---
tags:
- generated_from_trainer
metrics:
- accuracy
- precision
- recall
model-index:
- name: AraBERT-finetuned-CrossVal-fnd
  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. -->

# AraBERT-finetuned-CrossVal-fnd

This model is a fine-tuned version of [aubmindlab/bert-base-arabertv02](https://huggingface.co/aubmindlab/bert-base-arabertv02) on an unknown dataset.
It achieves the following results on the evaluation set:
- Loss: 0.1603
- Macro F1: 0.9387
- Accuracy: 0.9410
- Precision: 0.9415
- Recall: 0.9363

## 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: 16
- eval_batch_size: 32
- seed: 123
- gradient_accumulation_steps: 2
- total_train_batch_size: 32
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 4

### Training results

| Training Loss | Epoch | Step | Validation Loss | Macro F1 | Accuracy | Precision | Recall |
|:-------------:|:-----:|:----:|:---------------:|:--------:|:--------:|:---------:|:------:|
| 0.3283        | 1.0   | 798  | 0.1603          | 0.9387   | 0.9410   | 0.9415    | 0.9363 |
| 0.2274        | 2.0   | 1596 | 0.1826          | 0.9254   | 0.9271   | 0.9225    | 0.9298 |


### Framework versions

- Transformers 4.20.1
- Pytorch 1.11.0
- Datasets 2.1.0
- Tokenizers 0.12.1