
<!-- 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-large-germaner

This model is a fine-tuned version of [deepset/gbert-large](https://huggingface.co/deepset/gbert-large) on the germaner dataset.
It achieves the following results on the evaluation set:
- precision: 0.8693
- recall: 0.8856
- f1: 0.8774
- accuracy: 0.9784

## 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: 8
- eval_batch_size: 8
- learning_rate: 2e-05
- weight_decay_rate: 0.01
- num_warmup_steps: 0
- fp16: True

### Framework versions

- Transformers 4.18.0
- Datasets 1.18.0
- Tokenizers 0.12.1

{"language": ["de"], "license": "mit", "datasets": ["germaner"], "metrics": ["precision", "recall", "f1", "accuracy"], "model-index": [{"name": "gbert-large-germaner", "results": [{"task": {"type": "token-classification", "name": "Token Classification"}, "dataset": {"name": "germaner", "type": "germaner", "args": "default"}, "metrics": [{"type": "precision", "value": 0.8693333333333333, "name": "precision"}, {"type": "recall", "value": 0.885640362225097, "name": "recall"}, {"type": "f1", "value": 0.8774110861903236, "name": "f1"}, {"type": "accuracy", "value": 0.9784210744831022, "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-large-germaner

This model is a fine-tuned version of deepset/gbert-large on the germaner dataset.
It achieves the following results on the evaluation set:
- precision: 0.8693
- recall: 0.8856
- f1: 0.8774
- accuracy: 0.9784

## 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: 8
- eval_batch_size: 8
- learning_rate: 2e-05
- weight_decay_rate: 0.01
- num_warmup_steps: 0
- fp16: True

### Framework versions

- Transformers 4.18.0
- Datasets 1.18.0
- Tokenizers 0.12.1

# gbert-large-germaner

This model is a fine-tuned version of deepset/gbert-large on the germaner dataset.
It achieves the following results on the evaluation set:
- precision: 0.8693
- recall: 0.8856
- f1: 0.8774
- accuracy: 0.9784 ## 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: 8
- eval_batch_size: 8
- learning_rate: 2e-05
- weight_decay_rate: 0.01
- num_warmup_steps: 0
- fp16: True ### Framework versions

- Transformers 4.18.0
- Datasets 1.18.0
- Tokenizers 0.12.1
TAGS
#transformers #tf #tensorboard #bert #token-classification #de #dataset-germaner #license-mit #model-index #autotrain_compatible #endpoints_compatible #region-us 
 # gbert-large-germaner

This model is a fine-tuned version of deepset/gbert-large on the germaner dataset.
It achieves the following results on the evaluation set:
- precision: 0.8693
- recall: 0.8856
- f1: 0.8774
- accuracy: 0.9784 ## 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: 8
- eval_batch_size: 8
- learning_rate: 2e-05
- weight_decay_rate: 0.01
- num_warmup_steps: 0
- fp16: True ### Framework versions

- Transformers 4.18.0
- Datasets 1.18.0
- Tokenizers 0.12.1
passage: TAGS
#transformers #tf #tensorboard #bert #token-classification #de #dataset-germaner #license-mit #model-index #autotrain_compatible #endpoints_compatible #region-us 
# gbert-large-germaner

This model is a fine-tuned version of deepset/gbert-large on the germaner dataset.
It achieves the following results on the evaluation set:
- precision: 0.8693
- recall: 0.8856
- f1: 0.8774
- accuracy: 0.9784## 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: 8
- eval_batch_size: 8
- learning_rate: 2e-05
- weight_decay_rate: 0.01
- num_warmup_steps: 0
- fp16: True### Framework versions

- Transformers 4.18.0
- Datasets 1.18.0
- Tokenizers 0.12.1