chunk_id large_stringlengths 8 12 ⌀ | question_id large_stringlengths 5 8 | language large_stringclasses 1
value | chunk_index int64 0 13 ⌀ | chunk_text large_stringlengths 129 3.08k ⌀ | sparse_text large_stringlengths 114 9.64k | dense_vector listlengths 3.58k 3.58k ⌀ | sparse_indices listlengths 2 251 ⌀ | sparse_values listlengths 2 251 ⌀ | export_source large_stringclasses 1
value |
|---|---|---|---|---|---|---|---|---|---|
56440710_c0 | 56440710 | scala | 0 | Title: scala, filter RDD
Problem title: scala, filter RDD
Tags: scala, rdd, apache-spark
Problem: scala, filter RDD
Code signals: RDD, Seq, file2, Array | scala, filter RDD scala, filter RDD scala rdd apache-spark RDD Seq file2 Array scala, filter RDD i have val: and I would like to filter in file2 exists element in "key" I want something like this: | [
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-0.0244140625,
0.0103759765625,
0.01287841796875,
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0.03662109375,
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0.0235595703125,
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0.01080322265625,
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0.00921630859375,
0.0073... | [
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3502,
2806,
1884,
32316,
7,
12830,
23,
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3444,
903
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0.234619140625,
0.2626953125,
0.0924072265625,
0.2333984375,
0.0150909423828125,
0.03741455078125,
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0.07171630859375,
0.0780029296875,
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0.066162109375,
0.13330078125,
0.1666259765625,
0.1304931640625,
0.09332275390625,
0.1744384765625,
0.03909301757812... | embed |
46440770_c0 | 46440770 | scala | 0 | Title: Scala Tail Recursion
Problem title: Scala Tail Recursion
Tags: recursion, scala
Problem: Scala Tail Recursion
Code signals: Scala, Tail, Recursion | Scala Tail Recursion Scala Tail Recursion recursion scala Scala Tail Recursion Scala Tail Recursion So I have this recursive function that multiplies two numbers, simple enough. Now I'm trying to turn it into a tail recursive function, and I tried this: However I get the following error: Question: Can you explain to me... | [
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0.0169677734375,
0.032958984375,
-0.001777648925... | [
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31577,
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46741,
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33306,
2046,
25632,
18499,
4171,
73342,
15400,
74918,
5608,
456,
144810,
18151
... | [
0.137939453125,
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0.187255859375,
0.15478515625,
0.124267578125,
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0.1917724609375,
0.1962890625,
0.0750732421875,
0.1563720703125,
0.1663... | embed |
20220013_c0 | 20220013 | scala | 0 | Title: List of lists in Scala?
Problem title: List of lists in Scala?
Tags: scala, list
Problem: List of lists in Scala?
Code signals: List, Scala, Lists | List of lists in Scala? List of lists in Scala? scala list List Scala Lists List of lists in Scala? I have a grid (that is declared as a List (of Lists) ) and each 3*3 block for example is indexed as 0 , the next 3*3 block is indexed as 1 , etc. I need to write a method that returns the elements ( integers ) in a parti... | [
0.0224609375,
0.00860595703125,
0.0026702880859375,
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0.0013580322265625,
0.0084228515625,
0.0031280517578125,
0.00311279296875,
0.018310546875,
-0.0050048828125,
-0.029052734375,
0.0078125,
0.01507568359375,
0.00811767578125,
-0.00299072265625,
... | [
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32,
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87,
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237,
4390,
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138,
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363,
46389,
63262,
757,
11737,
106,
3871,
33022,
55300,
30646,
80854,
892,
26255,
17311,
34475,
27781,
876,
177609,
5608,
8337,
201,
11... | [
0.23486328125,
0.09100341796875,
0.240234375,
0.12548828125,
0.1153564453125,
0.236328125,
0.205810546875,
0.030548095703125,
0.208984375,
0.02801513671875,
0.0091552734375,
0.19091796875,
0.0706787109375,
0.014404296875,
0.044769287109375,
0.0154876708984375,
0.04962158203125,
0.0... | embed |
