---
library_name: transformers
license: mit
base_model: AnonymousCS/populism_xlmr_base
tags:
- generated_from_trainer
metrics:
- accuracy
model-index:
- name: populism_classifier_bsample_195
  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_195

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: 0.8624
- Accuracy: 0.0534
- 1-f1: 0.1014
- 1-recall: 1.0
- 1-precision: 0.0534
- 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 |
|:-------------:|:-----:|:----:|:---------------:|:--------:|:------:|:--------:|:-----------:|:------------:|
| 0.8175        | 1.0   | 13   | 1.0490          | 0.0534   | 0.1014 | 1.0      | 0.0534      | 0.5          |
| 0.7624        | 2.0   | 26   | 1.0185          | 0.0534   | 0.1014 | 1.0      | 0.0534      | 0.5          |
| 0.8583        | 3.0   | 39   | 0.9908          | 0.0534   | 0.1014 | 1.0      | 0.0534      | 0.5          |
| 0.7144        | 4.0   | 52   | 0.9653          | 0.0534   | 0.1014 | 1.0      | 0.0534      | 0.5          |
| 0.718         | 5.0   | 65   | 0.9438          | 0.0534   | 0.1014 | 1.0      | 0.0534      | 0.5          |
| 0.7514        | 6.0   | 78   | 0.9251          | 0.0534   | 0.1014 | 1.0      | 0.0534      | 0.5          |
| 0.6915        | 7.0   | 91   | 0.9108          | 0.0534   | 0.1014 | 1.0      | 0.0534      | 0.5          |
| 0.7727        | 8.0   | 104  | 0.8979          | 0.0534   | 0.1014 | 1.0      | 0.0534      | 0.5          |
| 0.7329        | 9.0   | 117  | 0.8895          | 0.0534   | 0.1014 | 1.0      | 0.0534      | 0.5          |
| 0.7947        | 10.0  | 130  | 0.8810          | 0.0534   | 0.1014 | 1.0      | 0.0534      | 0.5          |
| 0.7162        | 11.0  | 143  | 0.8744          | 0.0534   | 0.1014 | 1.0      | 0.0534      | 0.5          |
| 0.679         | 12.0  | 156  | 0.8699          | 0.0534   | 0.1014 | 1.0      | 0.0534      | 0.5          |
| 0.7156        | 13.0  | 169  | 0.8664          | 0.0534   | 0.1014 | 1.0      | 0.0534      | 0.5          |
| 0.7271        | 14.0  | 182  | 0.8633          | 0.0534   | 0.1014 | 1.0      | 0.0534      | 0.5          |
| 0.7828        | 15.0  | 195  | 0.8624          | 0.0534   | 0.1014 | 1.0      | 0.0534      | 0.5          |


### Framework versions

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