MMarco MiniLMv2 L12 H384 Reranker
cross-encoder/mmarco-mMiniLMv2-L12-H384-v1
published Jun 2022 · updated Apr 2025
MMarco MiniLMv2 L12 H384 Reranker is a cross-encoder rerank model trained on the multilingual MMARCO dataset for information retrieval.
specs
| Task | Reranking (cross-encoder) |
| Architecture | Multilingual MiniLMv2 (distilled from XLM-R Large) |
| Parameters | ~22M |
| License | MIT |
about this model
Multilingual Reranking
The model accepts a query-passage pair and outputs a relevance score. It is intended for use in a two-stage retrieval pipeline: first retrieve a set of candidate passages (e.g., via sparse or dense retrieval), then rerank them with this cross-encoder to obtain a more accurate ordering. In experiments, the model demonstrated strong performance not only on the 14 translated languages but also on additional languages not seen during training, indicating useful cross-lingual transfer.
Key Strengths
- Multilingual: covers 14 languages from the MMARCO dataset and generalises to others.
- Efficient: based on the lightweight MiniLMv2 architecture (12 layers, 384 hidden dimensions).
- Proven framework: built using the SentenceTransformers library, with training code and usage examples available from the UKPLab repository.
Hosted API
On gigarouter, this model is available as a managed, OpenAI-compatible API. You submit a query and a list of passages, and the API returns relevance scores for each pair. No local installation, hardware, or model weights management is required.
best for
- ·Multilingual search result reranking
- ·Cross-lingual information retrieval
- ·Improving top-k retrieval precision
FAQ
It is best for reranking passages retrieved by a first-stage retriever, especially in multilingual or cross-lingual search scenarios.
With ~22M parameters, it is a compact model offering fast inference suitable for production reranking pipelines.
The model is released under the MIT license, allowing free use, modification, and distribution.
It expects pairs of (query, passage) text strings, which are tokenized and fed to the cross-encoder to produce relevance scores.
Use the OpenAI-compatible endpoint with your API key, sending query-passage pairs to the rerank endpoint.
We're benchmarking and onboarding MMarco MiniLMv2 L12 H384 Reranker as a hosted, OpenAI-compatible API. Sign in for free credit and be ready when it lands, or tell us you want it and we'll prioritize it.