license: apache-2.0
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-xsmodel is used as the embeddings model.
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.