readme: ---
  language:
  - de
  license: mit
  widget:
   - text: |
       Philipp ist 26 Jahre alt und lebt in Nürnberg, Deutschland. Derzeit arbeitet er als Machine Learning Engineer und Tech Lead bei Hugging Face, um künstliche Intelligenz durch Open Source und Open Science zu demokratisieren.
  datasets:
  - germaner
  metrics:
  - precision
  - recall
  - f1
  - accuracy
  model-index:
  - name: gbert-base-germaner
    results:
    - task:
        name: Token Classification
        type: token-classification
      dataset:
        name: germaner
        type: germaner
        args: default
      metrics:
      - name: precision
        type: precision
        value: 0.8520523797532108
      - name: recall
        type: recall
        value: 0.8754204398447607
      - name: f1
        type: f1
        value: 0.8635783563042368
      - name: accuracy
        type: accuracy
        value: 0.976147969774973
  ---
  
  <!-- 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. -->
  
  # gbert-base-germaner
  
  This model is a fine-tuned version of [deepset/gbert-base](https://huggingface.co/deepset/gbert-base) on the germaner dataset.
  It achieves the following results on the evaluation set:
  - precision: 0.8521
  - recall: 0.8754
  - f1: 0.8636
  - accuracy: 0.9761
  
  If you want to learn how to fine-tune BERT yourself using Keras and Tensorflow check out this blog post: 
  
  https://www.philschmid.de/huggingface-transformers-keras-tf
  
  ## 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:
  - num_train_epochs: 5
  - train_batch_size: 16
  - eval_batch_size: 32
  - learning_rate: 2e-05
  - weight_decay_rate: 0.01
  - num_warmup_steps: 0
  - fp16: True
  
  ### Framework versions
  
  - Transformers 4.14.1
  - Datasets 1.16.1
  - Tokenizers 0.10.3
  