---
language: de
license: cc-by-4.0
tags:
- named-entity-recognition, legal, ner
datasets:
- german-ler
metrics:
- precision
- recall
- f1
model-index:
- name: elenanereiss/bert-german-ler
  results:
  - task:
      name: Token Classification
      type: token-classification
    dataset:
      name: german-ler
      type: german-ler
      args: german-ler
    metrics:
    - name: F1
      type: f1
      value: 0.9546215361725869
    - name: Precision
      type: precision
      value: 0.9449558173784978
    - name: Recall
      type: recall
      value: 0.9644870349492672
pipeline_tag: token-classification
widget:
- text: "Herr W. verstieß gegen § 36 Abs. 7 IfSG."
---


# bert-german-ler

## Model description

This model is a fine-tuned version of [bert-base-german-cased](https://huggingface.co/bert-base-german-cased) on the 
[German LER Dataset](https://huggingface.co/datasets/elenanereiss/german-ler).
Model fine-tuning is done via [T-NER](https://github.com/asahi417/tner)'s hyper-parameter search (see the repository
for more detail). It achieves the following results on the test set:

## Intended uses & limitations

to do

## Training procedure

### Training hyperparameters

The following hyperparameters were used during training:
- learning_rate: 1e-05
- train_batch_size: 12
- eval_batch_size: 16
- max_seq_length: 512
- num_epochs: 3

## Results 

```
eval_loss = 0.020239440724253654
eval_accuracy_score = 0.9953227664227791
eval_precision = 0.9212203128016991
eval_recall = 0.9458762886597938
eval_f1 = 0.9333855032769246
eval_runtime = 111.4147
eval_samples_per_second = 59.875
eval_steps_per_second = 3.743
epoch = 3.0
```

```
test_loss = 0.011871221475303173
test_accuracy_score = 0.9969460436964865
test_precision = 0.9449558173784978
test_recall = 0.9644870349492672
test_f1 = 0.9546215361725869
test_runtime = 111.5143
test_samples_per_second = 59.849
test_steps_per_second = 3.748
```

### Usage
to do

### Reference
```
@misc{https://doi.org/10.48550/arxiv.2003.13016,
  doi = {10.48550/ARXIV.2003.13016},
  url = {https://arxiv.org/abs/2003.13016},  
  author = {Leitner, Elena and Rehm, Georg and Moreno-Schneider, Julián},  
  keywords = {Computation and Language (cs.CL), Information Retrieval (cs.IR), FOS: Computer and information sciences, FOS: Computer and information sciences},  
  title = {A Dataset of German Legal Documents for Named Entity Recognition},  
  publisher = {arXiv},  
  year = {2020},  
  copyright = {arXiv.org perpetual, non-exclusive license}
}

```