readme: ---
  license: apache-2.0
  tags:
  - generated_from_trainer
  datasets:
  - conll2003
  metrics:
  - precision
  - recall
  - f1
  - accuracy
  model-index:
  - name: distilbert-base-uncased-finetuned-pos
    results:
    - task:
        name: Token Classification
        type: token-classification
      dataset:
        name: conll2003
        type: conll2003
        args: conll2003
      metrics:
      - name: Precision
        type: precision
        value: 0.9109037731744458
      - name: Recall
        type: recall
        value: 0.9143515710299648
      - name: F1
        type: f1
        value: 0.9126244157605404
      - name: Accuracy
        type: accuracy
        value: 0.9245555785025498
  ---
  
  <!-- 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. -->
  
  # distilbert-base-uncased-finetuned-pos
  
  This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co/distilbert-base-uncased) on the conll2003 dataset.
  It achieves the following results on the evaluation set:
  - Loss: 0.3165
  - Precision: 0.9109
  - Recall: 0.9144
  - F1: 0.9126
  - Accuracy: 0.9246
  
  ## 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: 16
  - eval_batch_size: 16
  - 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 |
  |:-------------:|:-----:|:----:|:---------------:|:---------:|:------:|:------:|:--------:|
  | 0.7941        | 1.0   | 878  | 0.3504          | 0.8995    | 0.9026 | 0.9011 | 0.9176   |
  | 0.2533        | 2.0   | 1756 | 0.3216          | 0.9091    | 0.9104 | 0.9098 | 0.9233   |
  | 0.2047        | 3.0   | 2634 | 0.3165          | 0.9109    | 0.9144 | 0.9126 | 0.9246   |
  
  
  ### Framework versions
  
  - Transformers 4.17.0
  - Pytorch 1.10.0+cu111
  - Datasets 2.0.0
  - Tokenizers 0.11.6
  