| --- |
| dataset_info: |
| features: |
| - name: Source |
| dtype: string |
| - name: Date |
| dtype: int64 |
| - name: Text |
| dtype: string |
| - name: Token_count |
| dtype: int64 |
| splits: |
| - name: train |
| num_bytes: 8122744210 |
| num_examples: 6366648 |
| download_size: 3707767805 |
| dataset_size: 8122744210 |
| configs: |
| - config_name: default |
| data_files: |
| - split: train |
| path: data/train-* |
| pretty_name: Project_CodeNet |
| size_categories: |
| - 1M<n<10M |
| task_categories: |
| - text-generation |
| language: |
| - code |
| license: other |
| --- |
| |
| # Project_CodeNet |
| |
| ## Overview |
| |
| This dataset is constructed from the **Project CodeNet** corpus, consisting of competitive programming submissions collected from online judges. |
| |
| We extract a large-scale code corpus designed for pretraining language models, with a focus on: |
| - clean executable code |
| - temporal metadata (submission time) |
| - minimal preprocessing to preserve the original distribution |
| |
| --- |
| |
| ## Dataset Statistics |
| |
| - **Total samples:** ~6.37M |
| - **Total tokens:** ~3.06B |
| - **Average tokens per sample:** 480.44 |
| |
| ### Token Length Distribution |
| - P50: 162 tokens |
| - P90: 679 tokens |
| - P95: 1035 tokens |
| - P99: 2702 tokens |
| --- |
| |
| ## Construction |
| |
| ### Source |
| - Project CodeNet https://github.com/IBM/Project_CodeNet |
|
|
| ### Filtering Rules |
|
|
| We apply the following steps: |
|
|
| 1. **Keep only Accepted submissions** |
| - Removes incorrect or incomplete code. |
|
|
| 2. **Deduplication at metadata level** |
| - For each `(problem_id, user_id, language)`, keep the **last accepted submission** |
| - This approximates the user's final solution |
|
|
| 3. **No content-based deduplication** |
| - Similar solutions across users are preserved |
| - Reflects real-world submission distribution |
|
|
| 4. **No balancing** |
| - Language and temporal distributions are kept as-is |
|
|
| --- |
|
|
| ## Fields |
|
|
| Each sample contains: |
|
|
| | Field | Description | |
| |------|------------| |
| | `Source` | Dataset name (`Project_CodeNet`) | |
| | `Date` | Submission year | |
| | `Text` | Source code | |
| | `Token_count` | Token count computed using `tiktoken` | |
|
|
| --- |
|
|
| ## Tokenization |
|
|
| - Tokenizer: `tiktoken` |
| - Encoding: `cl100k_base` |
|
|
| --- |
|
|
| ## Distribution Characteristics |
|
|
| ### Language Distribution |
| The dataset is highly skewed toward C++: |
|
|
| - C++ dominates (~60%) |
| - Python is the second largest (~23%) |
| - Other languages form a long tail |
|
|
| ### Temporal Distribution |
| The dataset is heavily concentrated in recent years: |
|
|
| - Majority of samples from **2019–2020** |
| - Reflects real submission activity in CodeNet |
|
|
| --- |
|
|
| ## Important Notes |
|
|
| - This dataset preserves the **original submission distribution** of CodeNet. |
| - It is **not balanced** across languages or time. |
| - It is primarily composed of **competitive programming code**, which may differ from production software code. |
| - Some level of **near-duplicate solutions** exists due to similar problem-solving strategies. |
|
|
| --- |
|
|
| ## Intended Use |
|
|
| - Pretraining code language models |
| - Studying temporal evolution of programming patterns |
| - Benchmarking under real-world distribution settings |
|
|
| --- |
|
|
| ## Limitations |
|
|
| - Not representative of general software engineering code |
| - Strong bias toward: |
| - competitive programming tasks |
| - algorithmic problem solving |
| - Language and temporal imbalance |
|
|
| --- |
|
|
| ## License |
|
|
| Please refer to the original **Project CodeNet** dataset for licensing details. |
|
|
| --- |
|
|
| ## Citation |
|
|
| If you use this dataset, please cite Project CodeNet: |
|
|
| @article{puri2021project, |
| title={Project CodeNet: A Large-Scale AI for Code Dataset for Learning a Diversity of Coding Tasks}, |
| author={Puri, Ruchir and others}, |
| year={2021} |
| } |
|
|
|
|
