update model card README.md

README.md

@@ -22,16 +22,16 @@ model-index:
     metrics:
     - name: Accuracy
       type: accuracy
-      value: 0.527
+      value: 0.5422
     - name: F1
       type: f1
-      value: 0.5262492788715516
+      value: 0.543454465221178
     - name: Precision
       type: precision
-      value: 0.5266767693980432
+      value: 0.5452336215624385
     - name: Recall
       type: recall
-      value: 0.527
+      value: 0.5422
 ---
 
 <!-- This model card has been generated automatically according to the information the Trainer had access to. You
@@ -41,11 +41,11 @@ should probably proofread and complete it, then remove this comment. -->
 
 This model is a fine-tuned version of [microsoft/Multilingual-MiniLM-L12-H384](https://huggingface.co/microsoft/Multilingual-MiniLM-L12-H384) on the amazon_reviews_multi dataset.
 It achieves the following results on the evaluation set:
-- Loss: 1.1588
-- Accuracy: 0.527
-- F1: 0.5262
-- Precision: 0.5267
-- Recall: 0.527
+- Loss: 1.2436
+- Accuracy: 0.5422
+- F1: 0.5435
+- Precision: 0.5452
+- Recall: 0.5422
 
 ## Model description
 
@@ -75,20 +75,13 @@ The following hyperparameters were used during training:
 
 ### Training results
 
-| Training Loss | Epoch | Step | Validation Loss | Accuracy | F1     | Precision | Recall |
-|:-------------:|:-----:|:----:|:---------------:|:--------:|:------:|:---------:|:------:|
-| 1.3182        | 0.4   | 500  | 1.1930          | 0.4684   | 0.4325 | 0.4439    | 0.4684 |
-| 1.1715        | 0.8   | 1000 | 1.1570          | 0.4782   | 0.4639 | 0.4629    | 0.4782 |
-| 1.0959        | 1.2   | 1500 | 1.1253          | 0.4976   | 0.4962 | 0.5008    | 0.4976 |
-| 1.0682        | 1.6   | 2000 | 1.0928          | 0.5128   | 0.5080 | 0.5094    | 0.5128 |
-| 1.0272        | 2.0   | 2500 | 1.0936          | 0.5144   | 0.5120 | 0.5138    | 0.5144 |
-| 0.956         | 2.4   | 3000 | 1.1047          | 0.5228   | 0.5179 | 0.5159    | 0.5228 |
-| 0.9539        | 2.8   | 3500 | 1.0970          | 0.5236   | 0.5211 | 0.5194    | 0.5236 |
-| 0.9064        | 3.2   | 4000 | 1.1232          | 0.5278   | 0.5238 | 0.5259    | 0.5278 |
-| 0.8595        | 3.6   | 4500 | 1.1256          | 0.5296   | 0.5286 | 0.5313    | 0.5296 |
-| 0.8731        | 4.0   | 5000 | 1.1400          | 0.5296   | 0.5238 | 0.5228    | 0.5296 |
-| 0.7876        | 4.4   | 5500 | 1.1518          | 0.5244   | 0.5271 | 0.5314    | 0.5244 |
-| 0.7959        | 4.8   | 6000 | 1.1588          | 0.527    | 0.5262 | 0.5267    | 0.527  |
+| Training Loss | Epoch | Step  | Validation Loss | Accuracy | F1     | Precision | Recall |
+|:-------------:|:-----:|:-----:|:---------------:|:--------:|:------:|:---------:|:------:|
+| 1.0049        | 1.0   | 2500  | 1.0616          | 0.5352   | 0.5268 | 0.5347    | 0.5352 |
+| 0.9172        | 2.0   | 5000  | 1.0763          | 0.5432   | 0.5412 | 0.5444    | 0.5432 |
+| 0.8285        | 3.0   | 7500  | 1.1077          | 0.5408   | 0.5428 | 0.5494    | 0.5408 |
+| 0.7361        | 4.0   | 10000 | 1.1743          | 0.5342   | 0.5399 | 0.5531    | 0.5342 |
+| 0.6538        | 5.0   | 12500 | 1.2436          | 0.5422   | 0.5435 | 0.5452    | 0.5422 |
 
 
 ### Framework versions