update model card README.md

README.md

@@ -18,20 +18,20 @@ model-index:
     dataset:
       name: amazon_reviews_multi
       type: amazon_reviews_multi
-      args: es
+      args: en
     metrics:
     - name: Accuracy
       type: accuracy
-      value: 0.5332
+      value: 0.5504
     - name: F1
       type: f1
-      value: 0.5334040143743698
+      value: 0.5457527808330634
     - name: Precision
       type: precision
-      value: 0.5338753575602949
+      value: 0.5428695841337288
     - name: Recall
       type: recall
-      value: 0.5332
+      value: 0.5504
 ---
 
 <!-- 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 [google/electra-small-discriminator](https://huggingface.co/google/electra-small-discriminator) on the amazon_reviews_multi dataset.
 It achieves the following results on the evaluation set:
-- Loss: 1.1142
-- Accuracy: 0.5332
-- F1: 0.5334
-- Precision: 0.5339
-- Recall: 0.5332
+- Loss: 1.0560
+- Accuracy: 0.5504
+- F1: 0.5458
+- Precision: 0.5429
+- Recall: 0.5504
 
 ## Model description
 
@@ -70,18 +70,16 @@ The following hyperparameters were used during training:
 - seed: 42
 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
 - lr_scheduler_type: linear
-- num_epochs: 5
+- num_epochs: 3
 - mixed_precision_training: Native AMP
 
 ### Training results
 
-| Training Loss | Epoch | Step  | Validation Loss | Accuracy | F1     | Precision | Recall |
-|:-------------:|:-----:|:-----:|:---------------:|:--------:|:------:|:---------:|:------:|
-| 1.1234        | 1.0   | 2500  | 1.1013          | 0.513    | 0.5133 | 0.5161    | 0.513  |
-| 1.0437        | 2.0   | 5000  | 1.0858          | 0.5228   | 0.5145 | 0.5132    | 0.5228 |
-| 0.9842        | 3.0   | 7500  | 1.0740          | 0.525    | 0.5280 | 0.5336    | 0.525  |
-| 0.9279        | 4.0   | 10000 | 1.0973          | 0.5308   | 0.5329 | 0.5371    | 0.5308 |
-| 0.8812        | 5.0   | 12500 | 1.1142          | 0.5332   | 0.5334 | 0.5339    | 0.5332 |
+| Training Loss | Epoch | Step | Validation Loss | Accuracy | F1     | Precision | Recall |
+|:-------------:|:-----:|:----:|:---------------:|:--------:|:------:|:---------:|:------:|
+| 1.2172        | 1.0   | 1000 | 1.1014          | 0.5216   | 0.4902 | 0.4954    | 0.5216 |
+| 1.0027        | 2.0   | 2000 | 1.0388          | 0.549    | 0.5471 | 0.5494    | 0.549  |
+| 0.9035        | 3.0   | 3000 | 1.0560          | 0.5504   | 0.5458 | 0.5429    | 0.5504 |
 
 
 ### Framework versions