Datasets:

jblitzar
/

github-python

---
annotations_creators:
  - author
license:
  - gpl-3.0
multilinguality:
  - monolingual
pretty_name: GitHub-Python
dataset_name: github-python
dataset_type: code
tags:
  - code
  - python
size_categories:
  - 100K<n⩽1M
task_categories:
  - text-generation
---
# GitHub-Python — Licensed & Elaborated Variants

This repository ships **two complementary Python-code corpora** extracted from
public GitHub:

- **Licensed Subset** – strictly _permissive-licensed_ files suitable for
  commercial redistribution / model training (main corpus used in our
  experiments).
- **Elaborated Collection** – a broader crawl that additionally contains files
  under _copyleft_ or unclear licenses (GPL/AGPL/LGPL, etc.). Useful for
  analysis or pre-training where license mixing is acceptable.

Both variants target **code-completion / generation** research.

## Dataset at a glance

|                     | **Licensed Subset** | **Elaborated Collection** |
| ------------------- | ------------------- | ------------------------- |
| Files (.py)         | 53,017              | 186,066                   |
| Unique repositories | 16,447              | 59,852                    |
| Repository owners   | 12,515              | 43,517                    |
| Compressed size     | 732 MB              | 2.4 GB \*                 |
| Vocabulary (tokens) | 443,431             | 443,431 †                 |
| License coverage    | Permissive only     | Mixed (perm. + copyleft)  |
| Secrets redacted    | ✅                  | ⚠️ not guaranteed         |
| Time window         | ≥ 2015-01-01        | ≥ 2015-01-01              |

\* estimated – elaborated corpus is distributed as raw file list, not a single
text file.  
† same tokenizer file is shared by both variants.

Numbers were obtained from the final redacted corpus and companion metadata.

---

## Dataset structure

```
huggingface_dataset/
 ├─ mega_licensed_corpus_redacted.txt      # Licensed Subset – concatenated code
 ├─ python_files.txt                       # Licensed Subset – raw file URLs
 ├─ python_files_elaborated.txt            # Elaborated Collection – raw file URLs
 ├─ python_files_elaborated_metadata.csv   # Elaborated Collection metadata
 └─ custom_tokens_vocab.txt             # `<token>\t<id>` vocabulary file
```

## Important Note

For technical reasons, seperate splits have been stored as seperate Dataset instances. See https://huggingface.co/datasets/jblitzar/github-python-metadata, https://huggingface.co/datasets/jblitzar/github-python-meta-elaborated, and https://huggingface.co/datasets/jblitzar/github-python-corpus .

### File separator

Individual files are concatenated with the sentinel line:

```
# <FILESEP>
```

Anything following the sentinel until the next sentinel (or EOF) is the source
code of one file.

---

## Dataset variants

### 1. Licensed Subset (`mega_licensed_corpus_redacted.txt`)
• 53 K permissively-licensed files (MIT/BSD/Apache/ISC/Unlicense).  
• All API keys & credentials removed.  
• Ready for redistribution & commercial use (respect upstream NOTICE files).
### 2. Elaborated Collection (`python_files_elaborated.txt`)
• 186 K files from a much larger crawl.  
• Contains **GPL / LGPL / AGPL and other copyleft** licenses.  
• Shipped _as URL list_ + metadata CSV; you must download the files yourself
(`datasets.load_dataset` streaming, `wget`, etc.).  
• **No license filtering or secret-redaction performed** – use with caution.

When first loading the dataset, decide which variant aligns with your use case
(e.g. proprietary model training → Licensed Subset only).


---

## Collection methodology

1. **Repository discovery**

   - Queried GitHub REST API for projects with **≥ 10 stars**  
     (earlier iterations used 100+, later expanded for coverage).
   - Only repositories with primary language _Python_ and last commit ≥ 2015.
2. **File filtering**

   - Retain files whose **size ∈ [1 KB, 100 KB]**.
   - Exclude common build/packaging scripts (`setup.py`, `__init__.py`, etc.).

3. **License compliance**

   - Allowed: MIT, Apache-2.0, BSD-2/3-Clause, ISC, Unlicense.
   - GPL, LGPL, AGPL and proprietary licenses were **excluded**.

4. **Deduplication**

   - Unique file SHA hashes; duplicates skipped.

5. **Formatting & cleaning**

   - Formatted with _autopep8_ to normalise whitespace.
   - Custom script removed trailing whitespace & normalised newlines.

