---
library_name: transformers
license: mit
base_model: AnonymousCS/populism_multilingual_roberta_base
tags:
- generated_from_trainer
metrics:
- accuracy
- f1
- recall
- precision
model-index:
- name: populism_model69
  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_model69

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: 0.5978
- Accuracy: 0.6997
- F1: 0.2378
- Recall: 0.5862
- Precision: 0.1491

## 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: 128
- eval_batch_size: 128
- 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: 5
- mixed_precision_training: Native AMP

### Training results

| Training Loss | Epoch | Step | Validation Loss | Accuracy | F1     | Recall | Precision |
|:-------------:|:-----:|:----:|:---------------:|:--------:|:------:|:------:|:---------:|
| No log        | 1.0   | 12   | 0.5366          | 0.6612   | 0.2454 | 0.6897 | 0.1493    |
| No log        | 2.0   | 24   | 0.5615          | 0.6749   | 0.2532 | 0.6897 | 0.1550    |
| No log        | 3.0   | 36   | 0.5669          | 0.6860   | 0.25   | 0.6552 | 0.1545    |
| No log        | 4.0   | 48   | 0.6013          | 0.7273   | 0.2556 | 0.5862 | 0.1635    |
| 0.5275        | 5.0   | 60   | 0.5978          | 0.6997   | 0.2378 | 0.5862 | 0.1491    |


### Framework versions

- Transformers 4.47.1
- Pytorch 2.5.1+cu121
- Datasets 3.2.0
- Tokenizers 0.21.0