tuskanny/nfcorpus_lateon

Dataset

NFCorpus, LateOn

3 commits

1 linked in READMEs

updated Sep 23, 2026

See the code

README

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 @ 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

beir

colbert

late-interaction

multivector

nfcorpus

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Linked in READMEs

TusKANNy/awesome-multivector-retrieval

An extensive and commented list of resources on Late-Interaction Multivector Retrieval.

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tuskanny/nfcorpus_lateon

Dataset

NFCorpus, LateOn

3 commits

1 linked in READMEs

updated Sep 23, 2026

See the code

README

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 @ 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.