readme: ---
  license: mit
  tags:
  - generated_from_trainer
  metrics:
  - f1
  - recall
  - precision
  model-index:
  - name: cold_reman_gpu_v1
    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. -->
  
  # cold_reman_gpu_v1
  
  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: 0.4520
  - F1: 0.6592
  - Roc Auc: 0.7559
  - Recall: 0.6197
  - Precision: 0.704
  
  ## 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: 2
  - eval_batch_size: 2
  - seed: 42
  - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
  - lr_scheduler_type: linear
  - lr_scheduler_warmup_ratio: 0.1
  - num_epochs: 3.0
  
  ### Training results
  
  | Training Loss | Epoch | Step | Validation Loss | F1     | Roc Auc | Recall | Precision |
  |:-------------:|:-----:|:----:|:---------------:|:------:|:-------:|:------:|:---------:|
  | No log        | 1.0   | 452  | 0.4556          | 0.6    | 0.7160  | 0.5282 | 0.6944    |
  | 0.4832        | 2.0   | 904  | 0.4520          | 0.6592 | 0.7559  | 0.6197 | 0.704     |
  | 0.3505        | 3.0   | 1356 | 0.4658          | 0.6543 | 0.7530  | 0.6197 | 0.6929    |
  
  
  ### Framework versions
  
  - Transformers 4.25.1
  - Pytorch 1.13.1+cu117
  - Datasets 2.8.0
  - Tokenizers 0.13.2
  