id string | text string | indices list | values list | labels list |
|---|---|---|---|---|
s1 | this is a test | [
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1.66885614... | [
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s2 | hello world | [
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0.8142920732498169,
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2.1904382705688477,
1... | [
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"earth",
"beautiful",
"global",
"planet",
"universe",
"birthday",
"welcome",
"hello",
"hi",
"worlds"
] |
s3 | neural search sparse retrieval | [
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1.4478073120117188,
0.4965222477912903,
1.63233482837677,
0.027435... | [
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"scan",
"sensor",
"neurons",
"neural",
"browser",
"sparse",
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] |
s4 | hybrid search combines dense and sparse signals | [
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0.8002380132675171,
0.005709056276828051,
0.22329793870449066,
... | [
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... |
s5 | the quick brown fox jumps over the lazy dog | [
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0.21801874041557312,
1.681915521621704,
0.0887... | [
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"sudden",
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"employee",
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] |
s6 | error handling and retries in distributed systems | [
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1... | [
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"distributed",
"error",
"procedure",
"handling",
"algorithm",
"errors",
"##try",
"violation",
"null",
"##tries",
"... |
s7 | golang benchmarking with realistic workloads | [
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0.2... | [
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"architecture",
"strategy",
"technique",
"bench",
"tool",
"load",
"ideal",
"employee",
"##mark",
"##load",
"realistic",
"realism",
"##l... |
s8 | database indexing for low latency retrieval | [
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1.784194... | [
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... |
s9 | vector search and inverted indexes | [
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2229,
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0.33943840861320496,
1.5217386484146118,
0.1... | [
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"equation",
"vector",
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"vectors",
"inverse",
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"indices"
] |
s10 | what is the best way to test model regressions | [
2000,
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1.6081128120422363,
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1.5097099542617798,
1.1895514726638794,
0.177918... | [
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"lab",
"procedure",
"ideal",
"equation",
"algorithm",
"correlation",
"calculate",
"null",
"rehab",
"regression"
] |
s11 | مرحبا بالعالم | [
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... | [
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0.... | [
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"iraqi... |
s12 | こんにちは 世界 | [
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... | [
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] |
s13 | hola mundo | [
1010,
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0... | [
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"planet",
"universe",
"favor",
"genre",
"latino",
"ho",
"casa",
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"mundo"
] |
s14 | bonjour le monde | [
1010,
2010,
2040,
2099,
2413,
2605,
3393,
4241,
14753,
23099,
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] | [
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] |
s15 | privacy preserving telemetry and observability | [
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... | [
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s16 | sparse expansion helps lexical matching | [
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0.1... | [
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s17 | bm25 baseline for retrieval | [
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s18 | tokenization affects downstream ranking quality | [
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0.2... | [
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] |
s19 | unit tests should be deterministic | [
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2204,
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0.05134565010666847,
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1.076686143875122,
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0.8865... | [
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s20 | production incidents require fast rollback | [
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SPLADE Endpoint Golden Dataset (v1)
This dataset contains sparse vectors generated from a SPLADE inference service.
Source
- Model:
prithivida/Splade_PP_en_v1 - Generated at (UTC):
2026-02-21T19:09:51.715028+00:00 - Rows:
20 - Digest (JSONL SHA-256):
050715eeb0a288daa486444e6bad006a98705f1c0a6e8f3780e0c46b1614105c
Notes
- Inputs are short synthetic test prompts for regression parity.
- Labels are included in this release for parity validation.
- No private endpoint addresses are included in dataset artifacts.
Files
splade_endpoint_golden/v1/splade_pp_en_v1_endpoint_topk24_labels_v1.jsonlsplade_endpoint_golden/v1/metadata.json
JSONL schema
Each line contains:
id(string)text(string)indices(array[int])values(array[float])labels(array[string], may be empty)
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