| import torch | |
| from transformers import T5Tokenizer | |
| def abstractive_summarizer(tokenizer, model, text): | |
| device = torch.device("cpu") | |
| preprocess_text = text.strip().replace("\n", "") | |
| t5_prepared_text = "summarize: " + preprocess_text | |
| tokenized_text = tokenizer.encode(t5_prepared_text, return_tensors="pt").to(device) | |
| # summmarize | |
| summary_ids = model.generate( | |
| tokenized_text, | |
| num_beams=4, | |
| no_repeat_ngram_size=2, | |
| min_length=30, | |
| max_length=300, | |
| early_stopping=True, | |
| ) | |
| abs_summarized_text = tokenizer.decode(summary_ids[0], skip_special_tokens=True) | |
| return abs_summarized_text | |