---
library_name: transformers
license: mit
base_model: FacebookAI/xlm-roberta-base
tags:
- generated_from_trainer
metrics:
- accuracy
model-index:
- name: populism_classifier_bsample_095
  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_bsample_095

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: 0.8900
- Accuracy: 0.8039
- 1-f1: 0.2754
- 1-recall: 0.8636
- 1-precision: 0.1638
- Balanced Acc: 0.8324

## 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: 3e-05
- train_batch_size: 16
- eval_batch_size: 16
- 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: 15
- mixed_precision_training: Native AMP

### Training results

| Training Loss | Epoch | Step | Validation Loss | Accuracy | 1-f1   | 1-recall | 1-precision | Balanced Acc |
|:-------------:|:-----:|:----:|:---------------:|:--------:|:------:|:--------:|:-----------:|:------------:|
| 0.412         | 1.0   | 13   | 0.8969          | 0.7569   | 0.2439 | 0.9091   | 0.1408      | 0.8295       |
| 0.049         | 2.0   | 26   | 0.6885          | 0.7471   | 0.2543 | 1.0      | 0.1457      | 0.8678       |
| 0.02          | 3.0   | 39   | 1.1921          | 0.6922   | 0.2189 | 1.0      | 0.1229      | 0.8391       |
| 0.1254        | 4.0   | 52   | 0.8900          | 0.8039   | 0.2754 | 0.8636   | 0.1638      | 0.8324       |


### Framework versions

- Transformers 4.46.3
- Pytorch 2.4.1+cu121
- Datasets 3.1.0
- Tokenizers 0.20.3