zeroentropy
/

zerank-1

@@ -18,7 +18,7 @@ tags:
 This model is an open-weights reranker model meant to be integrated into RAG applications to rerank results from preliminary search methods such as embeddings, BM25, and hybrid search.
-This reranker outperforms other popular rerankers such as coherer-rerank-v3.5 and Salesforce/Llama-rank-v1 across a wide variety of task domains, including on finance, legal, code, STEM, medical, and conversational data. See [this post](https://evals_blog_post) for more details.
 This model is trained on an innovative multi-stage pipeline that models query-document relevance scores using adjusted Elo-like ratings. See [this post](https://technical_blog_post) and our Technical Report (Coming soon!) for more details.
@@ -45,14 +45,14 @@ print(scores)
 Comparing NDCG@10 starting from top 100 documents by embedding (using text-3-embedding-small):
-| Task           | Embedding | cohere-rerank-v3.5 | Salesforce/Llama-rank-v1 | zerank-1-small | zerank-1 |
-|----------------|-----------|--------------------|--------------------------|----------------|----------|
-| Code           |    0.678  |       0.724        |           0.694          |      0.730     |   0.754  |
-| Conversational |    0.250  |       0.571        |           0.484          |      0.556     |   0.596  |
-| Finance        |    0.839  |       0.824        |           0.828          |      0.861     |   0.894  |
-| Legal          |    0.703  |       0.804        |           0.767          |      0.817     |   0.821  |
-| Medical        |    0.619  |       0.750        |           0.719          |      0.773     |   0.796  |
-| STEM           |    0.401  |       0.510        |           0.595          |      0.680     |   0.694  |
 Comparing BM25 and Hybrid Search without and with zerank-1:

 This model is an open-weights reranker model meant to be integrated into RAG applications to rerank results from preliminary search methods such as embeddings, BM25, and hybrid search.
+This reranker outperforms other popular rerankers such as cohere-rerank-v3.5 and Salesforce/Llama-rank-v1 across a wide variety of task domains, including on finance, legal, code, STEM, medical, and conversational data. See [this post](https://evals_blog_post) for more details.
 This model is trained on an innovative multi-stage pipeline that models query-document relevance scores using adjusted Elo-like ratings. See [this post](https://technical_blog_post) and our Technical Report (Coming soon!) for more details.
 Comparing NDCG@10 starting from top 100 documents by embedding (using text-3-embedding-small):
+| Task           | Embedding | cohere-rerank-v3.5 | Salesforce/Llama-rank-v1 | zerank-1-small | **zerank-1** |
+|----------------|-----------|--------------------|--------------------------|----------------|--------------|
+| Code           |    0.678  |       0.724        |           0.694          |      0.730     |   **0.754**  |
+| Conversational |    0.250  |       0.571        |           0.484          |      0.556     |   **0.596**  |
+| Finance        |    0.839  |       0.824        |           0.828          |      0.861     |   **0.894**  |
+| Legal          |    0.703  |       0.804        |           0.767          |      0.817     |   **0.821**  |
+| Medical        |    0.619  |       0.750        |           0.719          |      0.773     |   **0.796**  |
+| STEM           |    0.401  |       0.510        |           0.595          |      0.680     |   **0.694**  |
 Comparing BM25 and Hybrid Search without and with zerank-1: