---
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-doc`](https://huggingface.co/naver/efficient-splade-V-large-doc) for documents, [`naver/efficient-splade-V-large-query`](https://huggingface.co/naver/efficient-splade-V-large-query) for 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](https://github.com/TusKANNy/seismic), [kANNolo](https://github.com/TusKANNy/kannolo) and [Vectorium](https://github.com/TusKANNy/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.