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We introduce MLFMF, a collection of data sets for benchmarking recommendationsystems used to support formalization of mathematics with proof assistants.These systems help humans identify which previous entries (theorems,constructions, datatypes, and postulates) are relevant in proving a new theoremor carrying out a new construction. Each data set is derived from a library offormalized mathematics written in proof assistants Agda or Lean. The collectionincludes the largest Lean~4 library Mathlib, and some of the largest Agdalibraries: the standard library, the library of univalent mathematicsAgda-unimath, and the TypeTopology library. Each data set represents thecorresponding library in two ways: as a heterogeneous network, and as a list ofs-expressions representing the syntax trees of all the entries in the library.The network contains the (modular) structure of the library and the referencesbetween entries, while the s-expressions give complete and easily parsedinformation about every entry. We report baseline results using standard graphand word embeddings, tree ensembles, and instance-based learning algorithms.The MLFMF data sets provide solid benchmarking support for furtherinvestigation of the numerous machine learning approaches to formalizedmathematics. The methodology used to extract the networks and the s-expressionsreadily applies to other libraries, and is applicable to other proofassistants. With more than $250\,000$ entries in total, this is currently thelargest collection of formalized mathematical knowledge in machine learnableformat.

MLFMF: Data Sets for Machine Learning for Mathematical Formalization