---
language:
- de
license: mit
base_model: deepset/gbert-base
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: 0.8403996101364523
    - name: recall
      type: recall
      value: 0.8674547283702213
    - name: f1
      type: f1
      value: 0.8537128712871287
    - name: accuracy
      type: accuracy
      value: 0.9760785008915815
---

<!-- This model card has been generated automatically according to the information the Trainer had access to. You
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# 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: 0.8404
- recall: 0.8675
- f1: 0.8537
- accuracy: 0.9761

## 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-06
- weight_decay_rate: 0.01
- num_warmup_steps: 0
- fp16: True

### Framework versions

- Transformers 4.31.0
- Pytorch 2.0.1+cu118
- Datasets 2.14.4
- Tokenizers 0.13.3