---
library_name: transformers
license: mit
base_model: AnonymousCS/populism_xlmr_base
tags:
- generated_from_trainer
metrics:
- accuracy
model-index:
- name: populism_classifier_bsample_192
  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_192

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: 0.6731
- Accuracy: 0.9713
- 1-f1: 0.0
- 1-recall: 0.0
- 1-precision: 0.0
- 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 |
|:-------------:|:-----:|:----:|:---------------:|:--------:|:------:|:--------:|:-----------:|:------------:|
| 0.7181        | 1.0   | 17   | 0.7579          | 0.0287   | 0.0558 | 1.0      | 0.0287      | 0.5          |
| 0.6644        | 2.0   | 34   | 0.7431          | 0.0287   | 0.0558 | 1.0      | 0.0287      | 0.5          |
| 0.7257        | 3.0   | 51   | 0.7321          | 0.0287   | 0.0558 | 1.0      | 0.0287      | 0.5          |
| 0.7576        | 4.0   | 68   | 0.7196          | 0.0287   | 0.0558 | 1.0      | 0.0287      | 0.5          |
| 0.7521        | 5.0   | 85   | 0.7113          | 0.0287   | 0.0558 | 1.0      | 0.0287      | 0.5          |
| 0.7068        | 6.0   | 102  | 0.7053          | 0.0287   | 0.0558 | 1.0      | 0.0287      | 0.5          |
| 0.735         | 7.0   | 119  | 0.6979          | 0.0287   | 0.0558 | 1.0      | 0.0287      | 0.5          |
| 0.6718        | 8.0   | 136  | 0.6920          | 0.9713   | 0.0    | 0.0      | 0.0         | 0.5          |
| 0.6794        | 9.0   | 153  | 0.6874          | 0.9713   | 0.0    | 0.0      | 0.0         | 0.5          |
| 0.7529        | 10.0  | 170  | 0.6814          | 0.9713   | 0.0    | 0.0      | 0.0         | 0.5          |
| 0.714         | 11.0  | 187  | 0.6773          | 0.9713   | 0.0    | 0.0      | 0.0         | 0.5          |
| 0.6746        | 12.0  | 204  | 0.6759          | 0.9713   | 0.0    | 0.0      | 0.0         | 0.5          |
| 0.7143        | 13.0  | 221  | 0.6741          | 0.9713   | 0.0    | 0.0      | 0.0         | 0.5          |
| 0.7303        | 14.0  | 238  | 0.6731          | 0.9713   | 0.0    | 0.0      | 0.0         | 0.5          |
| 0.7097        | 15.0  | 255  | 0.6731          | 0.9713   | 0.0    | 0.0      | 0.0         | 0.5          |


### Framework versions

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