Update README.md

README.md

@@ -1,22 +1,22 @@
----
-license: apache-2.0
-tags:
-- clip
-- ecommerce
-- multimodal retrieval
-- transformers
-- openCLIP
-datasets:
-- Marqo/amazon-products-eval
-- Marqo/google-shopping-general-eval
----
+---
+license: apache-2.0
+tags:
+- clip
+- ecommerce
+- multimodal retrieval
+- transformers
+- openCLIP
+datasets:
+- Marqo/amazon-products-eval
+- Marqo/google-shopping-general-eval
+---
 
 # Marqo Ecommerce Embedding Models
 In this work, we introduce two state-of-the-art embedding models for ecommerce products: Marqo-Ecommerce-B and Marqo-Ecommerce-L. 
 
 The benchmarking results highlight a remarkable performance by marqo-ecommerce models, which both consistently outperformed all other models across various metrics. Specifically, for the Google Shopping Text-to-Image task, marqo-ecommerce-L achieved an improvement of 43% in MRR, 41% in nDCG@10 and 33% in Recall@10 when compared to ViT-B-16-SigLIP which is our baseline model for these benchmarks. For the Google Shopping Category-to-Image task, we saw an improvement of 67% in mAP, 41% in nDCG@10 and 42% in Precision@10. 
 
-<img src="https://raw.githubusercontent.com/marqo-ai/marqo-ecommerce-embeddings/refs/heads/main/performance.png?token=GHSAT0AAAAAACZY3OVLSH7USBXOC3SYCQ3OZZL6LVQ" alt="multi split visual" width="700"/>
+<img src="https://raw.githubusercontent.com/marqo-ai/marqo-ecommerce-embeddings/main/performance.png?token=GHSAT0AAAAAACZY3OVL7HD6UZTBOJ7FLG7MZZOCJSA" alt="multi split visual" width="700"/>
 
 More benchmarking results can be found below.