Update README.md

README.md

@@ -1,5 +1,5 @@
 ---
-license: cc-by-nc-4.0
+license: apache-2.0
 language:
 - en
 base_model:
@@ -12,16 +12,16 @@ tags:
 - stem
 - medical
 ---
-# zerank-1-small: Smaller, faster version of zerank-1
 
-<!-- Provide a quick summary of what the model is/does. -->
+<img src="https://i.imgur.com/oxvhvQu.png"/>
 
+# Releasing zeroentropy/zerank-1-small
 
-This model is the smaller version of [zeroentropy/zerank-1](https://huggingface.co/zeroentropy/zerank-1). Though the model is over 2x smaller, it maintains nearly the same standard of performance, continuing to outperform other popular rerankers.
-
-It 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.
+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.
 
+This 1.7B reranker is the smaller version of our flagship model [zeroentropy/zerank-1](https://huggingface.co/zeroentropy/zerank-1). Though the model is over 2x smaller, it maintains nearly the same standard of performance, continuing to outperform other popular rerankers, and displaying massive accuracy gains over traditional vector search.
 
+We release this model under the open-source Apache 2.0 license, in order to support the open-source community and push the frontier of what's possible with open-source models.
 
 ## How to Use
 
@@ -40,9 +40,11 @@ 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-small` 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 |
 |----------------|-----------|--------------------|--------------------------|----------------|----------|
@@ -53,18 +55,6 @@ Comparing NDCG@10 starting from top 100 documents by embedding (using text-3-emb
 | Medical        |    0.619  |       0.750        |           0.719          |      **0.773**     |   0.796  |
 | STEM           |    0.401  |       0.510        |           0.595          |      **0.680**     |   0.694  |
 
-Comparing BM25 and Hybrid Search without and with zerank-1:
-
-<img src="https://cdn-uploads.huggingface.co/production/uploads/67776f9dcd9c9435499eafc8/2GPVHFrI39FspnSNklhsM.png" alt="Description" width="400"/> <img src="https://cdn-uploads.huggingface.co/production/uploads/67776f9dcd9c9435499eafc8/dwYo2D7hoL8QiE8u3yqr9.png" alt="Description" width="400"/>
-
-
-## Citation
-
-**BibTeX:**
-
-Coming soon!
-
-**APA:**
-
-Coming soon!
+Comparing BM25 and Hybrid Search without and with `zerank-1-small`:
 
+<img src="https://cdn-uploads.huggingface.co/production/uploads/67776f9dcd9c9435499eafc8/2GPVHFrI39FspnSNklhsM.png" alt="Description" width="400"/> <img src="https://cdn-uploads.huggingface.co/production/uploads/67776f9dcd9c9435499eafc8/dwYo2D7hoL8QiE8u3yqr9.png" alt="Description" width="400"/>