Datasets:
File size: 3,530 Bytes
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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}
}
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