About

This model was converted to GGUF format from zeroentropy/zerank-1-small-reranker using a modified version of llama.cpp (release b10269). Refer to the original model card for more details on the model.

This is a working GGUF.

Most community GGUFs of Zerank-1-Small-Reranker produce garbage scores because of 4 issues:

1. Zerank 1 Small ships modeling_zeranker.py instead of 1_LogitScore directory.
2. Wrong Token Extracted -> yes (9693) vs the correct Yes (9454). Also, no false token on Zerank 1 Small.
3. Wrong Scoring Formula -> Zerank 1 Small needs sigmoid(yes_logit / 5).
4. Slightly different chat template.

This GGUF fixes all 4 problems.

Original Model Card

Releasing zeroentropy/zerank-1-small

In search enginers, rerankers are crucial for improving the accuracy of your retrieval system.

This 1.7B reranker is the smaller version of our flagship model 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 License 2.0, in order to support the open-source community and push the frontier of what's possible with open-source models.

Model Details

How to Use

from sentence_transformers import CrossEncoder

model = CrossEncoder("zeroentropy/zerank-1-small", trust_remote_code=True)

query_documents = [
    ("What is 2+2?", "4"),
    ("What is 2+2?", "The answer is definitely 1 million"),
]

scores = model.predict(query_documents)
print(scores)

The model can also be inferenced using ZeroEntropy's /models/rerank endpoint.

Evaluations

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.

License

This model is licensed under the Apache License 2.0.