---
library_name: transformers
license: mit
base_model: google-bert/bert-base-german-cased
tags:
- generated_from_trainer
metrics:
- accuracy
model-index:
- name: populism_model013
  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. -->

# populism_model013

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: 0.4882
- Accuracy: 0.9158
- 1-f1: 0.4333
- 1-recall: 0.4815
- 1-precision: 0.3939
- Balanced Acc: 0.7142

## 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: 1e-05
- train_batch_size: 32
- eval_batch_size: 32
- 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 | Accuracy | 1-f1   | 1-recall | 1-precision | Balanced Acc |
|:-------------:|:-----:|:----:|:---------------:|:--------:|:------:|:--------:|:-----------:|:------------:|
| 0.4617        | 1.0   | 51   | 0.4239          | 0.8936   | 0.4267 | 0.5926   | 0.3333      | 0.7539       |
| 0.3418        | 2.0   | 102  | 0.4725          | 0.9183   | 0.4590 | 0.5185   | 0.4118      | 0.7327       |
| 0.2711        | 3.0   | 153  | 0.4882          | 0.9158   | 0.4333 | 0.4815   | 0.3939      | 0.7142       |


### Framework versions

- Transformers 4.49.0.dev0
- Pytorch 2.5.1+cu124
- Datasets 3.2.0
- Tokenizers 0.21.0