Add new SentenceTransformer model.

README.md

@@ -372,19 +372,19 @@ model-index:
       type: bge-base-en-train
     metrics:
     - type: cosine_accuracy
-      value: 0.8125
+      value: 0.8269230769230769
       name: Cosine Accuracy
     - type: dot_accuracy
-      value: 0.1875
+      value: 0.17307692307692307
       name: Dot Accuracy
     - type: manhattan_accuracy
-      value: 0.8173076923076923
+      value: 0.8269230769230769
       name: Manhattan Accuracy
     - type: euclidean_accuracy
-      value: 0.8125
+      value: 0.8269230769230769
       name: Euclidean Accuracy
     - type: max_accuracy
-      value: 0.8173076923076923
+      value: 0.8269230769230769
       name: Max Accuracy
   - task:
       type: triplet
@@ -394,19 +394,19 @@ model-index:
       type: bge-base-en-eval
     metrics:
     - type: cosine_accuracy
-      value: 0.9393939393939394
+      value: 0.9696969696969697
       name: Cosine Accuracy
     - type: dot_accuracy
-      value: 0.06060606060606061
+      value: 0.030303030303030304
       name: Dot Accuracy
     - type: manhattan_accuracy
-      value: 0.9545454545454546
+      value: 0.9696969696969697
       name: Manhattan Accuracy
     - type: euclidean_accuracy
-      value: 0.9393939393939394
+      value: 0.9696969696969697
       name: Euclidean Accuracy
     - type: max_accuracy
-      value: 0.9545454545454546
+      value: 0.9696969696969697
       name: Max Accuracy
 ---
 
@@ -508,11 +508,11 @@ You can finetune this model on your own dataset.
 
 | Metric             | Value      |
 |:-------------------|:-----------|
-| cosine_accuracy    | 0.8125     |
-| dot_accuracy       | 0.1875     |
-| manhattan_accuracy | 0.8173     |
-| euclidean_accuracy | 0.8125     |
-| **max_accuracy**   | **0.8173** |
+| cosine_accuracy    | 0.8269     |
+| dot_accuracy       | 0.1731     |
+| manhattan_accuracy | 0.8269     |
+| euclidean_accuracy | 0.8269     |
+| **max_accuracy**   | **0.8269** |
 
 #### Triplet
 * Dataset: `bge-base-en-eval`
@@ -520,11 +520,11 @@ You can finetune this model on your own dataset.
 
 | Metric             | Value      |
 |:-------------------|:-----------|
-| cosine_accuracy    | 0.9394     |
-| dot_accuracy       | 0.0606     |
-| manhattan_accuracy | 0.9545     |
-| euclidean_accuracy | 0.9394     |
-| **max_accuracy**   | **0.9545** |
+| cosine_accuracy    | 0.9697     |
+| dot_accuracy       | 0.0303     |
+| manhattan_accuracy | 0.9697     |
+| euclidean_accuracy | 0.9697     |
+| **max_accuracy**   | **0.9697** |
 
 <!--
 ## Bias, Risks and Limitations
@@ -712,17 +712,17 @@ You can finetune this model on your own dataset.
 ### Training Logs
 | Epoch | Step | bge-base-en-eval_max_accuracy | bge-base-en-train_max_accuracy |
 |:-----:|:----:|:-----------------------------:|:------------------------------:|
-| 0     | 0    | -                             | 0.8173                         |
-| 5.0   | 65   | 0.9545                        | -                              |
+| 0     | 0    | -                             | 0.8269                         |
+| 5.0   | 65   | 0.9697                        | -                              |
 
 
 ### Framework Versions
-- Python: 3.11.11
+- Python: 3.9.6
 - Sentence Transformers: 3.1.1
 - Transformers: 4.45.2
-- PyTorch: 2.5.1+cu124
+- PyTorch: 2.6.0
 - Accelerate: 1.3.0
-- Datasets: 3.3.2
+- Datasets: 3.5.0
 - Tokenizers: 0.20.3
 
 ## Citation

config_sentence_transformers.json

@@ -2,7 +2,7 @@
   "__version__": {
     "sentence_transformers": "3.1.1",
     "transformers": "4.45.2",
-    "pytorch": "2.5.1+cu124"
+    "pytorch": "2.6.0"
   },
   "prompts": {},
   "default_prompt_name": null,

model.safetensors

@@ -1,3 +1,3 @@
 version https://git-lfs.github.com/spec/v1
-oid sha256:a87f57598ae86b5e141557e4f00e5ef707c841421bd96a04a440cdb5e6b45cd0
+oid sha256:97e6bfe80a4590dcf864959ba8a6dc1d8b6528a13cfb645a01060f078a852265
 size 437951328