---
library_name: transformers
license: mit
base_model: FacebookAI/xlm-roberta-large
tags:
- generated_from_trainer
metrics:
- accuracy
model-index:
- name: populism_classifier_bsample_106
  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_106

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: 0.9105
- Accuracy: 0.7967
- 1-f1: 0.1842
- 1-recall: 0.7778
- 1-precision: 0.1045
- Balanced Acc: 0.7875

## 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 |
|:-------------:|:-----:|:----:|:---------------:|:--------:|:------:|:--------:|:-----------:|:------------:|
| 0.3625        | 1.0   | 8    | 0.9670          | 0.6787   | 0.1404 | 0.8889   | 0.0762      | 0.7806       |
| 0.1057        | 2.0   | 16   | 0.5026          | 0.7803   | 0.1928 | 0.8889   | 0.1081      | 0.8330       |
| 0.1987        | 3.0   | 24   | 0.9663          | 0.7049   | 0.1667 | 1.0      | 0.0909      | 0.8480       |
| 0.0029        | 4.0   | 32   | 0.9105          | 0.7967   | 0.1842 | 0.7778   | 0.1045      | 0.7875       |


### Framework versions

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