my_model

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.

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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

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Model Details

• Framework: PyTorch
• Architecture: Custom EOUClassifier
• Task: Binary classification (EOU detection)
• Language: Arabic

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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.

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Files

• model.py — Model architecture (EOUClassifier)
• model.pt — Trained model weights
• config.json — Model configuration
• README.md — This file

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Loading the Model

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