convmodel provides a conversation model based on GPT-2.
convmodel provides a conversation model based on transformers GPT-2 model :wink:
:sparkles: Features :sparkles:
- Utilizes GPT2 model to generate response
- Handles multi-turn conversation
- Provides useuful interfaces to fine-tune model and generate a response from a given context
A simple example of fine-tune GPT-2 model and generate a response:
from convmodel import ConversationModel from convmodel import ConversationExample # Load model on GPU model = ConversationModel.from_pretrained("gpt2") # Define training/validation examples train_iterator = [ ConversationExample(conversation=[ "Hello", "Hi, how are you?", "Good, thank you, how about you?", "Good, thanks!" ]), ConversationExample(conversation=[ "I am hungry", "How about eating pizza?" ]), ] valid_iterator = [ ConversationExample(conversation=[ "Tired...", "Let's have a break!", "Nice idea!" ]), ] # Fine-tune model model.fit(train_iterator=train_iterator, valid_iterator=valid_iterator) # Generate response model.generate(context=["Hello", "How are you"], do_sample=True, top_p=0.95, top_k=50) # Output could be like below if sufficient examples were given. # => ConversationModelOutput(responses=['Good thank you'], context=['Hello', 'How are you'])
Please refer to document for more details of installation, model architecture and usage.
Enjoy talking with your conversational AI :wink:
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