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--- |
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license: apache-2.0 |
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datasets: |
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- Shuu12121/javascript-treesitter-filtered-datasetsV2 |
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- Shuu12121/ruby-treesitter-filtered-datasetsV2 |
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- Shuu12121/go-treesitter-dedupe_doc-filtered-dataset |
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- Shuu12121/java-treesitter-dedupe_doc-filtered-dataset |
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- Shuu12121/rust-treesitter-filtered-datasetsV2 |
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- Shuu12121/php-treesitter-filtered-datasetsV2 |
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- Shuu12121/python-treesitter-filtered-datasetsV2 |
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- Shuu12121/typescript-treesitter-filtered-datasetsV2 |
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language: |
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- en |
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pipeline_tag: fill-mask |
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tags: |
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- code |
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- python |
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- java |
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- javascript |
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- typescript |
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- go |
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- ruby |
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- rust |
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- php |
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--- |
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# CodeModernBERT-Owl-v1🦉 |
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## Model Details |
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* **Model type**: Bi-encoder architecture based on ModernBERT |
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* **Architecture**: |
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* Hidden size: 768 |
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* Layers: 22 |
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* Attention heads: 12 |
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* Intermediate size: 1,152 |
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* Max position embeddings: 8,192 |
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* Local attention window size: 128 |
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* RoPE positional encoding: θ = 160,000 |
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* Local RoPE positional encoding: θ = 10,000 |
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* **Sequence length**: up to 2,048 tokens for code and docstring inputs during pretraining |
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* **Implementation**: Back-end in Python; integrated into **OwlSpotLight**, a Visual Studio Code extension. |
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## Pretraining |
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* **Tokenizer**: Custom BPE tokenizer trained for code and docstring pairs. |
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* **Data**: Functions and natural language descriptions extracted from GitHub repositories. |
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* **Masking strategy**: Two-phase pretraining. |
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* **Phase 1: Random Masked Language Modeling (MLM)** |
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30% of tokens in code functions are randomly masked and predicted using standard MLM. |
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* **Phase 2: Line-level Span Masking** |
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Inspired by SpanBERT, continued pretraining on the same data with span masking at line granularity: |
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1. Convert input tokens back to strings. |
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2. Detect newline tokens with regex and segment inputs by line. |
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3. Exclude whitespace-only tokens from masking. |
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4. Apply padding to align sequence lengths. |
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5. Randomly mask 30% of tokens in each line segment and predict them. |
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* **Pretraining hyperparameters**: |
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* Batch size: 20 |
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* Gradient accumulation steps: 6 |
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* Effective batch size: 120 |
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* Optimizer: AdamW |
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* Learning rate: 5e-5 |
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* Scheduler: Cosine |
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* Epochs: 2 |
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* Precision: Mixed precision (fp16) using `transformers` |
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