TaniyaHaghighi
/

multiple_answer_QA

@@ -16,8 +16,8 @@ should probably proofread and complete it, then remove this comment. -->
 This model is a fine-tuned version of [bert-base-uncased](https://huggingface.co/bert-base-uncased) on the None dataset.
 It achieves the following results on the evaluation set:
-- Loss: 1.3879
-- Accuracy: 0.22
 ## Model description
@@ -36,22 +36,48 @@ More information needed
 ### Training hyperparameters
 The following hyperparameters were used during training:
-- learning_rate: 5e-05
 - train_batch_size: 16
 - eval_batch_size: 16
 - seed: 42
 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
 - lr_scheduler_type: linear
-- num_epochs: 4
 ### Training results
 | Training Loss | Epoch | Step | Validation Loss | Accuracy |
 |:-------------:|:-----:|:----:|:---------------:|:--------:|
-| No log        | 1.0   | 125  | 1.3866          | 0.265    |
-| No log        | 2.0   | 250  | 1.3867          | 0.235    |
-| No log        | 3.0   | 375  | 1.3871          | 0.235    |
-| 1.3893        | 4.0   | 500  | 1.3879          | 0.22     |
 ### Framework versions

 This model is a fine-tuned version of [bert-base-uncased](https://huggingface.co/bert-base-uncased) on the None dataset.
 It achieves the following results on the evaluation set:
+- Loss: 5.7334
+- Accuracy: 0.2675
 ## Model description
 ### Training hyperparameters
 The following hyperparameters were used during training:
+- learning_rate: 2e-05
 - train_batch_size: 16
 - eval_batch_size: 16
 - seed: 42
 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
 - lr_scheduler_type: linear
+- num_epochs: 30
 ### Training results
 | Training Loss | Epoch | Step | Validation Loss | Accuracy |
 |:-------------:|:-----:|:----:|:---------------:|:--------:|
+| No log        | 1.0   | 175  | 1.3886          | 0.23     |
+| No log        | 2.0   | 350  | 1.3892          | 0.255    |
+| 1.3936        | 3.0   | 525  | 1.3863          | 0.2375   |
+| 1.3936        | 4.0   | 700  | 1.3868          | 0.255    |
+| 1.3936        | 5.0   | 875  | 1.4511          | 0.26     |
+| 1.2973        | 6.0   | 1050 | 1.6738          | 0.24     |
+| 1.2973        | 7.0   | 1225 | 1.9684          | 0.265    |
+| 1.2973        | 8.0   | 1400 | 2.5002          | 0.28     |
+| 0.6916        | 9.0   | 1575 | 2.9872          | 0.28     |
+| 0.6916        | 10.0  | 1750 | 3.2354          | 0.28     |
+| 0.6916        | 11.0  | 1925 | 3.6618          | 0.3025   |
+| 0.2428        | 12.0  | 2100 | 4.1750          | 0.275    |
+| 0.2428        | 13.0  | 2275 | 4.1384          | 0.275    |
+| 0.2428        | 14.0  | 2450 | 4.5173          | 0.26     |
+| 0.1118        | 15.0  | 2625 | 4.6013          | 0.275    |
+| 0.1118        | 16.0  | 2800 | 4.2549          | 0.2525   |
+| 0.1118        | 17.0  | 2975 | 5.2751          | 0.275    |
+| 0.0482        | 18.0  | 3150 | 4.9489          | 0.275    |
+| 0.0482        | 19.0  | 3325 | 5.6077          | 0.28     |
+| 0.0261        | 20.0  | 3500 | 5.3054          | 0.2625   |
+| 0.0261        | 21.0  | 3675 | 5.1955          | 0.2625   |
+| 0.0261        | 22.0  | 3850 | 5.6210          | 0.2575   |
+| 0.0175        | 23.0  | 4025 | 5.6576          | 0.25     |
+| 0.0175        | 24.0  | 4200 | 5.6687          | 0.2725   |
+| 0.0175        | 25.0  | 4375 | 5.6082          | 0.255    |
+| 0.0133        | 26.0  | 4550 | 5.5999          | 0.27     |
+| 0.0133        | 27.0  | 4725 | 5.7972          | 0.26     |
+| 0.0133        | 28.0  | 4900 | 5.6427          | 0.2575   |
+| 0.0089        | 29.0  | 5075 | 5.7253          | 0.2675   |
+| 0.0089        | 30.0  | 5250 | 5.7334          | 0.2675   |
 ### Framework versions