat([chat_history_ids, new_user_input_ids], dim=-1) if step > 0 else new_user_input_ids
base_tokens = bot_input_ids.shape[-1]
chat_history_ids = model.generate(
bot_input_ids,
max_length=base_tokens+64, # 单次回复的最大token数量
do_sample=True,
pad_token_id=tokenizer.eos_token_id)
response = tokenizer.decode(
chat_history_ids[:, bot_input_ids.shape[-1]:][0],
skip_special_tokens=True)
print(f"系统: {response}\n")
chat(turns=5)
``` |