update model card README.md

README.md

@@ -1,6 +1,4 @@
 ---
-language:
-- en
 tags:
 - generated_from_trainer
 datasets:
@@ -15,7 +13,7 @@ model-index:
       name: Text Classification
       type: text-classification
     dataset:
-      name: GLUE MRPC
+      name: glue
       type: glue
       config: mrpc
       split: validation
@@ -23,10 +21,10 @@ model-index:
     metrics:
     - name: Accuracy
       type: accuracy
-      value: 0.6862745098039216
+      value: 0.6838235294117647
     - name: F1
       type: f1
-      value: 0.7999999999999999
+      value: 0.7809847198641766
 ---
 
 <!-- This model card has been generated automatically according to the information the Trainer had access to. You
@@ -34,12 +32,12 @@ should probably proofread and complete it, then remove this comment. -->
 
 # hBERTv1_mrpc
 
-This model is a fine-tuned version of [gokuls/bert_12_layer_model_v1](https://huggingface.co/gokuls/bert_12_layer_model_v1) on the GLUE MRPC dataset.
+This model is a fine-tuned version of [gokuls/bert_12_layer_model_v1](https://huggingface.co/gokuls/bert_12_layer_model_v1) on the glue dataset.
 It achieves the following results on the evaluation set:
-- Loss: 0.6051
-- Accuracy: 0.6863
-- F1: 0.8000
-- Combined Score: 0.7431
+- Loss: 0.9708
+- Accuracy: 0.6838
+- F1: 0.7810
+- Combined Score: 0.7324
 
 ## Model description
 
@@ -70,20 +68,20 @@ The following hyperparameters were used during training:
 
 ### Training results
 
-| Training Loss | Epoch | Step | Accuracy | Combined Score | F1     | Validation Loss |
-|:-------------:|:-----:|:----:|:--------:|:--------------:|:------:|:---------------:|
-| 0.6536        | 1.0   | 15   | 0.6838   | 0.7480         | 0.8122 | 0.6243          |
-| 0.6275        | 2.0   | 30   | 0.7010   | 0.7564         | 0.8117 | 0.6174          |
-| 0.6129        | 3.0   | 45   | 0.6961   | 0.7571         | 0.8182 | 0.6089          |
-| 0.6087        | 4.0   | 60   | 0.6887   | 0.7508         | 0.8130 | 0.6062          |
-| 0.5939        | 5.0   | 75   | 0.6863   | 0.7399         | 0.7935 | 0.6104          |
-| 0.5707        | 6.0   | 90   | 0.7083   | 0.7633         | 0.8183 | 0.6184          |
-| 0.5426        | 7.0   | 105  | 0.6863   | 0.7431         | 0.8000 | 0.6051          |
-| 0.4819        | 8.0   | 120  | 0.6560   | 0.6936         | 0.8019 | 0.7478          |
-| 0.4279        | 9.0   | 135  | 0.6673   | 0.6887         | 0.7678 | 0.7283          |
-| 0.3374        | 10.0  | 150  | 0.8092   | 0.6863         | 0.7902 | 0.7382          |
-| 0.2789        | 11.0  | 165  | 0.9342   | 0.6887         | 0.7935 | 0.7411          |
-| 0.2216        | 12.0  | 180  | 0.9708   | 0.6838         | 0.7810 | 0.7324          |
+| Training Loss | Epoch | Step | Validation Loss | Accuracy | F1     | Combined Score |
+|:-------------:|:-----:|:----:|:---------------:|:--------:|:------:|:--------------:|
+| 0.6536        | 1.0   | 15   | 0.6243          | 0.6838   | 0.8122 | 0.7480         |
+| 0.6275        | 2.0   | 30   | 0.6174          | 0.7010   | 0.8117 | 0.7564         |
+| 0.6129        | 3.0   | 45   | 0.6089          | 0.6961   | 0.8182 | 0.7571         |
+| 0.6087        | 4.0   | 60   | 0.6062          | 0.6887   | 0.8130 | 0.7508         |
+| 0.5939        | 5.0   | 75   | 0.6104          | 0.6863   | 0.7935 | 0.7399         |
+| 0.5707        | 6.0   | 90   | 0.6184          | 0.7083   | 0.8183 | 0.7633         |
+| 0.5426        | 7.0   | 105  | 0.6051          | 0.6863   | 0.8000 | 0.7431         |
+| 0.4819        | 8.0   | 120  | 0.6560          | 0.6936   | 0.8019 | 0.7478         |
+| 0.4279        | 9.0   | 135  | 0.6673          | 0.6887   | 0.7678 | 0.7283         |
+| 0.3374        | 10.0  | 150  | 0.8092          | 0.6863   | 0.7902 | 0.7382         |
+| 0.2789        | 11.0  | 165  | 0.9342          | 0.6887   | 0.7935 | 0.7411         |
+| 0.2216        | 12.0  | 180  | 0.9708          | 0.6838   | 0.7810 | 0.7324         |
 
 
 ### Framework versions