Datasets:
Request access to Project CodeNet (code corpus)
Project CodeNet (code corpus) is released by The Fin AI for research. Access is granted automatically after you complete this short form.
By accessing this dataset you agree to its license and to cite the The Fin AI paper in any resulting publication.
Log in or Sign Up to review the conditions and access this dataset content.
Project_CodeNet
Source: IBM Project CodeNet — https://github.com/IBM/Project_CodeNet. Competitive-programming submissions from IBM Project CodeNet. License: kept from the original source (
other).
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:
Keep only Accepted submissions
- Removes incorrect or incomplete code.
Deduplication at metadata level
- For each
(problem_id, user_id, language), keep the last accepted submission - This approximates the user's final solution
- For each
No content-based deduplication
- Similar solutions across users are preserved
- Reflects real-world submission distribution
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} }
- Downloads last month
- 76