6. **Secret redaction**
   - `truffleHog` + custom regex pass removed >150 active credentials.
   - Redacted corpus stored as `mega_licensed_corpus_redacted.txt`.

---

## Custom tokenisation

The accompanying `custom_tokens_vocab.txt` implements a **Python-aware
sub-token scheme**:

1. Strip doc-strings & comments.
2. Split on:
   - Camel-Case boundaries (`Camel` → `Camel`, `Case`)
   - Underscores, spaces
   - Indentation & newlines (preserved as `<newline>` token)
3. Rare tokens (frequency < 10) were dropped → 443 k vocabulary.

Example:

```python
def helloWorld(value):
    return value + 1
```

tokenises to:

```
def hello world ( value ) <newline>     return value + 1 <newline>
```

---

## Usage

```python
from datasets import load_dataset
ds = load_dataset("jblitzar/github-python-corpus", split="train")
print(ds[0]["code"][:300])        # raw source code
```

If you prefer token level examples (small reasons: memory), map the tokenizer:

```python
from tokenizers import Tokenizer
tok = Tokenizer.from_file("custom_tokens_vocab.txt")
def encode(ex):
    ex["input_ids"] = tok.encode(ex["code"]).ids
    return ex
ds = ds.map(encode, remove_columns=["code"])
```

---

## Ethical considerations & limitations

- **Licenses respected** – only permissive licenses included; retain NOTICE
  files when redistributing derivative works.
- **Secrets removed** – automated & manual audits performed, yet users **must
  not assume zero secrets**; re-audit before public deployments.
- **Code quality** – projects vary in style & correctness. Generated models
  may replicate bugs or vulnerable patterns.

---

## Citation

If you use this dataset, please cite:

```
@misc{github-python-2024,
  author       = {JBlitzar},
  title        = {GitHub-Python: A Permissively Licensed Corpus of Python Code},
  year         = {2024},
  howpublished = {\url{https://huggingface.co/datasets/jblitzar/github-python}},
  note         = {Version 1.0}
}
```

---

## License

Dataset card and aggregation scripts: **GPLv3**.  
Each code snippet remains under its **original repository license** (MIT,
Apache-2.0, BSD, ISC, etc.). Users must comply with upstream notices when
redistributing code or derivatives.

	---
	annotations_creators:
	- author
	license:
	- gpl-3.0
	multilinguality:
	- monolingual
	pretty_name: GitHub-Python
	dataset_name: github-python
	dataset_type: code
	tags:
	- code
	- python
	size_categories:
	- 100K<n⩽1M
	task_categories:
	- text-generation
	---

	# GitHub-Python — Licensed & Elaborated Variants

	This repository ships two complementary Python-code corpora extracted from
	public GitHub:

	- Licensed Subset – strictly _permissive-licensed_ files suitable for
	commercial redistribution / model training (main corpus used in our
	experiments).
	- Elaborated Collection – a broader crawl that additionally contains files
	under _copyleft_ or unclear licenses (GPL/AGPL/LGPL, etc.). Useful for
	analysis or pre-training where license mixing is acceptable.

	Both variants target code-completion / generation research.

	## Dataset at a glance

	\| \| Licensed Subset \| Elaborated Collection \|
	\| ------------------- \| ------------------- \| ------------------------- \|
	\| Files (.py) \| 53,017 \| 186,066 \|
	\| Unique repositories \| 16,447 \| 59,852 \|
	\| Repository owners \| 12,515 \| 43,517 \|
	\| Compressed size \| 732 MB \| 2.4 GB \* \|
	\| Vocabulary (tokens) \| 443,431 \| 443,431 † \|
	\| License coverage \| Permissive only \| Mixed (perm. + copyleft) \|
	\| Secrets redacted \| ✅ \| ⚠️ not guaranteed \|
	\| Time window \| ≥ 2015-01-01 \| ≥ 2015-01-01 \|

	\* estimated – elaborated corpus is distributed as raw file list, not a single
	text file.
	† same tokenizer file is shared by both variants.

	Numbers were obtained from the final redacted corpus and companion metadata.

	---

	## Dataset structure

	```
	huggingface_dataset/
	├─ mega_licensed_corpus_redacted.txt # Licensed Subset – concatenated code
	├─ python_files.txt # Licensed Subset – raw file URLs
	├─ python_files_elaborated.txt # Elaborated Collection – raw file URLs
	├─ python_files_elaborated_metadata.csv # Elaborated Collection metadata
	└─ custom_tokens_vocab.txt # `<token>\t<id>` vocabulary file
	```

	## Important Note

	For technical reasons, seperate splits have been stored as seperate Dataset instances. See https://huggingface.co/datasets/jblitzar/github-python-metadata, https://huggingface.co/datasets/jblitzar/github-python-meta-elaborated, and https://huggingface.co/datasets/jblitzar/github-python-corpus .

