Update dataset README

README.md

@@ -4,64 +4,95 @@ license: apache-2.0
 language:
 - en
 ---
-
-# PubMed-Multi-Vector
+# PubMed-MultiVector (PubMed-VE)
 
 ## Overview
-This dataset is a large slice of pre-embedded PubMed abstracts -- embedded with BGE-M3. It is designed to benchmark hybrid retrieval methods with corpus variables held constant. It generates dense, sparse, and ColBERT-style multi-vector representations over the exact same text corpus, accumulating over 8.37 billion multi-vector tokens across nearly 35 TB of data.
 
-The dataset includes:
+**PubMed-VE** is a retrieval benchmark built from [PubMed abstracts](https://huggingface.co/datasets/MedRAG/pubmed) using [BGE-M3](https://huggingface.co/BAAI/bge-m3).
+
+Each document is represented in all three formats produced by BGE-M3:
+
+- **~24 million dense embeddings**
+- **~24 million sparse embeddings**
+- **~8.77B multi-vector (token-level) embeddings**
+
 
-- **23.9M dense embeddings**
-- **23.9M sparse embeddings**
-- **8.37B multi-vector token embeddings**
-- **Exact top-1000 ground truth for dense, sparse, and filtered retrieval**
+
+|Statistic |Value |
+|---|---|
+| Documents | 23,898,701 |
+| Queries | 1,000 |
+| Ground truth | exact top-1000, one list per modality (dense / sparse / multi-vector) |
+| Embeddings | [`BAAI/bge-m3`](https://huggingface.co/BAAI/bge-m3) (dense 1024-d, sparse lexical weights, ColBERT-style multi-vector) |
+| Corpus size | ~35 TiB  |
 
 ## Dataset Contents
 
-Each row carries the document's dense and sparse embeddings, and all eight FineWeb source fields are retained verbatim:
+The corpus is [`MedRAG/pubmed`](https://huggingface.co/datasets/MedRAG/pubmed), which consists of 23,898,701 documents.
+
+Each row contains the following fields:
 
-| Field | Type | Notes |
-| --- | --- | --- |
-| `id` | `string` | Unique document identifier |
+| **Field** | **Type** | **Notes** |
+|---|---|---|
+| `id` | `string` | MedRAG snippet ID |
 | `title` | `string` | Article title |
-| `content` | `string` | Main article text or abstract |
-| `contents` | `string` | Alternative or full document text |
-| `PMID` | `int64` | PubMed Unique Identifier |
-| `source_file_name` | `string` | Origin source file path |
-| `source_row_number` | `int64` | Line/row index within the source file |
-| `bge_m3_dense_embedding` | `list<float>` | BGE-M3 dense vector representation |
-| `bge_m3_sparse_embedding` | `struct<indices: list<uint32>, values: list<float>>` | BGE-M3 sparse token weights (lexical indices and values) |
-| `bge_m3_multivector_embedding` | `list<list<float>>` | BGE-M3 multi-vector representation (ColBERT-style multi-token embeddings) |
+| `content` | `string` | Abstract text |
+| `contents` | `string` | Title + abstract |
+| `PMID` | `int64` | PubMed ID |
+| `source_file_name` | `string` | Source file in the MedRAG corpus |
+| `source_row_number` | `int64` | Row within the MedRAG source file |
+| `bge_m3_dense_embedding` | `list<float32>` | 1024-D dense vector |
+| `bge_m3_sparse_embedding` | `struct<indices: list<uint32>, values: list<float32>>` | Sparse lexical weights |
+| `bge_m3_multivector_embedding` | `list<list<float32>>` | 1024-D vector per token |
 
 ### Embeddings
-Each document is represented using [BAAI/bge-m3](https://huggingface.co/BAAI/bge-m3).
 
-- **Dense:** 1024 dimensions; Vectors are unit-norm. Compare with cosine, or equivalently dot product; do not re-normalize.
-- **Sparse:** BM25-style sparse embeddings for hybrid-retrieval.
-- **Multivector:** ColBERT-style dense multi-vectr embeddings for advaced reranking and rescoring workloads.
+Each PubMed abstract is represented using BGE-M3 in three retrieval modalities:
 
-### Intended Uses
+- **Dense:** 1024D float32 embeddings.
+- **Sparse:** BGE-M3 lexical weights over the XLM-RoBERTa vocabulary (250,002 tokens). Indices are token ids, values are float32 weights.
+- **Multi-vector:** A set of multi-vectors, where there is one 1024-d float32 vector per input token, ColBERT-style.
 
