作为基因分析OpenAI模型的替代品的小型开源文本嵌入模型
Dailin Gan1, Jun Li1
1Department of Applied and Computational Mathematics and Statistics, University of Notre Dame, Notre Dame, IN, USA.
Computational and structural biotechnology journal
|August 20, 2025
概括
开源文本嵌入模型为基因表达分析提供了具有成本效益和隐私保护的 OpenAI 替代方案. 这些模型在没有大量微调的情况下与GenePT性能相匹配或超过.
科学领域:
- 生物信息学
- 计算生物学
- 基因组学
背景情况:
- 基因表达分析的基础变压器模型在计算上昂贵.
- GenePT使用OpenAI的文本嵌入功能来编码基因信息.
- 由于其封闭源码的在线性质,OpenAI的服务引发了数据隐私和可访问性的担忧.
研究的目的:
- 调查替换OpenAI的文本嵌入模型以开源替代方案的可行性.
- 评估用于基因表达数据分析的轻量级开源变压器模型的性能.
- 为了确定这些开源模型的最佳性能是否需要微调.
主要方法:
- 识别了十个小型的,基于计算的轻型变压器嵌入式文本模型.
- 在四个不同的基因分类任务中评估模型性能.
- 将开源模型的性能与OpenAI的文本嵌入功能进行比较.
主要成果:
- 一些已识别的开源模型在基因分类任务中与OpenAI的文本嵌入功能匹配或超越.
- 选择的开源模型更小,更容易安装,并且需要更少的计算资源.
- 微调开源模型并不总是带来显著的性能改善.
结论:
- 开源文本嵌入模型提供了像OpenAI这样的专有解决方案的可行和潜在的优越替代方案.
- 这些模型提供了数据隐私,成本效益和可访问性.
- 这项研究表明,经过预先训练的开源模型可以在没有进行大量微调的基因分类任务时有效地使用.
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