old_T5_summarizer.py

import torch             

from transformers import AutoTokenizer, AutoModelWithLMHead

tokenizer = AutoTokenizer.from_pretrained('t5-base')                     
model = AutoModelWithLMHead.from_pretrained('t5-base', return_dict=True)

with open('summarizer_input.txt', 'r', encoding='utf-8') as file:
    text = file.read()

inputs = tokenizer.encode("summarize: " + text, return_tensors='pt', max_length=512, truncation=True)

summary_ids = model.generate(inputs, max_length=150, min_length=80, length_penalty=5., num_beams=2)
summary = tokenizer.decode(summary_ids[0])
print(summary)