The Ettin Reranker Family
The Ettin Reranker Family has been introduced, featuring six new state-of-the-art Sentence Transformers CrossEncoder rerankers. These models are built on the Ettin ModernBERT encoders and are designed for improved relevance scoring in information retrieval tasks. The release includes training recipes and usage instructions for those interested in implementing or training their own models.
- ▪The new rerankers include models with sizes ranging from 17 million to 1 billion parameters.
- ▪They utilize a distillation recipe for training, enhancing their performance on retrieval tasks.
- ▪The models can be easily integrated into existing workflows with minimal code.
Hacker News (Newest) files mainly under programming. We currently carry 5,306 of its stories.
Story provenance
Source · retrieval · rights · ranking — open for full record
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Story provenance
Attribution is not the same as permission. This drawer separates discovery metadata, excerpts, WeSearch-generated summaries, reuse status, and whether the publisher receives the visit. Nothing here claims a legal grant the publisher has not made.
Record
| Original publisher | Huggingface |
| Canonical URL | https://huggingface.co/blog/ettin-reranker |
| Publication time | Thu, 21 May 2026 20:49:06 +0000 |
| Retrieval time | 2026-05-21T21:01:35.754Z |
| Last seen | 2026-05-21T21:01:35.754Z |
| Headline source | Publisher (no WeSearch rewrite) |
| Excerpt source | publisher body |
| Excerpt method | First ~120 words (~800 chars) of extracted publisher body, fair-use limited. |
| Summary | WeSearch · cerebras-chat (WeSearch summarizer) |
| Summary source text | contentText |
| Citation coverage | Summary is a WeSearch-generated derivative; primary citation is the original publisher URL. |
| Cluster | 6h0-H1XYsFvv |
| Cluster logic | Grouped by semantic title/content similarity across sources within a rolling window. Same-publisher template collisions are excluded from coverage comparison. |
| Ranking reason | Story pages are not engagement-ranked. Hub feeds use recency, with optional source-diversified chronological ordering (cap consecutive stories per source). No personalized ranking. |
| Publisher visit | Yes — open original |
| Substitutes article? | No — link-out required for full text |
Rights status (four layers)
WeSearch handling by dimension
| Indexing | May the item be indexed (stored, ranked, made findable)? | Allowed |
| Snippet | May a short excerpt of the publisher's text be shown? | Allowed |
| AI summary | May WeSearch generate its own short summary of the article? | Limited |
| Retrieval / RAG | May the content be exposed for third-party retrieval-augmented generation? | Not asserted |
| Model training | May the content be used to train AI models? | Not asserted |
| Commercial reuse | May the content be reused commercially? | Not permitted |
Basis: Derived from the published RSS/Atom feed. Contact: [email protected]. Reviewed: 2026-07-24.
Opening excerpt (first ~120 words) tap to expand
Back to Articles Introducing the Ettin Reranker Family Published May 19, 2026 Update on GitHub Upvote 38 +32 Tom Aarsen tomaarsen Follow TL;DR Table of contents What is a reranker, and why pair one with an embedder? Usage End-to-end retrieve-then-rerank pipeline Architecture Details Results MTEB(eng, v2) Retrieval Speed Training Distillation recipe Dataset Training Arguments Evaluation Overall Training Script Conclusion Acknowledgements Citation TL;DR Today I'm releasing six new Sentence Transformers CrossEncoder rerankers, state-of-the-art at their respective sizes, built on top of the Ettin ModernBERT encoders, together with the data and full training recipe that produced them: cross-encoder/ettin-reranker-17m-v1 cross-encoder/ettin-reranker-32m-v1 cross-encoder/ettin-reranker-68m-v1…
Excerpt limited to ~120 words for fair-use compliance. The full article is at Huggingface.
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