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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: 0.8521
- 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

{"language": ["de"], "license": "mit", "datasets": ["germaner"], "metrics": ["precision", "recall", "f1", "accuracy"], "widget": [{"text": "Philipp ist 26 Jahre alt und lebt in N\u00fcrnberg, Deutschland. Derzeit arbeitet er als Machine Learning Engineer und Tech Lead bei Hugging Face, um k\u00fcnstliche Intelligenz durch Open Source und Open Science zu demokratisieren.\n"}], "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"}]}]}]}
transformers tf tensorboard bert token-classification de dataset:germaner license:mit model-index autotrain_compatible endpoints_compatible region:us
TAGS
#transformers #tf #tensorboard #bert #token-classification #de #dataset-germaner #license-mit #model-index #autotrain_compatible #endpoints_compatible #region-us 




# gbert-base-germaner

This model is a fine-tuned version of 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: 

URL

## 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

# gbert-base-germaner

This model is a fine-tuned version of 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: 

URL ## 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
TAGS
#transformers #tf #tensorboard #bert #token-classification #de #dataset-germaner #license-mit #model-index #autotrain_compatible #endpoints_compatible #region-us 
 # gbert-base-germaner

This model is a fine-tuned version of 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: 

URL ## 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
passage: TAGS
#transformers #tf #tensorboard #bert #token-classification #de #dataset-germaner #license-mit #model-index #autotrain_compatible #endpoints_compatible #region-us 
# gbert-base-germaner

This model is a fine-tuned version of 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: 

URL## 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