Update README.md

README.md

@@ -64,13 +64,15 @@ Results on the [CoIR benchmark](https://github.com/CoIR-team/coir) on [MTEB](htt
 | Model | Params | AVG | AppsRetrieval | COIRCodeSearchNet | CodeFeedbackMT | CodeFeedbackST | CodeSearchNetCC | CodeTransContest | CodeTransDL | CosQA | StackOverflow | Text2SQL |
 |---|---|---|---|---|---|---|---|---|---|---|---|---|
 | CodeRankEmbed | 137M | 59.14 | 23.46 | 94.70 | 42.61 | 78.11 | 76.39 | 66.43 | 34.84 | 35.92 | 80.53 | 58.37 |
+| **potion-code-16M-v2 + BM25 (hybrid)** | **16M** | **43.36** | **6.08** | **47.71** | **45.38** | **61.10** | **51.68** | **53.80** | **33.42** | **21.39** | **66.73** | **46.29** |
 | BM25 | — | 42.31 | 4.76 | 40.86 | 59.19 | 68.15 | 53.97 | 47.78 | 34.42 | 18.75 | 70.26 | 24.94 |
-| **potion-code-16M-v2** | **16M** | **40.89** | **5.20** | **46.32** | **37.97** | **53.43** | **43.70** | **43.63** | **32.64** | **27.80** | **59.63** | **58.62** |
-| potion-code-16M | 16M | 37.31 | 3.96 | 41.93 | 36.26 | 50.17 | 43.70 | 39.76 | 31.72 | 23.80 | 57.47 | 44.29 |
+| **potion-code-16M-v2** | **16M** | **39.08** | **5.19** | **46.37** | **38.02** | **53.22** | **43.66** | **43.66** | **32.64** | **24.36** | **59.57** | **44.07** |
+| potion-code-16M | 16M | 37.05 | 3.97 | 42.99 | 36.26 | 50.27 | 43.40 | 39.76 | 31.72 | 21.37 | 57.47 | 43.34 |
 | potion-retrieval-32M | 32M | 32.10 | 4.22 | 31.80 | 36.71 | 45.11 | 38.64 | 29.97 | 32.62 | 8.70 | 56.26 | 36.93 |
 | potion-base-32M | 32M | 31.42 | 3.37 | 29.58 | 34.77 | 42.69 | 37.88 | 28.51 | 30.55 | 14.61 | 53.36 | 38.88 |
 
-CoIR covers a broad range of code retrieval scenarios. For the use case of finding code given a natural language query, **CosQA** and **CodeFeedback (ST/MT)** are the most relevant tasks. Others are less so: **COIRCodeSearchNetRetrieval** retrieves text given a code query (the reverse direction), and the **CodeTransOcean** tasks target cross-language code translation. The hybrid row combines dense retrieval with BM25 using min-max score normalization and equal weighting (alpha=0.5).
+CoIR covers a broad range of code retrieval scenarios. For the use case of finding code given a natural language query, **CosQA** and **CodeFeedback (ST/MT)** are the most relevant tasks. Others are less so: **COIRCodeSearchNetRetrieval** retrieves text given a code query (the reverse direction), and the **CodeTransOcean** tasks target cross-language code translation.
+The hybrid row combines dense retrieval with BM25 using Reciprocal Rank Fusion (k=60).
 
 ## Model Details