readme: ---
  license: mit
  tags:
  - generated_from_trainer
  datasets:
  - germa_ner
  metrics:
  - precision
  - recall
  - f1
  - accuracy
  model-index:
  - name: bert-base-german-cased-fine-tuned-ner
    results:
    - task:
        name: Token Classification
        type: token-classification
      dataset:
        name: germa_ner
        type: germa_ner
        args: default
      metrics:
      - name: Precision
        type: precision
        value: 0.8089260808926081
      - name: Recall
        type: recall
        value: 0.872836719337848
      - name: F1
        type: f1
        value: 0.8396670285921101
      - name: Accuracy
        type: accuracy
        value: 0.9748511630761677
  ---
  
  <!-- 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-base-german-cased-fine-tuned-ner
  
  This model is a fine-tuned version of [bert-base-german-cased](https://huggingface.co/bert-base-german-cased) on the germa_ner dataset.
  It achieves the following results on the evaluation set:
  - Loss: 0.0966
  - Precision: 0.8089
  - Recall: 0.8728
  - F1: 0.8397
  - Accuracy: 0.9749
  
  ## 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: 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 |
  |:-------------:|:-----:|:----:|:---------------:|:---------:|:------:|:------:|:--------:|
  | 0.159         | 1.0   | 737  | 0.0922          | 0.7472    | 0.8461 | 0.7936 | 0.9703   |
  | 0.0714        | 2.0   | 1474 | 0.0916          | 0.7886    | 0.8713 | 0.8279 | 0.9731   |
  | 0.0319        | 3.0   | 2211 | 0.0966          | 0.8089    | 0.8728 | 0.8397 | 0.9749   |
  
  
  ### Framework versions
  
  - Transformers 4.18.0
  - Pytorch 1.9.0+cu111
  - Datasets 2.1.0
  - Tokenizers 0.12.1
  