Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: Training and Evaluating a Jupyter Notebook Data Science Assistant   We study the feasibility of a Data Science assistant powered by a
sequence-to-sequence transformer by training a new model JuPyT5 on all publicly
available Jupyter Notebook GitHub repositories and developing a new metric:
Data Science Problems (DSP). DSP is a collection of 1119 problems curated from
306 pedagogical notebooks with 92 dataset dependencies, natural language and
Markdown problem descriptions, and assert-based unit tests. These notebooks
were designed to test university students' mastery of various Python
implementations of Math and Data Science, and we now leverage them to study the
ability of JuPyT5 to understand and pass the tests. We analyze the content of
DSP, validate its quality, and we find that given 100 sampling attempts JuPyT5
is able to solve 77.5\% of the DSP problems. We further present various
ablation and statistical analyses and compare DSP to other recent natural
language to code benchmarks.

29
The categories of this paper are: funct-an
11
The categories of this paper are: hep-lat
22
The categories of this paper are: chao-dyn
31
The categories of this paper are: ao-sci
7
The categories of this paper are: hep-th
14
The categories of this paper are: math-ph
0
The categories of this paper are: hep-ph
6
The categories of this paper are: astro-ph
33
The categories of this paper are: plasm-ph
34
The categories of this paper are: chem-ph
8
The categories of this paper are: hep-ex
23
The categories of this paper are: solv-int
9
The categories of this paper are: nlin
2
The categories of this paper are: math
26
The categories of this paper are: bayes-an
32
The categories of this paper are: atom-ph