Blame for README.md

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      ]
    }
  ],
  "language": [
    "en"
  ],
  "license": "cc-by-sa-4.0",
  "pretty_name": "Stack2Graph vectors \u2014 lua",
  "tags": [
    "stack-overflow",
    "embeddings",
    "dense",
    "sparse",
    "stack2graph"
  ],
  "task_categories": [
    "text-retrieval"
  ],
  "viewer": true
}
---

# Stack2Graph vector database: lua

This release contains 25042 text chunks from Stack Overflow with dense and
sparse embeddings, selected by the producer's programming-language memberships.
Memberships can overlap: a chunk may occur in several language datasets. These
labels describe programming languages, not the natural language of the text.
`extra-languages`, when present, contains source rows without a language match.
An absent language is never replaced with an empty dataset.

## Files and fields

`data/vectors-*.parquet` contains Zstandard-compressed tables. Each row has a
stable `id`, `question_id`, `chunk_index`, `embedding_text`, model token counts,
`languages`, `tags`, `dense`, `sparse_indices`, `sparse_values` and `payload_json`.
The last field preserves the complete original Qdrant payload, including source
snapshot identity, source dates, scores and answer flags when available.
Dense values are float32 representations of the producer's stored float16
values; this does not recover precision discarded during embedding. Sparse
weights preserve float32 values and uint32 token indices.

On Hugging Face, load the `vectors` config with `datasets.load_dataset` and use
streaming for large releases. The dataset viewer uses only the explicitly listed
Parquet files. `data/dataset.tar.gz` is the portable package also used by MDC:
extract it to obtain the same Parquet bytes, README and `publication.json`.
The archive is an exchange dataset, not a Qdrant binary snapshot. Import in
bounded batches using each row's `id`, named `dense`/`sparse` vectors and decoded
`payload_json`. Recreate collection settings deliberately for your Qdrant version.

## Provenance and reproducibility

```json
{
  "after_id": -9223372036854775808,
  "chunk_overlap": 128,
  "chunk_tokens": 1024,
  "collection": "stackoverflow_questions",
  "dense_dimension": 4096,
  "dense_model": "Qwen/Qwen3-Embedding-8B",
  "dense_revision": "1d8ad4ca9b3dd8059ad90a75d4983776a23d44af",
  "question_limit": null,
  "registry_digest": "d2a7357c04e7f47d9bb598189aeeb7db0680d6b35f0d9695d8d2e544121afb8e",
  "snapshot_digest": "56fe643952cec74b17ea13d91edc8ad95181326156177c94cce48d3fed9834b9",
  "snapshot_id": "stackexchange_20260630_sakura",
  "sparse_model": "BAAI/bge-m3",
  "sparse_revision": "5617a9f61b028005a4858fdac845db406aefb181"
}
```

Export identity: `bc9f696c9588560e3be547879c58b96c50c64e3828495471bee43e504392ec7f`.
Point-ID configuration digest: `d16d58c9e7b8007664e3f77b4e9f4a1689a8b8de2c6727b479595621b25e3460`.
Point IDs use the same snapshot/configuration/question/chunk UUID derivation as
the project's Qdrant loader. `publication.json` lists exact file sizes, SHA-256
hashes, row count, source identity and model/chunking settings. On HF,
`release.json` additionally verifies all managed remote files.
Only locally finalized runs with matching stage/report counts are published.
The uploader does not re-embed text or query a running database.

## Uses and limitations

Intended for dense, sparse and hybrid retrieval, retrieval-augmented generation
and reproducible search experiments. Retrieval quality depends on the pinned
models and chunking configuration. Overlapping chunks and language memberships
are not independent samples; split evaluations by source question to avoid
leakage. Source questions can contain personal data, obsolete code, errors and
biases. Source-window limits are recorded above. No retrieval benchmark or
completeness beyond the recorded source window is claimed.

## Attribution and licensing

Derived from Stack Overflow content. Preserve the original post/author
attribution and applicable Stack Overflow licensing when redistributing or
displaying source content. Question IDs and source snapshot are retained to
resolve source posts. This publication declares CC BY-SA 4.0; embedding model
licenses and any upstream obligations still apply. Pinned model names and
revisions above identify their model cards for review.

Source and procedures: https://github.com/tha-atlas/Stack2Graph.
When citing this dataset, include the dataset repository, immutable release
revision, source snapshot, export identity, model revisions and the project.

Project citation: Kleybolte, Ventura and Zarcone (2026), *Stack2Graph*, KG-LLM
at LREC26. https://doi.org/10.63317/583q4d3mwbdu.