---
library_name: transformers
license: mit
base_model: AnonymousCS/populism_xlmr_base
tags:
- generated_from_trainer
metrics:
- accuracy
model-index:
- name: populism_classifier_bsample_189
  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_189

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: 0.6058
- Accuracy: 0.9710
- 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 |
|:-------------:|:-----:|:----:|:---------------:|:--------:|:----:|:--------:|:-----------:|:------------:|
| 0.7379        | 1.0   | 10   | 0.6291          | 0.9710   | 0.0  | 0.0      | 0.0         | 0.5          |
| 0.6749        | 2.0   | 20   | 0.6250          | 0.9710   | 0.0  | 0.0      | 0.0         | 0.5          |
| 0.6725        | 3.0   | 30   | 0.6218          | 0.9710   | 0.0  | 0.0      | 0.0         | 0.5          |
| 0.6775        | 4.0   | 40   | 0.6177          | 0.9710   | 0.0  | 0.0      | 0.0         | 0.5          |
| 0.6815        | 5.0   | 50   | 0.6154          | 0.9710   | 0.0  | 0.0      | 0.0         | 0.5          |
| 0.6848        | 6.0   | 60   | 0.6154          | 0.9710   | 0.0  | 0.0      | 0.0         | 0.5          |
| 0.6977        | 7.0   | 70   | 0.6140          | 0.9710   | 0.0  | 0.0      | 0.0         | 0.5          |
| 0.7019        | 8.0   | 80   | 0.6117          | 0.9710   | 0.0  | 0.0      | 0.0         | 0.5          |
| 0.7114        | 9.0   | 90   | 0.6099          | 0.9710   | 0.0  | 0.0      | 0.0         | 0.5          |
| 0.7529        | 10.0  | 100  | 0.6085          | 0.9710   | 0.0  | 0.0      | 0.0         | 0.5          |
| 0.7301        | 11.0  | 110  | 0.6076          | 0.9710   | 0.0  | 0.0      | 0.0         | 0.5          |
| 0.6978        | 12.0  | 120  | 0.6071          | 0.9710   | 0.0  | 0.0      | 0.0         | 0.5          |
| 0.7564        | 13.0  | 130  | 0.6067          | 0.9710   | 0.0  | 0.0      | 0.0         | 0.5          |
| 0.6832        | 14.0  | 140  | 0.6062          | 0.9710   | 0.0  | 0.0      | 0.0         | 0.5          |
| 0.6789        | 15.0  | 150  | 0.6058          | 0.9710   | 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