readme: ---
  tags:
  - generated_from_trainer
  datasets:
  - universal_dependencies
  metrics:
  - precision
  - recall
  - f1
  - accuracy
  inference: false
  model-index:
  - name: distil-slovakbert-upos
    results:
    - task:
        name: Token Classification
        type: token-classification
      dataset:
        name: universal_dependencies sk_snk
        type: universal_dependencies
        args: sk_snk
      metrics:
      - name: Precision
        type: precision
        value: 0.9771104035797263
      - name: Recall
        type: recall
        value: 0.9785418821096173
      - name: F1
        type: f1
        value: 0.9778256189451022
      - name: Accuracy
        type: accuracy
        value: 0.9800851200513933
  ---
  
  <!-- 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. -->
  
  # distil-slovakbert-upos
  
  This model is a fine-tuned version of [crabz/distil-slovakbert](https://huggingface.co/crabz/distil-slovakbert) on the universal_dependencies sk_snk dataset.
  It achieves the following results on the evaluation set:
  - Loss: 0.1207
  - Precision: 0.9771
  - Recall: 0.9785
  - F1: 0.9778
  - Accuracy: 0.9801
  
  ## 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: 32
  - eval_batch_size: 32
  - seed: 42
  - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
  - lr_scheduler_type: linear
  - num_epochs: 10.0
  
  ### Training results
  
  | Training Loss | Epoch | Step | Validation Loss | Precision | Recall | F1     | Accuracy |
  |:-------------:|:-----:|:----:|:---------------:|:---------:|:------:|:------:|:--------:|
  | No log        | 1.0   | 266  | 0.2168          | 0.9570    | 0.9554 | 0.9562 | 0.9610   |
  | 0.3935        | 2.0   | 532  | 0.1416          | 0.9723    | 0.9736 | 0.9730 | 0.9740   |
  | 0.3935        | 3.0   | 798  | 0.1236          | 0.9722    | 0.9735 | 0.9728 | 0.9747   |
  | 0.0664        | 4.0   | 1064 | 0.1195          | 0.9722    | 0.9741 | 0.9732 | 0.9766   |
  | 0.0664        | 5.0   | 1330 | 0.1160          | 0.9764    | 0.9772 | 0.9768 | 0.9789   |
  | 0.0377        | 6.0   | 1596 | 0.1194          | 0.9763    | 0.9776 | 0.9770 | 0.9790   |
  | 0.0377        | 7.0   | 1862 | 0.1188          | 0.9740    | 0.9755 | 0.9748 | 0.9777   |
  | 0.024         | 8.0   | 2128 | 0.1188          | 0.9762    | 0.9777 | 0.9769 | 0.9793   |
  | 0.024         | 9.0   | 2394 | 0.1207          | 0.9774    | 0.9789 | 0.9781 | 0.9802   |
  | 0.0184        | 10.0  | 2660 | 0.1207          | 0.9771    | 0.9785 | 0.9778 | 0.9801   |
  
  
  ### Framework versions
  
  - Transformers 4.17.0.dev0
  - Pytorch 1.10.0
  - Datasets 1.16.1
  - Tokenizers 0.11.0
  