| # from transformers import MT5Tokenizer, MT5ForConditionalGeneration | |
| # local_model_path = "./t5m_pocet" | |
| # tokenizer = MT5Tokenizer.from_pretrained(local_model_path, legacy=False) | |
| # model = MT5ForConditionalGeneration.from_pretrained(local_model_path) | |
| model_directory = './t5m_pocet/model_directory' | |
| from transformers import MT5ForConditionalGeneration, MT5Tokenizer | |
| import torch | |
| tokenizer = MT5Tokenizer(sp_model_kwargs={"model_file": f"{model_directory}/spiece.model"}) | |
| # local_model_path = "./t5m_pocet" | |
| # tokenizer = MT5Tokenizer.from_pretrained(local_model_path, legacy=False) | |
| # model = MT5ForConditionalGeneration.from_pretrained(local_model_path) | |
| # Загрузка токенизатора и модели mT5 | |
| # model_name = "google/mt5-small" | |
| # tokenizer = MT5Tokenizer.from_pretrained(model_name) | |
| # model = MT5ForConditionalGeneration.from_pretrained(model_name) | |
| # # Пример входных данных | |
| # context = "Контекст, на основе которого нужно ответить на вопрос." | |
| # question = "Какой вопрос нужно задать?" | |
| # # Форматирование входных данных для модели | |
| # input_text = f"question: {question} context: {context}" | |
| # input_ids = tokenizer(input_text, return_tensors="pt").input_ids | |
| # # Генерация ответа | |
| # outputs = model.generate(input_ids) | |
| # answer = tokenizer.decode(outputs[0], skip_special_tokens=True) | |
| # print(answer) | |