readme: ---
  license: apache-2.0
  tags:
  - generated_from_trainer
  datasets:
  - udpos28
  metrics:
  - precision
  - recall
  - f1
  - accuracy
  model-index:
  - name: udpos28-sm-all-POS
    results:
    - task:
        name: Token Classification
        type: token-classification
      dataset:
        name: udpos28
        type: udpos28
        args: en
      metrics:
      - name: Precision
        type: precision
        value: 0.9586517032792105
      - name: Recall
        type: recall
        value: 0.9588997472284696
      - name: F1
        type: f1
        value: 0.9587757092110369
      - name: Accuracy
        type: accuracy
        value: 0.964820639556654
  ---
  
  <!-- 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. -->
  
  # udpos28-sm-all-POS
  
  This model is a fine-tuned version of [bert-base-cased](https://huggingface.co/bert-base-cased) on the udpos28 dataset.
  It achieves the following results on the evaluation set:
  - Loss: 0.1479
  - Precision: 0.9587
  - Recall: 0.9589
  - F1: 0.9588
  - Accuracy: 0.9648
  
  ## 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: 4
  - eval_batch_size: 4
  - 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
  
  ### Training results
  
  | Training Loss | Epoch | Step  | Validation Loss | Precision | Recall | F1     | Accuracy |
  |:-------------:|:-----:|:-----:|:---------------:|:---------:|:------:|:------:|:--------:|
  | 0.1261        | 1.0   | 4978  | 0.1358          | 0.9513    | 0.9510 | 0.9512 | 0.9581   |
  | 0.0788        | 2.0   | 9956  | 0.1326          | 0.9578    | 0.9578 | 0.9578 | 0.9642   |
  | 0.0424        | 3.0   | 14934 | 0.1479          | 0.9587    | 0.9589 | 0.9588 | 0.9648   |
  
  
  ### Framework versions
  
  - Transformers 4.18.0
  - Pytorch 1.10.2+cu102
  - Datasets 2.2.2
  - Tokenizers 0.12.1
  