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library_name: transformers
license: mit
base_model: AnonymousCS/populism_xlmr_base
tags:
- generated_from_trainer
metrics:
- accuracy
model-index:
- name: populism_classifier_bsample_191
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_191
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: 0.8839
- Accuracy: 0.0552
- 1-f1: 0.1047
- 1-recall: 1.0
- 1-precision: 0.0552
- 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 |
|:-------------:|:-----:|:----:|:---------------:|:--------:|:------:|:--------:|:-----------:|:------------:|
| 0.7631 | 1.0 | 14 | 1.0478 | 0.0552 | 0.1047 | 1.0 | 0.0552 | 0.5 |
| 0.9633 | 2.0 | 28 | 1.0210 | 0.0552 | 0.1047 | 1.0 | 0.0552 | 0.5 |
| 0.5929 | 3.0 | 42 | 0.9960 | 0.0552 | 0.1047 | 1.0 | 0.0552 | 0.5 |
| 0.746 | 4.0 | 56 | 0.9764 | 0.0552 | 0.1047 | 1.0 | 0.0552 | 0.5 |
| 0.7519 | 5.0 | 70 | 0.9590 | 0.0552 | 0.1047 | 1.0 | 0.0552 | 0.5 |
| 0.8273 | 6.0 | 84 | 0.9430 | 0.0552 | 0.1047 | 1.0 | 0.0552 | 0.5 |
| 0.7775 | 7.0 | 98 | 0.9296 | 0.0552 | 0.1047 | 1.0 | 0.0552 | 0.5 |
| 0.7445 | 8.0 | 112 | 0.9189 | 0.0552 | 0.1047 | 1.0 | 0.0552 | 0.5 |
| 0.7983 | 9.0 | 126 | 0.9096 | 0.0552 | 0.1047 | 1.0 | 0.0552 | 0.5 |
| 0.6858 | 10.0 | 140 | 0.9034 | 0.0552 | 0.1047 | 1.0 | 0.0552 | 0.5 |
| 0.8285 | 11.0 | 154 | 0.8959 | 0.0552 | 0.1047 | 1.0 | 0.0552 | 0.5 |
| 0.9547 | 12.0 | 168 | 0.8905 | 0.0552 | 0.1047 | 1.0 | 0.0552 | 0.5 |
| 0.6147 | 13.0 | 182 | 0.8866 | 0.0552 | 0.1047 | 1.0 | 0.0552 | 0.5 |
| 0.7742 | 14.0 | 196 | 0.8848 | 0.0552 | 0.1047 | 1.0 | 0.0552 | 0.5 |
| 0.6978 | 15.0 | 210 | 0.8839 | 0.0552 | 0.1047 | 1.0 | 0.0552 | 0.5 |
### Framework versions
- Transformers 4.46.3
- Pytorch 2.4.1+cu121
- Datasets 3.1.0
- Tokenizers 0.20.3
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