| ``` |
| ### encode the input paragraph to summarize |
| tokenized_text = tokenizer.encode_plus( |
| str(text_input), |
| return_attention_mask = True, |
| return_tensors="pt") |
| |
| ### pass the tokenized paragraph in model |
| generated_token = model.generate( |
| input_ids = tokenized_text["input_ids"], |
| attention_mask=tokenized_text["attention_mask"], |
| max_length = 256, |
| use_cache=True, |
| ) |
| |
| ### decode the summarized paragraph token |
| summarized_paragraph = [ |
| tokenizer.decode(token_ids=ids, skip_special_tokens=True) for ids in generated_token |
| ] |
| |
| Summarized paragraph is the final result. |
| ``` |
