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---
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
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: 0.8404
- 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
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