update README

README.md

@@ -18,10 +18,10 @@ Users need to make their own assessment regarding any obligations or responsibil
 ### Performance on CoIR Benchmark
 | Model                 | Model Size | CoIR AVG (NDCG@10) |
 |-----------------------|------------|---------------------|
-| SFR-Embedding-Code    | 2B         | 66.4               |
+| **SFR-Embedding-Code**    | 2B         | 67.4               |
 | CodeSage-Large-v2     | 1.3B       | 64.2               |
 | CodeSage-Large        | 1.3B       | 61.0               |
-| SFR-Embedding-Code    | 400M       | 61.9               |
+| **SFR-Embedding-Code**    | 400M       | 61.9               |
 | CodeRankEmbed         | 137M       | 60.1               |
 | CodeSage-Base         | 356M       | 57.5               |
 | Voyage-Code-002       | -          | 56.3               |
@@ -45,9 +45,9 @@ import torch.nn.functional as F
 from transformers import AutoModel, AutoTokenizer
 
 input_texts = [
+    "how to implement quick sort in Python?",
     "def quick_sort(arr):\n    if len(arr) <= 1:\n        return arr\n    pivot = arr[len(arr) // 2]\n    left = [x for x in arr if x < pivot]\n    middle = [x for x in arr if x == pivot]\n    right = [x for x in arr if x > pivot]\n    return quick_sort(left) + middle + quick_sort(right)",
     "def bubble_sort(arr):\n    n = len(arr)\n    for i in range(n):\n        for j in range(0, n-i-1):\n            if arr[j] > arr[j+1]:\n                arr[j], arr[j+1] = arr[j+1], arr[j]\n    return arr",
-    "how to implement quick sort in Python?"
 ]
 
 model_path = 'Salesforce/SFR-Embedding-Code-400M_R'