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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_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
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