CodeNLBERT-Tiny Open-Source Model - A Code and Natural Language Classification Tool with over 99% Accuracy

Codenlbert Tiny

Developed by vishnun

BERT-small based model for classifying code and natural language with over 99% accuracy

Supports Multiple LanguagesOpen Source License:MIT #High-precision code classification #Natural language understanding #BERT fine-tuning

Downloads 68

Release Time : 8/4/2023

Model Overview

This model is designed to distinguish between code snippets and natural language text, fine-tuned on the BERT-small architecture, excelling in code vs. natural language classification tasks.

Model Features

High Accuracy

Achieves 99.8% classification accuracy on the validation set

Lightweight

Based on BERT-small architecture with a compact model size

Ease of Use

Provides Hugging Face Space demo and Python package

Model Capabilities

Code snippet recognition

Natural language text recognition

Text classification

Use Cases

Code Processing

Code Snippet Extraction

Identify and extract code snippets from mixed text

Accurately recognizes over 99% of code snippets

Document Processing

Technical Document Analysis

Differentiate between code examples and explanatory text in technical documents

Epoch	Training Loss	Validation Loss	Accuracy
1	0.022500	0.012705	0.997203
2	0.008700	0.013107	0.996880
3	0.002700	0.014081	0.997633
4	0.001800	0.010666	0.997526
5	0.000900	0.010800	0.998063

Property	Details
Model Type	bert - small
Training Data	vishnun/CodevsNL
Pipeline Tag	text - classification
Metrics	accuracy
Library Name	transformers
Tags	code, nli

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Codenlbert Tiny

Model Overview

Model Features

Model Capabilities

Use Cases

🚀 Code vs Natural Language Classification

🚀 Quick Start

📚 Documentation

Training Metrics

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📄 License

📦 Information Table