Datasets:
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pretty_name: SCIDOCS multivector embeddings (ColBERTv2)
task_categories: [text-retrieval]
tags: [late-interaction, colbert, multivector, beir, scidocs]
---
# SCIDOCS, ColBERTv2
Token-level (late-interaction) embeddings of the BEIR SCIDOCS corpus and queries, encoded with ColBERTv2, in the TACHIOM multivector format.
## Source
- BEIR SCIDOCS, test (its only split) split. Corpus, queries and qrels were read from the official BEIR files via `ir_datasets` (`beir/scidocs`); PyLate only did the encoding
- 25,657 documents, 1,000 queries, 29,928 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: [`colbert-ir/colbertv2.0`](https://huggingface.co/colbert-ir/colbertv2.0) @ `c1e84128e85ef755c096a95bdb06b47793b13acf`
- Library: PyLate 1.6.0, CPU
- Document length cap: 180 tokens (model default)
- Query length: 32 tokens (model default)
- Query expansion: yes (model default)
- The model defaults come from `artifact.metadata` 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) | 3,770,957 |
| Avg vectors per document | 147.0 (max 180) |
| Vectors per query | always 32 (padded with `[MASK]` expansion tokens, which are real embeddings) |
| Avg vectors per query | 32.0 |
## Files
| File | dtype | Shape | Content |
|---|---|---|---|
| `documents.npy` | float16 (`<f2`) | `[3770957, 128]` | All document vectors, concatenated document by document |
| `doclens.npy` | int32 | `[25657]` | Vectors per document; `sum == N` |
| `token_ids.npy` | uint32 | `[3770957]` | Input token id of each row of `documents.npy` |
| `doc_ids.npy` | string | `[25657]` | BEIR doc id of each document |
| `queries.npy` | float32 | `[1000, 32, 128]` | Query vectors, zero-padded at the end |
| `query_lens.npy` | int32 | `[1000]` | True number of vectors per query |
| `queries_ids.npy` | string | `[1000]` | BEIR query id of each query |
| `qrels.test.tsv` | TREC | 29928 lines | `qid \t 0 \t docid \t relevance` |
| `groundtruth/gt_top100.tsv` | TSV | 100000 lines | Exhaustive top-100: `query_idx \t doc_idx \t rank \t score` (0-based positions) |
| `groundtruth/gt_ids.npy` | int32 | `[1000, 100]` | Same, as doc positions |
| `groundtruth/gt_scores.npy` | float32 | `[1000, 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 |
|---:|---:|
| 0.1581 | 0.3578 |
|