Update README.md

README.md

@@ -64,9 +64,13 @@ Therefore, we will use this benchmark to improve Sagecontinuum's Text-to-Image R
 
 **Dataset Details**
 
-This dataset was build off of [**INQUIRE-Rerank**](https://huggingface.co/datasets/evendrow/INQUIRE-Rerank) with additional modifications. Please refer to [modify_inquire_rerank.ipynb](https://huggingface.co/datasets/sagecontinuum/INQUIRE-Benchmark-small/blob/main/modify_inquire_rerank.ipynb) to see
+This dataset was build off of [**INQUIRE-Rerank**](https://huggingface.co/datasets/evendrow/INQUIRE-Rerank) with additional modifications to be able to do full-dataset retrieval. Please refer to [modify_inquire_rerank.ipynb](https://huggingface.co/datasets/sagecontinuum/INQUIRE-Benchmark-small/blob/main/modify_inquire_rerank.ipynb) to see
 the modifications we did.
 
+**INQUIRE-Rerank Details**
+
+The INQUIRE-Rerank is created from 250 expert-level queries. This task fixes an initial ranking of 100 images per query, obtained using CLIP ViT-H-14 zero-shot retrieval on the entire 5 million image iNat24 dataset. The challenge is to rerank all 100 images for each query with the goal of assigning high scores to the relevant images (there are potentially many relevant images for each query). This fixed starting point makes reranking evaluation consistent, and saves time from running the initial retrieval yourself. If you're interested in full-dataset retrieval, check out INQUIRE-Fullrank available from the github repo.
+
 **Loading the Dataset**
 
 To load the dataset using HugginFace `datasets`, you first need to `pip install datasets`, then run the following code: