Dataset Viewer (First 5GB)
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what are five themes of geography
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who invented window wipers
| 2 |
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why were teacher unions formed
| 3 |
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A meta-analysis is used when
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where is atrial natriuretic hormone secreted from
| 5 |
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how does the choice of a narrator affect the plot of a narrative
| 6 |
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what show did art linkletter host
| 7 |
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dgs definition of indirect cost rates
| 8 |
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how to recover messages in messenger
| 9 |
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what are the first signs of mono
| 10 |
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[
803,
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what is the plasmodium falciparum
| 11 |
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why won't garmin express recognize my device
| 12 |
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can i resetting remote control for a garage door opener
| 13 |
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what is special education advocacy
| 14 |
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when can i workout after a tattoo
| 16 |
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steven cambrice therapist
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End of preview. Expand
in Data Studio
HSEB MSMARCO 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 queries100K: 100K documents, 1K queries1M: 1M documents, 1K queries
- Filter selectivity:
10%for high selectivity,90%for low selectivity,100%for no filters at all- each document has a tag based on sampled selectivity, so 10% of docs have a tag
10, and 50% of docs have tag50
- 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("hseb-benchmark/msmarco", "query-all-MiniLM-L6-v2-1M")
corpus = load_dataset("hseb-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,100K,1M
License
Apache 2.0
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