---
library_name: transformers
license: mit
base_model: AnonymousCS/populism_xlmr_base
tags:
- generated_from_trainer
metrics:
- accuracy
model-index:
- name: populism_classifier_bsample_200
  results: []
---

<!-- This model card has been generated automatically according to the information the Trainer had access to. You
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# populism_classifier_bsample_200

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: 0.7183
- Accuracy: 0.0588
- 1-f1: 0.1111
- 1-recall: 1.0
- 1-precision: 0.0588
- 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 |
|:-------------:|:-----:|:----:|:---------------:|:--------:|:------:|:--------:|:-----------:|:------------:|
| 0.6358        | 1.0   | 11   | 0.7573          | 0.0588   | 0.1111 | 1.0      | 0.0588      | 0.5          |
| 0.7499        | 2.0   | 22   | 0.7548          | 0.0588   | 0.1111 | 1.0      | 0.0588      | 0.5          |
| 0.7009        | 3.0   | 33   | 0.7522          | 0.0588   | 0.1111 | 1.0      | 0.0588      | 0.5          |
| 0.7169        | 4.0   | 44   | 0.7465          | 0.0588   | 0.1111 | 1.0      | 0.0588      | 0.5          |
| 0.672         | 5.0   | 55   | 0.7413          | 0.0588   | 0.1111 | 1.0      | 0.0588      | 0.5          |
| 0.6805        | 6.0   | 66   | 0.7366          | 0.0588   | 0.1111 | 1.0      | 0.0588      | 0.5          |
| 0.7068        | 7.0   | 77   | 0.7318          | 0.0588   | 0.1111 | 1.0      | 0.0588      | 0.5          |
| 0.7164        | 8.0   | 88   | 0.7288          | 0.0588   | 0.1111 | 1.0      | 0.0588      | 0.5          |
| 0.6938        | 9.0   | 99   | 0.7257          | 0.0588   | 0.1111 | 1.0      | 0.0588      | 0.5          |
| 0.7176        | 10.0  | 110  | 0.7231          | 0.0588   | 0.1111 | 1.0      | 0.0588      | 0.5          |
| 0.7314        | 11.0  | 121  | 0.7218          | 0.0588   | 0.1111 | 1.0      | 0.0588      | 0.5          |
| 0.6862        | 12.0  | 132  | 0.7205          | 0.0588   | 0.1111 | 1.0      | 0.0588      | 0.5          |
| 0.7093        | 13.0  | 143  | 0.7197          | 0.0588   | 0.1111 | 1.0      | 0.0588      | 0.5          |
| 0.7162        | 14.0  | 154  | 0.7188          | 0.0588   | 0.1111 | 1.0      | 0.0588      | 0.5          |
| 0.6544        | 15.0  | 165  | 0.7183          | 0.0588   | 0.1111 | 1.0      | 0.0588      | 0.5          |


### Framework versions

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