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| from transformers import AutoTokenizer, BartForConditionalGeneration | |
| model = BartForConditionalGeneration.from_pretrained("facebook/bart-large-cnn") | |
| tokenizer = AutoTokenizer.from_pretrained("facebook/bart-large-cnn") | |
| def summarize_text(text_to_summarize): | |
| inputs = tokenizer([text_to_summarize], max_length=1024, return_tensors="pt") | |
| summary_ids = model.generate( | |
| inputs["input_ids"], num_beams=2, min_length=0, max_length=100 | |
| ) | |
| summarized_text = tokenizer.batch_decode( | |
| summary_ids, skip_special_tokens=True, clean_up_tokenization_spaces=False | |
| )[0] | |
| return summarized_text | |