---
language:
- de
license: mit
datasets:
- germaner
metrics:
- precision
- recall
- f1
- accuracy
model-index:
- name: gbert-base-germaner
  results:
  - task:
      name: Token Classification
      type: token-classification
    dataset:
      name: germaner
      type: germaner
      args: default
    metrics:
    - name: precision
      type: precision
      value: 0.8520523797532108
    - name: recall
      type: recall
      value: 0.8754204398447607
    - name: f1
      type: f1
      value: 0.8635783563042368
    - name: accuracy
      type: accuracy
      value: 0.976147969774973
---

<!-- 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. -->

# gbert-base-germaner

This model is a fine-tuned version of [deepset/gbert-base](https://huggingface.co/deepset/gbert-base) on the germaner dataset.
It achieves the following results on the evaluation set:
- precision: 0.8521
- recall: 0.8754
- f1: 0.8636
- accuracy: 0.9761

If you want to learn how to fine-tune BERT yourself using Keras and Tensorflow check out this blog post: 

https://www.philschmid.de/huggingface-transformers-keras-tf

## 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:
- num_train_epochs: 5
- train_batch_size: 16
- eval_batch_size: 32
- learning_rate: 2e-05
- weight_decay_rate: 0.01
- num_warmup_steps: 0
- fp16: True

### Framework versions

- Transformers 4.14.1
- Datasets 1.16.1
- Tokenizers 0.10.3