---
library_name: transformers
license: mit
base_model: FacebookAI/xlm-roberta-base
tags:
- generated_from_trainer
metrics:
- accuracy
model-index:
- name: populism_classifier_092
  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_092

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: 0.2866
- Accuracy: 0.9904
- 1-f1: 0.8257
- 1-recall: 0.7895
- 1-precision: 0.8654
- Balanced Acc: 0.8929

## 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: 128
- eval_batch_size: 128
- 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 |
|:-------------:|:-----:|:----:|:---------------:|:--------:|:------:|:--------:|:-----------:|:------------:|
| 0.019         | 1.0   | 62   | 0.1108          | 0.9863   | 0.7692 | 0.7895   | 0.75        | 0.8908       |
| 0.1966        | 2.0   | 124  | 0.1625          | 0.9873   | 0.7788 | 0.7719   | 0.7857      | 0.8828       |
| 0.0228        | 3.0   | 186  | 0.0987          | 0.9878   | 0.8033 | 0.8596   | 0.7538      | 0.9257       |
| 0.2046        | 4.0   | 248  | 0.3138          | 0.9929   | 0.8654 | 0.7895   | 0.9574      | 0.8942       |
| 0.0006        | 5.0   | 310  | 0.2866          | 0.9904   | 0.8257 | 0.7895   | 0.8654      | 0.8929       |


### Framework versions

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