convmodel
convmodel provides a conversation model based on transformers GPT-2 model
- 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
