Update README.md

README.md

@@ -4,40 +4,92 @@ license: cc-by-nc-sa-4.0
 pipeline_tag: text-ranking
 ---
 
+<div align="center">
+
 # Contextual AI Reranker v2 6B
 
+<img src="Contextual_AI_Brand_Mark_Dark.png" width="10%" alt="Contextual_AI"/>
+
+[![Blog Post](https://img.shields.io/badge/📝%20Blog-ContextualReranker-green)](https://contextual.ai/blog/rerank-v2)
+[![Hugging Face Collection](https://img.shields.io/badge/🤗%20Hugging%20Face-Model%20Collection-yellow)](https://huggingface.co/collections/ContextualAI/contextual-ai-reranker-v2)
+
+</div>
+
+<hr>
+
 ## Highlights
 
-Our reranker is on the cost/performance Pareto frontier across 5 key areas:
-- Instruction following (including capability to rank more recent information higher)
-- Question answering
-- Multilinguality
-- Product search / recommendation systems
+Contextual AI's reranker is the **first instruction-following reranker** capable of handling retrieval conflicts and ranking with custom instructions (e.g., prioritizing recent information). It achieves state-of-the-art performance on BEIR and sits on the cost/performance Pareto frontier across:
+
+- Instruction following
+- Question answering  
+- Multilinguality (100+ languages)
+- Product search & recommendation
 - Real-world use cases
 
 <p align="center">
     <img src="main_benchmark.png" width="1200"/>
 <p>
 
-For more details on these and other benchmarks, please refer to our [blogpost](https://contextual.ai/blog/rerank-v2).
+For detailed benchmarks, see our [blog post](https://contextual.ai/blog/rerank-v2).
 
 ## Overview
 
-- Model Type: Text Reranking
-- Supported Languages: 100+
-- Number of Paramaters: 6B
-- Context Length: up to 32K
-- Blogpost: https://contextual.ai/blog/rerank-v2
+- **Model Type**: Text Reranking
+- **Supported Languages**: 100+
+- **Parameters**: 6B
+- **Context Length**: up to 32K
+
+## When to Use This Model
+
+Use this reranker when you need to:
+- Re-rank retrieved documents with custom instructions
+- Handle conflicting information in retrieval results
+- Prioritize documents by recency or other criteria
+- Support multilingual search (100+ languages)
+- Process long contexts (up to 32K tokens)
 
 ## Quickstart
 
-### vLLM usage
+### Basic Usage
+
+```python
+# Choose vLLM (recommended for production) or Transformers (simpler setup)
+# See full implementation in sections below
+
+model_path = "ContextualAI/reranker_v2_6b"
 
-Requires vllm==0.10.0 for NVFP4 or vllm>=0.8.5 for BF16.
+query = "What are the health benefits of exercise?"
+instruction = "Prioritize recent medical research"
+documents = [
+    "Regular exercise reduces risk of heart disease and improves mental health.",
+    "A 2024 study shows exercise enhances cognitive function in older adults.",
+    "Ancient Greeks valued physical fitness for military training."
+]
+
+# Using vLLM (see full code below):
+infer_w_vllm(model_path, query, instruction, documents)
+
+# OR using Transformers (see full code below):
+infer_w_hf(model_path, query, instruction, documents)
+```
+
+**Expected Output:**
+```
+Query: What are the health benefits of exercise?
+Instruction: Prioritize recent medical research
+Score: 0.8542 | Doc: A 2024 study shows exercise enhances cognitive function in older adults.
+Score: 0.7891 | Doc: Regular exercise reduces risk of heart disease and improves mental health.
+Score: 0.4123 | Doc: Ancient Greeks valued physical fitness for military training.
+```
+
+### vLLM Usage (Recommended for Production)
+
+Requires `vllm==0.10.0` for NVFP4 or `vllm>=0.8.5` for BF16.
 
 ```python
 import os
-os.environ['VLLM_USE_V1'] = '0'  # v1 engine doesn’t support logits processor yet
+os.environ['VLLM_USE_V1'] = '0'  # v1 engine doesn't support logits processor yet
 
 import torch
 from vllm import LLM, SamplingParams
@@ -97,12 +149,26 @@ def infer_w_vllm(model_path: str, query: str, instruction: str, documents: list[
     print(f"Instruction: {instruction}")
     for score, doc_id, doc in results:
         print(f"Score: {score:.4f} | Doc: {doc}")
+
+
+# Example usage
+if __name__ == "__main__":
+    model_path = "ContextualAI/reranker_v2_6b"
+    query = "What are the health benefits of exercise?"
+    instruction = "Prioritize recent medical research"
+    documents = [
+        "Regular exercise reduces risk of heart disease and improves mental health.",
+        "A 2024 study shows exercise enhances cognitive function in older adults.",
+        "Ancient Greeks valued physical fitness for military training."
+    ]
+    
+    infer_w_vllm(model_path, query, instruction, documents)
 ```
 
 
-### Transformers Usage
+### Transformers Usage (Simpler Setup)
 
-Requires transformers>=4.51.0 for BF16. Not supported for NVFP4.
+Requires `transformers>=4.51.0` for BF16. Not supported for NVFP4.
 
 ```python
 import torch
@@ -166,7 +232,7 @@ If you use this model, please cite:
 ```bibtex
 @misc{ctxl_rerank_v2_instruct_multilingual,
       title={Contextual AI Reranker v2}, 
-      author={George Halal, Sheshansh Agrawal},
+      author={Halal, George and Agrawal, Sheshansh},
       year={2025},
       url={https://contextual.ai/blog/rerank-v2}, 
 }
@@ -178,4 +244,4 @@ Creative Commons Attribution Non Commercial Share Alike 4.0 (cc-by-nc-sa-4.0)
 
 ## Contact
 
-For questions or issues, please open an issue on the model repository or contact george@contextual.ai.
+For questions or issues, please open an issue on the model repository.