Data Portrait: StarCoder training data
A Data Portrait of bigcode/starcoderdata (The Stack v1.2 in 86 languages,
GitHub issues, Jupyter notebooks and git commits), the training data of bigcode/starcoderbase (and its 1B/3B/7B versions) and
bigcode/starcoder. StarCoder is StarCoderBase
further trained on the Python subset, so this portrait covers both. StarCoder's special tokens (<reponame>,
<gh_stars>, <issue_comment>, <jupyter_code>, ...) inside the stored text are replaced by newlines.
Text is normalised as in data-portraits and split into 50-character n-grams with stride 50, stored in 1 Bloom filter(s) sized to fit in RAM. A query ORs membership across filters: combined false-positive rate <= 0.01 (each filter 0.010000).
Each partition is a separate revision of this repo (tag p<i>of<k>, branch partition-<n>):
| tag | files | sources | size |
|---|---|---|---|
p0of1 |
863 | ada, agda, alloy, antlr, applescript, assembly, augeas, awk, batchfile, bluespec, c, c-sharp, clojure, cmake, coffeescript, common-lisp, cpp, css, cuda, dart, dockerfile, elixir, elm, emacs-lisp, erlang, f-sharp, fortran, git-commits-cleaned, github-issues-filtered-structured, glsl, go, groovy, haskell, html, idris, isabelle, java, java-server-pages, javascript, json, julia, jupyter-scripts-dedup-filtered, jupyter-structured-clean-dedup, kotlin, lean, literate-agda, literate-coffeescript, literate-haskell, lua, makefile, maple, markdown, mathematica, matlab, ocaml, pascal, perl, php, powershell, prolog, protocol-buffer, python, r, racket, restructuredtext, rmarkdown, ruby, rust, sas, scala, scheme, shell, smalltalk, solidity, sparql, sql, stan, standard-ml, stata, systemverilog, tcl, tcsh, tex, thrift, typescript, verilog, vhdl, visual-basic, xslt, yacc, yaml, zig | 17 GiB |
partitions.json lists the exact files (pinned dataset revision) in each partition. Query with
starcoder_portrait.query:
from starcoder_portrait.query import SequentialPortrait
portrait = SequentialPortrait("aalkaswan/starcoder-portrait", ["p0of1"])
report = portrait.contains_from_text([text], sort_chains_by_length=True)[0]