chunk_id large_stringlengths 7 11 ⌀ | question_id large_stringlengths 4 8 | language large_stringclasses 1
value | chunk_index int64 0 5 ⌀ | chunk_text large_stringlengths 128 2.78k ⌀ | sparse_text large_stringlengths 116 3.65k | dense_vector listlengths 3.58k 3.58k ⌀ | sparse_indices listlengths 9 228 ⌀ | sparse_values listlengths 9 228 ⌀ | export_source large_stringclasses 1
value |
|---|---|---|---|---|---|---|---|---|---|
19195346_c0 | 19195346 | fsharp | 0 | Title: returning boolean value in function
Problem title: returning boolean value in function
Tags: f%23
Problem: returning boolean value in function
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76625066_c0 | 76625066 | fsharp | 0 | Title: F# - type conversion bool to int
Problem title: F# - type conversion bool to int
Tags: type-conversion, f%23
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7284809_c0 | 7284809 | fsharp | 0 | Title: F# - Sort a matrix containing tuples
Problem title: F# - Sort a matrix containing tuples
Tags: sorting, matrix, f%23, tuples
Problem: F# - Sort a matrix containing tuples
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8448_c0 | 8448 | fsharp | 0 | "Title: F# curried function\nProblem title: F# curried function\nTags: currying, functional-programm(...TRUNCATED) | "F# curried function F# curried function currying functional-programming f%23 F# curried function An(...TRUNCATED) | [0.0167236328125,-0.0089111328125,0.023193359375,-0.0032806396484375,0.0164794921875,0.016845703125,(...TRUNCATED) | [563,4904,9709,46572,32354,198555,214,123309,28966,1238,3949,3742,3630,765,10,8,16797,27781,12601,78(...TRUNCATED) | [0.1431884765625,0.2059326171875,0.1314697265625,0.2030029296875,0.2064208984375,0.2259521484375,0.1(...TRUNCATED) | embed |
49211743_c0 | 49211743 | fsharp | 0 | "Title: Gerenics changes parameter from 'a to obj\nProblem title: Gerenics changes parameter fr(...TRUNCATED) | "Gerenics changes parameter from 'a to obj Gerenics changes parameter from 'a to obj f%23 (...TRUNCATED) | [-0.006866455078125,0.006561279296875,0.00128173828125,0.016845703125,0.007415771484375,-0.018798828(...TRUNCATED) | [8022,33,28021,65572,171859,1295,54764,3768,74,11,47,995,170,1238,3742,158,2816,4288,189534,212059,1(...TRUNCATED) | [0.18359375,0.154296875,0.140625,0.18017578125,0.252197265625,0.1102294921875,0.0251312255859375,0.0(...TRUNCATED) | embed |
3189035_c0 | 3189035 | fsharp | 0 | "Title: Seq count number of elements\nProblem title: Seq count number of elements\nTags: f%23, count(...TRUNCATED) | "Seq count number of elements Seq count number of elements f%23 count sequence Seq Seq count number (...TRUNCATED) | [0.0130615234375,0.02001953125,-0.0026702880859375,0.020751953125,0.0203857421875,-0.000459671020507(...TRUNCATED) | [
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2946103_c0 | 2946103 | fsharp | 0 | "Title: A simple way to swap rows in a matrix for F#\nProblem title: A simple way to swap rows in a (...TRUNCATED) | "A simple way to swap rows in a matrix for F# A simple way to swap rows in a matrix for F# f%23 matr(...TRUNCATED) | [-0.0035552978515625,0.01275634765625,0.0198974609375,0.00433349609375,-0.004180908203125,-0.0480957(...TRUNCATED) | [
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71344405_c5 | 71344405 | fsharp | 5 | "006671355318000576, -0.02449338530147379, 0.04478202409774672, -0.2629400383137885, 0.1954843319620(...TRUNCATED) | "006671355318000576, -0.02449338530147379, 0.04478202409774672, -0.2629400383137885, 0.1954843319620(...TRUNCATED) | [0.0206298828125,0.0021820068359375,0.0186767578125,-0.006622314453125,0.004486083984375,0.028930664(...TRUNCATED) | [7227,142745,2681,158976,156874,8194,11835,4,20,99929,2357,12977,104342,141520,89678,157376,170665,1(...TRUNCATED) | [0.164794921875,0.1883544921875,0.035491943359375,0.1563720703125,0.134033203125,0.1162109375,0.1884(...TRUNCATED) | embed |
End of preview. Expand in Data Studio
F# StackOverflow Vector Dataset Datasheet
1. What This Dataset Is
This dataset is the F#-specific vector shard of the Stack2Graph StackOverflow retrieval corpus. Each Hugging Face dataset repository contains exactly one language dataset.
