| | --- |
| | 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. |
