30 lines
736 B
Python
30 lines
736 B
Python
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#!/usr/bin/python3
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from transformers import GPT2LMHeadModel, GPT2Tokenizer
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import torch
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device = 'cuda:0'
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# Inicjalizacja tokenizera i modelu
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tokenizer = GPT2Tokenizer.from_pretrained("gpt2-medium")
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model = GPT2LMHeadModel.from_pretrained("gpt2-medium").to(device)
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model.half()
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# Tekst, który chcesz kontynuować
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input_text = "Yesterday morning, a flying saucer has landed in Poznan and rt"
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# Kodowanie tekstu wejściowego
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input_ids = tokenizer.encode(input_text, return_tensors='pt').to(device)
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# Generowanie tekstu
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output = model(input_ids)
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distrib = torch.softmax(output[0][0][-1], dim=0)
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values, indices = torch.topk(distrib, 11)
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for val, idx in zip(values, indices):
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print(f'{tokenizer.decode([idx])} {idx} {val}')
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