Update app.py

app.py

@@ -140,7 +140,7 @@ with gr.Blocks(css="""
     gr.Markdown("# Ecommerce Embedding Model Benchmarks")
     
     gr.Markdown("This Space contains benchmark results conducted as part of the release of our ecommerce embedding models: [**`Marqo-Ecommerce-L`**](https://huggingface.co/Marqo/marqo-ecommerce-embeddings-L) and [**`Marqo-Ecommerce-B`**](https://huggingface.co/Marqo/marqo-ecommerce-embeddings-B). ")
-    gr.Markdown("Our benchmarking process was divided into two distinct regimes, each using different datasets of ecommerce product listings: **marqo-ecommerce-hard** and **marqo-ecommerce-easy**. Both datasets contained product images and text and only differed in size. The "easy" dataset is approximately 10-30 times smaller (200k vs 4M products), and designed to accommodate rate-limited models, specifically Cohere-Embeddings-v3 and GCP-Vertex (with limits of 0.66 rps and 2 rps respectively). The "hard" dataset represents the true challenge, since it contains four million ecommerce product listings and is more representative of real-world ecommerce search scenarios.")
+    gr.Markdown("Our benchmarking process was divided into two distinct regimes, each using different datasets of ecommerce product listings: **marqo-ecommerce-hard** and **marqo-ecommerce-easy**. Both datasets contained product images and text and only differed in size. The \"easy\" dataset is approximately 10-30 times smaller (200k vs 4M products), and designed to accommodate rate-limited models, specifically Cohere-Embeddings-v3 and GCP-Vertex (with limits of 0.66 rps and 2 rps respectively). The \"hard\" dataset represents the true challenge, since it contains four million ecommerce product listings and is more representative of real-world ecommerce search scenarios.")
     gr.Markdown('Within both these scenarios, the models were benchmarked against three different tasks:')
     gr.Markdown('- **Google Shopping Text-to-Image**')
     gr.Markdown('- **Google Shopping Category-to-Image**')