  Symbolic regression, i.e. predicting a function from the observation of its
values, is well-known to be a challenging task. In this paper, we train
Transformers to infer the function or recurrence relation underlying sequences
of integers or floats, a typical task in human IQ tests which has hardly been
tackled in the machine learning literature. We evaluate our integer model on a
subset of OEIS sequences, and show that it outperforms built-in Mathematica
functions for recurrence prediction. We also demonstrate that our float model
is able to yield informative approximations of out-of-vocabulary functions and
constants, e.g. $\operatorname{bessel0}(x)\approx
\frac{\sin(x)+\cos(x)}{\sqrt{\pi x}}$ and $1.644934\approx \pi^2/6$. An
interactive demonstration of our models is provided at
https://symbolicregression.metademolab.com.
