模型描述

PEGASUS 微调用于文本复述任务

实战演示 🚀

import torch
from transformers import PegasusForConditionalGeneration, PegasusTokenizer
model_name = 'tuner007/pegasus_paraphrase'
torch_device = 'cuda' if torch.cuda.is_available() else 'cpu'
tokenizer = PegasusTokenizer.from_pretrained(model_name)
model = PegasusForConditionalGeneration.from_pretrained(model_name).to(torch_device)

def get_response(input_text,num_return_sequences,num_beams):
  batch = tokenizer([input_text],truncation=True,padding='longest',max_length=60, return_tensors="pt").to(torch_device)
  translated = model.generate(**batch,max_length=60,num_beams=num_beams, num_return_sequences=num_return_sequences, temperature=1.5)
  tgt_text = tokenizer.batch_decode(translated, skip_special_tokens=True)
  return tgt_text

使用示例：

num_beams = 10
num_return_sequences = 10
context = "The ultimate test of your knowledge is your capacity to convey it to another."
get_response(context,num_return_sequences,num_beams)
# 输出结果：
['知识的终极考验在于你能否将其传达给他人',
 '衡量知识的标准是你传达它的能力',
 '传达知识的能力是对知识最重要的检验',
 '你传递知识的能力是知识的终极测试',
 '知识的测试在于你沟通它的能力',
 '你传达知识的能力是知识的终极考验',
 '向他人传达知识的能力是知识的终极测试',
 '传递知识的能力是对知识最重要的考验',
 '知识的考验在于你能多好地传达它',
 '传达知识的能力就是终极的检验']

由 Arpit Rajauria 创建