# ManyTypes4Py: A Benchmark Python Dataset for Machine Learning-based Type Inference

arXiv: https://arxiv.org/abs/2104.04706 · HTML: https://mlsanity.com/paper/2104.04706/

## Key facts

- Cited by 9 papers in the map (data through 14 Oct 2026).
- Builds on 5 of its 15 references that are in the map.
- First posted on arXiv 10 Apr 2021.

## Builds on (most cited first, 5 of 5)

- [Revisiting Unreasonable Effectiveness of Data in Deep Learning Era](https://mlsanity.com/paper/1707.02968/) (2017)
- [The Adverse Effects of Code Duplication in Machine Learning Models of Code](https://mlsanity.com/paper/1812.06469/) (2018)
- [Pythia: AI-assisted Code Completion System](https://mlsanity.com/paper/1912.00742/) (2019)
- [TypeWriter: Neural Type Prediction with Search-based Validation](https://mlsanity.com/paper/1912.03768/) (2019)
- [Typilus: Neural Type Hints](https://mlsanity.com/paper/2004.10657/) (2020)

## Built on by (most cited first, 9 of 9)

- [TypeT5: Seq2seq Type Inference using Static Analysis](https://mlsanity.com/paper/2303.09564/) (2023)
- [Type Prediction With Program Decomposition and Fill-in-the-Type Training](https://mlsanity.com/paper/2305.17145/) (2023)
- [Understanding How CodeLLMs (Mis)Predict Types with Activation Steering](https://mlsanity.com/paper/2404.01903/) (2024)
- [JEMMA: An Extensible Java Dataset for ML4Code Applications](https://mlsanity.com/paper/2212.09132/) (2022)
- [Cross-Domain Evaluation of a Deep Learning-Based Type Inference System](https://mlsanity.com/paper/2208.09189/) (2022)
- [TypyBench: Evaluating LLM Type Inference for Untyped Python Repositories](https://mlsanity.com/paper/2507.22086/) (2025)
- [Automated Type Annotation in Python Using Large Language Models](https://mlsanity.com/paper/2508.00422/) (2025)
- [Extending LLM Context via Associative Recurrent Memory](https://mlsanity.com/paper/2607.11614/) (2026)
- [On the Lexical Superstition of Large Language Models for Code Comprehension: Re-evaluation on Code of Low Lexical Quality](https://mlsanity.com/paper/2609.26388/) (2026)

## Abstract (arXiv)

In this paper, we present ManyTypes4Py, a large Python dataset for machine learning (ML)-based type inference. The dataset contains a total of 5,382 Python projects with more than 869K type annotations. Duplicate source code files were removed to eliminate the negative effect of the duplication bias. To facilitate training and evaluation of ML models, the dataset was split into training, validation and test sets by files. To extract type information from abstract syntax trees (ASTs), a lightweight static analyzer pipeline is developed and accompanied with the dataset. Using this pipeline, the collected Python projects were analyzed and the results of the AST analysis were stored in JSON-formatted files. The ManyTypes4Py dataset is shared on zenodo and its tools are publicly available on GitHub.