readme: ---
  license: apache-2.0
  tags:
  - generated_from_trainer
  metrics:
  - precision
  - recall
  - f1
  - accuracy
  model-index:
  - name: tiny-vanilla-target-conll2003
    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. -->
  
  # tiny-vanilla-target-conll2003
  
  This model is a fine-tuned version of [google/bert_uncased_L-2_H-128_A-2](https://huggingface.co/google/bert_uncased_L-2_H-128_A-2) on the None dataset.
  It achieves the following results on the evaluation set:
  - Loss: 0.1431
  - Precision: 0.7507
  - Recall: 0.8177
  - F1: 0.7828
  - Accuracy: 0.9581
  
  ## 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: 3e-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: constant
  - training_steps: 5000
  
  ### Training results
  
  | Training Loss | Epoch | Step | Validation Loss | Precision | Recall | F1     | Accuracy |
  |:-------------:|:-----:|:----:|:---------------:|:---------:|:------:|:------:|:--------:|
  | 0.7673        | 1.14  | 500  | 0.4291          | 0.4793    | 0.5160 | 0.4970 | 0.8920   |
  | 0.3746        | 2.28  | 1000 | 0.2869          | 0.5976    | 0.6572 | 0.6260 | 0.9256   |
  | 0.2869        | 3.42  | 1500 | 0.2292          | 0.6411    | 0.7184 | 0.6776 | 0.9370   |
  | 0.236         | 4.56  | 2000 | 0.1988          | 0.6805    | 0.7516 | 0.7143 | 0.9438   |
  | 0.2026        | 5.69  | 2500 | 0.1772          | 0.7047    | 0.7718 | 0.7367 | 0.9482   |
  | 0.1798        | 6.83  | 3000 | 0.1649          | 0.7179    | 0.7864 | 0.7506 | 0.9514   |
  | 0.158         | 7.97  | 3500 | 0.1559          | 0.7256    | 0.7987 | 0.7604 | 0.9543   |
  | 0.1415        | 9.11  | 4000 | 0.1500          | 0.7379    | 0.8034 | 0.7693 | 0.9563   |
  | 0.127         | 10.25 | 4500 | 0.1462          | 0.7532    | 0.8134 | 0.7821 | 0.9573   |
  | 0.1173        | 11.39 | 5000 | 0.1431          | 0.7507    | 0.8177 | 0.7828 | 0.9581   |
  
  
  ### Framework versions
  
  - Transformers 4.26.0.dev0
  - Pytorch 1.13.0+cu116
  - Datasets 2.8.1.dev0
  - Tokenizers 0.13.2
  