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--- |
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library_name: transformers |
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base_model: aubmindlab/bert-base-arabertv02 |
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tags: |
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- generated_from_trainer |
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metrics: |
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- accuracy |
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- f1 |
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model-index: |
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- name: arabic-sentiment-model |
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results: [] |
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language: |
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- ar |
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pipeline_tag: text-classification |
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datasets: |
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- ramybaly/arsentd_lev |
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--- |
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<!-- This model card has been generated automatically according to the information the Trainer had access to. You |
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should probably proofread and complete it, then remove this comment. --> |
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# arabic-sentiment-model |
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This model is a fine-tuned version of [aubmindlab/bert-base-arabertv02](https://huggingface.co/aubmindlab/bert-base-arabertv02) on an [ramybaly/arsentd_lev](https://huggingface.co/datasets/ramybaly/arsentd_lev) dataset. |
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It achieves the following results on the evaluation set: |
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- Loss: 0.1512 |
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- Accuracy: 0.9454 |
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- F1: 0.9454 |
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## Model description |
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This model is a fine-tuned version of |
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[aubmindlab/bert-base-arabertv02](aubmindlab/bert-base-arabertv02) |
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, |
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adapted for Arabic Sentiment Analysis. |
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The model is trained to classify Arabic text into binary sentiment classes (Positive / Negative). |
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It is suitable for analyzing opinions expressed in Modern Standard Arabic (MSA) as well as dialectal Arabic, commonly found in social media posts, product reviews, and user feedback. |
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The model benefits from AraBERT’s strong contextual understanding of Arabic morphology and syntax, resulting in high classification accuracy. |
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## Intended uses & limitations |
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This model can be used for: |
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Arabic sentiment analysis |
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Social media opinion mining |
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Customer feedback analysis |
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Academic research and NLP experiments |
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Graduation and portfolio projects |
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It is designed for inference on short to medium-length Arabic texts. |
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Limitations |
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The model performs binary sentiment classification only (no neutral class). |
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Performance may degrade on very long documents. |
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## Training and evaluation data |
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Training and Evaluation Data |
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The model was trained and evaluated using the [ramybaly/arsentd_lev dataset](ramybaly/arsentd_lev) dataset, which consists of Arabic text labeled for sentiment polarity. |
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Dataset Characteristics |
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Language: Arabic |
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Labels: Positive, Negative |
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Text Type: Short Arabic opinions and statements |
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Domains: General opinionated text |
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The dataset was split into training, evaluation, and test sets following standard supervised learning practices. |
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## Training procedure |
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Preprocessing |
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Arabic text normalization handled by AraBERT tokenizer |
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Tokenization using the AraBERT v02 tokenizer |
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Padding and truncation applied to ensure fixed input length |
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### Training hyperparameters |
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The following hyperparameters were used during training: |
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- learning_rate: 2e-05 |
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- train_batch_size: 16 |
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- eval_batch_size: 16 |
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- seed: 42 |
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- gradient_accumulation_steps: 4 |
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- total_train_batch_size: 64 |
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- optimizer: Use OptimizerNames.ADAMW_TORCH_FUSED with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments |
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- lr_scheduler_type: cosine |
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- lr_scheduler_warmup_steps: 50 |
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- num_epochs: 3 |
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### Training results |
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| Training Loss | Epoch | Step | Validation Loss | Accuracy | F1 | |
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|:-------------:|:-----:|:----:|:---------------:|:--------:|:------:| |
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| 0.2134 | 1.0 | 588 | 0.1978 | 0.9274 | 0.9274 | |
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| 0.1571 | 2.0 | 1176 | 0.1482 | 0.9438 | 0.9438 | |
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| 0.1217 | 3.0 | 1764 | 0.1512 | 0.9454 | 0.9454 | |
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### Framework versions |
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- Transformers 4.57.3 |
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- Pytorch 2.9.0+cu126 |
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- Datasets 4.0.0 |
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- Tokenizers 0.22.1 |
