README.md

---
dataset_info:
  features:
  - name: zip
    dtype: string
  - name: filename
    dtype: string
  - name: contents
    dtype: string
  - name: type_annotations
    sequence: string
  - name: type_annotation_starts
    sequence: int64
  - name: type_annotation_ends
    sequence: int64
  splits:
  - name: train
    num_bytes: 4206116750
    num_examples: 548536
  download_size: 1334224020
  dataset_size: 4206116750
configs:
- config_name: default
  data_files:
  - split: train
    path: data/train-*
license: openrail
pretty_name: ManyTypes4Py Reconstruction
---

# ManyTypes4Py-Reconstructed

This is a reconstruction of the original code from the [ManyTypes4Py paper]
from the following paper

A. M. Mir, E. Latoškinas and G. Gousios, "ManyTypes4Py: A Benchmark Python
Dataset for Machine Learning-based Type Inference," *IEEE/ACM International
Conference on Mining Software Repositories (MSR)*, 2021, pp. 585-589

[The artifact] (v0.7) for ManyTypes4Py does not have the original Python files.
Instead, each file is pre-processed into a stream of types without comments,
and the contents of each repository are stored in a single JSON file.
This reconstructed dataset has raw Python code.

More specifically:

1. We extract the list of repositories from the "clean" subset of ManyTypes4Py,
   which are the repositories that type-check with *mypy*.

2. We attempt to download all repositories, but only succeed in fetching
   4,663 (out of ~5.2K).

3. We augment each file with the text of each type annotation, as well as their
   start and end positions (in bytes) in the code.


## Internal Note

The dataset construction code is on the Discovery cluster at `/work/arjunguha-research-group/arjun/projects/ManyTypesForPy_reconstruction`.

[ManyTypes4Py paper]: https://arxiv.org/abs/2104.04706
[The artifact]: https://zenodo.org/records/4719447