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Як реалізувати декодування розмов в AI через python

To implement decoding conversations in AI using Python, you can use natural language processing (NLP) libraries like Hugging Face's Transformers, which provide pre-trained models for various NLP tasks, including conversation.

Here's a simple example using the DialoGPT model from Hugging Face's Transformers library:

  1. First, install the necessary libraries:
    pip install transformers
  2. Then, write a Python script to handle the conversation with the AI model:
    from transformers import AutoModelForCausalLM, AutoTokenizer
    import torch
    # Load the pre-trained model and tokenizer
    model_name = "microsoft/DialoGPT-medium"
    tokenizer = AutoTokenizer.from_pretrained(model_name)
    model = AutoModelForCausalLM.from_pretrained(model_name)
    def generate_response(user_input):
        # Encode the user input and add end-of-sentence token
        input_tokens = tokenizer.encode(user_input + tokenizer.eos_token, return_tensors="pt")
        # Generate a response from the model
        with torch.no_grad():
            response_tokens = model.generate(input_tokens, max_length=1000, pad_token_id=tokenizer.eos_token_id)
        # Decode the response tokens into text
        response = tokenizer.decode(response_tokens[:, input_tokens.shape[-1]:][0], skip_special_tokens=True)
        return response
    # Example conversation
    user_input = "What is the capital of France?"
    response = generate_response(user_input)

This example demonstrates how to use the DialoGPT model to generate a response for a given user input. You can modify the generate_response function and conversation logic to fit your specific use case.

Please note that running the DialoGPT model may require a powerful GPU and might consume a significant amount of memory. To use a smaller model, you can replace the "microsoft/DialoGPT-medium" with "microsoft/DialoGPT-small" or other models available in the Transformers library.

Автор: Рудюк С.А. 2023. K2 Cloud ERP.