readme: ---
  tags:
  - generated_from_trainer
  model-index:
  - name: scideberta-cs-tdm-pretrained
    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. -->
  
  # scideberta-cs-tdm-pretrained
  
  This model is a fine-tuned version of [KISTI-AI/scideberta-cs](https://huggingface.co/KISTI-AI/scideberta-cs) on the None dataset.
  It achieves the following results on the evaluation set:
  - Loss: 0.8211
  - Overall Precision: 0.6247
  - Overall Recall: 0.7665
  - Overall F1: 0.6884
  - Overall Accuracy: 0.9288
  - Datasetname F1: 0.6345
  - Metricname F1: 0.8177
  - Taskname F1: 0.6622
  
  ## 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: 100
  
  ### Training results
  
  | Training Loss | Epoch | Step | Validation Loss | Overall Precision | Overall Recall | Overall F1 | Overall Accuracy | Datasetname F1 | Metricname F1 | Taskname F1 |
  |:-------------:|:-----:|:----:|:---------------:|:-----------------:|:--------------:|:----------:|:----------------:|:--------------:|:-------------:|:-----------:|
  | No log        | 1.0   | 191  | 0.3160          | 0.4684            | 0.6953         | 0.5597     | 0.9187           | 0.5439         | 0.7008        | 0.5135      |
  | No log        | 2.0   | 382  | 0.3152          | 0.4370            | 0.6544         | 0.5240     | 0.9084           | 0.3623         | 0.7527        | 0.5303      |
  | 0.4334        | 3.0   | 573  | 0.3713          | 0.4900            | 0.6768         | 0.5684     | 0.9116           | 0.5525         | 0.7692        | 0.5025      |
  | 0.4334        | 4.0   | 764  | 0.4347          | 0.5554            | 0.6807         | 0.6117     | 0.9253           | 0.5943         | 0.7074        | 0.5795      |
  | 0.4334        | 5.0   | 955  | 0.5098          | 0.5777            | 0.7309         | 0.6453     | 0.9258           | 0.6478         | 0.7902        | 0.5868      |
  | 0.1097        | 6.0   | 1146 | 0.5453          | 0.5784            | 0.7401         | 0.6493     | 0.9265           | 0.5782         | 0.7642        | 0.6390      |
  | 0.1097        | 7.0   | 1337 | 0.6200          | 0.6264            | 0.7586         | 0.6862     | 0.9349           | 0.6513         | 0.7826        | 0.6629      |
  | 0.0499        | 8.0   | 1528 | 0.6072          | 0.6448            | 0.7401         | 0.6892     | 0.9380           | 0.6783         | 0.7935        | 0.6496      |
  | 0.0499        | 9.0   | 1719 | 0.6568          | 0.6329            | 0.7414         | 0.6829     | 0.9347           | 0.6413         | 0.8086        | 0.6487      |
  | 0.0499        | 10.0  | 1910 | 0.6726          | 0.6264            | 0.7520         | 0.6835     | 0.9312           | 0.6618         | 0.7967        | 0.6472      |
  | 0.0247        | 11.0  | 2101 | 0.8104          | 0.6635            | 0.7282         | 0.6943     | 0.9395           | 0.6514         | 0.8159        | 0.6635      |
  | 0.0247        | 12.0  | 2292 | 0.7022          | 0.6320            | 0.7704         | 0.6944     | 0.9376           | 0.6452         | 0.8122        | 0.6704      |
  | 0.0247        | 13.0  | 2483 | 0.8143          | 0.6655            | 0.7216         | 0.6924     | 0.9366           | 0.6321         | 0.8122        | 0.6700      |
  | 0.0176        | 14.0  | 2674 | 0.7723          | 0.6434            | 0.7309         | 0.6844     | 0.9329           | 0.6190         | 0.7934        | 0.6699      |
  | 0.0176        | 15.0  | 2865 | 0.7726          | 0.6071            | 0.7480         | 0.6702     | 0.9320           | 0.6174         | 0.8122        | 0.6391      |
  | 0.0132        | 16.0  | 3056 | 0.8124          | 0.6404            | 0.7493         | 0.6906     | 0.9329           | 0.6326         | 0.8098        | 0.6682      |
  | 0.0132        | 17.0  | 3247 | 0.8269          | 0.6374            | 0.7467         | 0.6877     | 0.9336           | 0.6071         | 0.8268        | 0.6714      |
  | 0.0132        | 18.0  | 3438 | 0.8826          | 0.6315            | 0.7573         | 0.6887     | 0.9343           | 0.6456         | 0.8142        | 0.6573      |
  | 0.0125        | 19.0  | 3629 | 0.8602          | 0.6446            | 0.7467         | 0.6919     | 0.9320           | 0.6190         | 0.8156        | 0.6760      |
  | 0.0125        | 20.0  | 3820 | 1.0048          | 0.6679            | 0.7216         | 0.6937     | 0.9350           | 0.6683         | 0.7932        | 0.6634      |
  | 0.0093        | 21.0  | 4011 | 0.8211          | 0.6247            | 0.7665         | 0.6884     | 0.9288           | 0.6345         | 0.8177      