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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_240
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_240
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: 1.0545
- Accuracy: 0.9219
- 1-f1: 0.4118
- 1-recall: 0.4242
- 1-precision: 0.4
- Balanced Acc: 0.6902
## 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 |
|:-------------:|:-----:|:----:|:---------------:|:--------:|:------:|:--------:|:-----------:|:------------:|
| 0.4838 | 1.0 | 32 | 0.4396 | 0.7578 | 0.3261 | 0.9091 | 0.1987 | 0.8282 |
| 0.2209 | 2.0 | 64 | 0.4442 | 0.8965 | 0.4301 | 0.6061 | 0.3333 | 0.7613 |
| 0.279 | 3.0 | 96 | 0.4339 | 0.8984 | 0.4694 | 0.6970 | 0.3538 | 0.8046 |
| 0.2208 | 4.0 | 128 | 1.0429 | 0.9277 | 0.3934 | 0.3636 | 0.4286 | 0.6651 |
| 0.0531 | 5.0 | 160 | 0.9513 | 0.9082 | 0.3733 | 0.4242 | 0.3333 | 0.6829 |
| 0.0195 | 6.0 | 192 | 1.0545 | 0.9219 | 0.4118 | 0.4242 | 0.4 | 0.6902 |
### Framework versions
- Transformers 4.46.3
- Pytorch 2.4.1+cu121
- Datasets 3.1.0
- Tokenizers 0.20.3
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