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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_247
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_247
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: 0.7165
- Accuracy: 0.8812
- 1-f1: 0.2169
- 1-recall: 0.3913
- 1-precision: 0.15
- Balanced Acc: 0.6470
## 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 |
|:-------------:|:-----:|:----:|:---------------:|:--------:|:------:|:--------:|:-----------:|:------------:|
| 0.5153 | 1.0 | 35 | 0.4105 | 0.8190 | 0.2774 | 0.8261 | 0.1667 | 0.8224 |
| 0.276 | 2.0 | 70 | 0.6425 | 0.9287 | 0.0930 | 0.0870 | 0.1 | 0.5263 |
| 0.304 | 3.0 | 105 | 0.3778 | 0.8483 | 0.2385 | 0.5652 | 0.1512 | 0.7130 |
| 0.2302 | 4.0 | 140 | 0.5725 | 0.8885 | 0.2469 | 0.4348 | 0.1724 | 0.6716 |
| 0.1769 | 5.0 | 175 | 0.8414 | 0.9269 | 0.1304 | 0.1304 | 0.1304 | 0.5461 |
| 0.9633 | 6.0 | 210 | 0.7165 | 0.8812 | 0.2169 | 0.3913 | 0.15 | 0.6470 |
### Framework versions
- Transformers 4.46.3
- Pytorch 2.4.1+cu121
- Datasets 3.1.0
- Tokenizers 0.20.3
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