-PubMed-Multi-Vector is intended for large-scale evaluation of:
+All three vectors are produced in a single forward pass per document by the same BGE-M3 checkpoint, so the dense, sparse, and multi-vector views of a document are exactly consistent with one another.
 
-- dense vector search
-- sparse vector search
-- multi vector search
-- hybrid retrieval
-- vector database ingestion and indexing
-- approximate nearest-neighbor algorithms
-- distributed retrieval systems
 
-It is also useful for work that needs a very large embedded corpus rather than a retrieval benchmark: quantization and compression studies, index-build cost and memory-footprint measurement, sharding and routing strategies, embedding-space analysis at web scale, and cost/recall trade-off curves where 10B vectors make the trade-off visible in a way 1M-vector benchmarks do not.
+### Queries and Ground Truth
 
+The benchmark includes ground truth at k=1000 for 1,000 queries sampled from [MTEB's raw bioRxiv dataset](https://huggingface.co/datasets/mteb/raw_biorxiv). Each query is the **title** of an existing bioRxiv preprint, embedded in dense, sparse, and multi-vector form, modeling a literature-review workflow in which a paper is used to retrieve related PubMed literature. Queries are short relative to the corpus, which makes the benchmark a asymmetric short-query / long-document retrieval task rather than a document-to-document similarity task. [Supernova](https://github.com/qdrant-labs/supernova) was used to compute exact, brute-force ground truth. Note that multi-vector ground truth uses MaxSim to determine relevance.
 
-## Licensing
+Each row in the queries files contain the following columns:
+
+| Column | Type | Description |
+|---|---|---|
+| `query_id` | `string` | `q00000` … `q00999` |
+| `query` | `string` | bioRxiv preprint title |
+| `doc_id` | `string` | Source bioRxiv DOI, e.g. `10.1101/181198` |
+| `category` | `string` | bioRxiv subject category |
+| `bge_m3_dense_embedding` | `list<float>` | 1024-d, L2-normalized |
+| `bge_m3_sparse_embedding` | `struct<indices: list<uint32>, values: list<float>>` | Lexical weights |
+| `bge_m3_multivector_embedding` | `list<list<float>>` | One 1024-d vector per token |
+| `hit_ids` | `list<string>` | Top-1000 PMIDs, best first |
+| `hit_scores` | `list<float>` | Parallel similarity scores, non-increasing |
 
-We release this dataset under the Apache 2.0 license
+
+## Intended Uses
+
+PubMed-VE is intended for evaluating and comparing dense, sparse, multi-vector, hybrid, and multi-stage retrieval systems over a common scientific corpus.
+
+Because the three modalities share a corpus, a query set, and an embedding model, differences in measured quality are attributable to the retrieval method rather than to the data or the encoder. Concretely, it supports:
+
+- Recall@k against exact ground truth for an ANN index, per modality, at any k up to 1000.
+- Modality comparison -- dense vs. sparse vs. multi-vector on identical inputs.
+- Hybrid retrieval -- score fusion, reciprocal-rank or other result fusion, and multi-stage pipelines (e.g. dense or sparse candidate generation followed by MaxSim reranking), each with an exact reference to measure against.
+
+
+## Licensing
+We release this dataset under the Apache 2.0 license.
 
 ## Acknowledgments
-This dataset would not exist without the upstream work it is built on:
 
-- **HuggingFace**
-- **SkyPilot**
+We thank [HuggingFace](https://huggingface.co/) for providing a grant to offset a portion of the cost of hosting this dataset. Additionally,  this benchmark would not exist without the upstream work it builds on:
+
+- Xiong, Jin, Lu, and Zhang for the [MedRAG](https://github.com/Teddy-XiongGZ/MedRAG) PubMed corpus, and the U.S. National Library of Medicine for maintaining and distributing PubMed.
+- bioRxiv and the preprint authors whose titles form the query set, and the **MTEB** team for the `raw_biorxiv` collection.
+- BAAI for releasing [BGE-M3](https://huggingface.co/BAAI/bge-m3).