Update README.md

README.md

@@ -12,17 +12,22 @@ tags:
 - stem
 - medical
 ---
-# zerank-1: ZeroEntropy Inc.'s SoTA reranker
 
-<!-- Provide a quick summary of what the model is/does. -->
+<img src="https://i.imgur.com/oxvhvQu.png"/>
 
-This model is an open-weights reranker model meant to be integrated into RAG applications to rerank results from preliminary search methods such as embeddings, BM25, and hybrid search.
+# Releasing zeroentropy/zerank-1
 
-This reranker outperforms other popular rerankers such as cohere-rerank-v3.5 and Salesforce/Llama-rank-v1 across a wide variety of task domains, including on finance, legal, code, STEM, medical, and conversational data. See [this post](https://evals_blog_post) for more details.
-This model is trained on an innovative multi-stage pipeline that models query-document relevance scores using adjusted Elo-like ratings. See [this post](https://technical_blog_post) and our Technical Report (Coming soon!) for more details.
+In search enginers, [rerankers are crucial](https://www.zeroentropy.dev/blog/what-is-a-reranker-and-do-i-need-one) for improving the accuracy of your retrieval system.
 
-For this model's smaller twin, see [zerank-1-small](https://huggingface.co/zeroentropy/zerank-1-small)
+However, SOTA rerankers are closed-source and proprietary. At ZeroEntropy, we've trained a SOTA reranker outperforming closed-source competitors, and we're launching our model here on HuggingFace.
 
+This reranker outperforms proprietary rerankers such as `cohere-rerank-v3.5` and `Salesforce/LlamaRank-v1` across a wide variety of domains, including finance, legal, code, STEM, medical, and conversational data.
+
+At ZeroEntropy we've developed an innovative multi-stage pipeline that models query-document relevance scores as adjusted [Elo ratings](https://en.wikipedia.org/wiki/Elo_rating_system). See our Technical Report (Coming soon!) for more details.
+
+Since we're a small company, this model is only released under a non-commercial license. If you'd like a commercial license, please contact us at [email protected] and we'll get you a license ASAP.
+
+For this model's smaller twin, see [zerank-1-small](https://huggingface.co/zeroentropy/zerank-1-small), which we've fully open-sourced under an Apache 2.0 License.
 
 ## How to Use
 
@@ -41,9 +46,11 @@ scores = model.predict(query_documents)
 print(scores)
 ```
 
+The model can also be inferenced using ZeroEntropy's [/models/rerank](https://docs.zeroentropy.dev/api-reference/models/rerank) endpoint.
+
 ## Evaluations
 
-Comparing NDCG@10 starting from top 100 documents by embedding (using text-3-embedding-small):
+NDCG@10 scores between `zerank-1` and competing closed-source proprietary rerankers. Since we are evaluating rerankers, OpenAI's `text-embedding-3-small` is used as an initial retriever for the Top 100 candidate documents.
 
 | Task           | Embedding | cohere-rerank-v3.5 | Salesforce/Llama-rank-v1 | zerank-1-small | **zerank-1** |
 |----------------|-----------|--------------------|--------------------------|----------------|--------------|