Fine-Tuning Cohere's Reranker
Blog post from Weaviate
Weaviate, a vector database, introduced reranking at the second stage in version 1.20. This feature allows users to improve search relevance by adding reranking to the second-stage of their search process. Cohere's rerank endpoint enables users to build search systems that add reranking at the last stage, and fine-tuning boosts the model's performance in unique domains. The blog post demonstrates how to fine-tune Cohere's reranker model using Weaviate's blogs dataset and DSPy's signature and chain-of-thought module. It also explains how to re-index data with a new schema that includes the fine-tuned model ID, and how to query the database with and without reranking.
| Trend | Post Mentions | Total Month Mentions | Posts | Companies | MoM |
|---|---|---|---|---|---|
| LLM | 8 | 2,401 | 292 | 122 | -7% |
| AI Model Fine-tuning | 4 | 474 | 91 | 59 | +12% |
| Vector Search | 4 | 2,087 | 216 | 81 | +23% |
| RAG | 2 | 1,125 | 154 | 56 | -17% |
| Real-time | 2 | 2,379 | 618 | 172 | -8% |
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