readme: ---
  license: mit
  tags:
  - generated_from_trainer
  datasets:
  - conll2003
  metrics:
  - precision
  - recall
  - f1
  - accuracy
  model-index:
  - name: hmBERT-CoNLL-cp2
    results:
    - task:
        name: Token Classification
        type: token-classification
      dataset:
        name: conll2003
        type: conll2003
        args: conll2003
      metrics:
      - name: Precision
        type: precision
        value: 0.8931730929727926
      - name: Recall
        type: recall
        value: 0.9005385392123864
      - name: F1
        type: f1
        value: 0.8968406938741306
      - name: Accuracy
        type: accuracy
        value: 0.983217164440637
  ---
  
  <!-- 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-cp2
  
  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: 0.0666
  - Precision: 0.8932
  - Recall: 0.9005
  - F1: 0.8968
  - Accuracy: 0.9832
  
  ## 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: 2
  
  ### Training results
  
  | Training Loss | Epoch | Step | Validation Loss | Precision | Recall | F1     | Accuracy |
  |:-------------:|:-----:|:----:|:---------------:|:---------:|:------:|:------:|:--------:|
  | No log        | 0.06  | 25   | 0.4116          | 0.3632    | 0.3718 | 0.3674 | 0.9005   |
  | No log        | 0.11  | 50   | 0.2247          | 0.6384    | 0.6902 | 0.6633 | 0.9459   |
  | No log        | 0.17  | 75   | 0.1624          | 0.7303    | 0.7627 | 0.7461 | 0.9580   |
  | No log        | 0.23  | 100  | 0.1541          | 0.7338    | 0.7688 | 0.7509 | 0.9588   |
  | No log        | 0.28  | 125  | 0.1349          | 0.7610    | 0.8095 | 0.7845 | 0.9643   |
  | No log        | 0.34  | 150  | 0.1230          | 0.7982    | 0.8253 | 0.8115 | 0.9694   |
  | No log        | 0.4   | 175  | 0.0997          | 0.8069    | 0.8406 | 0.8234 | 0.9727   |
  | No log        | 0.46  | 200  | 0.1044          | 0.8211    | 0.8410 | 0.8309 | 0.9732   |
  | No log        | 0.51  | 225  | 0.0871          | 0.8413    | 0.8603 | 0.8507 | 0.9760   |
  | No log        | 0.57  | 250  | 0.1066          | 0.8288    | 0.8465 | 0.8376 | 0.9733   |
  | No log        | 0.63  | 275  | 0.0872          | 0.8580    | 0.8667 | 0.8624 | 0.9766   |
  | No log        | 0.68  | 300  | 0.0834          | 0.8522    | 0.8706 | 0.8613 | 0.9773   |
  | No log        | 0.74  | 325  | 0.0832          | 0.8545    | 0.8834 | 0.8687 | 0.9783   |
  | No log        | 0.8   | 350  | 0.0776          | 0.8542    | 0.8834 | 0.8685 | 0.9787   |
  | No log        | 0.85  | 375  | 0.0760          | 0.8629    | 0.8896 | 0.8760 | 0.9801   |
  | No log        | 0.91  | 400  | 0.0673          | 0.8775    | 0.9004 | 0.8888 | 0.9824   |
  | No log        | 0.97  | 425  | 0.0681          | 0.8827    | 0.8938 | 0.8882 | 0.9817   |
  | No log        | 1.03  | 450  | 0.0659          | 0.8844    | 0.8950 | 0.8897 | 0.9824   |
  | No log        | 1.08  | 475  | 0.0690          | 0.8833    | 0.9015 | 0.8923 | 0.9832   |
  | 0.1399        | 1.14  | 500  | 0.0666          | 0.8932    | 0.9005 | 0.8968 | 0.9832   |
  | 0.1399        | 1.2   | 525  | 0.0667          | 0.8891    | 0.8997 | 0.8944 | 0.9825   |
  | 0.1399        | 1.25  | 550  | 0.0699          | 0.8751    | 0.8953 | 0.8851 | 0.9820   |
  | 0.1399        | 1.31  | 575  | 0.0617          | 0.8947    | 0.9068 | 0.9007 | 0.9840   |
  | 0.1399        | 1.37  | 600  | 0.0633          | 0.9       | 0.9058 | 0.9029 | 0.9841   |
  | 0.1399        | 1.42  | 625  | 0.0639          | 0.8966    | 0.9116 | 0.9040 | 0.9843   |
  | 0.1399        | 1.48  | 650  | 0.0624          | 0.8972    | 0.9110 | 0.9041 | 0.9845   |
  | 0.1399        | 1.54  | 675  | 0.0619          | 0.8980    | 0.9081 | 0.9030 | 0.9842   |
  | 0.1399        | 1.59  | 700  | 0.0615          | 0.9002    | 0.9090 | 0.9045 | 0.9843   |
  | 0.1399        | 1.65  | 725  | 0.0601          | 0.9037    | 0.9128 | 0.9082 | 0.9850   |
  | 0.1399        | 1.71  | 750  | 0.0585          | 0.9031    | 0.9142 | 0.9086 | 0.9849   |
  | 0.1399        | 1.77  | 775  | 0.0582          | 0.9035    | 0.9143 | 0.9089 | 0.9851   |
  | 0.1399        | 1.82  | 800  | 0.0580          | 0.9044    | 0.9157 | 0.9100 | 0.9853   |
  | 0.1399        | 1.88  | 825  | 0.0583          | 0.9034    | 0.9160 | 0.9097 | 0.9851   |
  | 0.1399        | 1.94  | 850  | 0.0578          | 0.9058    | 0.9170 | 0.9114 | 0.9854   |
  | 0.1399        | 1.99  | 875  | 0.0576          | 0.9060    | 0