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---
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
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