Model save

README.md

@@ -18,8 +18,8 @@ should probably proofread and complete it, then remove this comment. -->
 
 This model is a fine-tuned version of [FacebookAI/roberta-base](https://huggingface.co/FacebookAI/roberta-base) on an unknown dataset.
 It achieves the following results on the evaluation set:
-- Loss: 0.7933
-- Accuracy: 0.7696
+- Loss: 0.6189
+- Accuracy: 0.7964
 
 ## Model description
 
@@ -38,52 +38,24 @@ More information needed
 ### Training hyperparameters
 
 The following hyperparameters were used during training:
-- learning_rate: 2e-06
+- learning_rate: 3e-06
 - train_batch_size: 16
 - eval_batch_size: 8
 - seed: 42
 - optimizer: Use OptimizerNames.ADAMW_TORCH with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments
 - lr_scheduler_type: linear
 - lr_scheduler_warmup_ratio: 0.1
-- num_epochs: 32
+- num_epochs: 4
 - mixed_precision_training: Native AMP
 
 ### Training results
 
-| Training Loss | Epoch | Step  | Validation Loss | Accuracy |
-|:-------------:|:-----:|:-----:|:---------------:|:--------:|
-| 2.2522        | 1.0   | 1564  | 1.9637          | 0.3163   |
-| 1.0671        | 2.0   | 3128  | 0.8108          | 0.7662   |
-| 0.7146        | 3.0   | 4692  | 0.6397          | 0.7953   |
-| 0.6354        | 4.0   | 6256  | 0.5867          | 0.8027   |
-| 0.5794        | 5.0   | 7820  | 0.5669          | 0.8107   |
-| 0.5303        | 6.0   | 9384  | 0.5505          | 0.8105   |
-| 0.504         | 7.0   | 10948 | 0.5560          | 0.8081   |
-| 0.4664        | 8.0   | 12512 | 0.5553          | 0.8071   |
-| 0.4559        | 9.0   | 14076 | 0.5613          | 0.8073   |
-| 0.4393        | 10.0  | 15640 | 0.5734          | 0.8044   |
-| 0.4292        | 11.0  | 17204 | 0.5735          | 0.7995   |
-| 0.4065        | 12.0  | 18768 | 0.5987          | 0.8007   |
-| 0.3905        | 13.0  | 20332 | 0.6292          | 0.7951   |
-| 0.3828        | 14.0  | 21896 | 0.6233          | 0.7927   |
-| 0.3658        | 15.0  | 23460 | 0.6490          | 0.7887   |
-| 0.3663        | 16.0  | 25024 | 0.6571          | 0.7913   |
-| 0.3551        | 17.0  | 26588 | 0.6533          | 0.7852   |
-| 0.3439        | 18.0  | 28152 | 0.6814          | 0.7833   |
-| 0.3316        | 19.0  | 29716 | 0.6764          | 0.7817   |
-| 0.3297        | 20.0  | 31280 | 0.6896          | 0.7798   |
-| 0.318         | 21.0  | 32844 | 0.6947          | 0.7775   |
-| 0.323         | 22.0  | 34408 | 0.7231          | 0.7767   |
-| 0.304         | 23.0  | 35972 | 0.7333          | 0.7707   |
-| 0.3145        | 24.0  | 37536 | 0.7491          | 0.7742   |
-| 0.2975        | 25.0  | 39100 | 0.7502          | 0.7708   |
-| 0.2812        | 26.0  | 40664 | 0.7632          | 0.7719   |
-| 0.2825        | 27.0  | 42228 | 0.7734          | 0.7664   |
-| 0.2828        | 28.0  | 43792 | 0.7767          | 0.7697   |
-| 0.2872        | 29.0  | 45356 | 0.7865          | 0.7683   |
-| 0.2759        | 30.0  | 46920 | 0.7849          | 0.7700   |
-| 0.2758        | 31.0  | 48484 | 0.7915          | 0.7692   |
-| 0.2797        | 32.0  | 50048 | 0.7933          | 0.7696   |
+| Training Loss | Epoch | Step | Validation Loss | Accuracy |
+|:-------------:|:-----:|:----:|:---------------:|:--------:|
+| 0.7894        | 1.0   | 1585 | 0.6954          | 0.7818   |
+| 0.6249        | 2.0   | 3170 | 0.6336          | 0.7936   |
+| 0.5562        | 3.0   | 4755 | 0.6232          | 0.7953   |
+| 0.5416        | 4.0   | 6340 | 0.6189          | 0.7964   |
 
 
 ### Framework versions