Update model card with paper details and GitHub link

This PR significantly enhances the model card for `zeroentropy/zerank-1-small` by:

* Adding a clear introduction linking the model to its associated paper, [zELO: ELO-inspired Training Method for Rerankers and Embedding Models](https://huggingface.co/papers/2509.12541).
* Including the paper's abstract to provide a comprehensive overview of the model's methodology.
* Adding an explicit "Code" section that links to the [zbench GitHub repository](https://github.com/zeroentropy-ai/zbench), improving discoverability of the underlying code and benchmarking framework.
* Removing the internal "File information" section, as it's not relevant for public display.

These updates aim to provide users with accurate and complete information directly on the Hugging Face Hub, making it easier for researchers and developers to understand and utilize the model.

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.