---
license: mit
---

This repository contains the development data files used in the SISAP2025 indexing challenge.

## Datasets

- Common Crawl CCNEWS:
  - repo: <https://huggingface.co/datasets/sentence-transformers/ccnews/>
  - Sentence BERT model: sentence-transformers/all-MiniLM-L6-v2
  - similarity: Cosine / dot product
  - dimension: 384
  - corpus size: 614,664
  - fields: _title_ and _article_, train embeddings come from _article_.

- Google Questions & Answers:
  - repo: <https://huggingface.co/datasets/sentence-transformers/gooaq/>
  - Sentence BERT model: sentence-transformers/all-MiniLM-L6-v2
  - similarity: Cosine / dot product
  - dimension: 384
  - corpus size: 3,012,496
  - fields: _questions_ and _answers_, train embeddings come from _answers_.

- PUBMED23 from MegRag:
  - repo: <https://huggingface.co/datasets/MedRAG/pubmed>
  - Sentence BERT model: sentence-transformers/all-MiniLM-L6-v2
  - similarity: Cosine / dot product
  - dimension: 384
  - corpus size: 23,898,701
  - fields: _title_ and _content_, train embeddings come from _content_.

- Yahoo answers:
  - repo: <https://huggingface.co/datasets/sentence-transformers/yahoo-answers>
  - Sentence BERT model: sentence-transformers/all-MiniLM-L6-v2
  - similarity: Cosine / dot product
  - dimension: 384
  - corpus size: 681,164
  - fields: _question_ and _answer_, train embeddings come from _answer_.

## Tasks

- **Task 1:** $k=30$ nearest neighbor search on out-of-distribution queries in PUBMED23. -- sentence bert embeddings `sentence-transformers/all-MiniLM-L6-v2` from <https://huggingface.co/datasets/MedRAG/pubmed>.
- **Task 2:** $k=15$ nearest neighbor graph (a.k.a. self-join problem) on Google QA  -- sentence bert embeddings `sentence-transformers/all-MiniLM-L6-v2` from <https://huggingface.co/datasets/sentence-transformers/gooaq/>

*ALL* of these files correspond to development files. The evaluation process will use other querysets and subsets.

Additionally to PUBMED23 and Google QA we provide benchmark files for CCNEWS and Yahoo QA to help on tuning and experimentation.

## h5 datasets
The benchmark files are provided in h5 format and contain datasets and attributes
```
🗂️  HDF5.File: (read-only) benchmark-dev-pubmed23.h5
├─ 📂 itest
│  ├─ 🏷️  algo
│  ├─ 🏷️  querytime
│  ├─ 🔢 dists
│  ├─ 🔢 knns
│  └─ 🔢 queries
├─ 📂 otest
│  ├─ 🏷️  algo
│  ├─ 🏷️  querytime
│  ├─ 🔢 dists
│  ├─ 🔢 knns
│  └─ 🔢 queries
└─ 🔢 train

```

Where
- `train` is the main dataset to be indexed (a $384 \times n$ matrix)
- `otest/queries` is the out-of-distribution query set (a $384 \times n$ matrix)
- `otest/knns` (identifiers starting at 1) and `otest/dists` (distances) are the pre-computed gold standard for the out-of-distribution queries ($384 \times 11000$ matrices)
- attributes of `otest`:
  - `algo` the algorithm used to create the gold-standard.
  - `querytime` the time in seconds used to compute the gold standard (using an Intel(R) Xeon(R) Silver 4216 CPU @ 2.10GHz with 60 threads)
- `itest/queries` is the in-distribution query set, useful for testing and comparing with the out-of-distribution ones (a $384 \times n$ matrix)
- `itest/knns` (identifiers starting at 1) and `itest/dists` (distances) are the pre-computed gold standard for the in-distribution queries ($384 \times 11000$ matrices)
- attributes of `itest`:
  - `algo` the algorithm used to create the gold-standard.
  - `querytime` the time in seconds used to compute the gold standard (using an Intel(R) Xeon(R) Silver 4216 CPU @ 2.10GHz with 60 threads)

### Task2 h5 files
`benchmark-dev-*.h5` files can be used for development and both gold-standards follow **task1**. For **task2** we provide an additional gold-standard 
`allknn-benchmark-dev-gooaq.h5` that has the following structure:

```
🗂️  HDF5.File: (read-only) allknn-benchmark-dev-gooaq.h5
├─ 🏷️  algo
├─ 🏷️  querytime
├─ 🔢 dists 
└─ 🔢 knns
```

The descriptions of these h5 datasets are similar to the previous ones, however:

- `knns` (identifiers) and `dists` (distances) corresponding to the outgoing vertices of the $k$ nearest neighbor graph, i.e., the adjacency list of the graph. (a 16 \times n matrix)
- the edges contain a self loop that will be ignored by our scoring function.