| --- |
| 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 around 1000 chunks per programming language, the data is 31,100 chunks (entries) as 512 tokens per chunk, being a snippet of the code. |
| - Picked from the 18th epoch out of 20 done. |
|
|
| # 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. |
|
|
| - **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. |
|
|
| # Testing/Training Datasets |
| I have put here the samples entered into the training/testing pipeline, its a very small amount. |
|
|
| | Language | Testing Count | Training Count | |
| |--------------|---------------|----------------| |
| | Ada | 20 | 80 | |
| | Assembly | 20 | 80 | |
| | C | 20 | 80 | |
| | C# | 20 | 80 | |
| | C++ | 20 | 80 | |
| | COBOL | 14 | 55 | |
| | Common Lisp | 20 | 80 | |
| | Dart | 20 | 80 | |
| | Erlang | 20 | 80 | |
| | F# | 20 | 80 | |
| | Go | 20 | 80 | |
| | Haskell | 20 | 80 | |
| | Java | 20 | 80 | |
| | JavaScript | 20 | 80 | |
| | Julia | 20 | 80 | |
| | Kotlin | 20 | 80 | |
| | Lua | 20 | 80 | |
| | MATLAB | 20 | 80 | |
| | PHP | 20 | 80 | |
| | Perl | 20 | 80 | |
| | Prolog | 1 | 4 | |
| | Python | 20 | 80 | |
| | R | 20 | 80 | |
| | Ruby | 20 | 80 | |
| | Rust | 20 | 80 | |
| | SQL | 20 | 80 | |
| | Scala | 20 | 80 | |
| | Swift | 20 | 80 | |
| | TypeScript | 20 | 80 | |
|
|
| # Example Code |
|
|
| ```python |
| import torch.nn as nn |
| import torch.nn.functional as F |
| |
| class CodeClassifier(nn.Module): |
| def __init__(self, num_classes, embedding_dim, hidden_dim, num_layers, bidirectional=False): |
| super(CodeClassifier, self).__init__() |
| self.feature_extractor = nn.LSTM(embedding_dim, hidden_dim, num_layers, batch_first=True, bidirectional=bidirectional) |
| self.dropout = nn.Dropout(0.5) # Reintroduce dropout |
| self.fc1 = nn.Linear(hidden_dim * (2 if bidirectional else 1), hidden_dim) # Intermediate layer |
| self.fc2 = nn.Linear(hidden_dim, num_classes) # Output layer |
| |
| def forward(self, x): |
| x = x.unsqueeze(1) # Add sequence dimension |
| x, _ = self.feature_extractor(x) |
| x = x.squeeze(1) # Remove sequence dimension |
| x = self.fc1(x) |
| x = self.dropout(x) # Apply dropout |
| x = self.fc2(x) |
| return x |
| |
| import torch |
| from transformers import AutoTokenizer, AutoModel |
| from pathlib import Path |
| |
| def infer(text, model_path, embedding_model_name): |
| device = torch.device("cuda" if torch.cuda.is_available() else "cpu") |
| |
| # Load tokenizer and embedding model |
| tokenizer = AutoTokenizer.from_pretrained(embedding_model_name) |
| embedding_model = AutoModel.from_pretrained(embedding_model_name).to(device) |
| embedding_model.eval() |
| |
| # Prepare inputs |
| inputs = tokenizer(text, return_tensors="pt", padding=True, truncation=True) |
| inputs = {k: v.to(device) for k, v in inputs.items()} |
| |
| # Generate embeddings |
| with torch.no_grad(): |
| embeddings = embedding_model(**inputs)[0][:, 0] |
| |
| # Load classifier model |
| model = CodeClassifier(num_classes=31, embedding_dim=embeddings.size(-1), hidden_dim=64, num_layers=2, bidirectional=True) |
| model.load_state_dict(torch.load(model_path, map_location=device)) |
| model = model.to(device) |
| model.eval() |
| |
| # Predict class |
| with torch.no_grad(): |
| output = model(embeddings) |
| _, predicted = torch.max(output, dim=1) |
| |
| # Language labels |
| languages = [ |
| 'Ada', 'Assembly', 'C', 'C#', 'C++', 'COBOL', 'Common Lisp', 'Dart', 'Erlang', 'F#', |
| 'Fortran', 'Go', 'Haskell', 'Java', 'JavaScript', 'Julia', 'Kotlin', 'Lua', 'MATLAB', |
| 'Objective-C', 'PHP', 'Perl', 'Prolog', 'Python', 'R', 'Ruby', 'Rust', 'SQL', 'Scala', |
| 'Swift', 'TypeScript' |
| ] |
| |
| return languages[predicted.item()] |
| |
| # Example usage |
| if __name__ == "__main__": |
| example_text = "print('Hello, world!')" # Replace with actual text for inference |
| model_file_path = Path("./model.safetensors") |
| predicted_language = infer(example_text, model_file_path, "Snowflake/snowflake-arctic-embed-xs") |
| print(f"Predicted programming language: {predicted_language}") |
| |
| ``` |
|
|
