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id
string
question_id
int64
chunk_index
int32
embedding_text
string
dense_tokens
int32
sparse_tokens
int32
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tags
list
payload_json
string
dense
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1ea0da66-4b81-5acf-a084-a97a16cb38aa
337
0
Title: XML Processing in Python I am about to build a piece of a project that will need to construct and post an XML document to a web service and I'd like to do it in Python, as a means to expand my skills in it. Unfortunately, whilst I know the XML model fairly well in .NET, I'm uncertain what the pros and cons a...
113
120
[ "python" ]
[ "python", "xml" ]
{"accepted_answer_id":342,"answer_count":12,"chunk_count":1,"chunk_index":0,"closed_date":null,"creation_date":"2008-08-02T03:35:55.697000+00:00","has_accepted_answer":true,"is_closed":false,"languages":["python"],"last_activity_date":"2021-11-12T18:56:21.143000+00:00","last_edit_date":"2016-12-30T12:56:21.493000+00:00...
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1747982c-cf50-50e4-b5be-0eef66b4f1fb
469
0
Title: How can I find the full path to a font from its display name on a Mac? I am using the Photoshop's javascript API to find the fonts in a given PSD. Given a font name returned by the API, I want to find the actual physical font file that font name corresponds to on the disc. This is all happening in a python ...
108
111
[ "python" ]
[ "python", "macos", "fonts", "photoshop" ]
{"accepted_answer_id":3040,"answer_count":6,"chunk_count":1,"chunk_index":0,"closed_date":null,"creation_date":"2008-08-02T15:11:16.430000+00:00","has_accepted_answer":true,"is_closed":false,"languages":["python"],"last_activity_date":"2022-12-15T07:51:30.287000+00:00","last_edit_date":"2016-12-22T03:53:45.467000+00:00...
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2e7bbed2-eda6-52c4-8e92-b25f3e468a23
502
0
"Title: Get a preview JPEG of a PDF on Windows?\n\nI have a cross-platform (Python) application whic(...TRUNCATED)
59
66
[ "python" ]
[ "python", "windows", "image", "pdf" ]
"{\"accepted_answer_id\":7090,\"answer_count\":4,\"chunk_count\":1,\"chunk_index\":0,\"closed_date\"(...TRUNCATED)
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39e79c76-e882-5dbe-b252-5183816201b0
535
0
"Title: Continuous Integration System for a Python Codebase\n\nI am starting to work on a hobby proj(...TRUNCATED)
144
156
[ "python" ]
[ "python", "continuous-integration", "extreme-programming" ]
"{\"accepted_answer_id\":541,\"answer_count\":7,\"chunk_count\":1,\"chunk_index\":0,\"closed_date\":(...TRUNCATED)
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f54dc076-4522-5bce-b9b2-9f73b1ebcdbe
594
0
"Title: cx_Oracle: How do I iterate over a result set?\n\nThere are several ways to iterate over a r(...TRUNCATED)
36
43
[ "python", "sql" ]
[ "python", "sql", "database", "oracle-database", "cx-oracle" ]
"{\"accepted_answer_id\":595,\"answer_count\":3,\"chunk_count\":1,\"chunk_index\":0,\"closed_date\":(...TRUNCATED)
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2a31fffa-ff98-5db6-80fb-974bc3000629
683
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"Title: Using 'in' to match an attribute of Python objects in an array\n\nI don't remember whether I(...TRUNCATED)
77
92
[ "python" ]
[ "python", "arrays", "iteration" ]
"{\"accepted_answer_id\":57833,\"answer_count\":8,\"chunk_count\":1,\"chunk_index\":0,\"closed_date\(...TRUNCATED)
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e22bec48-4f0c-5293-9ae0-2060976afc2e
742
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"Title: Class views in Django\n\nDjango view points to a function, which can be a problem if you wan(...TRUNCATED)
135
139
[ "python" ]
[ "python", "django", "view", "oop" ]
"{\"accepted_answer_id\":33957,\"answer_count\":9,\"chunk_count\":1,\"chunk_index\":0,\"closed_date\(...TRUNCATED)
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89b25954-cdef-5092-966d-c6b341320a82
766
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"Title: Python and MySQL\n\nI can get Python to work with Postgresql but I cannot get it to work wit(...TRUNCATED)
121
128
[ "python" ]
[ "python", "mysql", "postgresql", "bpgsql" ]
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773
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"Title: How do I use itertools.groupby()?\n\nI haven't been able to find an understandable explanati(...TRUNCATED)
138
167
[ "python" ]
[ "python", "python-itertools" ]
"{\"accepted_answer_id\":7286,\"answer_count\":16,\"chunk_count\":1,\"chunk_index\":0,\"closed_date\(...TRUNCATED)
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c7c65d81-fc56-59fd-8991-9873612ec624
972
0
"Title: Adding a method to an existing object instance in Python\n\nHow do I add a method to an exis(...TRUNCATED)
58
60
[ "python" ]
[ "python", "oop", "methods", "monkeypatching" ]
"{\"accepted_answer_id\":2982,\"answer_count\":19,\"chunk_count\":1,\"chunk_index\":0,\"closed_date\(...TRUNCATED)
[0.00418853759765625,0.022186279296875,-0.01309967041015625,-0.00156402587890625,0.00861358642578125(...TRUNCATED)
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End of preview. Expand in Data Studio

Stack2Graph vector database: python

This release contains 2728396 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

{
  "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.

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