---
library_name: transformers
license: mit
base_model: AnonymousCS/populism_xlmr_base
tags:
- generated_from_trainer
metrics:
- accuracy
model-index:
- name: populism_classifier_bsample_196
  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_196

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: 0.5068
- Accuracy: 0.9733
- 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 |
|:-------------:|:-----:|:----:|:---------------:|:--------:|:------:|:--------:|:-----------:|:------------:|
| 0.7368        | 1.0   | 38   | 0.7313          | 0.0267   | 0.0521 | 1.0      | 0.0267      | 0.5          |
| 0.6993        | 2.0   | 76   | 0.6730          | 0.9733   | 0.0    | 0.0      | 0.0         | 0.5          |
| 0.677         | 3.0   | 114  | 0.6288          | 0.9733   | 0.0    | 0.0      | 0.0         | 0.5          |
| 0.6889        | 4.0   | 152  | 0.5966          | 0.9733   | 0.0    | 0.0      | 0.0         | 0.5          |
| 0.7637        | 5.0   | 190  | 0.5709          | 0.9733   | 0.0    | 0.0      | 0.0         | 0.5          |
| 0.6929        | 6.0   | 228  | 0.5567          | 0.9733   | 0.0    | 0.0      | 0.0         | 0.5          |
| 0.7174        | 7.0   | 266  | 0.5389          | 0.9733   | 0.0    | 0.0      | 0.0         | 0.5          |
| 0.6366        | 8.0   | 304  | 0.5298          | 0.9733   | 0.0    | 0.0      | 0.0         | 0.5          |
| 0.6762        | 9.0   | 342  | 0.5243          | 0.9733   | 0.0    | 0.0      | 0.0         | 0.5          |
| 0.7455        | 10.0  | 380  | 0.5184          | 0.9733   | 0.0    | 0.0      | 0.0         | 0.5          |
| 0.7218        | 11.0  | 418  | 0.5129          | 0.9733   | 0.0    | 0.0      | 0.0         | 0.5          |
| 0.6597        | 12.0  | 456  | 0.5100          | 0.9733   | 0.0    | 0.0      | 0.0         | 0.5          |
| 0.6704        | 13.0  | 494  | 0.5084          | 0.9733   | 0.0    | 0.0      | 0.0         | 0.5          |
| 0.6379        | 14.0  | 532  | 0.5072          | 0.9733   | 0.0    | 0.0      | 0.0         | 0.5          |
| 0.7379        | 15.0  | 570  | 0.5068          | 0.9733   | 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