---
library_name: transformers
license: mit
base_model: FacebookAI/xlm-roberta-base
tags:
- generated_from_trainer
metrics:
- accuracy
model-index:
- name: populism_classifier_bsample_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_bsample_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: 0.7838
- Accuracy: 0.8245
- 1-f1: 0.3408
- 1-recall: 0.9504
- 1-precision: 0.2076
- Balanced Acc: 0.8843

## 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 |
|:-------------:|:-----:|:----:|:---------------:|:--------:|:------:|:--------:|:-----------:|:------------:|
| 0.5269        | 1.0   | 333  | 0.8559          | 0.7152   | 0.2444 | 0.9654   | 0.1399      | 0.8340       |
| 0.075         | 2.0   | 666  | 0.4378          | 0.8218   | 0.3276 | 0.9098   | 0.1998      | 0.8636       |
| 0.3245        | 3.0   | 999  | 0.5634          | 0.8349   | 0.3514 | 0.9368   | 0.2162      | 0.8833       |
| 0.0832        | 4.0   | 1332 | 0.3395          | 0.8799   | 0.4085 | 0.8692   | 0.2670      | 0.8748       |
| 0.2264        | 5.0   | 1665 | 0.3105          | 0.9179   | 0.4654 | 0.7489   | 0.3376      | 0.8376       |
| 0.2779        | 6.0   | 1998 | 0.8355          | 0.7710   | 0.2866 | 0.9639   | 0.1683      | 0.8626       |
| 0.1703        | 7.0   | 2331 | 0.7838          | 0.8245   | 0.3408 | 0.9504   | 0.2076      | 0.8843       |


### Framework versions

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