Datasets:
id stringlengths 36 36 | question_id int64 7.55M 80M | chunk_index int32 0 17 | embedding_text stringlengths 45 18.2k | dense_tokens int32 12 1.02k | sparse_tokens int32 12 1.02k | languages listlengths 1 4 | tags listlengths 1 5 | payload_json stringlengths 407 565 | dense list | sparse_indices listlengths 8 485 | sparse_values listlengths 8 485 |
|---|---|---|---|---|---|---|---|---|---|---|---|
6677710a-5b01-55d3-842f-3183c6bcc602 | 7,546,412 | 0 | Title: How can Google's Dart get better performance?
I've read the article about Google's upcoming DASH/DART language, which I found quite interesting.
One thing I stumbled upon is that they say they will remove the inherent performance problems of JavaScript. But what are these performance problems exactly? There a... | 130 | 140 | [
"dart",
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"dart"
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9ffdc6ec-53ec-5d38-9395-b08a3a30293b | 7,574,900 | 0 | Title: How is google dart related to google closure?
I have just started using Google Closure and I have also heard about Google Dart which seems very similar. How are the two related?
| 39 | 43 | [
"dart"
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e15bf177-9dea-5213-9787-c7cffae6cab1 | 7,711,025 | 0 | "Title: Does Google Dart support mixins?\n\nI've skimmed through the language documentation and it s(...TRUNCATED) | 64 | 81 | [
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54f96156-28e8-5ef1-b127-1a82418f3486 | 7,712,107 | 0 | "Title: What is the benefit of the Dart language over JavaScript (Node.js)\n\nI don't see the point (...TRUNCATED) | 77 | 83 | [
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825b1322-e901-55c2-950e-7209918b2dcf | 7,714,302 | 0 | "Title: Is there Dart VM available?\n\nJust read news that Google had announced an early preview of (...TRUNCATED) | 143 | 156 | [
"dart"
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"runtime",
"interpreter",
"vm-implementation"
] | "{\"accepted_answer_id\":7714390,\"answer_count\":3,\"chunk_count\":1,\"chunk_index\":0,\"closed_dat(...TRUNCATED) | [0.0078887939453125,0.0015077590942382812,-0.026947021484375,-0.017303466796875,0.030609130859375,-0(...TRUNCATED) | [18,47,190,201,449,479,1011,1328,1467,1478,1528,1815,2071,2499,2541,2685,2811,3066,3525,5609,5744,71(...TRUNCATED) | [0.2373046875,0.0615234375,0.061279296875,0.076171875,0.009765625,0.060546875,0.1708984375,0.1103515(...TRUNCATED) |
0a58ce8e-0387-53c7-aee9-263990c7cb6a | 7,715,537 | 0 | "Title: What does Dart's output look like?\n\nDart, Google's new web language, says it supports outp(...TRUNCATED) | 38 | 41 | [
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"dart"
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f82a7567-5cfb-5d98-902a-94f85649732b | 7,715,948 | 0 | "Title: How to perform runtime type checking in Dart?\n\nDart specification states:\n \n \nReified t(...TRUNCATED) | 88 | 103 | [
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8d5b7d12-ff16-56fa-befd-67dd70873219 | 7,716,004 | 0 | "Title: Will Dart support the use of existing JavaScript libraries?\n\nI understand Dart compiles to(...TRUNCATED) | 84 | 93 | [
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a4173079-47f2-559a-be98-94ad398ddde4 | 7,716,245 | 0 | "Title: Will Dart scripts run natively in the browser?\n\nThe Dart language looks like a very nice t(...TRUNCATED) | 110 | 130 | [
"dart",
"html"
] | [
"html",
"dart"
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b4442a28-6cb6-5e4f-8eb2-9c3f072ae6d3 | 7,717,804 | 0 | "Title: How can I evaluate Dart performance?\n\nGoogle is launching a new language, promising that i(...TRUNCATED) | 180 | 213 | [
"dart"
] | [
"performance",
"web",
"performance-testing",
"dart"
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Stack2Graph vector database: dart
This release contains 115044 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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