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---
language: ar
license: mit
base_model: UBC-NLP/MARBERT
tags:
- sentiment-analysis
- arabic
- bert
pipeline_tag: text-classification
---
<!-- 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. -->
# arabic-sentiment-analysis-model
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: 0.0339
## Model description
This model is fine-tuned for Arabic Sentiment Analysis. It can classify Arabic text into different emotional categories (Positive/Negative).
- **Developed by:** Hager Abbas
- **Language:** Arabic
- **Model Type:** Text Classification
- **Fine-tuned from:** [اسم الموديل الأصلي اللي استخدمناه، غالباً bert-base-arabic]
## Intended uses & limitations
This model is intended for analyzing social media posts, customer reviews, and general Arabic text to determine sentiment.
## How to use
```python
from transformers import pipeline
classifier = pipeline("sentiment-analysis", model="HagerAbbas/اسم-الموديل-بتاعك")
classifier("أنا سعيد جداً بهذا الإنجاز")
## 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: 8
- 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: 3
### Training results
| Training Loss | Epoch | Step | Validation Loss |
|:-------------:|:-----:|:----:|:---------------:|
| 0.168 | 1.0 | 113 | 0.2174 |
| 0.1619 | 2.0 | 226 | 0.0477 |
| 0.014 | 3.0 | 339 | 0.0339 |
### Framework versions
- Transformers 4.57.3
- Pytorch 2.9.0+cu126
- Datasets 4.4.2
- Tokenizers 0.22.1
|