Datasets:

Playfulbug
/

python_dataset

license: apache-2.0
language:
  - en
tags:
  - code
  - python
  - pretraining
  - programming
  - pip
  - datasets
  - gradio
  - keras
  - pytorch-lightning
  - pytest
  - transformers
  - ternsorflow
  - pytorch
  - numpy
  - pandas
  - matpotlib
  - django
size_categories:
  - 100K<n<1M
pretty_name: Goodman

Python and Docs Training Dataset

A massive, multi-format code and documentation corpus built from 70+ major open-source Python repositories (including the Pip, Keras, Gradio, Transformers, Datasets, and more).

Dataset Structure

The dataset is processed using exact BPE token chunking (512-token chunks with zero overlap via OpenAI's cl100k_base tokenizer) while preserving original Python indentation, newlines, and <|file|>path document boundaries—eliminating boundary duplication and allowing direct fixed-window pre-training without truncation or padding.

Each record in the JSONL and Parquet shards contains:

id: Unique sequential identifier for the 512-token chunk (e.g., combine_chunk_0).
source: Originating combined corpus file (combine.txt).
chunk_index: Zero-based sequential index preserving repository-grouped ordering (Docs -> Code).
token_count: Exact BPE token length (512 tokens per row; < 512 only on the final EOF tail row if retained).
content: Raw, indentation-preserved block of Python code, type stubs, or documentation separated by <|file|>path headers.

(An accompanying flat binary shard Python_pretrain.bin stores the same continuous token stream as headerless uint32 token IDs for zero-copy np.memmap training.)

Corpus Breakdown

Combined Repository Corpus (combine.txt): 22,551,967 lines (~850.3 MB / ~220M+ cl100k_base tokens) curated across 74 permissive-license (MIT, Apache-2.0, BSD-3) repositories, with line lengths capped at 999 characters and test/vendored directories excluded.
Documentation (.md, .rst): Repository guides, API references, and technical manuals grouped ahead of each library's source tree.
Python Source & Type Stubs (.py, .pyi): Production library implementations, core modules, and type annotations across foundational Python, data science, web, and ML frameworks (including CPython, PyTorch, TensorFlow, NumPy, SciPy, Pandas, Django, FastAPI/Starlette, and Transformers).

License

Distributed under the Apache-2.0 open-source license.