---
library_name: transformers
base_model: aubmindlab/bert-base-arabertv02-twitter
tags:
- generated_from_trainer
metrics:
- accuracy
model-index:
- name: Twitter_concatenatewithPrompt_Augmentation-fold4
  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. -->

# Twitter_concatenatewithPrompt_Augmentation-fold4

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: 0.4110
- Accuracy: 0.8605
- Macro F1: 0.8602
- Weighted F1: 0.8606
- F1 Pro: 0.8789
- F1 Against: 0.856
- F1 Neutral: 0.8458

## 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
- gradient_accumulation_steps: 2
- total_train_batch_size: 32
- 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: cosine
- lr_scheduler_warmup_steps: 0.1
- num_epochs: 8
- mixed_precision_training: Native AMP

### Training results

| Training Loss | Epoch  | Step | Validation Loss | Accuracy | Macro F1 | Weighted F1 | F1 Pro | F1 Against | F1 Neutral |
|:-------------:|:------:|:----:|:---------------:|:--------:|:--------:|:-----------:|:------:|:----------:|:----------:|
| 1.7120        | 2.3294 | 100  | 0.5647          | 0.7656   | 0.7664   | 0.7661      | 0.7867 | 0.7479     | 0.7644     |
| 0.8256        | 4.6588 | 200  | 0.4331          | 0.8427   | 0.8422   | 0.8427      | 0.8610 | 0.8392     | 0.8265     |
| 0.4433        | 6.9882 | 300  | 0.4109          | 0.8605   | 0.8602   | 0.8606      | 0.8789 | 0.856      | 0.8458     |


### Framework versions

- Transformers 5.0.0
- Pytorch 2.9.0+cu128
- Datasets 4.0.0
- Tokenizers 0.22.2