readme: ---
  license: mit
  tags:
  - generated_from_trainer
  metrics:
  - f1
  - accuracy
  - recall
  model-index:
  - name: roberta_comp
    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. -->
  
  # roberta_comp
  
  This model is a fine-tuned version of [ibm/ColD-Fusion](https://huggingface.co/ibm/ColD-Fusion) on the None dataset.
  It achieves the following results on the evaluation set:
  - Loss: 0.3066
  - F1: 0.8077
  - Roc Auc: 0.8650
  - Accuracy: 0.5765
  - Recall: 0.8105
  
  ## 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: 5
  
  ### Training results
  
  | Training Loss | Epoch | Step | Validation Loss | F1     | Roc Auc | Accuracy | Recall |
  |:-------------:|:-----:|:----:|:---------------:|:------:|:-------:|:--------:|:------:|
  | No log        | 1.0   | 231  | 0.3455          | 0.7594 | 0.8284  | 0.5306   | 0.7474 |
  | No log        | 2.0   | 462  | 0.2986          | 0.7986 | 0.8569  | 0.5714   | 0.7930 |
  | 0.3143        | 3.0   | 693  | 0.3006          | 0.8056 | 0.8632  | 0.5867   | 0.8070 |
  | 0.3143        | 4.0   | 924  | 0.3066          | 0.8077 | 0.8650  | 0.5765   | 0.8105 |
  | 0.1365        | 5.0   | 1155 | 0.3117          | 0.8028 | 0.8618  | 0.5663   | 0.8070 |
  
  
  ### Framework versions
  
  - Transformers 4.25.1
  - Pytorch 1.13.1+rocm5.2
  - Datasets 2.8.0
  - Tokenizers 0.13.2
  