pretty_name: MS MARCO passages, Efficient-SPLADE (Seismic binary format)
task_categories:
- text-retrieval
tags:
- sparse
- learned-sparse-retrieval
- splade
- msmarco
- approximate-nearest-neighbor
MS MARCO passages, Efficient-SPLADE
Learned sparse embeddings of the MS MARCO v1 passage corpus and its dev (small) queries, encoded with Efficient-SPLADE (V-large), ready for approximate nearest neighbor search by inner product. The query encoder is trained to produce queries with few nonzero entries, so queries are much shorter than documents.
Source
- MS MARCO v1 passage ranking: 8,841,823 passages, 6,980 queries (dev, small)
- Models:
naver/efficient-splade-V-large-docfor documents,naver/efficient-splade-V-large-queryfor queries - Only the vectors are included, not the text
Statistics
| Documents | 8,841,823 |
| Queries | 6,980 |
| Avg nonzeros per document | 182.8 |
| Avg nonzeros per query | 5.9 |
Terms in token_to_id_mapping.json |
29,452 |
Files
| File | Size | Content |
|---|---|---|
documents.bin |
13.0 GB | Document vectors |
queries.bin |
360 kB | Query vectors |
groundtruth.tsv |
1.7 MB | Exact top-10 per query |
doc_ids.npy |
71 MB | MS MARCO passage id of each document (int64) |
queries_ids.npy |
56 kB | MS MARCO query id of each query (int64) |
token_to_id_mapping.json |
510 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.