readme: ---
  license: mit
  tags:
  - generated_from_trainer
  datasets:
  - conll2003
  metrics:
  - precision
  - recall
  - f1
  - accuracy
  model-index:
  - name: hmBERT-CoNLL-cp3
    results:
    - task:
        name: Token Classification
        type: token-classification
      dataset:
        name: conll2003
        type: conll2003
        args: conll2003
      metrics:
      - name: Precision
        type: precision
        value: 0.9121408403919614
      - name: Recall
        type: recall
        value: 0.9242679232581622
      - name: F1
        type: f1
        value: 0.9181643400484828
      - name: Accuracy
        type: accuracy
        value: 0.9862154900510105
  ---
  
  <!-- 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. -->
  
  # hmBERT-CoNLL-cp3
  
  This model is a fine-tuned version of [dbmdz/bert-base-historic-multilingual-cased](https://huggingface.co/dbmdz/bert-base-historic-multilingual-cased) on the conll2003 dataset.
  It achieves the following results on the evaluation set:
  - Loss: 0.0572
  - Precision: 0.9121
  - Recall: 0.9243
  - F1: 0.9182
  - Accuracy: 0.9862
  
  ## 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: 3
  
  ### Training results
  
  | Training Loss | Epoch | Step | Validation Loss | Precision | Recall | F1     | Accuracy |
  |:-------------:|:-----:|:----:|:---------------:|:---------:|:------:|:------:|:--------:|
  | No log        | 0.06  | 25   | 0.4115          | 0.3643    | 0.3728 | 0.3685 | 0.9007   |
  | No log        | 0.11  | 50   | 0.2243          | 0.6393    | 0.6908 | 0.6641 | 0.9460   |
  | No log        | 0.17  | 75   | 0.1617          | 0.7319    | 0.7637 | 0.7475 | 0.9580   |
  | No log        | 0.23  | 100  | 0.1544          | 0.7282    | 0.7637 | 0.7455 | 0.9585   |
  | No log        | 0.28  | 125  | 0.1341          | 0.7595    | 0.8117 | 0.7847 | 0.9644   |
  | No log        | 0.34  | 150  | 0.1221          | 0.7980    | 0.8251 | 0.8114 | 0.9693   |
  | No log        | 0.4   | 175  | 0.1013          | 0.7968    | 0.8344 | 0.8152 | 0.9719   |
  | No log        | 0.46  | 200  | 0.1076          | 0.8265    | 0.8403 | 0.8333 | 0.9732   |
  | No log        | 0.51  | 225  | 0.0883          | 0.8453    | 0.8635 | 0.8543 | 0.9763   |
  | No log        | 0.57  | 250  | 0.0973          | 0.8439    | 0.8633 | 0.8535 | 0.9763   |
  | No log        | 0.63  | 275  | 0.0883          | 0.8497    | 0.8655 | 0.8575 | 0.9765   |
  | No log        | 0.68  | 300  | 0.0879          | 0.8462    | 0.8642 | 0.8551 | 0.9766   |
  | No log        | 0.74  | 325  | 0.0781          | 0.8592    | 0.8834 | 0.8711 | 0.9787   |
  | No log        | 0.8   | 350  | 0.0725          | 0.8697    | 0.8928 | 0.8811 | 0.9803   |
  | No log        | 0.85  | 375  | 0.0755          | 0.8687    | 0.8943 | 0.8813 | 0.9807   |
  | No log        | 0.91  | 400  | 0.0666          | 0.8781    | 0.9004 | 0.8891 | 0.9822   |
  | No log        | 0.97  | 425  | 0.0658          | 0.8877    | 0.8995 | 0.8936 | 0.9823   |
  | No log        | 1.03  | 450  | 0.0645          | 0.8951    | 0.9036 | 0.8993 | 0.9837   |
  | No log        | 1.08  | 475  | 0.0697          | 0.8864    | 0.9039 | 0.8951 | 0.9831   |
  | 0.1392        | 1.14  | 500  | 0.0688          | 0.8824    | 0.8994 | 0.8908 | 0.9824   |
  | 0.1392        | 1.2   | 525  | 0.0681          | 0.8950    | 0.9049 | 0.8999 | 0.9827   |
  | 0.1392        | 1.25  | 550  | 0.0676          | 0.8855    | 0.8977 | 0.8915 | 0.9823   |
  | 0.1392        | 1.31  | 575  | 0.0618          | 0.8940    | 0.9088 | 0.9014 | 0.9842   |
  | 0.1392        | 1.37  | 600  | 0.0644          | 0.8945    | 0.9076 | 0.9010 | 0.9840   |
  | 0.1392        | 1.42  | 625  | 0.0641          | 0.8936    | 0.9086 | 0.9010 | 0.9837   |
  | 0.1392        | 1.48  | 650  | 0.0619          | 0.8969    | 0.9120 | 0.9044 | 0.9846   |
  | 0.1392        | 1.54  | 675  | 0.0608          | 0.9045    | 0.9105 | 0.9075 | 0.9848   |
  | 0.1392        | 1.59  | 700  | 0.0624          | 0.9038    | 0.9143 | 0.9091 | 0.9851   |
  | 0.1392        | 1.65  | 725  | 0.0596          | 0.9062    | 0.9170 | 0.9116 | 0.9852   |
  | 0.1392        | 1.71  | 750  | 0.0580          | 0.8995    | 0.9143 | 0.9069 | 0.9848   |
  | 0.1392        | 1.77  | 775  | 0.0582          | 0.9082    | 0.9172 | 0.9127 | 0.9858   |
  | 0.1392        | 1.82  | 800  | 0.0588          | 0.9024    | 0.9179 | 0.9101 | 0.9852   |
  | 0.1392        | 1.88  | 825  | 0.0592          | 0.9020    | 0.9219 | 0.9119 | 0.9856   |
  | 0.1392        | 1.94  | 850  | 0.0600          | 0.9054    | 0.9182 | 0.9118 | 0.9852   |
  | 0.1392        | 1.99  | 875  | 0.0568          | 0.9068    | 