---
library_name: transformers
base_model: aubmindlab/bert-base-arabertv02-twitter
tags:
- generated_from_trainer
metrics:
- accuracy
model-index:
- name: Arabertv2-fold5
  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. -->

# Arabertv2-fold5

This model is a fine-tuned version of [aubmindlab/bert-base-arabertv02-twitter](https://huggingface.co/aubmindlab/bert-base-arabertv02-twitter) on an unknown dataset.
It achieves the following results on the evaluation set:
- Loss: 0.6600
- Accuracy: 0.8155
- Macro F1: 0.8151
- Weighted F1: 0.8151
- F1 Pro: 0.8037
- F1 Against: 0.8226
- F1 Neutral: 0.8190

## 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: 16
- seed: 42
- optimizer: Use OptimizerNames.ADAMW_TORCH_FUSED with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments
- lr_scheduler_type: linear
- num_epochs: 10
- mixed_precision_training: Native AMP

### Training results

| Training Loss | Epoch  | Step | Validation Loss | Accuracy | Macro F1 | Weighted F1 | F1 Pro | F1 Against | F1 Neutral |
|:-------------:|:------:|:----:|:---------------:|:--------:|:--------:|:-----------:|:------:|:----------:|:----------:|
| 0.9553        | 1.1628 | 50   | 0.7744          | 0.6667   | 0.6675   | 0.6667      | 0.6829 | 0.6387     | 0.6809     |
| 0.6022        | 2.3256 | 100  | 0.5448          | 0.7619   | 0.7617   | 0.7620      | 0.7748 | 0.7603     | 0.75       |
| 0.3637        | 3.4884 | 150  | 0.5355          | 0.7976   | 0.7961   | 0.7969      | 0.7961 | 0.8154     | 0.7767     |
| 0.2713        | 4.6512 | 200  | 0.5420          | 0.8036   | 0.8031   | 0.8035      | 0.8037 | 0.8130     | 0.7925     |
| 0.1627        | 5.8140 | 250  | 0.5662          | 0.7917   | 0.7914   | 0.7914      | 0.7748 | 0.8033     | 0.7961     |
| 0.1126        | 6.9767 | 300  | 0.6165          | 0.8095   | 0.8095   | 0.8092      | 0.7963 | 0.8130     | 0.8190     |
| 0.0886        | 8.1395 | 350  | 0.6597          | 0.8155   | 0.8151   | 0.8151      | 0.8037 | 0.8226     | 0.8190     |
| 0.0803        | 9.3023 | 400  | 0.6821          | 0.8095   | 0.8089   | 0.8091      | 0.7963 | 0.8226     | 0.8077     |


### Framework versions

- Transformers 5.0.0
- Pytorch 2.10.0+cu128
- Datasets 4.0.0
- Tokenizers 0.22.2