Add new SentenceTransformer model

README.md

@@ -78,28 +78,28 @@ model-index:
       type: test
     metrics:
     - type: cosine_accuracy@1
-      value: 0.5761591648590022
+      value: 0.5763286334056399
       name: Cosine Accuracy@1
     - type: cosine_precision@1
-      value: 0.5761591648590022
+      value: 0.5763286334056399
       name: Cosine Precision@1
     - type: cosine_recall@1
-      value: 0.5588122182164516
+      value: 0.5589816867630893
       name: Cosine Recall@1
     - type: cosine_ndcg@10
-      value: 0.7618942742503089
+      value: 0.7619419081029518
       name: Cosine Ndcg@10
     - type: cosine_mrr@1
-      value: 0.5761591648590022
+      value: 0.5763286334056399
       name: Cosine Mrr@1
     - type: cosine_map@100
-      value: 0.7107009769861719
+      value: 0.7107794631883741
       name: Cosine Map@100
     - type: cosine_auc_precision_cache_hit_ratio
-      value: 0.3491200519822629
+      value: 0.3488530268041688
       name: Cosine Auc Precision Cache Hit Ratio
     - type: cosine_auc_similarity_distribution
-      value: 0.1635457705044361
+      value: 0.1634818016054941
       name: Cosine Auc Similarity Distribution
 ---
 
@@ -165,9 +165,9 @@ print(embeddings.shape)
 # Get the similarity scores for the embeddings
 similarities = model.similarity(embeddings, embeddings)
 print(similarities)
-# tensor([[1.0000, 1.0000, 0.3764],
-#         [1.0000, 1.0000, 0.3764],
-#         [0.3764, 0.3764, 1.0000]])
+# tensor([[1.0000, 1.0000, 0.3428],
+#         [1.0000, 1.0000, 0.3428],
+#         [0.3428, 0.3428, 1.0000]])
 ```
 
 <!--
@@ -205,13 +205,13 @@ You can finetune this model on your own dataset.
 
 | Metric                               | Value      |
 |:-------------------------------------|:-----------|
-| cosine_accuracy@1                    | 0.5762     |
-| cosine_precision@1                   | 0.5762     |
-| cosine_recall@1                      | 0.5588     |
+| cosine_accuracy@1                    | 0.5763     |
+| cosine_precision@1                   | 0.5763     |
+| cosine_recall@1                      | 0.559      |
 | **cosine_ndcg@10**                   | **0.7619** |
-| cosine_mrr@1                         | 0.5762     |
-| cosine_map@100                       | 0.7107     |
-| cosine_auc_precision_cache_hit_ratio | 0.3491     |
+| cosine_mrr@1                         | 0.5763     |
+| cosine_map@100                       | 0.7108     |
+| cosine_auc_precision_cache_hit_ratio | 0.3489     |
 | cosine_auc_similarity_distribution   | 0.1635     |
 
 <!--
@@ -286,8 +286,8 @@ You can finetune this model on your own dataset.
 #### Non-Default Hyperparameters
 
 - `eval_strategy`: steps
-- `per_device_train_batch_size`: 512
-- `per_device_eval_batch_size`: 512
+- `per_device_train_batch_size`: 1152
+- `per_device_eval_batch_size`: 1152
 - `gradient_accumulation_steps`: 2
 - `weight_decay`: 0.001
 - `adam_beta2`: 0.98
@@ -310,8 +310,8 @@ You can finetune this model on your own dataset.
 - `do_predict`: False
 - `eval_strategy`: steps
 - `prediction_loss_only`: True
-- `per_device_train_batch_size`: 512
-- `per_device_eval_batch_size`: 512
+- `per_device_train_batch_size`: 1152
+- `per_device_eval_batch_size`: 1152
 - `per_gpu_train_batch_size`: None
 - `per_gpu_eval_batch_size`: None
 - `gradient_accumulation_steps`: 2
@@ -431,7 +431,7 @@ You can finetune this model on your own dataset.
 ### Training Logs
 | Epoch | Step | Validation Loss | test_cosine_ndcg@10 |
 |:-----:|:----:|:---------------:|:-------------------:|
-| 0     | 0    | 0.5769          | 0.7619              |
+| 0     | 0    | 0.6981          | 0.7619              |
 
 
 ### Framework Versions

config.json

@@ -4,7 +4,7 @@
   ],
   "attention_probs_dropout_prob": 0.1,
   "classifier_dropout": null,
-  "dtype": "bfloat16",
+  "dtype": "float32",
   "gradient_checkpointing": false,
   "hidden_act": "gelu",
   "hidden_dropout_prob": 0.1,

model.safetensors

@@ -1,3 +1,3 @@
 version https://git-lfs.github.com/spec/v1
-oid sha256:d9268ede7c1599df9c42af6a9d44d4795f65162440f7e3fd928f28cf599a3ee0
-size 45437864
+oid sha256:4652c7e874d7264659b94a56fddfeb099ad39cc4909b1947b1d06df95e701f72
+size 90864192