---
library_name: transformers
license: mit
base_model: FacebookAI/xlm-roberta-base
tags:
- generated_from_trainer
metrics:
- accuracy
model-index:
- name: populism_classifier_079
  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_079

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: 0.3726
- Accuracy: 0.9495
- 1-f1: 0.5652
- 1-recall: 0.6872
- 1-precision: 0.4800
- Balanced Acc: 0.8250

## 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 |
|:-------------:|:-----:|:----:|:---------------:|:--------:|:------:|:--------:|:-----------:|:------------:|
| 0.3081        | 1.0   | 436  | 0.2883          | 0.9422   | 0.5373 | 0.7038   | 0.4345      | 0.8289       |
| 0.2317        | 2.0   | 872  | 0.2401          | 0.9052   | 0.4688 | 0.8767   | 0.3200      | 0.8917       |
| 0.1213        | 3.0   | 1308 | 0.2857          | 0.9432   | 0.5605 | 0.7594   | 0.4442      | 0.8559       |
| 0.2189        | 4.0   | 1744 | 0.3726          | 0.9495   | 0.5652 | 0.6872   | 0.4800      | 0.8250       |


### Framework versions

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