	### File separator

	Individual files are concatenated with the sentinel line:

	```
	# <FILESEP>
	```

	Anything following the sentinel until the next sentinel (or EOF) is the source
	code of one file.

	---

	## Dataset variants

	### 1. Licensed Subset (`mega_licensed_corpus_redacted.txt`)

	• 53 K permissively-licensed files (MIT/BSD/Apache/ISC/Unlicense).
	• All API keys & credentials removed.
	• Ready for redistribution & commercial use (respect upstream NOTICE files).

	### 2. Elaborated Collection (`python_files_elaborated.txt`)

	• 186 K files from a much larger crawl.
	• Contains GPL / LGPL / AGPL and other copyleft licenses.
	• Shipped _as URL list_ + metadata CSV; you must download the files yourself
	(`datasets.load_dataset` streaming, `wget`, etc.).
	• No license filtering or secret-redaction performed – use with caution.

	When first loading the dataset, decide which variant aligns with your use case
	(e.g. proprietary model training → Licensed Subset only).


	---

	## Collection methodology

	1. Repository discovery

	- Queried GitHub REST API for projects with ≥ 10 stars
	(earlier iterations used 100+, later expanded for coverage).
	- Only repositories with primary language _Python_ and last commit ≥ 2015.

	2. File filtering

	- Retain files whose size ∈ [1 KB, 100 KB].
	- Exclude common build/packaging scripts (`setup.py`, `__init__.py`, etc.).

	3. License compliance

	- Allowed: MIT, Apache-2.0, BSD-2/3-Clause, ISC, Unlicense.
	- GPL, LGPL, AGPL and proprietary licenses were excluded.

	4. Deduplication

	- Unique file SHA hashes; duplicates skipped.

	5. Formatting & cleaning

	- Formatted with _autopep8_ to normalise whitespace.
	- Custom script removed trailing whitespace & normalised newlines.

	6. Secret redaction
	- `truffleHog` + custom regex pass removed >150 active credentials.
	- Redacted corpus stored as `mega_licensed_corpus_redacted.txt`.

	---

	## Custom tokenisation

	The accompanying `custom_tokens_vocab.txt` implements a **Python-aware
	sub-token scheme**:

	1. Strip doc-strings & comments.
	2. Split on:
	- Camel-Case boundaries (`Camel` → `Camel`, `Case`)
	- Underscores, spaces
	- Indentation & newlines (preserved as `<newline>` token)
	3. Rare tokens (frequency < 10) were dropped → 443 k vocabulary.

	Example:

	```python
	def helloWorld(value):
	return value + 1
	```

	tokenises to:

	```
	def hello world ( value ) <newline> return value + 1 <newline>
	```

	---

	## Usage

	```python
	from datasets import load_dataset

	ds = load_dataset("jblitzar/github-python-corpus", split="train")

	print(ds[0]["code"][:300]) # raw source code
	```

	If you prefer token level examples (small reasons: memory), map the tokenizer:

	```python
	from tokenizers import Tokenizer
	tok = Tokenizer.from_file("custom_tokens_vocab.txt")

	def encode(ex):
	ex["input_ids"] = tok.encode(ex["code"]).ids
	return ex

	ds = ds.map(encode, remove_columns=["code"])
	```

	---

	## Ethical considerations & limitations

	- Licenses respected – only permissive licenses included; retain NOTICE
	files when redistributing derivative works.
	- Secrets removed – automated & manual audits performed, yet users **must
	not assume zero secrets**; re-audit before public deployments.
	- Code quality – projects vary in style & correctness. Generated models
	may replicate bugs or vulnerable patterns.

	---

	## Citation

	If you use this dataset, please cite:

	```
	@misc{github-python-2024,
	author = {JBlitzar},
	title = {GitHub-Python: A Permissively Licensed Corpus of Python Code},
	year = {2024},
	howpublished = {\url{https://huggingface.co/datasets/jblitzar/github-python}},
	note = {Version 1.0}
	}
	```

	---

	## License

	Dataset card and aggregation scripts: GPLv3.
	Each code snippet remains under its original repository license (MIT,
	Apache-2.0, BSD, ISC, etc.). Users must comply with upstream notices when
	redistributing code or derivatives.