Update README.md

README.md

@@ -20,4 +20,144 @@ configs:
   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}
+}
+