---
library_name: transformers
license: apache-2.0
base_model: google/rembert
tags:
- generated_from_trainer
metrics:
- accuracy
model-index:
- name: populism_classifier_bsample_406
  results: []
---

<!-- This model card has been generated automatically according to the information the Trainer had access to. You
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# populism_classifier_bsample_406

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: 0.5690
- Accuracy: 0.8396
- 1-f1: 0.5275
- 1-recall: 0.96
- 1-precision: 0.3636
- Balanced Acc: 0.8936

## 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: 32
- 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 |
|:-------------:|:-----:|:----:|:---------------:|:--------:|:------:|:--------:|:-----------:|:------------:|
| 0.1398        | 1.0   | 4    | 0.9556          | 0.6604   | 0.3546 | 1.0      | 0.2155      | 0.8128       |
| 0.0567        | 2.0   | 8    | 0.4961          | 0.7463   | 0.4237 | 1.0      | 0.2688      | 0.8601       |
| 0.0276        | 3.0   | 12   | 0.4268          | 0.8470   | 0.5176 | 0.88     | 0.3667      | 0.8618       |
| 0.0019        | 4.0   | 16   | 0.4868          | 0.8545   | 0.5517 | 0.96     | 0.3871      | 0.9018       |
| 0.0183        | 5.0   | 20   | 0.5690          | 0.8396   | 0.5275 | 0.96     | 0.3636      | 0.8936       |


### Framework versions

- Transformers 4.46.3
- Pytorch 2.4.1+cu121
- Datasets 3.1.0
- Tokenizers 0.20.3