readme: ---
  license: mit
  tags:
  - generated_from_trainer
  metrics:
  - precision
  - recall
  - f1
  - accuracy
  model-index:
  - name: og-deberta-extra-o
    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. -->
  
  # og-deberta-extra-o
  
  This model is a fine-tuned version of [microsoft/deberta-base](https://huggingface.co/microsoft/deberta-base) on an unknown dataset.
  It achieves the following results on the evaluation set:
  - Loss: 0.5184
  - Precision: 0.5981
  - Recall: 0.6667
  - F1: 0.6305
  - Accuracy: 0.9226
  
  ## 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: 8
  - eval_batch_size: 8
  - seed: 42
  - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
  - lr_scheduler_type: linear
  - num_epochs: 25
  
  ### Training results
  
  | Training Loss | Epoch | Step | Validation Loss | Precision | Recall | F1     | Accuracy |
  |:-------------:|:-----:|:----:|:---------------:|:---------:|:------:|:------:|:--------:|
  | No log        | 1.0   | 55   | 0.4813          | 0.2863    | 0.3467 | 0.3136 | 0.8720   |
  | No log        | 2.0   | 110  | 0.3469          | 0.4456    | 0.4587 | 0.4520 | 0.9010   |
  | No log        | 3.0   | 165  | 0.3166          | 0.5206    | 0.5387 | 0.5295 | 0.9147   |
  | No log        | 4.0   | 220  | 0.3338          | 0.4899    | 0.584  | 0.5328 | 0.9087   |
  | No log        | 5.0   | 275  | 0.3166          | 0.5625    | 0.648  | 0.6022 | 0.9198   |
  | No log        | 6.0   | 330  | 0.3464          | 0.5707    | 0.6027 | 0.5863 | 0.9207   |
  | No log        | 7.0   | 385  | 0.3548          | 0.5489    | 0.6133 | 0.5793 | 0.9207   |
  | No log        | 8.0   | 440  | 0.4005          | 0.6125    | 0.6027 | 0.6075 | 0.9210   |
  | No log        | 9.0   | 495  | 0.4185          | 0.5763    | 0.6347 | 0.6041 | 0.9171   |
  | 0.2019        | 10.0  | 550  | 0.4174          | 0.5596    | 0.6507 | 0.6017 | 0.9179   |
  | 0.2019        | 11.0  | 605  | 0.4558          | 0.5603    | 0.632  | 0.5940 | 0.9179   |
  | 0.2019        | 12.0  | 660  | 0.4615          | 0.5632    | 0.6533 | 0.6049 | 0.9166   |
  | 0.2019        | 13.0  | 715  | 0.4899          | 0.5815    | 0.6187 | 0.5995 | 0.9208   |
  | 0.2019        | 14.0  | 770  | 0.4800          | 0.5581    | 0.64   | 0.5963 | 0.9186   |
  | 0.2019        | 15.0  | 825  | 0.4752          | 0.5905    | 0.6613 | 0.6239 | 0.9212   |
  | 0.2019        | 16.0  | 880  | 0.5014          | 0.5773    | 0.6373 | 0.6058 | 0.9174   |
  | 0.2019        | 17.0  | 935  | 0.5095          | 0.5917    | 0.6453 | 0.6173 | 0.9195   |
  | 0.2019        | 18.0  | 990  | 0.5249          | 0.5807    | 0.6427 | 0.6101 | 0.9203   |
  | 0.0077        | 19.0  | 1045 | 0.5086          | 0.5761    | 0.656  | 0.6135 | 0.9222   |
  | 0.0077        | 20.0  | 1100 | 0.5108          | 0.5962    | 0.6693 | 0.6307 | 0.9219   |
  | 0.0077        | 21.0  | 1155 | 0.5144          | 0.5977    | 0.6853 | 0.6385 | 0.9231   |
  | 0.0077        | 22.0  | 1210 | 0.5176          | 0.5990    | 0.6613 | 0.6286 | 0.9229   |
  | 0.0077        | 23.0  | 1265 | 0.5171          | 0.6039    | 0.6667 | 0.6337 | 0.9226   |
  | 0.0077        | 24.0  | 1320 | 0.5184          | 0.6043    | 0.672  | 0.6364 | 0.9226   |
  | 0.0077        | 25.0  | 1375 | 0.5184          | 0.5981    | 0.6667 | 0.6305 | 0.9226   |
  
  
  ### Framework versions
  
  - Transformers 4.23.1
  - Pytorch 1.12.1+cu113
  - Datasets 2.6.1
  - Tokenizers 0.13.1
  