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 QQP
+      name: glue
       type: glue
       config: qqp
       split: validation
@@ -23,10 +21,10 @@ model-index:
     metrics:
     - name: Accuracy
       type: accuracy
-      value: 0.6318327974276527
+      value: 0.8193173386099432
     - name: F1
       type: f1
-      value: 0.0
+      value: 0.7510988449350915
 ---
 
 <!-- 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_no_pretrain_qqp
 
-This model is a fine-tuned version of [](https://huggingface.co/) on the GLUE QQP dataset.
+This model is a fine-tuned version of [](https://huggingface.co/) on the glue dataset.
 It achieves the following results on the evaluation set:
-- Loss: 0.6598
-- Accuracy: 0.6318
-- F1: 0.0
-- Combined Score: 0.3159
+- Loss: 0.5245
+- Accuracy: 0.8193
+- F1: 0.7511
+- Combined Score: 0.7852
 
 ## Model description
 
@@ -58,31 +56,32 @@ More information needed
 ### Training hyperparameters
 
 The following hyperparameters were used during training:
-- learning_rate: 0.0005
-- train_batch_size: 128
-- eval_batch_size: 128
+- learning_rate: 4e-05
+- train_batch_size: 96
+- eval_batch_size: 96
 - seed: 10
 - distributed_type: multi-GPU
 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
 - lr_scheduler_type: linear
 - num_epochs: 50
-- mixed_precision_training: Native AMP
 
 ### Training results
 
-| Training Loss | Epoch | Step  | Validation Loss | Accuracy | F1  | Combined Score |
-|:-------------:|:-----:|:-----:|:---------------:|:--------:|:---:|:--------------:|
-| 0.6693        | 1.0   | 2843  | 0.6598          | 0.6318   | 0.0 | 0.3159         |
-| 0.6591        | 2.0   | 5686  | nan             | 0.6318   | 0.0 | 0.3159         |
-| 0.6588        | 3.0   | 8529  | nan             | 0.6318   | 0.0 | 0.3159         |
-| 0.6582        | 4.0   | 11372 | nan             | 0.6318   | 0.0 | 0.3159         |
-| 0.0           | 5.0   | 14215 | nan             | 0.6318   | 0.0 | 0.3159         |
-| 0.0           | 6.0   | 17058 | nan             | 0.6318   | 0.0 | 0.3159         |
+| Training Loss | Epoch | Step  | Validation Loss | Accuracy | F1     | Combined Score |
+|:-------------:|:-----:|:-----:|:---------------:|:--------:|:------:|:--------------:|
+| 0.5334        | 1.0   | 3791  | 0.4826          | 0.7676   | 0.6650 | 0.7163         |
+| 0.4491        | 2.0   | 7582  | 0.4493          | 0.7909   | 0.6926 | 0.7417         |
+| 0.3866        | 3.0   | 11373 | 0.4402          | 0.7954   | 0.7269 | 0.7612         |
+| 0.3657        | 4.0   | 15164 | 0.4990          | 0.7775   | 0.7211 | 0.7493         |
+| 0.3708        | 5.0   | 18955 | 0.4744          | 0.8077   | 0.7273 | 0.7675         |
+| 0.2948        | 6.0   | 22746 | 0.4693          | 0.8143   | 0.7379 | 0.7761         |
+| 0.2546        | 7.0   | 26537 | 0.4507          | 0.8120   | 0.7578 | 0.7849         |
+| 0.2225        | 8.0   | 30328 | 0.5245          | 0.8193   | 0.7511 | 0.7852         |
 
 
 ### Framework versions
 
-- Transformers 4.29.2
+- Transformers 4.30.2
 - Pytorch 1.14.0a0+410ce96
 - Datasets 2.12.0
 - Tokenizers 0.13.3