--- 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 (`