readme: ---
  license: apache-2.0
  tags:
  - generated_from_trainer
  metrics:
  - f1
  model-index:
  - name: iiif_manuscript_vit
    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. -->
  
  # iiif_manuscript_vit
  
  This model is a fine-tuned version of [google/vit-base-patch16-224-in21k](https://huggingface.co/google/vit-base-patch16-224-in21k) on the None dataset.
  It achieves the following results on the evaluation set:
  - Loss: 0.5684
  - F1: 0.5996
  
  ## 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: 64
  - eval_batch_size: 64
  - seed: 42
  - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
  - lr_scheduler_type: linear
  - num_epochs: 10
  - mixed_precision_training: Native AMP
  - label_smoothing_factor: 0.1
  
  ### Training results
  
  | Training Loss | Epoch | Step  | Validation Loss | F1     |
  |:-------------:|:-----:|:-----:|:---------------:|:------:|
  | 0.5639        | 1.0   | 2269  | 0.5822          | 0.5516 |
  | 0.5834        | 2.0   | 4538  | 0.5825          | 0.5346 |
  | 0.5778        | 3.0   | 6807  | 0.5794          | 0.6034 |
  | 0.5735        | 4.0   | 9076  | 0.5742          | 0.5713 |
  | 0.5731        | 5.0   | 11345 | 0.5745          | 0.6008 |
  | 0.5701        | 6.0   | 13614 | 0.5729          | 0.5499 |
  | 0.5696        | 7.0   | 15883 | 0.5717          | 0.5952 |
  | 0.5683        | 8.0   | 18152 | 0.5680          | 0.6005 |
  | 0.5648        | 9.0   | 20421 | 0.5679          | 0.5967 |
  | 0.564         | 10.0  | 22690 | 0.5684          | 0.5996 |
  
  
  ### Framework versions
  
  - Transformers 4.16.2
  - Pytorch 1.10.0+cu111
  - Datasets 1.18.3
  - Tokenizers 0.11.0
  