---
library_name: transformers
license: mit
base_model: AnonymousCS/populism_xlmr_base
tags:
- generated_from_trainer
metrics:
- accuracy
model-index:
- name: populism_classifier_bsample_193
  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_193

This model is a fine-tuned version of [AnonymousCS/populism_xlmr_base](https://huggingface.co/AnonymousCS/populism_xlmr_base) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 0.7076
- Accuracy: 0.0581
- 1-f1: 0.1098
- 1-recall: 1.0
- 1-precision: 0.0581
- Balanced Acc: 0.5

## 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-06
- 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
- lr_scheduler_warmup_ratio: 0.06
- 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 |
|:-------------:|:-----:|:----:|:---------------:|:--------:|:------:|:--------:|:-----------:|:------------:|
| 0.7227        | 1.0   | 12   | 0.7571          | 0.0581   | 0.1098 | 1.0      | 0.0581      | 0.5          |
| 0.7046        | 2.0   | 24   | 0.7470          | 0.0581   | 0.1098 | 1.0      | 0.0581      | 0.5          |
| 0.7114        | 3.0   | 36   | 0.7379          | 0.0581   | 0.1098 | 1.0      | 0.0581      | 0.5          |
| 0.7272        | 4.0   | 48   | 0.7297          | 0.0581   | 0.1098 | 1.0      | 0.0581      | 0.5          |
| 0.6995        | 5.0   | 60   | 0.7214          | 0.0581   | 0.1098 | 1.0      | 0.0581      | 0.5          |
| 0.6791        | 6.0   | 72   | 0.7193          | 0.0581   | 0.1098 | 1.0      | 0.0581      | 0.5          |
| 0.6834        | 7.0   | 84   | 0.7175          | 0.0581   | 0.1098 | 1.0      | 0.0581      | 0.5          |
| 0.6809        | 8.0   | 96   | 0.7154          | 0.0581   | 0.1098 | 1.0      | 0.0581      | 0.5          |
| 0.6887        | 9.0   | 108  | 0.7132          | 0.0581   | 0.1098 | 1.0      | 0.0581      | 0.5          |
| 0.6833        | 10.0  | 120  | 0.7128          | 0.0581   | 0.1098 | 1.0      | 0.0581      | 0.5          |
| 0.6723        | 11.0  | 132  | 0.7106          | 0.0581   | 0.1098 | 1.0      | 0.0581      | 0.5          |
| 0.7238        | 12.0  | 144  | 0.7098          | 0.0581   | 0.1098 | 1.0      | 0.0581      | 0.5          |
| 0.7287        | 13.0  | 156  | 0.7089          | 0.0581   | 0.1098 | 1.0      | 0.0581      | 0.5          |
| 0.7138        | 14.0  | 168  | 0.7080          | 0.0581   | 0.1098 | 1.0      | 0.0581      | 0.5          |
| 0.6918        | 15.0  | 180  | 0.7076          | 0.0581   | 0.1098 | 1.0      | 0.0581      | 0.5          |


### Framework versions

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