Datasets:
id string | question_id int64 | chunk_index int32 | embedding_text string | dense_tokens int32 | sparse_tokens int32 | languages list | tags list | payload_json string | dense list | sparse_indices list | sparse_values list |
|---|---|---|---|---|---|---|---|---|---|---|---|
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"
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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"
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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 | [
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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) | [0.0162200927734375,-0.0037593841552734375,-0.027496337890625,-0.0043487548828125,-0.003393173217773(...TRUNCATED) | [28,73,100,223,310,678,1257,1733,1884,2065,2684,2806,3034,3173,3229,3445,4197,4488,5423,5426,5701,58(...TRUNCATED) | [0.0169677734375,0.1259765625,0.0673828125,0.1171875,0.09326171875,0.002197265625,0.03515625,0.05639(...TRUNCATED) |
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) | [-0.00896453857421875,-0.0204315185546875,0.003696441650390625,-0.01448822021484375,0.00403976440429(...TRUNCATED) | [10,17,47,54,67,70,87,219,425,645,670,5423,8560,11030,11249,12638,16713,16750,40368,48322,48962,5235(...TRUNCATED) | [0.0211181640625,0.08984375,0.0546875,0.048583984375,0.1328125,0.005615234375,0.061767578125,0.05957(...TRUNCATED) |
2a31fffa-ff98-5db6-80fb-974bc3000629 | 683 | 0 | "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) | [0.0205230712890625,0.00595855712890625,-0.0185546875,0.013641357421875,0.0168304443359375,-0.038085(...TRUNCATED) | [13,17,23,31,42,47,53,54,73,111,214,345,720,1314,1379,1884,5775,6817,6953,8562,9351,10298,10336,1358(...TRUNCATED) | [0.0810546875,0.0927734375,0.23046875,0.189453125,0.1201171875,0.002685546875,0.1083984375,0.0300292(...TRUNCATED) |
e22bec48-4f0c-5293-9ae0-2060976afc2e | 742 | 0 | "Title: Class views in Django\n\nDjango view points to a function, which can be a problem if you wan(...TRUNCATED) | 135 | 139 | [
"python"
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"view",
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89b25954-cdef-5092-966d-c6b341320a82 | 766 | 0 | "Title: Python and MySQL\n\nI can get Python to work with Postgresql but I cannot get it to work wit(...TRUNCATED) | 121 | 128 | [
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15367acb-289e-5580-88f8-635d01314422 | 773 | 0 | "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) | [0.0154266357421875,0.004627227783203125,-0.0210418701171875,-0.006259918212890625,0.008712768554687(...TRUNCATED) | [5,7,17,47,54,67,87,96,107,111,112,132,645,720,903,1272,3060,3181,3642,3934,4127,4527,4692,5303,5608(...TRUNCATED) | [0.001708984375,0.107421875,0.0634765625,0.01220703125,0.044677734375,0.052734375,0.059326171875,0.0(...TRUNCATED) |
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"
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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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