---
pretty_name: NFCorpus multivector embeddings (LateOn)
task_categories: [text-retrieval]
tags: [late-interaction, colbert, multivector, beir, nfcorpus]
---

# NFCorpus, LateOn

Token-level (late-interaction) embeddings of the BEIR NFCorpus corpus and queries, encoded with LateOn, in the TACHIOM multivector format.

## Source
- BEIR NFCorpus, test split. Corpus, queries and qrels were read from the official BEIR files via `ir_datasets` (`beir/nfcorpus/test`); PyLate only did the encoding
- 3,633 documents, 323 queries, 12,334 qrels
- Text given to the encoder for each document: `title + " " + text` (BEIR title and body joined by a space). The text itself is not included, only its vectors
- Row order follows the BEIR corpus and query files; row *i* of `doc_ids.npy` / `queries_ids.npy` identifies row *i* of `doclens.npy` / `queries.npy`

## Encoding
- Model: [`lightonai/LateOn`](https://huggingface.co/lightonai/LateOn) @ `62911e105059585d244384c7d17826e35f669c17`
- Library: PyLate 1.6.0, CPU
- Document length cap: 300 tokens (model default)
- Query length: 32 tokens (model default)
- Query expansion: no (model default)
- The model defaults come from `config_sentence_transformers.json` in the model repository. We did not override any of them
- Documents: punctuation tokens are dropped (PyLate skiplist), as are padding tokens
- Vectors: 128-d, L2-normalized

## Statistics
| | |
|---|---|
| Token vectors (N) | 862,599 |
| Avg vectors per document | 237.4 (max 286) |
| Vectors per query | variable, 4 to 25 (no query expansion), zero-padded to 25 |
| Avg vectors per query | 8.6 |

## Files
| File | dtype | Shape | Content |
|---|---|---|---|
| `documents.npy` | float16 (`<f2`) | `[862599, 128]` | All document vectors, concatenated document by document |
| `doclens.npy` | int32 | `[3633]` | Vectors per document; `sum == N` |
| `token_ids.npy` | uint32 | `[862599]` | Input token id of each row of `documents.npy` |
| `doc_ids.npy` | string | `[3633]` | BEIR doc id of each document |
| `queries.npy` | float32 | `[323, 25, 128]` | Query vectors, zero-padded at the end |
| `query_lens.npy` | int32 | `[323]` | True number of vectors per query |
| `queries_ids.npy` | string | `[323]` | BEIR query id of each query |
| `qrels.test.tsv` | TREC | 12334 lines | `qid \t 0 \t docid \t relevance` |
| `groundtruth/gt_top100.tsv` | TSV | 32300 lines | Exhaustive top-100: `query_idx \t doc_idx \t rank \t score` (0-based positions) |
| `groundtruth/gt_ids.npy` | int32 | `[323, 100]` | Same, as doc positions |
| `groundtruth/gt_scores.npy` | float32 | `[323, 100]` | Same, MaxSim scores |

Zero padding does not change any score: a zero query vector adds 0 to the MaxSim of every document.

## Exhaustive-search effectiveness
Exact MaxSim over the full collection (`vectorium` `compute_groundtruth_multivec`). These are the reference numbers for approximate search on this data.

| nDCG@10 | R@100 |
|---:|---:|
| 0.3809 | 0.3366 |