Models
Using Models Developed by ZeroEntropy
Rerankers
Rerankers are cross-encoder neural networks that can boost the accuracy of any search system, you can read more about what rerankers are and when they are most useful in this blog post.
zerank-1 and zerank-1-small
zerank-1
and zerank-1-small
are reranker models developed by ZeroEntropy.
zerank-1
is our flagship state-of-the-art reranker, you can read more about its performance and cost in this blog post.
Both these models can be called using:
- Using the models/rerank API endpoint, which is callable via the Python and Node SDKs.
- By passing in the
reranker
query parameter into top-snippets - Downloading from our HuggingFace and self-hosting the models.
We’ve open-sourced zerank-1-small
under an Apache 2.0 license, and it is also available through HuggingFace and Baseten.
Our flagship model zerank-1
can be downloaded from HuggingFace under a non-commercial license. To use in a commercial setting, contact us at founders@zeroentropy.dev and we’ll get you a license ASAP!
Using the ZeroEntropy SDK
Using top-snippets
When querying for /top-snippets from a ZeroEntropy collection, you can easily apply the reranker and get a significantly better ranking. Scores from a reranker are deterministic and more readily interpretable, which is another benefit over just hybrid search.