Introducing the Ettin Reranker Family

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

cross-encoder/ettin-reranker-150m-v1

cross-encoder/ettin-reranker-400m-v1

cross-encoder/ettin-reranker-1b-v1

The models were trained with a distillation recipe: pointwise MSE on mixedbread-ai/mxbai-rerank-large-v2

scores over cross-encoder/ettin-reranker-v1-data

, which is a subset of lightonai/embeddings-pre-training

mixed with a reranked subset of lightonai/embeddings-fine-tuning

.

Our six rerankers paired with google/embeddinggemma-300m on MTEB(eng, v2) Retrieval. See Results for five more embedder pairings.

If you're new to rerankers and want the "why" first, jump to What is a reranker, and why pair one with an embedder?. If you just want to plug a model in, jump to Usage. If you want to train your own, jump to Training.

I bootstrapped the training recipe below with the new

train-sentence-transformers

Agent Skill shipped in Sentence Transformers v5.5.0. Install it withhf skills add train-sentence-transformers [--global] [--claude]

and ask your AI coding agent (Claude Code, Codex, Cursor, Gemini CLI, ...) to fine-tune aSentenceTransformer

,CrossEncoder

, orSparseEncoder

model on your data.

• What is a reranker, and why pair one with an embedder?
• Usage
• Architecture Details
• Results
• Training
• Conclusion
• Acknowledgements

A reranker (a.k.a. pointwise cross-encoder) is a neural model that takes a (query, document)

pair and outputs a single relevance score. Unlike an embedding model, which encodes the query and document separately and computes their similarity from the two embedding vectors, a reranker lets the two texts attend to each other through every transformer layer. That joint encoding is more accurate but also more expensive: the model has to be run once per (query, document)

pair rather than once per text.

Because cross-encoders are too expensive to run over a full corpus, the common production pattern is retrieve-then-rerank: a fast embedding model retrieves the top-K candidates (cheap), then a cross-encoder re-orders just those K with high accuracy. The total cost stays bounded while the final ranking is much closer to what an exhaustive cross-encoder pass would produce.

Throughout this blogpost I'll use "reranker" and "cross-encoder" interchangeably.

The released models are normal Sentence Transformers CrossEncoder

models, so you can use them with just 3 lines of code:

from sentence_transformers import CrossEncoder
model = CrossEncoder("cross-encoder/ettin-reranker-32m-v1")
scores = model.predict([
("Where was Apple founded?", "Apple Inc. was founded in Cupertino, California in 1976 by Steve Jobs, Steve Wozniak, and Ronald Wayne."),
("Where was Apple founded?", "The Fuji apple is an apple cultivar developed in the late 1930s and brought to market in 1962."),
])
print(scores)
# [11.393298 2.968891] <- larger means more relevant

For a query and a list of candidates, you can also use rank

to get back sorted indices and scores:

ranked = model.rank(
query="Which planet is known as the Red Planet?",
documents=[
"Venus is often called Earth's twin because of its similar size and proximity.",
"Mars, known for its reddish appearance, is often referred to as the Red Planet.",
"Jupiter, the largest planet in our solar system, has a prominent red spot.",
"Saturn, famous for its rings, is sometimes mistaken for the Red Planet.",
],
top_k=4,
return_documents=True,
)
for r in ranked:
print(f"({r['score']:.2f}): {r['text']}")
# (10.82): Mars, known for its reddish appearance, is often referred to as the Red Planet.
# (9.86): Saturn, famous for its rings, is sometimes mistaken for the Red Planet.
# (8.55): Jupiter, the largest planet in our solar system, has a prominent red spot.
# (6.21): Venus is often called Earth's twin because of its similar size and proximity.

You can swap cross-encoder/ettin-reranker-32m-v1

for any other size to trade quality for speed. All six accept up to 8K tokens of context (useful for long-document reranking) thanks to ModernBERT's long-context pre-training.

It is recommended to install kernels

and set model_kwargs={"dtype": "bfloat16", "attn_implementation": "flash_attention_2"}

for the highest throughput. See the Speed section below for more details, but in general you can expect a 1.7x-8.3x speedup over default loading depending on model size and sequence length.

from sentence_transformers import CrossEncoder
model = CrossEncoder(
"cross-encoder/ettin-reranker-32m-v1",
model_kwargs={"dtype": "bfloat16", "attn_implementation": "flash_attention_2"},
)

A complete example with a fast embedder for retrieval and the reranker for the final ordering:

from sentence_transformers import SentenceTransformer, CrossEncoder
# Fast retrieval with a static embedder (sub-millisecond on CPU per query)
embedder = SentenceTransformer("sentence-transformers/static-retrieval-mrl-en-v1")
reranker = CrossEncoder("cross-encoder/ettin-reranker-68m-v1")
corpus = [
"Apple Inc. was founded in Cupertino, California in 1976 by Steve Jobs, Steve Wozniak, and Ronald Wayne.",
"The Fuji apple is an apple cultivar developed in the late 1930s.",
"Steve Jobs introduced the iPhone in 2007 at Macworld.",
"Macintosh computers were sold by Apple from 1984 onward.",
# ... thousands or millions more in production
]
query = "Where was Apple founded?"
# Step 1: encode + retrieve top-100
query_emb = embedder.encode_query(query, convert_to_tensor=True)
corpus_emb = embedder.encode_document(corpus, convert_to_tensor=True)
scores = embedder.similarity(query_emb, corpus_emb)[0]
top_k_idx = scores.topk(min(100, len(corpus))).indices.tolist()
# Step 2: rerank
top_k_docs = [corpus[i] for i in top_k_idx]
ranked = reranker.rank(query, top_k_docs, top_k=5, return_documents=True)
for r in ranked:
print(f"({r['score']:.2f}): {r['text']}")
# (11.63): Apple Inc. was founded in Cupertino, California in 1976 by Steve Jobs, Steve Wozniak, and Ronald Wayne.
# (4.71): Steve Jobs introduced the iPhone in 2007 at Macworld.
# (1.96): The Fuji apple is an apple cultivar developed in the late 1930s.
# (1.49): Macintosh computers were sold by Apple from 1984 onward.

This is the same shape used by most modern search systems. The retriever decides what enters the funnel, the reranker decides what wins.

All six rerankers share the same architecture and differ only in their backbone size. The backbone is one of the six Ettin encoders from Johns Hopkins University's Ettin suite. These are ModernBERT-style models with unpadded attention, RoPE positional encodings, GeGLU, and 2T tokens of open-license pre-training, supporting up to 8192 tokens of context.

On top of each encoder, the reranker uses a 4-module classification head that mirrors ModernBertForSequenceClassification

but is built from Sentence Transformers' modular components. The underlying Transformer

is a plain AutoModel

rather than AutoModelForSequenceClassification

, which lets us use sequence unpadding for variable-length inputs for Flash Attention 2. At medium-document sequence lengths this is a 1.7x-8.3x speedup over fp32+SDPA depending on model size (see Speed for the full benchmark):

1. Transformer(FA2)
2. Pooling(cls)
3. Dense(H, H, bias=False, GELU)
4. LayerNorm(H)
5. Dense(H, 1, scores)

In my ablations, CLS pooling outperformed mean pooling. That was a little surprising. ModernBERT uses global attention only every third layer and the other two-thirds use local-window attention that cannot reach CLS from distant positions. Empirically, those few global layers carry enough signal to make CLS the better pooling choice.