Update model card with paper details and GitHub link

README.md

@@ -1,9 +1,10 @@
 ---
-license: apache-2.0
-language:
-- en
 base_model:
 - Qwen/Qwen3-4B
+language:
+- en
+library_name: sentence-transformers
+license: apache-2.0
 pipeline_tag: text-ranking
 tags:
 - finance
@@ -11,14 +12,23 @@ tags:
 - code
 - stem
 - medical
-library_name: sentence-transformers
 ---
 
 <img src="https://i.imgur.com/oxvhvQu.png"/>
 
-# Releasing zeroentropy/zerank-1-small
+# zeroentropy/zerank-1-small
+
+This model, `zeroentropy/zerank-1-small`, is a state-of-the-art open-weight reranker. It was introduced in the paper [zELO: ELO-inspired Training Method for Rerankers and Embedding Models](https://huggingface.co/papers/2509.12541).
+
+## Paper: zELO: ELO-inspired Training Method for Rerankers and Embedding Models
+
+### Abstract
+We introduce a novel training methodology named zELO, which optimizes retrieval performance via the analysis that ranking tasks are statically equivalent to a Thurstone model. Based on the zELO method, we use unsupervised data in order train a suite of state-of-the-art open-weight reranker models: zerank-1 and zerank-1-small. These models achieve the highest retrieval scores in multiple domains, including finance, legal, code, and STEM, outperforming closed-source proprietary rerankers on both NDCG@10 and Recall. These models also demonstrate great versatility, maintaining their 0-shot performance on out-of-domain and private customer datasets. The training data included 112,000 queries and 100 documents per query, and was trained end-to-end from unannotated queries and documents in less than 10,000 H100-hours.
+
+## Code
+The methodology and benchmarking framework associated with this model can be found in the [zbench GitHub repository](https://github.com/zeroentropy-ai/zbench).
 
-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.
+In search engines, [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.