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Supernova11c/
Supernova-Nepali-Language-Detector-V1

Name: Supernova-Nepali-Language-Detector-V1
Availability: InStock

language-identification

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Supernova Language Detector V1

A lightweight deterministic language identification system developed by Supernova.

Supported languages

Nepali
Hindi
Sanskrit
English
Nepali Latin
Mixed
Unknown

Design

This detector does not use neural networks or pretrained weights.

It uses deterministic language fingerprints, vocabulary statistics, Unicode/script analysis, and calibrated scoring.

Example

python

from detector import detect_language

result = detect_language('नेपाल सुन्दर देश हो।')
print(result)

Limitations

Nepali, Hindi, and Sanskrit share the Devanagari script. Very short or ambiguous text may therefore be difficult to classify.

Unknown is returned when there is insufficient evidence.

No pretrained weights

Supernova Language Detector V1 contains no neural-model weights.

Project

Developed as part of the Supernova AI project. How to Use

Supernova Nepali Language Detector V1 is a deterministic language identification system. It does not require a neural model or GPU.

Installation

Clone or download this repository:

git clone https://huggingface.co/Supernova11c/Supernova-Nepali-Language-Detector-V1 cd Supernova-Nepali-Language-Detector-V1

Python Usage

from detector import detect_final_language

text = "नेपाल सुन्दर देश हो।"

result = detect_final_language(text)

print(result)

Example output:

{ "language": "Nepali", "confidence": 0.17, "script": "Devanagari" }

Supported Languages

Supernova V1 can identify:

Nepali
Hindi
Sanskrit
English
Nepali Latin
Mixed
Unknown

Examples

from detector import detect_final_language

tests = [ "नेपाल सुन्दर देश हो।", "भारत एक विशाल देश है।", "संस्कृतं प्राचीनतमा भाषा अस्ति।", "Artificial intelligence is powerful.", "ma aaja school janchu", "म आज school जान्छु।", "xyz qqq zzz" ]

for text in tests: result = detect_final_language(text)

print(text)
print("Language:", result["language"])
print("Confidence:", result["confidence"])
print("Script:", result["script"])
print()

Output Fields

"language" — detected language or "Unknown".

"confidence" — confidence score between "0.0" and "1.0".

"script" — detected writing system:

"Devanagari"
"Latin"
"Mixed"
"Unknown"

Design

Supernova V1 is intentionally deterministic.

It does not use:

Neural inference
Transformers
Fine-tuned model weights
GPU
External API calls

The detector uses language fingerprints, vocabulary statistics, character patterns, script analysis, and dedicated Nepali-Latin rules.

This makes the system lightweight, reproducible, and suitable for local/offline inference.

Verification

Supernova V1 passed its final standalone benchmark with 7/7 tests.

The release package was also independently imported and tested after packaging.

License

See the repository license and accompanying project files for licensing information.

⚡ Performance & CPU Benchmarks

Supernova text processing architecture is engineered for extreme, zero-overhead systems efficiency. Running entirely on standard CPU hardware without any GPU acceleration or heavy vector models, it delivers elite-tier throughput:

Language Detection & Processing: 1,237,070,359+ characters/sec
Hardware Requirement: Standard CPU (Zero GPU dependency, ultra-low memory footprint)
Architecture: Modular, deterministic, and hallucination-free text pipeline.
Test Environment: Google Colab Free Tier (Standard Shared CPU Runtime)