---
library_name: transformers
license: mit
base_model: AnonymousCS/populism_xlmr_base
tags:
- generated_from_trainer
metrics:
- accuracy
model-index:
- name: populism_classifier_183
  results: []
---

<!-- This model card has been generated automatically according to the information the Trainer had access to. You
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# populism_classifier_183

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: 0.6137
- Accuracy: 0.9523
- 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-05
- train_batch_size: 128
- eval_batch_size: 128
- 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
- num_epochs: 20
- mixed_precision_training: Native AMP

### Training results

| Training Loss | Epoch | Step | Validation Loss | Accuracy | 1-f1 | 1-recall | 1-precision | Balanced Acc |
|:-------------:|:-----:|:----:|:---------------:|:--------:|:----:|:--------:|:-----------:|:------------:|
| 0.5977        | 1.0   | 436  | 0.6120          | 0.9523   | 0.0  | 0.0      | 0.0         | 0.5          |
| 0.6288        | 2.0   | 872  | 0.6115          | 0.9523   | 0.0  | 0.0      | 0.0         | 0.5          |
| 0.6311        | 3.0   | 1308 | 0.6110          | 0.9523   | 0.0  | 0.0      | 0.0         | 0.5          |
| 0.5575        | 4.0   | 1744 | 0.6115          | 0.9523   | 0.0  | 0.0      | 0.0         | 0.5          |
| 0.5841        | 5.0   | 2180 | 0.6137          | 0.9523   | 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