| import torch | |
| from transformers import GPT2Tokenizer, T5ForConditionalGeneration | |
| tokenizer = GPT2Tokenizer.from_pretrained('RussianNLP/FRED-T5-Summarizer', eos_token='</s>') | |
| model = T5ForConditionalGeneration.from_pretrained('RussianNLP/FRED-T5-Summarizer') | |
| device = 'cpu' | |
| model.to(device) | |
| input_text = "<LM> Сократи текст.\n " | |
| def make_summarization(user_text): | |
| processing_text = input_text + user_text | |
| input_ids = torch.tensor([tokenizer.encode(processing_text)]).to(device) | |
| outputs = model.generate(input_ids, eos_token_id=tokenizer.eos_token_id, | |
| num_beams=3, | |
| min_new_tokens=17, | |
| max_new_tokens=200, | |
| do_sample=True, | |
| no_repeat_ngram_size=4, | |
| top_p=0.9) | |
| return tokenizer.decode(outputs[0][1:]) | |