---
library_name: transformers
license: mit
base_model: FacebookAI/xlm-roberta-large
tags:
- generated_from_trainer
metrics:
- accuracy
model-index:
- name: populism_classifier_bsample_109
  results: []
---

<!-- This model card has been generated automatically according to the information the Trainer had access to. You
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# populism_classifier_bsample_109

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: 1.2216
- Accuracy: 0.6890
- 1-f1: 0.1667
- 1-recall: 0.8667
- 1-precision: 0.0922
- Balanced Acc: 0.7745

## 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 |
|:-------------:|:-----:|:----:|:---------------:|:--------:|:------:|:--------:|:-----------:|:------------:|
| 0.6574        | 1.0   | 13   | 0.6522          | 0.7010   | 0.1379 | 0.6667   | 0.0769      | 0.6844       |
| 0.6734        | 2.0   | 26   | 0.5561          | 0.7512   | 0.1613 | 0.6667   | 0.0917      | 0.7105       |
| 0.4632        | 3.0   | 39   | 0.5145          | 0.6435   | 0.1287 | 0.7333   | 0.0705      | 0.6868       |
| 0.7198        | 4.0   | 52   | 0.8734          | 0.6794   | 0.1519 | 0.8      | 0.0839      | 0.7375       |
| 0.2578        | 5.0   | 65   | 1.2216          | 0.6890   | 0.1667 | 0.8667   | 0.0922      | 0.7745       |


### Framework versions

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