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---
language:
- de
license: mit
datasets:
- germaner
metrics:
- precision
- recall
- f1
- accuracy
widget:
- text: 'Philipp ist 26 Jahre alt und lebt in Nürnberg, Deutschland. Derzeit arbeitet
er als Machine Learning Engineer und Tech Lead bei Hugging Face, um künstliche
Intelligenz durch Open Source und Open Science zu demokratisieren.
'
base_model: deepset/gbert-base
model-index:
- name: gbert-base-germaner
results:
- task:
type: token-classification
name: Token Classification
dataset:
name: germaner
type: germaner
args: default
metrics:
- type: precision
value: 0.8520523797532108
name: precision
- type: recall
value: 0.8754204398447607
name: recall
- type: f1
value: 0.8635783563042368
name: f1
- type: accuracy
value: 0.976147969774973
name: accuracy
---
<!-- 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
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