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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_184
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_184
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: 0.9426
- Accuracy: 0.0295
- 1-f1: 0.0573
- 1-recall: 1.0
- 1-precision: 0.0295
- 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 |
|:-------------:|:-----:|:----:|:---------------:|:--------:|:------:|:--------:|:-----------:|:------------:|
| 0.5448 | 1.0 | 8 | 1.0716 | 0.0295 | 0.0573 | 1.0 | 0.0295 | 0.5 |
| 0.8134 | 2.0 | 16 | 1.0558 | 0.0295 | 0.0573 | 1.0 | 0.0295 | 0.5 |
| 0.8261 | 3.0 | 24 | 1.0399 | 0.0295 | 0.0573 | 1.0 | 0.0295 | 0.5 |
| 0.9282 | 4.0 | 32 | 1.0228 | 0.0295 | 0.0573 | 1.0 | 0.0295 | 0.5 |
| 0.5454 | 5.0 | 40 | 1.0064 | 0.0295 | 0.0573 | 1.0 | 0.0295 | 0.5 |
| 0.9706 | 6.0 | 48 | 0.9933 | 0.0295 | 0.0573 | 1.0 | 0.0295 | 0.5 |
| 0.6615 | 7.0 | 56 | 0.9817 | 0.0295 | 0.0573 | 1.0 | 0.0295 | 0.5 |
| 0.5593 | 8.0 | 64 | 0.9710 | 0.0295 | 0.0573 | 1.0 | 0.0295 | 0.5 |
| 0.8249 | 9.0 | 72 | 0.9636 | 0.0295 | 0.0573 | 1.0 | 0.0295 | 0.5 |
| 0.9571 | 10.0 | 80 | 0.9570 | 0.0295 | 0.0573 | 1.0 | 0.0295 | 0.5 |
| 1.001 | 11.0 | 88 | 0.9510 | 0.0295 | 0.0573 | 1.0 | 0.0295 | 0.5 |
| 0.9016 | 12.0 | 96 | 0.9473 | 0.0295 | 0.0573 | 1.0 | 0.0295 | 0.5 |
| 0.8054 | 13.0 | 104 | 0.9450 | 0.0295 | 0.0573 | 1.0 | 0.0295 | 0.5 |
| 0.6523 | 14.0 | 112 | 0.9436 | 0.0295 | 0.0573 | 1.0 | 0.0295 | 0.5 |
| 0.6263 | 15.0 | 120 | 0.9426 | 0.0295 | 0.0573 | 1.0 | 0.0295 | 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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