| --- |
| language: en |
| tags: |
| - code |
| - algorithms |
| - competitive-programming |
| - multi-label-classification |
| - codebert |
| datasets: |
| - xCodeEval |
| metrics: |
| - f1 |
| - precision |
| - recall |
| library_name: transformers |
| pipeline_tag: text-classification |
| --- |
| |
| # CodeBERT Algorithm Tagger |
|
|
| A fine-tuned CodeBERT model for multi-label classification of algorithmic problems from competitive programming platforms like Codeforces. |
|
|
| ## Model Description |
|
|
| This model predicts algorithmic tags/categories for competitive programming problems based on their problem descriptions and solution code. |
|
|
| **Supported Tags:** |
| - math |
| - graphs |
| - strings |
| - number theory |
| - trees |
| - geometry |
| - games |
| - probabilities |
|
|
| ## Training Data |
|
|
| - **Dataset**: xCodeEval (Codeforces problems) |
| - **Training examples**: 2,147 problems (filtered for focus tags) |
| - **Test examples**: 531 problems |
| - **Source**: Problems and solutions from Codeforces platform |
|
|
| ### Model Architecture |
|
|
| - **Input**: Concatenated problem description and solution code |
| - **Encoder**: CodeBERT (RoBERTa-based architecture) |
| - **Output**: 8-dimensional binary classification (one per tag) |
|
|
| ## Usage |
|
|
| ### Installation |
|
|
| ```bash |
| pip install transformers torch |
| ``` |
|
|
|
|
