readme: ---
  license: apache-2.0
  tags:
  - generated_from_trainer
  metrics:
  - accuracy
  model-index:
  - name: DEREXP
    results: []
  ---
  
  <!-- This model card has been generated automatically according to the information the Trainer had access to. You
  should probably proofread and complete it, then remove this comment. -->
  
  # DEREXP
  
  This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co/distilbert-base-uncased) on the None dataset.
  It achieves the following results on the evaluation set:
  - Loss: 3.1590
  - Mse: 3.1590
  - Mae: 1.3397
  - R2: 0.4465
  - Accuracy: 0.2528
  
  ## Model description
  
  More information needed
  
  ## Intended uses & limitations
  
  More information needed
  
  ## Training and evaluation data
  
  More information needed
  
  ## Training procedure
  
  ### 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: 1
  
  ### Training results
  
  | Training Loss | Epoch | Step  | Validation Loss | Mse    | Mae    | R2     | Accuracy |
  |:-------------:|:-----:|:-----:|:---------------:|:------:|:------:|:------:|:--------:|
  | 14.7557       | 0.01  | 500   | 4.3307          | 4.3307 | 1.6240 | 0.2411 | 0.1976   |
  | 4.5754        | 0.02  | 1000  | 4.1273          | 4.1273 | 1.5719 | 0.2768 | 0.2084   |
  | 4.2925        | 0.02  | 1500  | 4.3074          | 4.3074 | 1.6155 | 0.2452 | 0.2012   |
  | 3.9816        | 0.03  | 2000  | 3.7767          | 3.7767 | 1.5008 | 0.3382 | 0.2134   |
  | 3.9171        | 0.04  | 2500  | 3.7033          | 3.7033 | 1.4732 | 0.3511 | 0.2304   |
  | 3.946         | 0.05  | 3000  | 3.6217          | 3.6217 | 1.4552 | 0.3654 | 0.2352   |
  | 4.1           | 0.06  | 3500  | 3.6101          | 3.6101 | 1.4612 | 0.3674 | 0.2216   |
  | 3.8535        | 0.06  | 4000  | 3.6160          | 3.6160 | 1.4576 | 0.3664 | 0.2294   |
  | 3.9037        | 0.07  | 4500  | 3.5864          | 3.5864 | 1.4476 | 0.3716 | 0.2374   |
  | 3.9358        | 0.08  | 5000  | 3.5087          | 3.5087 | 1.4237 | 0.3852 | 0.2414   |
  | 3.8062        | 0.09  | 5500  | 3.6085          | 3.6085 | 1.4595 | 0.3677 | 0.2256   |
  | 3.8802        | 0.1   | 6000  | 3.6371          | 3.6371 | 1.4615 | 0.3627 | 0.223    |
  | 3.7239        | 0.1   | 6500  | 3.5191          | 3.5191 | 1.4278 | 0.3834 | 0.2324   |
  | 3.7618        | 0.11  | 7000  | 3.8408          | 3.8408 | 1.4973 | 0.3270 | 0.2316   |
  | 3.7217        | 0.12  | 7500  | 3.8241          | 3.8241 | 1.5046 | 0.3299 | 0.2236   |
  | 3.8204        | 0.13  | 8000  | 3.5290          | 3.5290 | 1.4256 | 0.3816 | 0.2388   |
  | 3.7211        | 0.14  | 8500  | 3.6903          | 3.6903 | 1.4674 | 0.3534 | 0.227    |
  | 3.7243        | 0.14  | 9000  | 3.4718          | 3.4718 | 1.4201 | 0.3917 | 0.231    |
  | 3.7713        | 0.15  | 9500  | 3.8970          | 3.8970 | 1.5304 | 0.3171 | 0.2092   |
  | 3.6289        | 0.16  | 10000 | 3.5273          | 3.5273 | 1.4255 | 0.3819 | 0.2388   |
  | 3.7516        | 0.17  | 10500 | 3.9020          | 3.9020 | 1.5230 | 0.3163 | 0.2138   |
  | 3.7491        | 0.18  | 11000 | 3.4809          | 3.4809 | 1.4209 | 0.3901 | 0.2378   |
  | 3.7809        | 0.18  | 11500 | 3.8779          | 3.8779 | 1.5087 | 0.3205 | 0.229    |
  | 3.7163        | 0.19  | 12000 | 3.5177          | 3.5177 | 1.4330 | 0.3836 | 0.2298   |
  | 3.732         | 0.2   | 12500 | 3.9986          | 3.9986 | 1.5401 | 0.2993 | 0.218    |
  | 3.7381        | 0.21  | 13000 | 3.4782          | 3.4782 | 1.4277 | 0.3905 | 0.2302   |
  | 3.7652        | 0.22  | 13500 | 3.6239          | 3.6239 | 1.4587 | 0.3650 | 0.2244   |
  | 3.6003        | 0.22  | 14000 | 3.4873          | 3.4873 | 1.4288 | 0.3889 | 0.2316   |
  | 3.6865        | 0.23  | 14500 | 3.5895          | 3.5895 | 1.4511 | 0.3710 | 0.23     |
  | 3.7398        | 0.24  | 15000 | 3.8835          | 3.8835 | 1.5183 | 0.3195 | 0.2172   |
  | 3.5939        | 0.25  | 15500 | 3.6334          | 3.6334 | 1.4643 | 0.3633 | 0.2256   |
  | 3.691         | 0.26  | 16000 | 3.4251          | 3.4251 | 1.3994 | 0.3998 | 0.2488   |
  | 3.7279        | 0.26  | 16500 | 3.3956          | 3.3956 | 1.4034 | 0.4050 | 0.2336   |
  | 3.797         | 0.27  | 17000 | 3.4029          | 3.4029 | 1.3968 | 0.4037 | 0.2486   |
  | 3.684         | 0.28  | 17500 | 3.5831          | 3.5831 | 1.4451 | 0.3721 | 0.2304   |
  | 3.5894        | 0.29  | 18000 | 3.6120          | 3.6120 | 1.4492 | 0.3671 | 0.2338   |
  | 3.5938        | 0.3   | 18500 | 3.4975          | 3.4975 | 1.4240 | 0.3871 | 0.231    |
  | 3.4948        | 0.3   | 19000 | 3.4791          | 3.4791 | 1.4167 | 0.3904 | 0.24     |
  | 3.6527        | 0.31  | 19500 | 3.3409          | 3.3409 | 1.3817 | 0.4146 | 0.2474   |
  | 3.5545        | 0.32  | 20000 | 3.3412          | 3.3412 | 1.3860 | 0.4145 | 0.2466   |
  | 3.6102        | 0.33  | 20500 | 3.4148          | 3.4148 | 1.3961 | 0.4016 | 0.2488   |
  | 3.542         | 0.34  | 21000 | 3.5980          | 3.5980 | 1.4508