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index bed0738c7eeb449bca98b5d2f33c89a1ee56349a..7540e71ca9b04450df88af31d3d6ef76fb7d57da 100644
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diff --git a/README.md b/README.md
index 0d32c16dcaf9a0484d30b4c9ee56e3e0619be577..1036d8712236fa9fb685647b36243584cc049e7a 100644
--- a/README.md
+++ b/README.md
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 ---
-tags:
-  - stackoverflow
-  - qdrant
-  - embeddings
-  - retrieval
-  - question-answering
-task_categories:
-  - feature-extraction
-  - question-answering
-language:
-  - en
-pretty_name: LUA StackOverflow Vector Dataset
-license: cc-by-sa-4.0
-viewer: false
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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
+}
 ---
 
-# LUA StackOverflow Vector Dataset
+# Stack2Graph vector database: lua
 
-## Summary
+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.
 
-This Hugging Face dataset repository contains the LUA shard of the Stack2Graph vector-database component as restorable Qdrant artifacts plus portable Parquet fallback files.
-Hugging Face uses one dataset repository per programming language, so this repository is directly cloneable without an extra top-level archive wrapper.
+## Files and fields
 
-The artifacts are intended for semantic and hybrid retrieval, graph entry-point finding, and retrieval-augmented generation experiments over Stack Overflow content.
+`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.
 
-Stack2Graph source:
-[https://github.com/tha-atlas/Stack2Graph](https://github.com/tha-atlas/Stack2Graph)
+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.
 
-## Hugging Face Repository Layout
+## Provenance and reproducibility
 
-```text
-README.md
-dataset_manifest.json
-qdrant_snapshots/
-  collections_manifest.json
-  stackoverflow_lua_vector.tar.zst
-  stackoverflow_lua_vector.snapshot.zst.part_000
-  ...
-question_metadata/
-  r0_00000.parquet
-chunk_records/
-  r0_00000.parquet
+```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"
+}
 ```
 
-- `README.md`: Hugging Face dataset card rendered from this template.
-- `dataset_manifest.json`: language-scoped manifest listing the files in this repository.
-- `qdrant_snapshots/collections_manifest.json`: language-scoped Qdrant snapshot manifest.
-- `qdrant_snapshots/stackoverflow_lua_vector.*`: Qdrant restore artifacts for `stackoverflow_lua_vector`.
-- `question_metadata/*.parquet`: question-level metadata used by parent-child chunk retrieval.
-- `chunk_records/*.parquet`: chunk-level vector rows when parent-child indexing is enabled.
-- `question_records/*.parquet`: question-level vector rows when non-chunked export is used.
+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.
 
-Parent-child exports contain `question_metadata/` and `chunk_records/`. Non-chunked exports contain `question_records/` instead. A repository does not need both `chunk_records/` and `question_records/`.
+## Uses and limitations
 
-During Hugging Face upload, the local leading language directory is removed. A local file such as `lua/chunk_records/r0_00000.parquet` appears in this repository as `chunk_records/r0_00000.parquet`.
+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.
 
-## Content
+## Attribution and licensing
 
-Rows are derived from Stack Overflow questions tagged for LUA.
+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.
 
-The current Stack2Graph vector pipeline uses:
+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.
 
-- dense vectors from `Qwen/Qwen3-Embedding-8B`
-- 4096-dimensional dense embeddings with last-token pooling
-- instruction-aware query embedding for retrieval
-- sparse lexical vectors from `BAAI/bge-m3`
-- unified question text containing title, tags, cleaned problem text, and code
-- optional parent-child indexing where chunk hits collapse back to parent question IDs
-
-When Qdrant snapshots are included, they are the fastest restore path. The Parquet files remain the portable fallback for rebuilding the collection.
-
-## Coverage
-
-This repository contains only the LUA shard. A Stack Overflow question can appear in more than one language shard when it has multiple programming-language tags.
-
-The dataset is not a complete Stack Overflow mirror. Full question and answer graph context lives in the corresponding Stack2Graph knowledge graph artifacts; the vector dataset stores retrieval rows, vectors, sparse weights, and routing metadata.
-
-## Recommended Use
-
-Use this dataset for semantic and hybrid retrieval, Qdrant restore or ingestion tests, Stack2Graph RAG experiments, graph entry-point retrieval before QLever graph expansion, and vector database benchmarking.
-
-This dataset is not intended as a standalone supervised training dataset.
-
-## Restore With Stack2Graph
-
-You do not need to regenerate embeddings to use this dataset.
-
-Typical workflow:
-
-1. Clone Stack2Graph and configure `.env` with service paths and an HF token.
-2. Start local services:
-
-```bash
-docker compose up -d
-```
-
-3. Download and restore vector artifacts:
-
-```bash
-python -m experiment.sources.hf --skip-kg
-```
-
-The loader restores Qdrant snapshots when present and falls back to Parquet ingestion when snapshots are unavailable.
-
-## Manual Use
-
-For manual use, inspect `dataset_manifest.json`, then either restore the Qdrant snapshot artifacts under `qdrant_snapshots/` or ingest the listed Parquet shards into a compatible vector database.
-
-The target Qdrant collection name is:
-
-```text
-stackoverflow_lua_vector
-```
-
-## Quality Notes
-
-- Embedding quality depends on the configured Stack2Graph export pipeline and model versions.
-- Sparse text can be generated from the same unified text as dense embeddings or from a lexical variant, depending on export configuration.
-- Community-generated Stack Overflow content may contain errors, outdated information, bias, or incomplete answers.
-- Rebuilding from Parquet may produce operational differences if Qdrant collection settings differ from the original export.
-
-## License And Attribution
-
-This dataset is derived from Stack Overflow content and is distributed under `CC-BY-SA-4.0`.
-Preserve required attribution and license notices when redistributing derived artifacts.
-
-## Citation
-
-If you use this dataset, cite the Stack2Graph project and preserve Stack Overflow attribution requirements:
-
-- Kleybolte, L. A., Ventura, V., & Zarcone, A. (2026). Stack2Graph: A Structured Knowledge Representation of Stack Overflow Data for Retrieval-based Question Answering . In Proceedings of the Knowledge Graphs and Large Language Models Workshop (KG-LLM) @ LREC26 (pp. 120-133). European Language Resources Association (ELRA). https://doi.org/10.63317/583q4d3mwbdu.
+Project citation: Kleybolte, Ventura and Zarcone (2026), *Stack2Graph*, KG-LLM
+at LREC26. https://doi.org/10.63317/583q4d3mwbdu.
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