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# bert-base-german-cased-gnad10-finetuned-tagesschau-subcategories

This model is a fine-tuned version of [Mathking/bert-base-german-cased-gnad10](https://huggingface.co/Mathking/bert-base-german-cased-gnad10) on the None dataset.
It achieves the following results on the evaluation set:
- eval_loss: 0.4599
- eval_accuracy: 0.84
- eval_runtime: 71.9717
- eval_samples_per_second: 2.084
- eval_steps_per_second: 0.139
- epoch: 1.2
- step: 90

## 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:
- learning_rate: 2e-05
- train_batch_size: 16
- eval_batch_size: 16
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 5

### Framework versions

- Transformers 4.21.3
- Pytorch 1.13.0+cpu
- Datasets 2.8.0
- Tokenizers 0.12.1

{"tags": ["generated_from_trainer"], "model-index": [{"name": "bert-base-german-cased-gnad10-finetuned-tagesschau-subcategories", "results": []}]}
transformers pytorch tensorboard bert text-classification generated_from_trainer autotrain_compatible endpoints_compatible region:us
TAGS
#transformers #pytorch #tensorboard #bert #text-classification #generated_from_trainer #autotrain_compatible #endpoints_compatible #region-us 




# bert-base-german-cased-gnad10-finetuned-tagesschau-subcategories

This model is a fine-tuned version of Mathking/bert-base-german-cased-gnad10 on the None dataset.
It achieves the following results on the evaluation set:
- eval_loss: 0.4599
- eval_accuracy: 0.84
- eval_runtime: 71.9717
- eval_samples_per_second: 2.084
- eval_steps_per_second: 0.139
- epoch: 1.2
- step: 90

## 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:
- learning_rate: 2e-05
- train_batch_size: 16
- eval_batch_size: 16
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 5

### Framework versions

- Transformers 4.21.3
- Pytorch 1.13.0+cpu
- Datasets 2.8.0
- Tokenizers 0.12.1

# bert-base-german-cased-gnad10-finetuned-tagesschau-subcategories

This model is a fine-tuned version of Mathking/bert-base-german-cased-gnad10 on the None dataset.
It achieves the following results on the evaluation set:
- eval_loss: 0.4599
- eval_accuracy: 0.84
- eval_runtime: 71.9717
- eval_samples_per_second: 2.084
- eval_steps_per_second: 0.139
- epoch: 1.2
- step: 90 ## 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:
- learning_rate: 2e-05
- train_batch_size: 16
- eval_batch_size: 16
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 5 ### Framework versions

- Transformers 4.21.3
- Pytorch 1.13.0+cpu
- Datasets 2.8.0
- Tokenizers 0.12.1
TAGS
#transformers #pytorch #tensorboard #bert #text-classification #generated_from_trainer #autotrain_compatible #endpoints_compatible #region-us 
 # bert-base-german-cased-gnad10-finetuned-tagesschau-subcategories

This model is a fine-tuned version of Mathking/bert-base-german-cased-gnad10 on the None dataset.
It achieves the following results on the evaluation set:
- eval_loss: 0.4599
- eval_accuracy: 0.84
- eval_runtime: 71.9717
- eval_samples_per_second: 2.084
- eval_steps_per_second: 0.139
- epoch: 1.2
- step: 90 ## 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:
- learning_rate: 2e-05
- train_batch_size: 16
- eval_batch_size: 16
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 5 ### Framework versions

- Transformers 4.21.3
- Pytorch 1.13.0+cpu
- Datasets 2.8.0
- Tokenizers 0.12.1
TAGS
#transformers #pytorch #tensorboard #bert #text-classification #generated_from_trainer #autotrain_compatible #endpoints_compatible #region-us 
# bert-base-german-cased-gnad10-finetuned-tagesschau-subcategories

This model is a fine-tuned version of Mathking/bert-base-german-cased-gnad10 on the None dataset.
It achieves the following results on the evaluation set:
- eval_loss: 0.4599
- eval_accuracy: 0.84
- eval_runtime: 71.9717
- eval_samples_per_second: 2.084
- eval_steps_per_second: 0.139
- epoch: 1.2
- step: 90## 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:
- learning_rate: 2e-05
- train_batch_size: 16
- eval_batch_size: 16
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 5### Framework versions

- Transformers 4.21.3
- Pytorch 1.13.0+cpu
- Datasets 2.8.0
- Tokenizers 0.12.1