---
library_name: transformers
base_model: UBC-NLP/MARBERT
tags:
- generated_from_trainer
metrics:
- accuracy
- precision
- recall
- f1
model-index:
- name: UBC-NLP-MARBERT-arabic-fp16-allagree-cameltools
  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. -->

# UBC-NLP-MARBERT-arabic-fp16-allagree-cameltools

This model is a fine-tuned version of [UBC-NLP/MARBERT](https://huggingface.co/UBC-NLP/MARBERT) on an unknown dataset.
It achieves the following results on the evaluation set:
- Loss: 0.2191
- Accuracy: 0.9384
- Precision: 0.9385
- Recall: 0.9384
- F1: 0.9384

## 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: 64
- eval_batch_size: 64
- seed: 42
- gradient_accumulation_steps: 2
- total_train_batch_size: 128
- optimizer: Use OptimizerNames.ADAMW_TORCH with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments
- lr_scheduler_type: linear
- lr_scheduler_warmup_ratio: 0.3
- num_epochs: 10
- mixed_precision_training: Native AMP

### Training results

| Training Loss | Epoch  | Step | Validation Loss | Accuracy | Precision | Recall | F1     |
|:-------------:|:------:|:----:|:---------------:|:--------:|:---------:|:------:|:------:|
| 0.9917        | 0.7463 | 50   | 0.6973          | 0.7668   | 0.8138    | 0.7668 | 0.6876 |
| 0.3936        | 1.4925 | 100  | 0.2191          | 0.9384   | 0.9385    | 0.9384 | 0.9384 |
| 0.1789        | 2.2388 | 150  | 0.1868          | 0.9450   | 0.9448    | 0.9450 | 0.9448 |
| 0.1503        | 2.9851 | 200  | 0.2098          | 0.9291   | 0.9323    | 0.9291 | 0.9296 |
| 0.0871        | 3.7313 | 250  | 0.2069          | 0.9431   | 0.9435    | 0.9431 | 0.9433 |
| 0.0484        | 4.4776 | 300  | 0.2293          | 0.9506   | 0.9506    | 0.9506 | 0.9506 |


### Framework versions

- Transformers 4.52.4
- Pytorch 2.7.0+cu126
- Datasets 3.6.0
- Tokenizers 0.21.1