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README.md

@@ -1,8 +1,122 @@
----
-license: apache-2.0
-language:
-- ary
-metrics:
-- accuracy
-pipeline_tag: text-classification
----
+# Sentiment Analysis for Darija (Arabic Dialect)
+
+This repository hosts a **Sentiment Analysis model for Darija** (Moroccan Arabic dialect), built using **BERT**. The model is fine-tuned to classify text into two categories: **positive** and **negative** sentiment. It is designed to facilitate sentiment analysis in applications involving Darija text data, such as social media analysis, customer feedback, or market research.
+
+---
+
+## Model Details
+
+- **Base Model**: [SI2M-Lab/DarijaBERT](https://huggingface.co/SI2M-Lab/DarijaBERT)
+- **Task**: Sentiment Classification (Binary)
+- **Architecture**: BERT with a custom classification head and dropout regularization (0.3 dropout rate).
+- **Fine-Tuning Data**: Dataset of labeled Darija text samples (positive and negative).
+- **Max Sequence Length**: 128 tokens
+
+---
+
+## How to Use
+
+### Load the Model and Tokenizer
+
+To use this model for sentiment analysis, you can load it using the Transformers library:
+
+```python
+from transformers import AutoTokenizer, AutoModelForSequenceClassification
+
+# Load the tokenizer and model
+tokenizer = AutoTokenizer.from_pretrained("BenhamdaneNawfal/sentiment-analysis-darija")
+model = AutoModelForSequenceClassification.from_pretrained("BenhamdaneNawfal/sentiment-analysis-darija")
+
+# Example text
+test_text = "هذا المنتج رائع جدا"
+
+# Tokenize the text
+inputs = tokenizer(test_text, return_tensors="pt", truncation=True, padding=True, max_length=128)
+
+# Get model predictions
+outputs = model(**inputs)
+logits = outputs.logits
+predicted_class = logits.argmax().item()
+
+print(f"Predicted class: {predicted_class}")
+```
+
+### Output Classes
+- **0**: Negative
+- **1**: Positive
+
+---
+
+## Fine-Tuning Process
+
+The model was fine-tuned using the following:
+
+- **Dataset**: A dataset of Darija text labeled for sentiment.
+- **Loss Function**: Cross-entropy loss for binary classification.
+- **Optimizer**: AdamW with weight decay (0.01).
+- **Learning Rate**: 5e-5 with linear warmup.
+- **Batch Size**: 16 for training, 64 for evaluation.
+- **Early Stopping**: Training stops if validation loss does not improve after 1 epoch.
+
+---
+
+## Evaluation Metrics
+
+The model's performance was evaluated using the following metrics:
+
+- **Accuracy**: 85%
+- **Precision**: 87%
+- **Recall**: 83%
+- **F1-Score**: 85%
+
+---
+
+## Publishing on Hugging Face
+
+The model and tokenizer were saved and uploaded to Hugging Face using the `huggingface_hub` library. To reproduce or fine-tune this model, follow these steps:
+
+1. Save the model and tokenizer:
+   ```python
+   model.save_pretrained("darija-bert-model")
+   tokenizer.save_pretrained("darija-bert-model")
+   ```
+
+2. Upload the model to Hugging Face:
+   ```python
+   from huggingface_hub import upload_folder
+
+   upload_folder(
+       folder_path="darija-bert-model",
+       repo_id="BenhamdaneNawfal/sentiment-analysis-darija",
+       repo_type="model"
+   )
+   ```
+
+---
+
+## Future Work
+
+- Expand the dataset to include more labeled examples from diverse sources.
+- Fine-tune the model for multi-class sentiment analysis (e.g., neutral, positive, negative).
+- Explore the use of data augmentation techniques for better generalization.
+
+---
+
+## Citation
+If you use this model, please cite it as:
+
+```
+@misc{benhamdanenawfal2025darijabert,
+  author = {Benhamdane Nawfal},
+  title = {Sentiment Analysis for Darija (Arabic Dialect)},
+  year = {2025},
+  publisher = {Hugging Face},
+  url = {https://huggingface.co/BenhamdaneNawfal/sentiment-analysis-darija}
+}
+```
+
+---
+
+## Contact
+For any questions or issues, feel free to contact me at: [[email protected]].
+