---
library_name: transformers
license: mit
base_model: AnonymousCS/populism_multilingual_roberta_base
tags:
- generated_from_trainer
metrics:
- accuracy
- f1
- recall
- precision
model-index:
- name: populism_model56
  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_model56

This model is a fine-tuned version of [AnonymousCS/populism_multilingual_roberta_base](https://huggingface.co/AnonymousCS/populism_multilingual_roberta_base) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 0.4856
- Accuracy: 0.8519
- F1: 0.3103
- Recall: 0.6301
- Precision: 0.2058

## 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: 128
- eval_batch_size: 128
- seed: 42
- optimizer: Use 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: 5
- mixed_precision_training: Native AMP

### Training results

| Training Loss | Epoch | Step | Validation Loss | Accuracy | F1     | Recall | Precision |
|:-------------:|:-----:|:----:|:---------------:|:--------:|:------:|:------:|:---------:|
| 0.6562        | 1.0   | 87   | 0.5132          | 0.9247   | 0.1746 | 0.1507 | 0.2075    |
| 0.5           | 2.0   | 174  | 0.4700          | 0.8751   | 0.3086 | 0.5274 | 0.2181    |
| 0.4517        | 3.0   | 261  | 0.4616          | 0.8700   | 0.3214 | 0.5822 | 0.2219    |
| 0.4041        | 4.0   | 348  | 0.4681          | 0.7936   | 0.2692 | 0.7192 | 0.1656    |
| 0.37          | 5.0   | 435  | 0.4856          | 0.8519   | 0.3103 | 0.6301 | 0.2058    |


### Framework versions

- Transformers 4.47.1
- Pytorch 2.5.1+cu121
- Datasets 3.2.0
- Tokenizers 0.21.0