---
base_model: SI2M-Lab/DarijaBERT
tags:
- generated_from_trainer
metrics:
- accuracy
- recall
model-index:
- name: results
  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. -->

# results

This model is a fine-tuned version of [SI2M-Lab/DarijaBERT](https://huggingface.co/SI2M-Lab/DarijaBERT) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 0.5291
- Macro F1: 0.7697
- Accuracy: 0.8007
- Recall: 0.7687

## 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: 2e-05
- train_batch_size: 32
- eval_batch_size: 32
- seed: 42
- gradient_accumulation_steps: 4
- total_train_batch_size: 128
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 10

### Training results

| Training Loss | Epoch  | Step | Validation Loss | Macro F1 | Accuracy | Recall |
|:-------------:|:------:|:----:|:---------------:|:--------:|:--------:|:------:|
| 0.6848        | 0.9877 | 40   | 0.6040          | 0.6869   | 0.7504   | 0.6821 |
| 0.5937        | 2.0    | 81   | 0.5376          | 0.7396   | 0.7799   | 0.7286 |
| 0.4946        | 2.9877 | 121  | 0.5313          | 0.7474   | 0.7816   | 0.7434 |
| 0.386         | 4.0    | 162  | 0.5291          | 0.7697   | 0.8007   | 0.7687 |
| 0.3114        | 4.9877 | 202  | 0.5690          | 0.7391   | 0.7782   | 0.7329 |
| 0.2477        | 6.0    | 243  | 0.5891          | 0.7480   | 0.7834   | 0.7441 |
| 0.1804        | 6.9877 | 283  | 0.6194          | 0.7422   | 0.7764   | 0.7366 |


### Framework versions

- Transformers 4.41.2
- Pytorch 2.3.0+cu121
- Datasets 2.19.2
- Tokenizers 0.19.1