---
library_name: transformers
license: mit
base_model: AnonymousCS/populism_xlmr_base
tags:
- generated_from_trainer
metrics:
- accuracy
model-index:
- name: populism_classifier_bsample_187
  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_187

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: 0.7374
- Accuracy: 0.0359
- 1-f1: 0.0693
- 1-recall: 1.0
- 1-precision: 0.0359
- 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 |
|:-------------:|:-----:|:----:|:---------------:|:--------:|:------:|:--------:|:-----------:|:------------:|
| 0.7144        | 1.0   | 13   | 0.7651          | 0.0359   | 0.0693 | 1.0      | 0.0359      | 0.5          |
| 0.7109        | 2.0   | 26   | 0.7570          | 0.0359   | 0.0693 | 1.0      | 0.0359      | 0.5          |
| 0.7222        | 3.0   | 39   | 0.7542          | 0.0359   | 0.0693 | 1.0      | 0.0359      | 0.5          |
| 0.6849        | 4.0   | 52   | 0.7506          | 0.0359   | 0.0693 | 1.0      | 0.0359      | 0.5          |
| 0.7012        | 5.0   | 65   | 0.7478          | 0.0359   | 0.0693 | 1.0      | 0.0359      | 0.5          |
| 0.7035        | 6.0   | 78   | 0.7465          | 0.0359   | 0.0693 | 1.0      | 0.0359      | 0.5          |
| 0.6665        | 7.0   | 91   | 0.7447          | 0.0359   | 0.0693 | 1.0      | 0.0359      | 0.5          |
| 0.7684        | 8.0   | 104  | 0.7451          | 0.0359   | 0.0693 | 1.0      | 0.0359      | 0.5          |
| 0.7179        | 9.0   | 117  | 0.7447          | 0.0359   | 0.0693 | 1.0      | 0.0359      | 0.5          |
| 0.6673        | 10.0  | 130  | 0.7428          | 0.0359   | 0.0693 | 1.0      | 0.0359      | 0.5          |
| 0.7015        | 11.0  | 143  | 0.7410          | 0.0359   | 0.0693 | 1.0      | 0.0359      | 0.5          |
| 0.6969        | 12.0  | 156  | 0.7392          | 0.0359   | 0.0693 | 1.0      | 0.0359      | 0.5          |
| 0.7305        | 13.0  | 169  | 0.7378          | 0.0359   | 0.0693 | 1.0      | 0.0359      | 0.5          |
| 0.699         | 14.0  | 182  | 0.7374          | 0.0359   | 0.0693 | 1.0      | 0.0359      | 0.5          |
| 0.7246        | 15.0  | 195  | 0.7374          | 0.0359   | 0.0693 | 1.0      | 0.0359      | 0.5          |


### Framework versions

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