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library_name: transformers
license: mit
base_model: AnonymousCS/populism_multilingual_roberta_base
tags:
- generated_from_trainer
metrics:
- accuracy
model-index:
- name: populism_classifier_243
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_243
This model is a fine-tuned version of [AnonymousCS/populism_multilingual_roberta_base](https://huggingface.co/AnonymousCS/populism_multilingual_roberta_base) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 0.8283
- Accuracy: 0.9344
- 1-f1: 0.4516
- 1-recall: 0.5385
- 1-precision: 0.3889
- Balanced Acc: 0.7469
## 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-05
- train_batch_size: 64
- eval_batch_size: 64
- 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
- num_epochs: 20
- mixed_precision_training: Native AMP
### Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy | 1-f1 | 1-recall | 1-precision | Balanced Acc |
|:-------------:|:-----:|:----:|:---------------:|:--------:|:------:|:--------:|:-----------:|:------------:|
| 0.4982 | 1.0 | 33 | 0.4862 | 0.7181 | 0.2151 | 0.7692 | 0.125 | 0.7423 |
| 0.1442 | 2.0 | 66 | 0.6065 | 0.9517 | 0.4898 | 0.4615 | 0.5217 | 0.7196 |
| 0.2403 | 3.0 | 99 | 0.4708 | 0.8900 | 0.3871 | 0.6923 | 0.2687 | 0.7964 |
| 0.0461 | 4.0 | 132 | 0.5945 | 0.9189 | 0.4324 | 0.6154 | 0.3333 | 0.7752 |
| 0.1079 | 5.0 | 165 | 0.7173 | 0.9402 | 0.4561 | 0.5 | 0.4194 | 0.7317 |
| 0.0124 | 6.0 | 198 | 0.8283 | 0.9344 | 0.4516 | 0.5385 | 0.3889 | 0.7469 |
### Framework versions
- Transformers 4.46.3
- Pytorch 2.4.1+cu121
- Datasets 3.1.0
- Tokenizers 0.20.3
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