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---
library_name: transformers
license: apache-2.0
base_model: google/rembert
tags:
- generated_from_trainer
metrics:
- accuracy
model-index:
- name: populism_classifier_411
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_411
This model is a fine-tuned version of [google/rembert](https://huggingface.co/google/rembert) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 0.6686
- Accuracy: 0.9118
- 1-f1: 0.0
- 1-recall: 0.0
- 1-precision: 0.0
- 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-05
- train_batch_size: 16
- eval_batch_size: 32
- 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 |
|:-------------:|:-----:|:----:|:---------------:|:--------:|:----:|:--------:|:-----------:|:------------:|
| 0.6746 | 1.0 | 91 | 0.6774 | 0.9118 | 0.0 | 0.0 | 0.0 | 0.5 |
| 0.6333 | 2.0 | 182 | 0.6769 | 0.9118 | 0.0 | 0.0 | 0.0 | 0.5 |
| 0.7695 | 3.0 | 273 | 0.6672 | 0.9118 | 0.0 | 0.0 | 0.0 | 0.5 |
| 0.6843 | 4.0 | 364 | 0.6757 | 0.9118 | 0.0 | 0.0 | 0.0 | 0.5 |
| 0.7178 | 5.0 | 455 | 0.6668 | 0.9118 | 0.0 | 0.0 | 0.0 | 0.5 |
| 0.6505 | 6.0 | 546 | 0.6666 | 0.9118 | 0.0 | 0.0 | 0.0 | 0.5 |
| 0.5584 | 7.0 | 637 | 0.6691 | 0.9118 | 0.0 | 0.0 | 0.0 | 0.5 |
| 0.8056 | 8.0 | 728 | 0.6686 | 0.9118 | 0.0 | 0.0 | 0.0 | 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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