You can deploy this model and start earning money today!

zephrox/
banglabert-sentence-router-onnx

Name: banglabert-sentence-router-onnx
Availability: InStock

tafseer-nayeem/BengaliReadability

mit

Views

No views yet

BanglaBERT Sentence Router (ONNX) - Shohoj Lipi 🧠

This is an ONNX-optimized, binary sentence classification model designed to detect complex Bengali sentences that are difficult for dyslexic readers (ages 6–14). It was created for Shohoj Lipi (সহজ লিপি), a project for the SciBlitz AI Challenge 2026 (Track C).

Model Details

Base Model: csebuetnlp/banglabert (Electra architecture)
Format: ONNX (CPU optimized for ultra-fast, low-memory inference)
Task: Binary Classification (0 = Simple, 1 = Complex)
Training Data: 96,000+ sentences from the BengaliReadability dataset

Intended Use

This model acts as a "Router" in a text simplification pipeline. It rapidly scans Bengali sentences and flags the complex ones (Label 1) to be sent to a Large Language Model (like Gemini) for simplification and syllable-scaffolding, while allowing simple sentences (Label 0) to pass through untouched.

Why ONNX?

Exporting BanglaBERT to ONNX reduces CPU inference time from ~2.0 seconds to ~300ms and cuts RAM usage by over 60%, allowing it to run smoothly on free-tier hosting services (like Render or Railway) without needing a GPU.

Usage (Python)

You can load this model locally using onnxruntime and HuggingFace's tokenizers:

python

import numpy as np
from transformers import AutoTokenizer
from optimum.onnxruntime import ORTModelForSequenceClassification

# Load tokenizer and ONNX model
tokenizer = AutoTokenizer.from_pretrained("YOUR_USERNAME/banglabert-sentence-router-onnx")
model = ORTModelForSequenceClassification.from_pretrained("YOUR_USERNAME/banglabert-sentence-router-onnx")

text = "বাংলাদেশের অর্থনীতি মূলত কৃষিনির্ভর।"
inputs = tokenizer(text, return_tensors="pt")

# Inference
outputs = model(**inputs)
logits = outputs.logits.detach().numpy()
predicted_class = np.argmax(logits, axis=1)[0]
confidence = np.max(np.exp(logits) / np.sum(np.exp(logits), axis=1, keepdims=True))

print("Class:", "Complex" if predicted_class == 1 else "Simple")
print("Confidence:", confidence)

Performance

Validation Accuracy: 85.0%
F1 Score: > 0.80

Limitations

The model struggles with highly technical medical or legal jargon, as it was trained primarily on NCTB school textbooks and literature.
It evaluates complexity at the sentence level, not the document level. It should be used in conjunction with a document-level readability heuristic.

zephrox/banglabert-sentence-router-onnx