Update README.md

README.md

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----
-license: apache-2.0
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+# Arabic End-of-Utterance (EOU) Classifier
+
+## Overview
+This repository contains a custom PyTorch model for **End-of-Utterance (EOU) detection** in Arabic conversational text.  
+The model predicts whether a given text segment represents the end of a speaker’s turn.
+
+This is a **custom architecture** (not a Hugging Face `AutoModel`) and is intended for research and development use.
+
+---
+
+## Task
+Given an input text segment, the model outputs a binary prediction:
+
+- `0` → The speaker is expected to continue speaking  
+- `1` → The speaker has finished their turn  
+
+---
+
+## Model Details
+- Framework: PyTorch
+- Architecture: Custom `EOUClassifier`
+- Task: Binary classification (EOU detection)
+- Language: Arabic
+
+---
+
+## Tokenizer
+This model uses the tokenizer from:
+
+`Omartificial-Intelligence-Space/SA-BERT-V1`
+
+The tokenizer is **not included** in this repository and must be loaded separately.
+
+---
+
+## Files
+- `model.py` — Model architecture (`EOUClassifier`)
+- `model.pt` — Trained model weights
+- `config.json` — Model configuration
+- `README.md` — This file
+
+---
+
+## Loading the Model
+```python
+import torch
+from transformers import AutoTokenizer
+from model import EOUClassifier
+
+tokenizer = AutoTokenizer.from_pretrained(
+    "Omartificial-Intelligence-Space/SA-BERT-V1"
+)
+
+model = EOUClassifier()
+model.load_state_dict(
+    torch.load("model.pt", map_location="cpu")
+)
+model.eval()
+
+examples = ["مقصدي من الموضوع انه", "اتمنى تقدر تساعدني"]
+
+
+batch = tokenizer(examples, padding=True, truncation=True, return_tensors="pt")
+batch.to(device)
+
+out = model(batch["input_ids"], batch["attention_mask"])
+```
+
+## license 
+
+MIT