ebart.py

import spaces
import torch
from transformers import PegasusForConditionalGeneration
from tokenizers_pegasus import PegasusTokenizer

class PegasusSummarizer:
    _instance = None

    def __new__(cls, *args, **kwargs):
        if cls._instance is None:
            cls._instance = super().__new__(cls)
            cls._instance._init_model()
        return cls._instance

    def _init_model(self):
        # 加载标记器和模型
        model_name = "IDEA-CCNL/Randeng-Pegasus-523M-Summary-Chinese-V1"
        self.model = PegasusForConditionalGeneration.from_pretrained(model_name)
        self.tokenizer = PegasusTokenizer.from_pretrained(model_name)

        # 将模型移动到GPU
        self.device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
        self.model.to(self.device)

    def generate_summary(self, text, max_length=180, min_length=64):
        # 进行标记化并将输入数据移动到GPU
        inputs = self.tokenizer(text, max_length=1024, truncation=True, return_tensors="pt").to(self.device)

        # 生成摘要
        summary_ids = self.model.generate(
            inputs["input_ids"],
            max_length=max_length,
            min_length=min_length,
            num_beams=4,
            early_stopping=True,
            temperature=0.7,
            top_k=50,
            top_p=0.9,
            repetition_penalty=2.0,
            length_penalty=1.0,
            no_repeat_ngram_size=3,
            num_return_sequences=1,
            do_sample=True
        )

        # 解码并返回摘要
        clean_summary = self.tokenizer.batch_decode(summary_ids, skip_special_tokens=True, clean_up_tokenization_spaces=True)[0]

        # 处理并过滤掉不需要的特殊标记
        special_tokens = ['<pad>', '<unk>', '</s>']
        for token in special_tokens:
            clean_summary = clean_summary.replace(token, '')

        return clean_summary

@spaces.GPU
def generate_summary(text, max_length=180, min_length=64):
    if(len(text)< max_length):
        return text
    else:
        summarizer = PegasusSummarizer()
        return summarizer.generate_summary(text, max_length, min_length)

if __name__ == "__main__":
    text = (
        "东四路西侧之前有划分免费停车位，为什么后面被撤销，而道路东侧有划分免费停车车位，附近小区车位紧张导致很难找到停车位。"
    )
    summary = generate_summary(text, max_length=128, min_length=64)
    print(summary)