---
library_name: transformers
license: mit
base_model: AnonymousCS/populism_xlmr_base
tags:
- generated_from_trainer
metrics:
- accuracy
model-index:
- name: populism_classifier_bsample_206
  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_206

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: 0.9586
- Accuracy: 0.0576
- 1-f1: 0.1089
- 1-recall: 1.0
- 1-precision: 0.0576
- 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 |
|:-------------:|:-----:|:----:|:---------------:|:--------:|:------:|:--------:|:-----------:|:------------:|
| 0.6637        | 1.0   | 14   | 1.0498          | 0.0576   | 0.1089 | 1.0      | 0.0576      | 0.5          |
| 0.7143        | 2.0   | 28   | 1.0328          | 0.0576   | 0.1089 | 1.0      | 0.0576      | 0.5          |
| 0.6766        | 3.0   | 42   | 1.0204          | 0.0576   | 0.1089 | 1.0      | 0.0576      | 0.5          |
| 0.7573        | 4.0   | 56   | 1.0106          | 0.0576   | 0.1089 | 1.0      | 0.0576      | 0.5          |
| 0.729         | 5.0   | 70   | 0.9990          | 0.0576   | 0.1089 | 1.0      | 0.0576      | 0.5          |
| 0.6968        | 6.0   | 84   | 0.9919          | 0.0576   | 0.1089 | 1.0      | 0.0576      | 0.5          |
| 0.6379        | 7.0   | 98   | 0.9839          | 0.0576   | 0.1089 | 1.0      | 0.0576      | 0.5          |
| 0.718         | 8.0   | 112  | 0.9766          | 0.0576   | 0.1089 | 1.0      | 0.0576      | 0.5          |
| 0.6331        | 9.0   | 126  | 0.9706          | 0.0576   | 0.1089 | 1.0      | 0.0576      | 0.5          |
| 0.807         | 10.0  | 140  | 0.9697          | 0.0576   | 0.1089 | 1.0      | 0.0576      | 0.5          |
| 0.6774        | 11.0  | 154  | 0.9653          | 0.0576   | 0.1089 | 1.0      | 0.0576      | 0.5          |
| 0.6837        | 12.0  | 168  | 0.9622          | 0.0576   | 0.1089 | 1.0      | 0.0576      | 0.5          |
| 0.6331        | 13.0  | 182  | 0.9599          | 0.0576   | 0.1089 | 1.0      | 0.0576      | 0.5          |
| 0.7114        | 14.0  | 196  | 0.9590          | 0.0576   | 0.1089 | 1.0      | 0.0576      | 0.5          |
| 0.6857        | 15.0  | 210  | 0.9586          | 0.0576   | 0.1089 | 1.0      | 0.0576      | 0.5          |


### Framework versions

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