chunk_id large_stringlengths 6 11 ⌀ | question_id large_stringlengths 3 8 | language large_stringclasses 1
value | chunk_index int64 0 8 ⌀ | chunk_text large_stringlengths 120 3.18k ⌀ | sparse_text large_stringlengths 110 5.18k | dense_vector listlengths 3.58k 3.58k ⌀ | sparse_indices listlengths 3 257 ⌀ | sparse_values listlengths 3 257 ⌀ | export_source large_stringclasses 1
value |
|---|---|---|---|---|---|---|---|---|---|
22423495_c0 | 22423495 | objective-c | 0 | Title: Admob ios x64 error
Problem title: Admob ios x64 error
Tags: ios, admob, objective-c
Problem: Admob ios x64 error
Code signals: Admob, x64 | Admob ios x64 error Admob ios x64 error ios admob objective-c Admob x64 Admob ios x64 error I'm trying to test Admob Banner on iPad Mini Retina. Now I'm getting this error: I found a solution which removes some errors. It is to add some Frameworks. List of them: AudioToolbox.Framework MessageUI.Framework SystemConfigur... | [
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1193229_c0 | 1193229 | objective-c | 0 | Title: Allocate custom memory
Problem title: Allocate custom memory
Tags: objective-c
Problem: Allocate custom memory how can I allocate custom memory?
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30462790_c0 | 30462790 | objective-c | 0 | Title: iOS fmdb: Getting sqlite errors
Problem title: iOS fmdb: Getting sqlite errors
Tags: fmdb, ios, objective-c
Problem: iOS fmdb: Getting sqlite errors
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3172641_c0 | 3172641 | objective-c | 0 | "Title: Is objective-C garbage collected?\nProblem title: Is objective-C garbage collected?\nTags: o(...TRUNCATED) | "Is objective-C garbage collected? Is objective-C garbage collected? objective-c Is objective-C garb(...TRUNCATED) | [0.01397705078125,0.01019287109375,0.00070953369140625,0.007537841796875,-0.01531982421875,0.0211181(...TRUNCATED) | [
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3587031_c0 | 3587031 | objective-c | 0 | "Title: Retain my own classes?\nProblem title: Retain my own classes?\nTags: iphone, objective-c\nPr(...TRUNCATED) | "Retain my own classes? Retain my own classes? iphone objective-c Retain Retain my own classes? How (...TRUNCATED) | [-0.0022735595703125,0.0181884765625,-0.006256103515625,0.0057373046875,-0.01470947265625,0.01202392(...TRUNCATED) | [
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5730621_c0 | 5730621 | objective-c | 0 | "Title: Rounding float numbers\nProblem title: Rounding float numbers\nTags: objective-c\nProblem: R(...TRUNCATED) | "Rounding float numbers Rounding float numbers objective-c Rounding Rounding float numbers Fox examp(...TRUNCATED) | [0.004058837890625,0.01068115234375,-0.01416015625,-0.0146484375,0.01055908203125,-0.004913330078125(...TRUNCATED) | [
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4929781_c0 | 4929781 | objective-c | 0 | "Title: setTag: in Objective-C\nProblem title: setTag: in Objective-C\nTags: tags, objective-c\nProb(...TRUNCATED) | "setTag: in Objective-C setTag: in Objective-C tags objective-c Objective-C setTag: in Objective-C H(...TRUNCATED) | [0.01519775390625,0.022216796875,0.00927734375,0.01483154296875,0.01116943359375,-0.0308837890625,-0(...TRUNCATED) | [
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1322069_c0 | 1322069 | objective-c | 0 | "Title: latitude/longitude\nProblem title: latitude/longitude\nTags: iphone, objective-c\nProblem: l(...TRUNCATED) | "latitude/longitude latitude/longitude iphone objective-c latitude/longitude Is there any way to fin(...TRUNCATED) | [0.017822265625,0.0224609375,0.01171875,-0.0040283203125,0.00191497802734375,-0.02490234375,0.039794(...TRUNCATED) | [21,35810,64,10617,21753,13,71798,151814,9,238,2071,2685,2499,3917,47,7413,1810,70,111,10,3687,17368(...TRUNCATED) | [0.170166015625,0.279296875,0.1396484375,0.16162109375,0.228271484375,0.168212890625,0.2147216796875(...TRUNCATED) | embed |
13524343_c0 | 13524343 | objective-c | 0 | "Title: Error in UIAlertView\nProblem title: Error in UIAlertView\nTags: ios, uialertview, objective(...TRUNCATED) | "Error in UIAlertView Error in UIAlertView ios uialertview objective-c Error UIAlertView Error in UI(...TRUNCATED) | [0.0216064453125,0.01361083984375,-0.0004253387451171875,0.00341796875,0.00193023681640625,0.0050354(...TRUNCATED) | [
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End of preview. Expand in Data Studio
Objective-C StackOverflow Vector Dataset Datasheet
1. What This Dataset Is
This dataset is the Objective-C-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 Objective-C 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 Objective-C 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_objective-c_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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