nuha-ajp-trinary-onnx

nuha-ajp-trinary-onnx

Model Summary

nuha-ajp-trinary-onnx is a lightweight, ONNX-optimised Arabic text classifier that categorises Jordanian social media comments into three classes based on the NUHA methodology for online gender-based violence (OGBV). It fine-tunes nuha-ajp-mlm — a domain-adapted Arabic BERT — with a reduced 4-layer architecture for efficient CPU inference, and is exported to ONNX. It shares the same classification task and labels as nuha-ajp-trinary but is optimised for production deployment. This is the model powering the NUHA analysis platform.

This model was developed as part of a pilot proof-of-concept for the NUHA project by the Jordan Open Source Association (JOSA).

For the full-depth (12-layer) version of this classifier, see nuha-ajp-trinary.

Uses

Direct Use

from optimum.onnxruntime import ORTModelForSequenceClassification
from transformers import AutoTokenizer, pipeline

model = ORTModelForSequenceClassification.from_pretrained("thejosango/nuha-ajp-trinary-onnx")
tokenizer = AutoTokenizer.from_pretrained("thejosango/nuha-ajp-trinary-onnx")
classifier = pipeline("text-classification", model=model, tokenizer=tokenizer)

result = classifier("اخرسي يا غبية")
print(result)
# [{'label': 'Gender Based Violence', 'score': ...}]

For batch inference:

comments = ["يعطيكم العافية", "أنتِ ساحرة", "اخرسي يا غبية"]
results = classifier(comments)
for comment, result in zip(comments, results):
    print(f"{result['label']} ({result['score']:.2f}): {comment}")

Using the PyTorch Version

If you need the full PyTorch model (for fine-tuning or non-ONNX inference), use nuha-ajp-trinary directly.

Out-of-Scope Use

• Other Arabic dialects: The model was trained primarily on Jordanian Arabic. Performance on Egyptian, Gulf, or Modern Standard Arabic is not validated.
• Other hate speech targets: NUHA is calibrated for online gender-based violence. It is not designed to detect hate speech targeting race, religion, or other demographics.
• High-stakes automated decisions: Given the moderate performance (F1 ≈ 0.54) and pilot nature of this work, the model should not be used as the sole decision-maker in content moderation systems without human review.

Preprocessing

At inference time, apply the following normalisation to input text before passing it to the model:

1. URLs replaced with [رابط] token
2. @mentions replaced with [مستخدم] token
3. Email addresses replaced with [بريد] token
4. Numbers removed
5. Punctuation removed
6. Arabic diacritics (harakat) removed
7. Whitespace normalised

Evaluation Results

Evaluated on the validation split of thejosango/nuha-ajp-dataset (methodology configuration):

See nuha-ajp-trinary for full training details and evaluation discussion.

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This model was developed as part of an initial pilot study. Performance metrics reflect the complexity of the task and the proof-of-concept nature of this system.