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Pytorch-Code-50K

Tasks:
Text Generation
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parquet
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English
Size:
10K - 100K
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code
python
pytorch
deep-learning
beta
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metadata 
license: other
language:
  - en
pretty_name: Pytorch Code 50K
size_categories:
  - 10K<n<100K
tags:
  - code
  - python
  - pytorch
  - deep-learning
  - beta
task_categories:
  - text-generation

⚠️ Pytorch-Code-50K — BETA

Status: BETA. This is an early, unpolished release. The data is real but unaudited: it has not been manually reviewed, re-verified against upstream, or checked for toxic/low-quality content beyond the filters listed below. The sample may change between releases. Use at your own risk and do not treat it as a benchmark.

Overview

Pytorch-Code-50K is a collection of ~50,000 Python source files that use the PyTorch ecosystem, extracted from BigCode's The Stack (Python subset).

It is intended as a lightweight, easy-to-consume corpus for:

  • fine-tuning and pre-training small code models on PyTorch idioms,
  • studying how PyTorch APIs are actually used in the wild,
  • retrieval / RAG over real PyTorch examples,
  • quick prototyping where a full 3 TB dataset is overkill.

Source

Upstream bigcode/the-stack (data/python)
Curated by Monster-Code
Language Python
Domain PyTorch / deep learning code
Size ~50,000 files
Format Parquet (one row per file)
Release BETA v0.1

The Stack is a collection of source code from repositories with various licenses. Any use of all or part of the code in this dataset must abide by the terms of the original licenses, including attribution clauses when relevant. Provenance (repo, path, license, sha) is preserved so you can attribute and filter correctly.

Selection criteria

A file is included when all of the following hold:

  1. It comes from the Python subset of The Stack.

  2. Its source contains a top-level import from the PyTorch ecosystem, matched with this regex:

    ^[ \t]*(?:import|from)[ \t]+(
    torch(vision|audio|text|arrow|metrics|_geometric|_scatter|_sparse|
          _cluster|_spline_conv|_xla|_cpp|_jit|_onnx|_fx)?
  | pytorch_lightning
  | lightning(\.pytorch)?
  | skorch
)\b

  3. Its length is between 200 and 200,000 characters.

  4. Its content hash is unique within the sample (exact duplicates removed).

Sampling

The Stack stores files grouped by repository, so a naive "first 50K" pass would be dominated by whichever repositories sort first. Instead, update.py reservoir-samples over a randomly shuffled subset of the source shards, so the sample is approximately uniform over the scanned shards rather than biased toward any one repository. Pass --seed to reproduce or vary the sample.

Dataset schema

Column Type Description
content string The full source code of the file.
repo string Repository identifier (owner/name) when available.
path string File path within the repository, when available.
license string Detected license(s), comma-separated if multiple.
size int64 File size in bytes.
sha string Content hash from the source dataset.
lang string Language tag (always python here).
ext string File extension (.py).

Source column names vary between The Stack releases; update.py maps them defensively. Columns that are missing upstream are emitted as empty strings.

Usage 

from datasets import load_dataset

ds = load_dataset("Monster-Code/Pytorch-Code-50K", split="train")
print(ds)
# Dataset({
#     features: ['content', 'repo', 'path', 'license', 'size', 'sha', 'lang', 'ext'],
#     num_rows: 50000
# })

# Only permissively licensed files
ds = ds.filter(lambda x: "mit" in x["license"].lower())

# A single training example
print(ds[0]["content"][:500])

Stream it instead of downloading everything:

ds = load_dataset("Monster-Code/Pytorch-Code-50K", split="train", streaming=True)
for row in ds:
    ...

How it was built 

pip install huggingface_hub pyarrow

# accept the terms of the source dataset once:
#   https://huggingface.co/datasets/bigcode/the-stack

python update.py            # full 50K build
python update.py --dry-run  # scan and report, push nothing

update.py:

  1. lists the Python parquet shards of bigcode/the-stack,
  2. shuffles them with a seed and downloads them through the HF cache (re-runs are incremental),
  3. filters by the PyTorch import regex and length bounds,
  4. reservoir-samples 50,000 unique files,
  5. writes local parquet shards and pushes them plus this card to the Hub.

The script is idempotent and resumable: source shards are cached under cache/ and already-downloaded shards are skipped.

Known limitations (BETA)

  • Not exhaustively filtered. Files may contain security-sensitive code, secrets committed to version control, or license-incompatible snippets. No manual review has been performed.
  • Import-based selection. Files that use PyTorch only through a wrapper (e.g. transformers without importing torch) are not included. Files that mention import torch in a docstring/comment may be included.
  • Sampling bias. The sample is uniform over a random subset of shards, not over all PyTorch files on GitHub. Repositories whose code is heavily duplicated may still be over-represented.
  • Duplicates. The Stack is not deduplicated, so the same file can appear more than once (across repositories) and very similar files are common.
  • License metadata is noisy. license comes from upstream detection and may be missing, wrong, or list several licenses.
  • Beta may change. Rows and schema may change in future releases; pin a revision if you need reproducibility.

Terms of use

This dataset is distributed for research purposes. You are responsible for complying with the licenses of the original repositories. By using this dataset you agree to the Terms of Use of The Stack.

Citation

If you use this dataset, please cite The Stack and this release:

@misc{pytorch-code-50k,
  title        = {Pytorch-Code-50K (BETA)},
  author       = {Monster-Code},
  year         = {2026},
  howpublished = {\url{https://huggingface.co/datasets/Monster-Code/Pytorch-Code-50K}}
}

@article{BigCodeTheStack,
  title   = {The Stack: 3 TB of permissively licensed source code},
  author  = {BigCode Project},
  journal = {arXiv preprint arXiv:2211.15533},
  year    = {2022}
}

Contact

Questions, takedown requests, or complaints about specific files: open a discussion on the dataset repo or contact the Monster-Code maintainers.

This is a BETA release. Expect rough edges. Please report issues on the dataset's Hugging Face page.