Update README.md

README.md

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 # BlockRank-Mistral-7B: Scalable In-context Ranking with Generative Models
 
-Try in Colab notebook: [![Open In Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/github/nilesh2797/BlockRank/blob/main/quickstart.ipynb)
+[![Open In Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/github/nilesh2797/BlockRank/blob/main/quickstart.ipynb)
 
 **BlockRank-Mistral-7B** is a fine-tuned version of [Mistral-7B-Instruct-v0.3](https://huggingface.co/mistralai/Mistral-7B-Instruct-v0.3) optimized for efficient in-context document ranking. It implements BlockRank, a method that makes LLMs efficient and scalable for ranking by aligning their internal attention mechanisms with the structure of the ranking task.
 
@@ -30,10 +30,10 @@ Try in Colab notebook: [![Open In Colab](https://colab.research.google.com/asset
 
 ### Key Features
 
-- 🚀 **Linear Complexity Attention**: Structured sparse attention reduces complexity from O(n²) to O(n)
-- ⚡ **2-4× Faster Inference**: Attention-based scoring eliminates autoregressive decoding
-- 🎯 **Auxiliary Contrastive Loss**: Mid-layer contrastive objective improves relevance signals
-- 📊 **Strong Zero-shot Generalization**: SOTA performance on BEIR benchmarks
+- **Linear Complexity Attention**: Structured sparse attention reduces complexity from O(n²) to O(n)
+- **2-4× Faster Inference**: Attention-based scoring eliminates autoregressive decoding
+- **Auxiliary Contrastive Loss**: Mid-layer contrastive objective improves relevance signals
+- **Strong Zero-shot Generalization**: SOTA performance on BEIR benchmarks
 
 ## Citation