Omniracle

Llama3.1 Rouge Score评分怎么样

关于Llama 3.1的ROUGE Score评分,以下是相关的内容整理:

  1. ROUGE评分概述

    • ROUGE(Recall-Oriented Understudy for Gisting Evaluation)是一种专注于召回率的评估方法,主要用于自动摘要和文本生成的质量评估。
    • ROUGE可以细分为多个评估指标,包括ROUGE-N(n-gram重叠)、ROUGE-L(最长公共子序列)等。
  2. Llama 3.1的ROUGE Score

    • 在微调前,Llama 3.1的ROUGE-1得分为22.20,BLEU-4得分为5.93。
    • 在微调后,ROUGE-1得分提升至32.79,BLEU-4得分提升至11.30,显示出微调后模型在生成文本的准确性上有显著提高。
  3. ROUGE Score的计算

    • ROUGE-1是基于单个词的重叠计算,ROUGE-2则是基于双词组的重叠计算。
    • ROUGE-L则考虑了生成文本与参考文本之间的最长公共子序列,提供了更全面的评估。
  4. 模型性能提升

    • 微调后的Llama 3.1在生成文本的准确性上有明显提升,表明模型在处理复杂语言任务时的能力得到了增强。
  5. 使用的工具和方法

    • 在计算ROUGE Score时,使用了改进的句子切分机制,特别考虑了中文标点符号,以提高评分的准确性。
    • 采用了jieba库进行中文分词,确保了在中文文本处理中的有效性。

综上所述,Llama 3.1的ROUGE Score在微调后表现出色,显示了其在文本生成任务中的高效性和准确性。

- RESOURCES -

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