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how much do prisons have to pay prisoners who work | 12 | [
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what terminal does united operate out of at newark airport | 17 | [
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] |
how many cubic yards in a dump truck | 18 | [
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700,
10,
909,
109,
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710,
204,
205,
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507,
602,
504,
502,
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307,
201,
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306,
209,
106,
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508,
1,
603,
104,
405,
400,
607,
703,
609,
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802,
203,
610,
506,
900,
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500,
906,
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804,
107,
210,
708,
3,
302,
907,
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308,
901,
501,
701,
208,
905,
606,
102,
206,
801,
309,
407,
706,
305,
910,
605,
4,
608
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700,
118,
716,
10,
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204,
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205,
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515,
419,
705,
303,
417,
20,
312,
17,
105,
402,
911,
111,
120,
304,
7,
306,
209
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10,
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315,
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13,
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103,
126,
301,
409,
110,
101,
220,
926,
8,
727
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End of preview. Expand
in Data Studio
Nixiesearch benchmarking dataset
This collection is based on MSMARCO dataset:
- Embedding models:
- 384 dims: sentence-transformers/all-MiniLM-L6-v2
- 768 dims: intfloat/e5-base-v2
- 1024 dims: intfloat-e5-large-v2
- Splits:
1K: 1K documents, 1K queries10K: 10K documents, 10K queries100K: 100K documents, 10K queries1M: 1M documents, 10K queries
- Filter selectivity:
- from
10%to100%with10%step. - each document has a tag based on sampled selectivity, so 10% of docs have a tag
10, and 50% of docs have tag50
- from
- Exact match results:
- for each selectivity level for each query there are precomputed exact k-NN search results for top-100 documents.
Dataset structure
The dataset can be loaded with the Huggingface datasets library:
from datasets import load_dataset
query = load_dataset("nixiesearch/benchmark-msmarco", "query-all-MiniLM-L6-v2-1M")
corpus = load_dataset("nixiesearch/benchmark-msmarco", "corpus-all-MiniLM-L6-v2-1M")
where 2nd argument to load_dataset is a config name, consisting of the following parts:
<query|corpus>-<model>-<size>
query|corpus- which side of the dataset you're loadingmodel: embedding model,all-MiniLM-L6-v2(384 dims),e5-base-v2(768 dims) ore5-large-v2(1024 dims)size: sample size,1K,10K,100K,1M
License
Apache 2.0
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