README.md

---
language:
- ar
license: apache-2.0
tags:
- eou-detection
- arabic
- saudi-dialect
- conversation
- livekit
metrics:
- f1
- precision
- recall
pipeline_tag: text-classification
---

# Arabic End-of-Utterance (EOU) Detection Model

## Model Description

Fine-tuned model for Arabic End-of-Utterance detection, optimized for Saudi dialect conversations.
Designed for real-time integration with LiveKit voice agents.

## Performance Metrics (Step 2400)

| Metric | Value |
|--------|-------|
| F1 Score | 0.534 |
| Precision | 0.431 |
| Recall | 0.702 |
| FPR | 0.150 |

## Intended Use

- Real-time voice agent turn detection
- Arabic conversational AI systems
- Saudi dialect speech processing

## Training Details

- Base Model: [specify your base model]
- Training Steps: 2400
- Validation Loss: 0.462
- Training Date: December 2024

## Usage
```python
from transformers import AutoModelForSequenceClassification, AutoTokenizer

model = AutoModelForSequenceClassification.from_pretrained("{username}/{repo_name}")
tokenizer = AutoTokenizer.from_pretrained("{username}/{repo_name}")

# Example inference
text = "نعم، أنا أفهم ما تقصد"
inputs = tokenizer(text, return_tensors="pt")
outputs = model(**inputs)
eou_probability = torch.softmax(outputs.logits, dim=-1)[0][1].item()
```

## Limitations

- Optimized for Saudi dialect
- May require threshold tuning for specific use cases
- Designed for conversational contexts

## Citation
```bibtex
@misc{arabic-eou-2024,
  author = {Your Name},
  title = {Arabic EOU Detection Model},
  year = {2024},
  publisher = {HuggingFace},
  url = {https://huggingface.co/{username}/{repo_name}}
}
```