---
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