readme: ---
  tags:
  - generated_from_trainer
  datasets:
  - udpos28
  metrics:
  - precision
  - recall
  - f1
  - accuracy
  model-index:
  - name: parsbert-finetuned-pos
    results:
    - task:
        name: Token Classification
        type: token-classification
      dataset:
        name: udpos28
        type: udpos28
        args: fa
      metrics:
      - name: Precision
        type: precision
        value: 0.9447937270415372
      - name: Recall
        type: recall
        value: 0.9486470191864382
      - name: F1
        type: f1
        value: 0.9467164522465448
      - name: Accuracy
        type: accuracy
        value: 0.9598951738759165
  ---
  
  <!-- 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. -->
  
  # parsbert-finetuned-pos
  
  This model is a fine-tuned version of [HooshvareLab/bert-base-parsbert-uncased](https://huggingface.co/HooshvareLab/bert-base-parsbert-uncased) on the udpos28 dataset.
  It achieves the following results on the evaluation set:
  - Loss: 0.1385
  - Precision: 0.9448
  - Recall: 0.9486
  - F1: 0.9467
  - Accuracy: 0.9599
  
  ## 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: 8
  - eval_batch_size: 8
  - seed: 42
  - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
  - lr_scheduler_type: linear
  - num_epochs: 3
  
  ### Training results
  
  | Training Loss | Epoch | Step | Validation Loss | Precision | Recall | F1     | Accuracy |
  |:-------------:|:-----:|:----:|:---------------:|:---------:|:------:|:------:|:--------:|
  | 0.122         | 1.0   | 3103 | 0.1215          | 0.9363    | 0.9424 | 0.9394 | 0.9561   |
  | 0.0735        | 2.0   | 6206 | 0.1297          | 0.9413    | 0.9474 | 0.9443 | 0.9582   |
  | 0.0373        | 3.0   | 9309 | 0.1385          | 0.9448    | 0.9486 | 0.9467 | 0.9599   |
  
  
  ### Framework versions
  
  - Transformers 4.18.0
  - Pytorch 1.10.0
  - Datasets 2.0.0
  - Tokenizers 0.11.6
  