Update README.md

README.md

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 ## Detailed Performance
-The benchmarks are separated into 'Marqo-Ecommerce-Hard' and '100k-Marqo-Ecommerce-Easy'. The "easy" dataset is about 10-30 times smaller, and designed to accommodate rate-limited models, specifically Cohere-Embeddings-v3 and GCP-Vertex. The "hard" dataset represents the true challenge, since it contains four million ecommerce product listings, which pushes these models to their limits in a real-world, ecommerce scenario.
+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. 
 
 Within both these scenarios, the models were benchmarked against three different tasks: