| | --- |
| | license: apache-2.0 |
| | datasets: |
| | - Novora/CodeClassifier_v1 |
| | pipeline_tag: text-classification |
| | --- |
| | |
| | # Introduction |
| |
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| | 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). |
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| | This model is designed to be able to run on CPU, but optimally runs on GPUs. |
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|
| | # 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 |
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| | 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: |
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| | - **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. |
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| | - **Adaptive Pooling**: Following the LSTM, adaptive average pooling reduces the feature dimension to a fixed size, accommodating variable-length inputs. |
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| | - **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. |
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| | The model's bidirectional nature and architectural components make it adept at understanding the syntax and structure crucial for code classification. |
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