| --- |
| 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. |