It is optimized for dense+sparse retrieval, Qdrant restoration, and embedding-based RAG experiments.
It is used in the Stack2Graph project as the vector counterpart to the language-scoped RDF knowledge graph shards.
See the Stack2Graph repository for more details: https://github.com/tha-atlas/Stack2Graph
2. Repository Layout
dataset_manifest.json
question_metadata_*.parquet
chunk_records_*.parquet
question_records_*.parquet
dataset_manifest.json: language-scoped manifest for this dataset shard.question_metadata_*.parquet: per-question metadata and retrieval bookkeeping.chunk_records_*.parquet: chunk-level vector rows when parent-child indexing is enabled.question_records_*.parquet: question-level vector rows when chunking is disabled or exported alongside chunk data.
3. Data Model And Coverage
The dataset is derived from Stack Overflow questions selected for the F# programming language. It contains the structured records needed to rebuild the Stack2Graph Qdrant collection for that language.
Coverage scope:
- records are retained when they match the Stack2Graph supported language-tag set
- this repository contains only the F# shard
- the archive may contain both metadata-only and retrieval-ready vector rows depending on the export mode
4. Recommended Preprocessing
- Read
dataset_manifest.jsonfirst and use it as the source of truth for included Parquet files. - Load all Parquet shards for this repository into your vector indexing pipeline.
- Rebuild or restore the Qdrant collection
stackoverflow_fsharp_vector. - Preserve attribution and license metadata during downstream export.
5. Automatic Download And Vector DB Setup
You do not need to regenerate embeddings from GraphDB to use this dataset.
In the Stack2Graph repository, you can use the automation script
python -m experiment.load_hf_datasets_into_services --skip-kg to download dataset artifacts
and prepare the vector database service state automatically.
Typical workflow:
- Clone and configure Stack2Graph (
.envwith HF token and service paths). - Clone and configure Stack2Graph (
.envwith HF token and service paths). - Start required local services:
docker compose up -d
- Run the loader script:
python -m experiment.load_hf_datasets_into_services --skip-kg
For manual usage without automation, directly ingest the listed Parquet files into your vector database.
6. Quality Notes And Caveats
- A Stack Overflow question may belong to multiple language shards when tagged with multiple languages.
- Embeddings and sparse representations depend on the configured export pipeline and model versions.
- As with community-generated data, content may include noise, bias, and temporal drift.
7. Intended Use
- semantic retrieval and reranking
- RAG and hybrid retriever experiments
- vector database benchmarking and diagnostics
- language-scoped developer tooling research
8. Limitations
- Not a complete mirror of all Stack Overflow content.
- Not all export modes include the same row types or chunk layouts.
- Best used together with the Stack2Graph retrieval pipeline and Qdrant-compatible tooling.
9. Licensing And Attribution
This dataset inherits Stack Overflow source licensing and attribution requirements. Ensure compliant attribution and redistribution practices in all derived artifacts.
10. Suggested Citation
If you use this dataset, cite the Stack2Graph work:
- Stack2Graph: A Structured Knowledge Representation of Stack Overflow Data for Retrieval-based Question Answering
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