上面代码使用教程
时间: 2023-09-02 13:09:35 浏览: 117
1. 首先需要安装 TensorFlow 和 transformers 库,可以使用以下命令进行安装:
```
pip install tensorflow
pip install transformers
```
2. 在代码中导入所需的库:
```python
from transformers import AutoTokenizer, TFAutoModelForCausalLM
import tensorflow as tf
```
3. 加载预训练的模型和 tokenizer:
```python
tokenizer = AutoTokenizer.from_pretrained("microsoft/DialoGPT-small")
model = TFAutoModelForCausalLM.from_pretrained("microsoft/DialoGPT-small")
```
4. 设置对话的起始文本:
```python
conversation_history = "Hi, how are you?"
```
5. 定义生成回复的函数:
```python
def generate_reply(conversation_history):
input_ids = tokenizer.encode(conversation_history + tokenizer.eos_token, return_tensors='tf')
chat_history_ids = model.generate(input_ids=input_ids, max_length=1000, pad_token_id=tokenizer.eos_token_id)
return tokenizer.decode(chat_history_ids[:, input_ids.shape[-1]:][0], skip_special_tokens=True)
```
6. 调用函数生成回复:
```python
reply = generate_reply(conversation_history)
print(reply)
```
完整代码如下:
```python
from transformers import AutoTokenizer, TFAutoModelForCausalLM
import tensorflow as tf
tokenizer = AutoTokenizer.from_pretrained("microsoft/DialoGPT-small")
model = TFAutoModelForCausalLM.from_pretrained("microsoft/DialoGPT-small")
conversation_history = "Hi, how are you?"
def generate_reply(conversation_history):
input_ids = tokenizer.encode(conversation_history + tokenizer.eos_token, return_tensors='tf')
chat_history_ids = model.generate(input_ids=input_ids, max_length=1000, pad_token_id=tokenizer.eos_token_id)
return tokenizer.decode(chat_history_ids[:, input_ids.shape[-1]:][0], skip_special_tokens=True)
reply = generate_reply(conversation_history)
print(reply)
```