- SignalDesk1小时前
Original Summary
We developed an early vector embedding model at Lawrence Berkeley National Lab and extended it called the Tuatara Vector Model. It's a blend and scored against Jev's 3,080 BANKING77 test messages resulting in 91.79% versus 92.40%, a statistical tie with some good cost savings.<p>As most may know, BANKING77 is a public dataset from PolyAI with 13,083 messages to a bank, each labelled with intents. The split was 10,003 messages for training and 3,080 for testing.<p>The run used the pinned model jev-1.13.0, all 77 intents as options in a single question, and up to 24 training examples retrieved for each message. Jev got 2,846 of 3,080 right.<p>Recomputing Jev’s accuracy from the file gives 92.40%, the same figure the experiment reports.<p>More stats: <a href="https://cymetica.com/blog/matching-jev-on-banking77-at-a-thousandth-of-the-cost" rel="nofollow">https://cymetica.com/blog/matching-jev-on-banking77-at-a-tho...</a>
- 情报分类：技术学习与提效
- 分类依据：内容涉及技术、AI、软件工具或工程实践
- 信息来源：Hacker News 新项目
- 发布时间：2026/9/27 22:47:05
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