---
library_name: transformers
license: mit
base_model: FacebookAI/xlm-roberta-large
tags:
- generated_from_trainer
metrics:
- accuracy
model-index:
- name: populism_classifier_bsample_117
  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_117

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: 0.7021
- Accuracy: 0.6240
- 1-f1: 0.2088
- 1-recall: 0.9286
- 1-precision: 0.1176
- Balanced Acc: 0.7677

## 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 |
|:-------------:|:-----:|:----:|:---------------:|:--------:|:------:|:--------:|:-----------:|:------------:|
| 0.6799        | 1.0   | 13   | 0.5534          | 0.8168   | 0.2258 | 0.5      | 0.1458      | 0.6673       |
| 0.6687        | 2.0   | 26   | 0.5016          | 0.7309   | 0.2034 | 0.6429   | 0.1208      | 0.6894       |
| 0.4943        | 3.0   | 39   | 0.5893          | 0.6508   | 0.2078 | 0.8571   | 0.1182      | 0.7481       |
| 0.227         | 4.0   | 52   | 0.7021          | 0.6240   | 0.2088 | 0.9286   | 0.1176      | 0.7677       |


### Framework versions

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