helmo
/

code-search-net-multilang-tokenizer

@@ -1,199 +1,110 @@
 ---
 library_name: transformers
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 ---
 library_name: transformers
+tags:
+- tokenizer
+- code
+- multilingual
+- programming
+license: apache-2.0
 ---
+# CodeSearchNet Multilingual Tokenizer
+A specialized tokenizer trained on code from 6 programming languages (Python, Java, JavaScript, PHP, Ruby, Go) using the CodeSearchNet dataset.
 ## Model Details
 ### Model Description
+This tokenizer is based on GPT-2's tokenizer but retrained specifically for source code across multiple programming languages. It provides more efficient tokenization for code compared to general-purpose tokenizers.
+- **Model type:** BPE Tokenizer
+- **Languages:** Python, Java, JavaScript, PHP, Ruby, Go
+- **Vocabulary size:** 64,000 tokens
+- **Finetuned from:** GPT-2 tokenizer
 ## Uses
 ### Direct Use
+This tokenizer is designed for preprocessing source code before training or inference with language models. It's particularly useful for:
+- Code generation models
+- Code completion systems
+- Code analysis and understanding tasks
+- Multi-language programming assistants
+## Performance
+Compared to the original GPT-2 tokenizer, this specialized tokenizer achieves:
+- **Python**: 25% fewer tokens on average
+- **Java**: 31% fewer tokens on average
+- **JavaScript**: 21% fewer tokens on average
+- **Go**: 14% fewer tokens on average
+- **PHP**: 14% fewer tokens on average
+- **Ruby**: 13% fewer tokens on average
+## How to Get Started
+```python
+from transformers import AutoTokenizer
+tokenizer = AutoTokenizer.from_pretrained("helmo/code-search-net-multilang-tokenizer")
+# Example usage
+code = '''public class Calculator {
+    public int add(int a, int b) {
+        return a + b;
+    }
+}'''
+tokens = tokenizer.tokenize(code)
+token_ids = tokenizer.encode(code)
+```
 ## Training Details
 ### Training Data
+Trained on the [CodeSearchNet dataset](https://github.com/github/CodeSearchNet) which contains:
+- ~2M code functions across 6 programming languages
+- Real-world code from GitHub repositories
+- Functions with associated documentation
 ### Training Procedure
+- **Base model:** GPT-2 tokenizer (50,257 vocab)
+- **Training method:** BPE (Byte-Pair Encoding)
+- **Final vocabulary:** 64,000 tokens
+- **Training corpus:** Combined functions from all 6 languages in CodeSearchNet
+## Technical Specifications
+### Model Architecture
+- **Algorithm:** Byte-Pair Encoding (BPE)
+- **Vocabulary size:** 64,000
+- **Special tokens:** Inherited from GPT-2 tokenizer
+- **Subword handling:** Optimized for code syntax and patterns
+## Citation
+```bibtex
+@misc{codesearchnet-multilang-tokenizer,
+  title={CodeSearchNet Multilingual Tokenizer},
+  author={Hélder Monteiro},
+  year={2025},
+  howpublished={Hugging Face Model Hub},
+}
+```
+## Dataset Reference
+```bibtex
+@article{husain2019codesearchnet,
+  title={CodeSearchNet Challenge: Evaluating the State of Semantic Code Search},
+  author={Husain, Hamel and Wu, Ho-Hsiang and Gazit, Tiferet and Allamanis, Miltiadis and Brockschmidt, Marc},
+  journal={arXiv preprint arXiv:1909.09436},
+  year={2019}
+}
+```