36446827_c0 | 36446827 | scala | 0 | "Title: Apache Spark Build error\nProblem title: Apache Spark Build error\nTags: scala, apache-spark(...TRUNCATED) | "Apache Spark Build error Apache Spark Build error scala apache-spark Apache Spark Build Apache Spar(...TRUNCATED) | [0.01214599609375,0.04296875,-0.018310546875,0.00311279296875,-0.003265380859375,-0.00421142578125,-(...TRUNCATED) | [9795,1430,152810,121137,18499,146232,2241,7,10237,33976,131999,31344,18151,23,198996,6370,39876,617(...TRUNCATED) | [0.1658935546875,0.166259765625,0.2978515625,0.2183837890625,0.182373046875,0.2205810546875,0.122802(...TRUNCATED) | embed |
33112727_c0 | 33112727 | scala | 0 | "Title: Get values from an RDD\nProblem title: Get values from an RDD\nTags: scala, hadoop, apache-s(...TRUNCATED) | "Get values from an RDD Get values from an RDD scala hadoop apache-spark an Get RDD Array Get values(...TRUNCATED) | [0.001678466796875,-0.0029449462890625,-0.0162353515625,-0.00250244140625,-0.01129150390625,0.000078(...TRUNCATED) | [20779,142424,1295,142,627,24057,146232,256,246,2146,2241,1430,10237,69253,53,75935,46560,25632,9384(...TRUNCATED) | [0.134033203125,0.202880859375,0.1458740234375,0.060211181640625,0.1279296875,0.300537109375,0.24353(...TRUNCATED) | embed |
40221310_c0 | 40221310 | scala | 0 | "Title: Add Tuple to Map?\nProblem title: Add Tuple to Map?\nTags: scala\nProblem: Add Tuple to Map?(...TRUNCATED) | "Add Tuple to Map? Add Tuple to Map? scala Add Tuple Map res8 scala.collection.immutable.Map res0 Ad(...TRUNCATED) | [0.024169921875,0.00860595703125,0.0047607421875,-0.012451171875,-0.0228271484375,-0.005706787109375(...TRUNCATED) | [29117,1371,8705,47,42719,146232,3332,1019,196352,1830,561,22819,166179,2389,152653,11,34513,119044,(...TRUNCATED) | [0.1650390625,0.18603515625,0.282958984375,0.12255859375,0.2484130859375,0.2064208984375,0.052429199(...TRUNCATED) | embed |
47160608_c0 | 47160608 | scala | 0 | "Title: Intellij SBT Timeout errors\nProblem title: Intellij SBT Timeout errors\nTags: intellij-idea(...TRUNCATED) | "Intellij SBT Timeout errors Intellij SBT Timeout errors intellij-idea sbt scala Intellij SBT Timeou(...TRUNCATED) | [0.0126953125,0.0284423828125,-0.0179443359375,0.000560760498046875,-0.0184326171875,-0.006195068359(...TRUNCATED) | [24018,150,170,159,52681,19055,6056,18499,33049,30610,91,16723,146232,3525,152653,11,1375,36497,2896(...TRUNCATED) | [0.1275634765625,0.1339111328125,0.2139892578125,0.100341796875,0.23095703125,0.1661376953125,0.1420(...TRUNCATED) | embed |
58982543_c0 | 58982543 | scala | 0 | "Title: Splitting dataframe\nProblem title: Splitting dataframe\nTags: parsing, scala\nProblem: Spli(...TRUNCATED) | "Splitting dataframe Splitting dataframe parsing scala Splitting Splitting dataframe How would you s(...TRUNCATED) | [0.0133056640625,0.0142822265625,-0.00653076171875,0.00872802734375,-0.01544189453125,-0.01989746093(...TRUNCATED) | [
46474,
1916,
2053,
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366,
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146232,
11249,
2806,
398,
65450,
23
] | [0.191650390625,0.1368408203125,0.147216796875,0.263427734375,0.1280517578125,0.093505859375,0.26293(...TRUNCATED) | embed |
8526691_c0 | 8526691 | scala | 0 | "Title: Scala Stream confusion\nProblem title: Scala Stream confusion\nTags: scala, recursion, strea(...TRUNCATED) | "Scala Stream confusion Scala Stream confusion scala recursion stream tail Scala Stream s.tail.map x(...TRUNCATED) | [0.007293701171875,0.025146484375,-0.000946044921875,-0.0198974609375,-0.0023651123046875,-0.0144042(...TRUNCATED) | [152653,11,115061,169463,146232,195625,1830,75973,46741,91,62346,1022,31251,78219,32036,28398,87,71,(...TRUNCATED) | [0.1707763671875,0.08685302734375,0.23486328125,0.215576171875,0.1827392578125,0.140869140625,0.0261(...TRUNCATED) | embed |
24172966_c0 | 24172966 | scala | 0 | "Title: scala, exception handling, promises\nProblem title: scala, exception handling, promises\nTag(...TRUNCATED) | "scala, exception handling, promises scala, exception handling, promises scala Exception p.future Aw(...TRUNCATED) | [0.01025390625,0.021728515625,-0.0250244140625,-0.027587890625,0.00066375732421875,-0.00044059753417(...TRUNCATED) | [146232,131527,45064,103036,7,5443,63928,915,3125,6644,62,634,217,2109,7136,9842,1679,152653,11,2967(...TRUNCATED) | [0.26025390625,0.224853515625,0.1920166015625,0.21044921875,0.061859130859375,0.094970703125,0.23864(...TRUNCATED) | embed |
End of preview. Expand in Data Studio
Scala StackOverflow Vector Dataset Datasheet
1. What This Dataset Is
This dataset is the Scala-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 Scala 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 Scala 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_scala_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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