---
library_name: transformers
license: mit
base_model: google-bert/bert-base-german-cased
tags:
- generated_from_trainer
metrics:
- precision
- recall
- f1
- accuracy
model-index:
- name: curated_normal_dataset
  results: []
---

<!-- 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. -->

# curated_normal_dataset

This model is a fine-tuned version of [google-bert/bert-base-german-cased](https://huggingface.co/google-bert/bert-base-german-cased) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 0.0261
- Precision: 0.9785
- Recall: 0.9681
- F1: 0.9733
- Accuracy: 0.9981

## 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: Use OptimizerNames.ADAMW_TORCH with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments
- lr_scheduler_type: linear
- num_epochs: 3
- mixed_precision_training: Native AMP

### Training results

| Training Loss | Epoch | Step | Validation Loss | Precision | Recall | F1     | Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:---------:|:------:|:------:|:--------:|
| 0.0267        | 1.0   | 278  | 0.0186          | 0.9712    | 0.9574 | 0.9643 | 0.9977   |
| 0.0168        | 2.0   | 556  | 0.0261          | 0.9785    | 0.9681 | 0.9733 | 0.9981   |


### Framework versions

- Transformers 4.49.0
- Pytorch 2.8.0+cu126
- Datasets 4.2.0
- Tokenizers 0.21.4