---
library_name: transformers
license: mit
base_model: FacebookAI/xlm-roberta-large
tags:
- generated_from_trainer
metrics:
- accuracy
model-index:
- name: populism_classifier_105
  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_105

This model is a fine-tuned version of [FacebookAI/xlm-roberta-large](https://huggingface.co/FacebookAI/xlm-roberta-large) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 0.5066
- Accuracy: 0.9602
- 1-f1: 0.6365
- 1-recall: 0.7293
- 1-precision: 0.5646
- Balanced Acc: 0.8506

## 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_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 |
|:-------------:|:-----:|:----:|:---------------:|:--------:|:------:|:--------:|:-----------:|:------------:|
| 0.0683        | 1.0   | 1742 | 0.3066          | 0.9614   | 0.5991 | 0.6045   | 0.5938      | 0.7919       |
| 0.3732        | 2.0   | 3484 | 0.2559          | 0.9332   | 0.5530 | 0.8662   | 0.4062      | 0.9014       |
| 0.0593        | 3.0   | 5226 | 0.4048          | 0.9637   | 0.6312 | 0.6511   | 0.6124      | 0.8152       |
| 0.0033        | 4.0   | 6968 | 0.5066          | 0.9602   | 0.6365 | 0.7293   | 0.5646      | 0.8506       |


### Framework versions

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