Update README.md

README.md

@@ -26,6 +26,42 @@ configs:
 This dataset contains the embeddings for the MSMARCO-V2.1 dataset which is used as the corpora for [TREC RAG](https://trec-rag.github.io/)
 All embeddings are created using [Snowflake's Arctic Embed L](https://huggingface.co/Snowflake/snowflake-arctic-embed-l) and are intended to serve as a simple baseline for dense retrieval-based methods. 
 
+
+## Retrieval Performance
+Retrieval performance for the TREC DL21-23, MSMARCOV2-Dev and Raggy Queries can be found below with BM25 as a baseline. For both systems retrieval is at the segment level and Doc Score = Max (passage score).
+Retrieval is done via dot product and happens in BF16.
+### NDCG@10
+| Dataset | BM25 | Snowflake Arctic Embed L |
+|---|---|---|
+| Deep Learning 2021 | 0.5778 | 0.70682 |
+| Deep Learning 2022 | 0.3576 | 0.5444 |
+| Deep Learning 2023 | 0.3356 | 0.47372 |
+| msmarcov2-dev | N/A | 0.35844 |
+| msmarcov2-dev2 | N/A | 0.35821 |
+| Raggy Queries | 0.4227 | 0.57759 |
+
+### Recall@100
+| Dataset | BM25 | Snowflake Arctic Embed L |
+|---|---|---|
+| Deep Learning 2021 | 0.3811 | 0.41361 |
+| Deep Learning 2022 | 0.233 | 0.31351 |
+| Deep Learning 2023 | 0.3049 | 0.34793 |
+| msmarcov2-dev | 0.6683 | 0.85131 |
+| msmarcov2-dev2 | 0.6771 | 0.84767 |
+| Raggy Queries | 0.2807 | 0.36228 |
+
+
+### Recall@1000
+| Dataset | BM25 | Snowflake Arctic Embed L |
+|---|---|---|
+| Deep Learning 2021 | 0.7115 | 0.7193 |
+| Deep Learning 2022 | 0.479 | 0.54566 |
+| Deep Learning 2023 | 0.5852 | 0.59577 |
+| msmarcov2-dev | 0.8528 | 0.93966 |
+| msmarcov2-dev2 | 0.8577 | 0.93947 |
+| Raggy Queries | 0.5745 | 0.63092 |
+
+
 ##  Loading the dataset
 
 ### Loading the document embeddings