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library_name: transformers
license: mit
base_model: FacebookAI/xlm-roberta-base
tags:
- generated_from_trainer
metrics:
- accuracy
model-index:
- name: populism_classifier_bsample_079
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_079
This model is a fine-tuned version of [FacebookAI/xlm-roberta-base](https://huggingface.co/FacebookAI/xlm-roberta-base) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 0.7838
- Accuracy: 0.8245
- 1-f1: 0.3408
- 1-recall: 0.9504
- 1-precision: 0.2076
- Balanced Acc: 0.8843
## 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: 3e-05
- 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
- 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 |
|:-------------:|:-----:|:----:|:---------------:|:--------:|:------:|:--------:|:-----------:|:------------:|
| 0.5269 | 1.0 | 333 | 0.8559 | 0.7152 | 0.2444 | 0.9654 | 0.1399 | 0.8340 |
| 0.075 | 2.0 | 666 | 0.4378 | 0.8218 | 0.3276 | 0.9098 | 0.1998 | 0.8636 |
| 0.3245 | 3.0 | 999 | 0.5634 | 0.8349 | 0.3514 | 0.9368 | 0.2162 | 0.8833 |
| 0.0832 | 4.0 | 1332 | 0.3395 | 0.8799 | 0.4085 | 0.8692 | 0.2670 | 0.8748 |
| 0.2264 | 5.0 | 1665 | 0.3105 | 0.9179 | 0.4654 | 0.7489 | 0.3376 | 0.8376 |
| 0.2779 | 6.0 | 1998 | 0.8355 | 0.7710 | 0.2866 | 0.9639 | 0.1683 | 0.8626 |
| 0.1703 | 7.0 | 2331 | 0.7838 | 0.8245 | 0.3408 | 0.9504 | 0.2076 | 0.8843 |
### Framework versions
- Transformers 4.46.3
- Pytorch 2.4.1+cu121
- Datasets 3.1.0
- Tokenizers 0.20.3
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