readme: ---
  tags:
  - generated_from_trainer
  datasets:
  - davanstrien/manuscript_noisy_labels_iiif
  model-index:
  - name: clip-roberta-finetuned
    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. -->
  
  # clip-roberta-finetuned
  
  This model is a fine-tuned version of [./clip-roberta](https://huggingface.co/./clip-roberta) on the davanstrien/manuscript_noisy_labels_iiif dataset.
  It achieves the following results on the evaluation set:
  - Loss: 2.5792
  
  ## 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: 5e-05
  - train_batch_size: 128
  - eval_batch_size: 256
  - seed: 42
  - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
  - lr_scheduler_type: linear
  - num_epochs: 10.0
  - mixed_precision_training: Native AMP
  
  ### Training results
  
  | Training Loss | Epoch | Step  | Validation Loss |
  |:-------------:|:-----:|:-----:|:---------------:|
  | 2.9841        | 0.07  | 500   | 3.4112          |
  | 2.72          | 0.15  | 1000  | 3.3430          |
  | 2.6319        | 0.22  | 1500  | 3.2295          |
  | 2.5781        | 0.29  | 2000  | 3.1645          |
  | 2.5339        | 0.36  | 2500  | 3.1226          |
  | 2.503         | 0.44  | 3000  | 3.0856          |
  | 2.4581        | 0.51  | 3500  | 3.0639          |
  | 2.4494        | 0.58  | 4000  | 3.0415          |
  | 2.4275        | 0.65  | 4500  | 3.0245          |
  | 2.3909        | 0.73  | 5000  | 2.9991          |
  | 2.3902        | 0.8   | 5500  | 2.9931          |
  | 2.3741        | 0.87  | 6000  | 2.9612          |
  | 2.3536        | 0.95  | 6500  | 2.9509          |
  | 2.3392        | 1.02  | 7000  | 2.9289          |
  | 2.3083        | 1.09  | 7500  | 2.9214          |
  | 2.3094        | 1.16  | 8000  | 2.9153          |
  | 2.2864        | 1.24  | 8500  | 2.9034          |
  | 2.2893        | 1.31  | 9000  | 2.8963          |
  | 2.2697        | 1.38  | 9500  | 2.8847          |
  | 2.2762        | 1.46  | 10000 | 2.8665          |
  | 2.2667        | 1.53  | 10500 | 2.8536          |
  | 2.2548        | 1.6   | 11000 | 2.8472          |
  | 2.238         | 1.67  | 11500 | 2.8491          |
  | 2.2423        | 1.75  | 12000 | 2.8257          |
  | 2.2406        | 1.82  | 12500 | 2.8287          |
  | 2.2248        | 1.89  | 13000 | 2.8193          |
  | 2.223         | 1.96  | 13500 | 2.8101          |
  | 2.1995        | 2.04  | 14000 | 2.8027          |
  | 2.1834        | 2.11  | 14500 | 2.7880          |
  | 2.1723        | 2.18  | 15000 | 2.7783          |
  | 2.1651        | 2.26  | 15500 | 2.7739          |
  | 2.1575        | 2.33  | 16000 | 2.7825          |
  | 2.1598        | 2.4   | 16500 | 2.7660          |
  | 2.1667        | 2.47  | 17000 | 2.7578          |
  | 2.1565        | 2.55  | 17500 | 2.7580          |
  | 2.1558        | 2.62  | 18000 | 2.7561          |
  | 2.1642        | 2.69  | 18500 | 2.7512          |
  | 2.1374        | 2.77  | 19000 | 2.7361          |
  | 2.1402        | 2.84  | 19500 | 2.7385          |
  | 2.1326        | 2.91  | 20000 | 2.7235          |
  | 2.1272        | 2.98  | 20500 | 2.7183          |
  | 2.0954        | 3.06  | 21000 | 2.7156          |
  | 2.0842        | 3.13  | 21500 | 2.7065          |
  | 2.0859        | 3.2   | 22000 | 2.7089          |
  | 2.0856        | 3.27  | 22500 | 2.6962          |
  | 2.0775        | 3.35  | 23000 | 2.6931          |
  | 2.0821        | 3.42  | 23500 | 2.6933          |
  | 2.0706        | 3.49  | 24000 | 2.7011          |
  | 2.0689        | 3.57  | 24500 | 2.7009          |
  | 2.0807        | 3.64  | 25000 | 2.6825          |
  | 2.0639        | 3.71  | 25500 | 2.6744          |
  | 2.0742        | 3.78  | 26000 | 2.6777          |
  | 2.0789        | 3.86  | 26500 | 2.6689          |
  | 2.0594        | 3.93  | 27000 | 2.6566          |
  | 2.056         | 4.0   | 27500 | 2.6676          |
  | 2.0223        | 4.08  | 28000 | 2.6711          |
  | 2.0185        | 4.15  | 28500 | 2.6568          |
  | 2.018         | 4.22  | 29000 | 2.6567          |
  | 2.0036        | 4.29  | 29500 | 2.6545          |
  | 2.0238        | 4.37  | 30000 | 2.6559          |
  | 2.0091        | 4.44  | 30500 | 2.6450          |
  | 2.0096        | 4.51  | 31000 | 2.6389          |
  | 2.0083        | 4.58  | 31500 | 2.6401          |
  | 2.0012        | 4.66  | 32000 | 2.6399          |
  | 2.0166        | 4.73  | 32500 | 2.6289          |
  | 1.9963        | 4.8   | 33000 | 2.6348          |
  | 1.9943        | 4.88  | 33500 | 2.6240          |
  | 2.0099        | 4.95  | 34000 | 2.6190          |
  | 1.9895        | 5.02  | 34500 | 2.6308          |
  | 1.9581        | 5.09  | 35000 | 2.6385          |
  | 1.9502        | 5.17  | 35500 | 2.6237          |
  | 1.9485        | 5.24  | 36000 | 2.6248          |
  | 1.9643        | 5.31  | 36500 | 2.6279  