readme: ---
  license: apache-2.0
  language: de
  tags:
  - generated_from_trainer
  datasets:
  - wikiann
  model-index:
  - name: ner-bert-german
    results: []
  examples: null
  widget:
  - text: "Herr Schmidt lebt in Berlin und arbeitet für die UN."
    example_title: Schmidt aus Berlin
  - text: "Die Deutsche Bahn hat ihren Hauptsitz in Frankfurt."
    example_title: Deutsche Bahn
  - text: "In München gibt es viele Unternehmen, z.B. BMW und Siemens."
    example_title: München
  metrics:
  - seqeval
  ---
  
  <!-- 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. -->
  
  # ner-bert-german
  
  This model can be used to do [named-entity recognition](https://en.wikipedia.org/wiki/Named-entity_recognition) in German. 
  It is trained on a fine-tuned version of [bert-base-multilingual-cased](https://huggingface.co/bert-base-multilingual-cased) on the German wikiann dataset.
  
  It achieves the following results on the evaluation set:
  - Loss: 0.2450
  - Overall Precision: 0.8767
  - Overall Recall: 0.8893
  - Overall F1: 0.8829
  - Overall Accuracy: 0.9606
  - Loc F1: 0.9067
  - Org F1: 0.8278
  - Per F1: 0.9152
  
  ## 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: 7
  
  ### Training results
  
  | Training Loss | Epoch | Step | Validation Loss | Overall Precision | Overall Recall | Overall F1 | Overall Accuracy | Loc F1 | Org F1 | Per F1 |
  |:-------------:|:-----:|:----:|:---------------:|:-----------------:|:--------------:|:----------:|:----------------:|:------:|:------:|:------:|
  | 0.252         | 0.8   | 1000 | 0.1724          | 0.8422            | 0.8368         | 0.8395     | 0.9501           | 0.8702 | 0.7593 | 0.8921 |
  | 0.1376        | 1.6   | 2000 | 0.1679          | 0.8388            | 0.8607         | 0.8497     | 0.9528           | 0.8814 | 0.7712 | 0.8971 |
  | 0.0982        | 2.4   | 3000 | 0.1880          | 0.8631            | 0.8598         | 0.8614     | 0.9564           | 0.8847 | 0.7915 | 0.9070 |
  | 0.0681        | 3.2   | 4000 | 0.1956          | 0.8599            | 0.8775         | 0.8686     | 0.9574           | 0.8905 | 0.8084 | 0.9097 |
  | 0.0477        | 4.0   | 5000 | 0.2115          | 0.8738            | 0.8814         | 0.8776     | 0.9593           | 0.9003 | 0.8207 | 0.9144 |
  | 0.031         | 4.8   | 6000 | 0.2274          | 0.8751            | 0.8826         | 0.8788     | 0.9598           | 0.9017 | 0.8246 | 0.9115 |
  | 0.0229        | 5.6   | 7000 | 0.2317          | 0.8715            | 0.8888         | 0.8801     | 0.9598           | 0.9061 | 0.8208 | 0.9145 |
  | 0.0181        | 6.4   | 8000 | 0.2450          | 0.8767            | 0.8893         | 0.8829     | 0.9606           | 0.9067 | 0.8278 | 0.9152 |
  
  
  ### Framework versions
  
  - Transformers 4.25.1
  - Pytorch 1.13.1
  - Datasets 2.8.0
  - Tokenizers 0.13.2