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from transformers import AutoTokenizer, AutoModelForSeq2SeqLM
model = AutoModelForSeq2SeqLM.from_pretrained("AlekseyKulnevich/Pegasus-HeaderGeneration")
tokenizer = PegasusTokenizer.from_pretrained('google/pegasus-large')
input_text # your text
input_ = tokenizer.batch_encode_plus([input_text], max_length=1024, pad_to_max_length=True,
truncation=True, padding='longest', return_tensors='pt')
input_ids = input_['input_ids']
input_mask = input_['attention_mask']
headers = model.generate(input_ids=input_ids,
attention_mask=input_mask,
num_beams=32,
no_repeat_ngram_size=2,
early_stopping=True,
num_return_sequences=10)
headers = tokenizer.batch_decode(headers, skip_special_tokens=True)headers = model.generate(input_ids=input_ids,
attention_mask=input_mask,
num_beams=32,
no_repeat_ngram_size=2,
early_stopping=True,
num_return_sequences=20)
tokenizer.batch_decode(headers, skip_special_tokens=True)