convmodel

convmodel

convmodel Transformers GPT-2 모델을 기반으로 한 대화 모델을 제공합니다.

특징

• GPT2 모델을 활용하여 응답 생성
• 여러 차례 대화를 처리합니다.
• 모델을 미세 조정하고 주어진 컨텍스트에서 응답을 생성하는 데 유용한 인터페이스를 제공합니다.

GPT-2 모델을 미세 조정하고 응답을 생성하는 간단한 예:

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'])">

 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'])

설치, 모델 아키텍처 및 사용법에 대한 자세한 내용은 문서를 참조하십시오.

• 설치하다
• 모델 아키텍처 개요
• 모델 훈련
• 응답 생성
• CLI(실험적)

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