sparse_datasets
Collection
Learnes sparse datasets (SPLADE-style) ready to use for retrieval • 3 items • Updated
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MS MARCO passages, SPLADE-v3
Learned sparse embeddings of the MS MARCO v1 passage corpus and its dev (small) queries, encoded with SPLADE-v3, ready for approximate nearest neighbor search by inner product.
Source
- MS MARCO v1 passage ranking: 8,841,823 passages, 6,980 queries (dev, small)
- Model:
naver/splade-v3 - Only the vectors are included, not the text
Statistics
| Documents | 8,841,823 |
| Queries | 6,980 |
| Avg nonzeros per document | 169.8 |
| Avg nonzeros per query | 24.5 |
Terms in token_to_id_mapping.json |
28,638 |
Files
| File | Size | Content |
|---|---|---|
documents.bin |
12.0 GB | Document vectors |
queries.bin |
1.4 MB | Query vectors |
groundtruth.tsv |
1.7 MB | Exact top-10 per query |
doc_ids.npy |
248 MB | MS MARCO passage id of each document (string) |
queries_ids.npy |
196 kB | MS MARCO query id of each query (string) |
token_to_id_mapping.json |
494 kB | Term to component id |
Format
Seismic binary format, little-endian, used by Seismic, kANNolo and Vectorium:
u32 n_vectors, then for each vector u32 nnz, nnz x u32 component ids (ascending), nnz x f32 values.
groundtruth.tsv has four tab-separated columns: query position, document position, rank (1-based), score.
Positions are 0-based rows of queries.bin and documents.bin. It holds the exact top-10 of every query by inner product, computed with an exhaustive scan.
Row i of doc_ids.npy / queries_ids.npy is the original id of row i of documents.bin / queries.bin.
Component ids are collection-specific term ids; token_to_id_mapping.json maps terms to component ids.
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