metadata

license: apache-2.0
datasets:
  - Novora/CodeClassifier_v1
pipeline_tag: text-classification

Introduction

Novora Code Classifier v1 Tiny, is a tiny Text Classification model, which classifies given code text input under 1 of 31 different classes (programming languages).

This model is designed to be able to run on CPU, but optimally runs on GPUs.

Info

1 of 31 classes output
512 token input dimension
64 hidden dimensions
2 linear layers
The snowflake-arctic-embed-xs model is used as the embeddings model.
Dataset split into 80% training set, 20% testing set.
The combined test and training data is 100 chunks per programming language, the data is 3,100 chunks (entries) as 512 tokens per chunk, being a snippet of the code.

Architecture

The CodeClassifier-v1-Tiny model employs a neural network architecture optimized for text classification tasks, specifically for classifying programming languages from code snippets. This model includes:

Bidirectional LSTM Feature Extractor: This bidirectional LSTM layer processes input embeddings, effectively capturing contextual relationships in both forward and reverse directions within the code snippets.
Adaptive Pooling: Following the LSTM, adaptive average pooling reduces the feature dimension to a fixed size, accommodating variable-length inputs.
Fully Connected Layers: The network includes two linear layers. The first projects the pooled features into a hidden feature space, and the second linear layer maps these to the output classes, which correspond to different programming languages. A dropout layer with a rate of 0.5 between these layers helps mitigate overfitting.

The model's bidirectional nature and architectural components make it adept at understanding the syntax and structure crucial for code classification.