readme: ---
  license: mit
  tags:
  - generated_from_trainer
  metrics:
  - precision
  - recall
  - f1
  - accuracy
  model-index:
  - name: bert-base-german-cased-20000-ner
    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
  
  This model is a fine-tuned version of [bert-base-german-cased](https://huggingface.co/bert-base-german-cased) on the None dataset.
  It achieves the following results on the evaluation set:
  - Loss: 0.0826
  - Precision: 0.8904
  - Recall: 0.8693
  - F1: 0.8797
  - Accuracy: 0.9832
  
  ## 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   | 0.0840          | 0.8076    | 0.7842 | 0.7957 | 0.9752   |
  | No log        | 0.23  | 128  | 0.0787          | 0.8119    | 0.7735 | 0.7922 | 0.9746   |
  | No log        | 0.34  | 192  | 0.0677          | 0.8264    | 0.8362 | 0.8313 | 0.9794   |
  | No log        | 0.45  | 256  | 0.0630          | 0.8440    | 0.8125 | 0.8280 | 0.9801   |
  | No log        | 0.57  | 320  | 0.0664          | 0.8035    | 0.8391 | 0.8209 | 0.9782   |
  | No log        | 0.68  | 384  | 0.0674          | 0.8850    | 0.8285 | 0.8558 | 0.9819   |
  | No log        | 0.79  | 448  | 0.0631          | 0.8834    | 0.8598 | 0.8714 | 0.9825   |
  | 0.094         | 0.9   | 512  | 0.0572          | 0.8933    | 0.8462 | 0.8691 | 0.9832   |
  | 0.094         | 1.02  | 576  | 0.0728          | 0.8520    | 0.8681 | 0.8600 | 0.9795   |
  | 0.094         | 1.13  | 640  | 0.0784          | 0.8496    | 0.8717 | 0.8605 | 0.9800   |
  | 0.094         | 1.24  | 704  | 0.0721          | 0.8868    | 0.8527 | 0.8695 | 0.9814   |
  | 0.094         | 1.36  | 768  | 0.0700          | 0.8755    | 0.8362 | 0.8554 | 0.9808   |
  | 0.094         | 1.47  | 832  | 0.0590          | 0.8662    | 0.8610 | 0.8636 | 0.9822   |
  | 0.094         | 1.58  | 896  | 0.0615          | 0.8692    | 0.8764 | 0.8728 | 0.9821   |
  | 0.094         | 1.7   | 960  | 0.0670          | 0.8812    | 0.8557 | 0.8683 | 0.9826   |
  | 0.0413        | 1.81  | 1024 | 0.0623          | 0.9061    | 0.8557 | 0.8802 | 0.9843   |
  | 0.0413        | 1.92  | 1088 | 0.0570          | 0.8891    | 0.8770 | 0.8830 | 0.9833   |
  | 0.0413        | 2.04  | 1152 | 0.0643          | 0.8859    | 0.8859 | 0.8859 | 0.9831   |
  | 0.0413        | 2.15  | 1216 | 0.0705          | 0.8824    | 0.8740 | 0.8782 | 0.9830   |
  | 0.0413        | 2.26  | 1280 | 0.0698          | 0.8818    | 0.8557 | 0.8685 | 0.9824   |
  | 0.0413        | 2.37  | 1344 | 0.0826          | 0.8904    | 0.8693 | 0.8797 | 0.9832   |
  
  
  ### Framework versions
  
  - Transformers 4.18.0
  - Pytorch 1.9.0+cu111
  - Datasets 2.1.0
  - Tokenizers 0.12.1
  