---
library_name: transformers
license: mit
base_model: FacebookAI/xlm-roberta-base
tags:
- generated_from_trainer
metrics:
- accuracy
model-index:
- name: populism_classifier_103
  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_classifier_103

This model is a fine-tuned version of [FacebookAI/xlm-roberta-base](https://huggingface.co/FacebookAI/xlm-roberta-base) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 0.4698
- Accuracy: 0.9400
- 1-f1: 0.4444
- 1-recall: 0.4571
- 1-precision: 0.4324
- Balanced Acc: 0.7120

## 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 OptimizerNames.ADAMW_TORCH_FUSED with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments
- lr_scheduler_type: linear
- num_epochs: 20
- mixed_precision_training: Native AMP

### Training results

| Training Loss | Epoch | Step | Validation Loss | Accuracy | 1-f1   | 1-recall | 1-precision | Balanced Acc |
|:-------------:|:-----:|:----:|:---------------:|:--------:|:------:|:--------:|:-----------:|:------------:|
| 0.4302        | 1.0   | 21   | 0.3729          | 0.8921   | 0.3571 | 0.5714   | 0.2597      | 0.7406       |
| 0.3379        | 2.0   | 42   | 0.3342          | 0.8051   | 0.3299 | 0.9143   | 0.2013      | 0.8567       |
| 0.3594        | 3.0   | 63   | 0.3794          | 0.7781   | 0.3019 | 0.9143   | 0.1808      | 0.8424       |
| 0.4089        | 4.0   | 84   | 0.4698          | 0.9400   | 0.4444 | 0.4571   | 0.4324      | 0.7120       |


### Framework versions

- Transformers 4.56.0.dev0
- Pytorch 2.8.0+cu126
- Datasets 4.0.0
- Tokenizers 0.21.4