readme: ---
  license: mit
  tags:
  - generated_from_trainer
  datasets:
  - conll2003
  metrics:
  - precision
  - recall
  - f1
  - accuracy
  model-index:
  - name: bert-german-ner
    results:
    - task:
        name: Token Classification
        type: token-classification
      dataset:
        name: conll2003
        type: conll2003
        config: conll2003
        split: validation
        args: conll2003
      metrics:
      - name: Precision
        type: precision
        value: 0.8333588604686782
      - name: Recall
        type: recall
        value: 0.8620088719898605
      - name: F1
        type: f1
        value: 0.8474417880227396
      - name: Accuracy
        type: accuracy
        value: 0.9292245320451997
  ---
  
  <!-- 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. -->
  
  # bert-german-ner
  
  This model is a fine-tuned version of [dbmdz/bert-base-german-cased](https://huggingface.co/dbmdz/bert-base-german-cased) on the conll2003 dataset.
  It achieves the following results on the evaluation set:
  - Loss: 0.3196
  - Precision: 0.8334
  - Recall: 0.8620
  - F1: 0.8474
  - Accuracy: 0.9292
  
  ## 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: 8
  
  ### Training results
  
  | Training Loss | Epoch | Step | Validation Loss | Precision | Recall | F1     | Accuracy |
  |:-------------:|:-----:|:----:|:---------------:|:---------:|:------:|:------:|:--------:|
  | No log        | 1.0   | 300  | 0.3617          | 0.7310    | 0.7733 | 0.7516 | 0.8908   |
  | 0.5428        | 2.0   | 600  | 0.2897          | 0.7789    | 0.8395 | 0.8081 | 0.9132   |
  | 0.5428        | 3.0   | 900  | 0.2805          | 0.8147    | 0.8465 | 0.8303 | 0.9221   |
  | 0.2019        | 4.0   | 1200 | 0.2816          | 0.8259    | 0.8498 | 0.8377 | 0.9260   |
  | 0.1215        | 5.0   | 1500 | 0.2942          | 0.8332    | 0.8599 | 0.8463 | 0.9285   |
  | 0.1215        | 6.0   | 1800 | 0.3053          | 0.8293    | 0.8619 | 0.8452 | 0.9287   |
  | 0.0814        | 7.0   | 2100 | 0.3190          | 0.8249    | 0.8634 | 0.8437 | 0.9267   |
  | 0.0814        | 8.0   | 2400 | 0.3196          | 0.8334    | 0.8620 | 0.8474 | 0.9292   |
  
  
  ### Framework versions
  
  - Transformers 4.26.0
  - Pytorch 1.13.1+cu116
  - Datasets 2.9.0
  - Tokenizers 0.13.2
  