Datasets:

s0duku
/

the-stack-go

stackbuild is the single repository-context data tool. It scans an immutable Repo snapshot, builds its dependency graph, and emits budgeted pretraining samples. The same package exposes the repository models and scanner; importing

from stackbuild import BuildConfig, Repo, RepoFile, build_samples, write_parquet

repo = Repo("owner/project", "revision", (
    RepoFile("lib.py", "VALUE = 1\n", "Python"),
    RepoFile("main.py", "import lib\nprint(lib.VALUE)\n", "Python"),
))
samples = build_samples(
    repo,
    config=BuildConfig(max_tokens=8_192),
)
write_parquet(samples, "samples.parquet")

By default one sample kind is emitted:

dependency: hard-dependency components in dependency-first order. Files with no hard edges are packed together under the same repository boundary.

For an additional target-oriented view, enable bounded deterministic target sampling:

config = BuildConfig(
    max_tokens=8_192,
    target_context=True,
    max_targets_per_repo=32,
    max_dependency_depth=3,
)

This adds target_context samples for a stable hash-ranked uniform sample of eligible source files; target selection is not ranked by reference count. Resolved dependencies are selected nearest-first and rendered dependency-first, then small local or root documentation/build context is added when budget allows. The target is last. Target-only samples are skipped because they add no context beyond the default repository samples. Metadata distinguishes target, dependency, and repository-context files.

Each sample uses the six native DeepSeek V4 repository/file boundary tokens:

<｜begin▁of▁repo▁name｜>owner/project<｜end▁of▁repo▁name｜>
<｜begin▁of▁file▁name｜>lib.py<｜end▁of▁file▁name｜>
<｜begin▁of▁file｜>
VALUE = 1
<｜end▁of▁file｜>
<｜begin▁of▁file▁name｜>main.py<｜end▁of▁file▁name｜>
<｜begin▁of▁file｜>
import lib
print(lib.VALUE)
<｜end▁of▁file｜>

The default tokenizer is pinned to deepseek-ai/DeepSeek-V4-Flash-Base, revision 8855555deef230a27a21a8d6f294b7b7497759b6. These tokens already have IDs 128815–128820. No vocabulary extension is required. The local directory the-stack-go/tokenizers/deepseek-v4-flash-base is preferred; an explicit tokenizer can also be supplied through the API or CLI.

revision_id, language, dependency, role, and content-policy information stay in the structured Sample metadata rather than the model text. No literal EOS marker is written into Sample.text; the training collator should append the model's existing EOS token after tokenization.

Jupyter notebooks are converted into ordered text, code, and output cells. Binary files and, by default, generated/vendor files are represented by file metadata without raw content. Every input path is represented by the dependency sample set, including metadata-only files.

When tokenizer is omitted, the pinned tokenizer is loaded and cached. Token counts are exact encodings of the complete sample with add_special_tokens=False. The 8192 budget applies to Sample.text; any BOS/EOS appended by training must be budgeted separately. Parquet stores nested files and metadata_json along with text and token counts, using ZSTD compression and 2048-row groups.

The stackbuild CLI performs the same end-to-end operation for a checkout:

stackbuild ./repository samples.parquet --info-output repository-info.json

Use stackbuild-info when only the dependency metadata JSON is needed.