readme: ---
  license: mit
  tags:
  - generated_from_trainer
  metrics:
  - precision
  - recall
  - f1
  - accuracy
  model-index:
  - name: bert-base-german-cased-20000-ner-uncased
    results: []
  ---
  
  <!-- 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-20000-ner-uncased
  
  This model is a fine-tuned version of [dbmdz/bert-base-german-uncased](https://huggingface.co/dbmdz/bert-base-german-uncased) on the None dataset.
  It achieves the following results on the evaluation set:
  - Loss: 0.0664
  - Precision: 0.9061
  - Recall: 0.8697
  - F1: 0.8875
  - Accuracy: 0.9838
  
  ## 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: 10
  
  ### Training results
  
  | Training Loss | Epoch | Step | Validation Loss | Precision | Recall | F1     | Accuracy |
  |:-------------:|:-----:|:----:|:---------------:|:---------:|:------:|:------:|:--------:|
  | No log        | 0.11  | 64   | 0.1129          | 0.8012    | 0.8547 | 0.8271 | 0.9729   |
  | No log        | 0.23  | 128  | 0.0879          | 0.7882    | 0.8426 | 0.8145 | 0.9771   |
  | No log        | 0.34  | 192  | 0.0662          | 0.8711    | 0.8523 | 0.8616 | 0.9815   |
  | No log        | 0.45  | 256  | 0.0627          | 0.8839    | 0.8553 | 0.8694 | 0.9820   |
  | No log        | 0.57  | 320  | 0.0669          | 0.8677    | 0.8709 | 0.8693 | 0.9806   |
  | No log        | 0.68  | 384  | 0.0568          | 0.8669    | 0.8685 | 0.8677 | 0.9823   |
  | No log        | 0.79  | 448  | 0.0620          | 0.9066    | 0.8631 | 0.8843 | 0.9827   |
  | 0.0861        | 0.9   | 512  | 0.0603          | 0.8743    | 0.8859 | 0.8801 | 0.9829   |
  | 0.0861        | 1.02  | 576  | 0.0552          | 0.8983    | 0.8697 | 0.8837 | 0.9845   |
  | 0.0861        | 1.13  | 640  | 0.0563          | 0.9007    | 0.8823 | 0.8914 | 0.9847   |
  | 0.0861        | 1.24  | 704  | 0.0605          | 0.8683    | 0.8829 | 0.8755 | 0.9834   |
  | 0.0861        | 1.36  | 768  | 0.0547          | 0.9199    | 0.8895 | 0.9044 | 0.9857   |
  | 0.0861        | 1.47  | 832  | 0.0585          | 0.9159    | 0.8703 | 0.8925 | 0.9845   |
  | 0.0861        | 1.58  | 896  | 0.0601          | 0.8818    | 0.8871 | 0.8844 | 0.9834   |
  | 0.0861        | 1.7   | 960  | 0.0664          | 0.9061    | 0.8697 | 0.8875 | 0.9838   |
  
  
  ### Framework versions
  
  - Transformers 4.18.0
  - Pytorch 1.9.0+cu111
  - Datasets 2.1.0
  - Tokenizers 0.12.1
  