Datasets:
id stringlengths 36 36 | question_id int64 9.53M 80M | chunk_index int32 0 22 | embedding_text stringlengths 77 8.89k | dense_tokens int32 21 1.02k | sparse_tokens int32 22 1.02k | languages listlengths 1 4 | tags listlengths 1 5 | payload_json stringlengths 416 545 | dense list | sparse_indices listlengths 13 397 | sparse_values listlengths 13 397 |
|---|---|---|---|---|---|---|---|---|---|---|---|
5e6a4dfd-9516-57a6-8fc2-52d34fba8bda | 9,533,139 | 0 | Title: What would Clojure lose by switching away from leading parenthesis like Dylan, Julia and Seph?
Three lispy homoiconic languages, Dylan, Julia and Seph all moved away from leading parenthesis - so a hypothetical function call in Common Lisp that would look like:
(print hello world)
Would look like the follow... | 233 | 264 | [
"julia",
"lisp"
] | [
"clojure",
"lisp",
"julia",
"language-design",
"dylan"
] | {"accepted_answer_id":9537572,"answer_count":9,"chunk_count":1,"chunk_index":0,"closed_date":null,"creation_date":"2012-03-02T12:17:16.083000+00:00","has_accepted_answer":true,"is_closed":false,"languages":["julia","lisp"],"last_activity_date":"2020-06-05T17:56:32.213000+00:00","last_edit_date":"2017-06-24T04:07:19.843... | [
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d8714092-f745-5274-b14c-e84438028739 | 9,965,747 | 0 | Title: Linking R and Julia?
Julia looks very promising for fast and syntax-sane computation (e.g. here), but I suspect it will not be anywhere near R in terms of overall statistics workflow for some time yet. So I'd like to use it where C++ is mainly used in R programs: to optimize slow portions of code. Before I inve... | 177 | 193 | [
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250720e7-de73-5bdd-ae88-915c10391992 | 9,968,578 | 0 | "Title: Speeding up Julia's poorly written R examples\n\nThe Julia examples to compare performance a(...TRUNCATED) | 484 | 486 | [
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b5c307c9-dee4-5eaf-b3b7-54e6ec2b9f60 | 10,268,028 | 0 | "Title: Julia compiles the script every time?\n\nJulia language compiles the script every time, can'(...TRUNCATED) | 153 | 159 | [
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] | [
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9b61faef-fa45-5909-b4a6-34e50942bea4 | 10,775,510 | 0 | "Title: Julia's Python performance example in pypy\n\nJulia is a new statistical programming languag(...TRUNCATED) | 183 | 213 | [
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"numpy",
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] | "{\"accepted_answer_id\":10801867,\"answer_count\":2,\"chunk_count\":1,\"chunk_index\":0,\"closed_da(...TRUNCATED) | [-0.0172882080078125,0.0286865234375,-0.031524658203125,-0.01099395751953125,0.032440185546875,-0.04(...TRUNCATED) | [7,18,24,47,188,275,289,399,420,442,449,461,493,678,831,1264,1528,1577,1937,2046,2729,3034,3501,3525(...TRUNCATED) | [0.00390625,0.059814453125,0.0198974609375,0.013671875,0.107421875,0.05419921875,0.001220703125,0.14(...TRUNCATED) |
2cc5b8d4-b4e2-5e59-901d-f38fb4b8c5c8 | 12,646,494 | 0 | "Title: Couple of basic questions about Julia on Windows\n\nI run Julia on Windows with the julia.ba(...TRUNCATED) | 159 | 170 | [
"julia"
] | [
"julia"
] | "{\"accepted_answer_id\":12664034,\"answer_count\":2,\"chunk_count\":1,\"chunk_index\":0,\"closed_da(...TRUNCATED) | [-0.0287628173828125,-0.000980377197265625,-0.0256805419921875,-0.0096282958984375,0.008438110351562(...TRUNCATED) | [5,11,34,54,98,111,132,461,678,903,959,1284,1311,1672,2848,3642,3773,4358,4527,6044,6397,6626,7884,8(...TRUNCATED) | [0.03515625,0.1845703125,0.040283203125,0.0322265625,0.09912109375,0.00146484375,0.099609375,0.16992(...TRUNCATED) |
0ea52341-c88f-5605-a5d2-b9194a2d899c | 13,479,274 | 0 | "Title: How to run Julia parallel code using distributed array computations (pre 1.x Julia versions)(...TRUNCATED) | 157 | 168 | [
"julia"
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52f8d521-d3cc-5790-a73d-7741e9d72c70 | 14,092,316 | 0 | "Title: Simplest way(s) to make a Julia package available to others\n\nJulia has a packaging system (...TRUNCATED) | 114 | 141 | [
"julia"
] | [
"module",
"julia"
] | "{\"accepted_answer_id\":14115836,\"answer_count\":2,\"chunk_count\":1,\"chunk_index\":0,\"closed_da(...TRUNCATED) | [-0.00370025634765625,-0.007724761962890625,-0.0161590576171875,-0.00637054443359375,0.0048942565917(...TRUNCATED) | [23,47,73,132,191,217,271,436,527,678,831,1632,1810,2633,3249,3917,4000,4028,4588,5303,5426,7783,851(...TRUNCATED) | [0.0260009765625,0.09521484375,0.07177734375,0.052001953125,0.0947265625,0.138671875,0.07177734375,0(...TRUNCATED) |
44dc0045-db5c-5935-a197-f74f34c795cf | 14,107,226 | 0 | "Title: Constructing a multi-dimensional array in Julia\n\nIn MATLAB, the following syntax can be us(...TRUNCATED) | 134 | 139 | [
"julia"
] | [
"multidimensional-array",
"julia"
] | "{\"accepted_answer_id\":14111889,\"answer_count\":4,\"chunk_count\":1,\"chunk_index\":0,\"closed_da(...TRUNCATED) | [-0.0134735107421875,0.0179290771484375,-0.018646240234375,-0.00896453857421875,0.0103912353515625,-(...TRUNCATED) | [10,23,53,71,102,136,360,425,449,617,831,876,1657,2203,4317,4720,4778,5701,6002,6024,7515,10298,1181(...TRUNCATED) | [0.095703125,0.07666015625,0.134765625,0.1220703125,0.08837890625,0.048583984375,0.037841796875,0.05(...TRUNCATED) |
bd24ffef-df62-53dd-b86a-a183d3e89aa0 | 14,115,088 | 0 | "Title: Defining print()-like functions for a new type in Julia\n\nIn order to make a new type print(...TRUNCATED) | 82 | 84 | [
"julia"
] | [
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] | "{\"accepted_answer_id\":null,\"answer_count\":1,\"chunk_count\":1,\"chunk_index\":0,\"closed_date\"(...TRUNCATED) | [-0.01522064208984375,-0.01116180419921875,-0.007312774658203125,-0.005313873291015625,0.00415039062(...TRUNCATED) | [7,23,71,100,132,449,456,2424,2448,3129,3249,3525,3871,4734,5062,5584,5608,7639,10644,18822,22819,28(...TRUNCATED) | [0.02734375,0.076171875,0.003662109375,0.0213623046875,0.09912109375,0.0181884765625,0.048095703125,(...TRUNCATED) |
Stack2Graph vector database: julia
This release contains 14294 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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