mist-reranker-150m
A compact English cross-encoder reranker for the second stage of a RAG / search pipeline. Given a query and a candidate passage, it outputs a relevance score to re-sort the top-k from a first-stage retriever (BM25 or a bi-encoder).
Built on answerdotai/ModernBERT-base
(~150M) and fine-tuned on
olaverse/reranker-general-en-llm-judged.
Competitive with purpose-built 150M ModernBERT rerankers on QA/fact retrieval, at
equal or smaller size.
📄 Model details
| Property | Value |
|---|---|
| Type | Cross-encoder reranker (single relevance score) |
| Backbone | answerdotai/ModernBERT-base |
| Parameters | ~150M |
| Max sequence length | 512 tokens |
| Training data | olaverse/reranker-general-en-llm-judged |
| Language | English |
| License | Apache-2.0 |
🏃 How to run
pip install -U sentence-transformers
from sentence_transformers import CrossEncoder
model = CrossEncoder("olaverse/mist-reranker-150m")
query = "What causes ocean tides?"
passages = [
"Tides are caused by the gravitational pull of the Moon and Sun on Earth's oceans.",
"The Pacific Ocean is the largest of Earth's oceanic divisions.",
"Tidal energy is a renewable power source generated from the movement of tides.",
]
scores = model.predict([(query, p) for p in passages])
for s, p in sorted(zip(scores, passages), reverse=True):
print(f"{s:.3f} {p}")
📈 Performance
NanoBEIR nDCG@10, all models run through the same evaluation harness
(CrossEncoderNanoBEIREvaluator). Every task is zero-shot — held out from
training for every model. Tasks are split into in-domain QA/fact and out-of-domain
argument retrieval.
| Model | params | nfcorpus | scifact | fiqa | dbpedia | QA mean | arguana | touche2020 | arg mean |
|---|---|---|---|---|---|---|---|---|---|
| granite-embedding-reranker-r2 | 150M | 0.437 | 0.811 | 0.561 | 0.723 | 0.633 | 0.555 | 0.597 | 0.576 |
| mist-reranker-150m | 150M | 0.440 | 0.785 | 0.578 | 0.711 | 0.628 | 0.421 | 0.581 | 0.501 |
| ms-marco-MiniLM-L12-v2 | 33M | 0.399 | 0.738 | 0.514 | 0.713 | 0.591 | 0.365 | 0.602 | 0.483 |
| bge-reranker-base | 278M | 0.385 | 0.734 | 0.447 | 0.697 | 0.566 | 0.360 | 0.422 | 0.391 |
On QA/fact retrieval, mist-reranker-150m (0.628) is within ~0.005 of granite (0.633) and ahead of bge-reranker-base (0.566) — 1.8× larger — and MiniLM-L12. It was trained on a fully-disclosed, MS-MARCO-free data mix; the comparison models use larger undisclosed training data. The parity is achieved with cleaner, verifiable data provenance.
🧪 Training
- Base:
answerdotai/ModernBERT-base, trained from scratch into a reranker. - Data: LLM-judged graded 0–3 relevance labels (Qwen2.5-72B, UMBRELA-style rubric), validated at Cohen's κ (quadratic) = 0.491 vs TREC-DL human qrels.
- Loss: BinaryCrossEntropyLoss on labels normalized to [0,1]; 2 epochs, best checkpoint by NanoBEIR mean nDCG@10.
- Raw ModernBERT-base scored 0.108 mean nDCG@10 before training; the dataset does effectively all the reranking learning.
License
Released under Apache-2.0.
Citation
@misc{mist-reranker-150m,
title = {mist-reranker-150m},
author = {Olaverse},
year = {2026},
url = {https://huggingface.co/olaverse/mist-reranker-150m}
}