# BIHEADS: Multi-Task BiLSTM+BiGRU for Egyptian Arabic

BIHEADS is a recurrent neural network ensemble designed to provide a complementary perspective to transformer-based models for Egyptian Arabic. It uses FastText embeddings and a parallel architecture to capture temporal nuances in dialectal speech.

## Model Details
- **Architecture:** Parallel BiLSTM + BiGRU shared backbone
- **Embeddings:** FastText `facebook/fasttext-arz-vectors` (300-dim)
- **Hidden Size:** 256 per direction
- **Layers:** 2-layer LSTM, 2-layer GRU

## Task Heads & Labels
- **Emotion (8 classes):** `none`, `anger`, `joy`, `sadness`, `love`, `sympathy`, `surprise`, `fear`
- **Sentiment (3 classes):** `negative`, `neutral`, `positive`
- **Sarcasm (2 classes):** `not sarcastic`, `sarcastic`

## Training Data
The model utilizes the same Egyptian-filtered training splits as MASRIHEADS to ensure feature alignment for downstream ensemble learners.

## Performance (Test Set F1)
| Task      | Macro-F1 |
|-----------|----------|
| Sarcasm   | 0.6293   |
| Sentiment | 0.6284   |
| Emotion   | 0.5514   |
| **Mean** | **0.6030**|