readme: ---
  license: mit
  tags:
  - generated_from_trainer
  datasets:
  - conll2003
  metrics:
  - precision
  - recall
  - f1
  - accuracy
  model-index:
  - name: hmBERT-CoNLL-cp1
    results:
    - task:
        name: Token Classification
        type: token-classification
      dataset:
        name: conll2003
        type: conll2003
        args: conll2003
      metrics:
      - name: Precision
        type: precision
        value: 0.8690143162744776
      - name: Recall
        type: recall
        value: 0.8887579939414338
      - name: F1
        type: f1
        value: 0.8787752724852317
      - name: Accuracy
        type: accuracy
        value: 0.9810170943499085
  ---
  
  <!-- 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-cp1
  
  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: 0.0710
  - Precision: 0.8690
  - Recall: 0.8888
  - F1: 0.8788
  - Accuracy: 0.9810
  
  ## 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: 1
  
  ### Training results
  
  | Training Loss | Epoch | Step | Validation Loss | Precision | Recall | F1     | Accuracy |
  |:-------------:|:-----:|:----:|:---------------:|:---------:|:------:|:------:|:--------:|
  | No log        | 0.06  | 25   | 0.4115          | 0.3593    | 0.3708 | 0.3649 | 0.9002   |
  | No log        | 0.11  | 50   | 0.2263          | 0.6360    | 0.6898 | 0.6618 | 0.9456   |
  | No log        | 0.17  | 75   | 0.1660          | 0.7250    | 0.7582 | 0.7412 | 0.9564   |
  | No log        | 0.23  | 100  | 0.1520          | 0.7432    | 0.7775 | 0.7600 | 0.9597   |
  | No log        | 0.28  | 125  | 0.1343          | 0.7683    | 0.8103 | 0.7888 | 0.9645   |
  | No log        | 0.34  | 150  | 0.1252          | 0.7973    | 0.8230 | 0.8099 | 0.9691   |
  | No log        | 0.4   | 175  | 0.1021          | 0.8118    | 0.8398 | 0.8255 | 0.9724   |
  | No log        | 0.46  | 200  | 0.1056          | 0.8153    | 0.8411 | 0.8280 | 0.9727   |
  | No log        | 0.51  | 225  | 0.0872          | 0.8331    | 0.8612 | 0.8469 | 0.9755   |
  | No log        | 0.57  | 250  | 0.1055          | 0.8226    | 0.8418 | 0.8321 | 0.9725   |
  | No log        | 0.63  | 275  | 0.0921          | 0.8605    | 0.8640 | 0.8623 | 0.9767   |
  | No log        | 0.68  | 300  | 0.0824          | 0.8600    | 0.8787 | 0.8692 | 0.9788   |
  | No log        | 0.74  | 325  | 0.0834          | 0.8530    | 0.8771 | 0.8649 | 0.9787   |
  | No log        | 0.8   | 350  | 0.0758          | 0.8646    | 0.8876 | 0.8759 | 0.9800   |
  | No log        | 0.85  | 375  | 0.0727          | 0.8705    | 0.8866 | 0.8784 | 0.9810   |
  | No log        | 0.91  | 400  | 0.0734          | 0.8717    | 0.8899 | 0.8807 | 0.9811   |
  | No log        | 0.97  | 425  | 0.0713          | 0.8683    | 0.8889 | 0.8785 | 0.9810   |
  
  
  ### Framework versions
  
  - Transformers 4.20.1
  - Pytorch 1.12.0
  - Datasets 2.4.0
  - Tokenizers 0.12.1
  