Text Classification
sentence-transformers
Safetensors
English
bert
cross-encoder
reranker
retrieval
sentence-similarity
Eval Results (legacy)
text-embeddings-inference
Instructions to use matulichpt/radlit-crossencoder with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- sentence-transformers
How to use matulichpt/radlit-crossencoder with sentence-transformers:
from sentence_transformers import CrossEncoder model = CrossEncoder("matulichpt/radlit-crossencoder") query = "Which planet is known as the Red Planet?" passages = [ "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." ] scores = model.predict([(query, passage) for passage in passages]) print(scores) - Notebooks
- Google Colab
- Kaggle
Upload README.md with huggingface_hub
Browse files
README.md
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- type: mrr
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value: 0.829
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name: MRR (with bi-encoder)
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name: MRR
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---
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# RadLITE-Reranker
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A domain-specialized cross-encoder for reranking radiology search results. This model takes a query-document pair and predicts a relevance score, providing more accurate ranking than bi-encoder similarity alone.
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> **Recommended:** Use this reranker together with [RadLITE-Encoder](https://huggingface.co/matulichpt/radlit-biencoder) in a two-stage pipeline for optimal performance. The bi-encoder handles fast retrieval over large corpora, then this cross-encoder reranks the top candidates for precision. This combination achieves **MRR 0.829** on radiology
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## Model Description
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| Bi-Encoder only | 0.78 | baseline |
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| **Bi-Encoder + Reranker** | **0.829** | **+6.3%** |
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###
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| Dataset |
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| Core Exam Chest | 0.533 | 0.409 |
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| Core Exam Combined | 0.466 | 0.381 |
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The reranker
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## Quick Start
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year = {2026},
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month = {January},
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url = {https://huggingface.co/matulichpt/radlit-crossencoder},
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note = {
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}
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```
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- type: mrr
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value: 0.829
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name: MRR (with bi-encoder)
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- type: mrr
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value: 0.533
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name: MRR on ABR Core Exam (Chest)
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---
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# RadLITE-Reranker
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A domain-specialized cross-encoder for reranking radiology search results. This model takes a query-document pair and predicts a relevance score, providing more accurate ranking than bi-encoder similarity alone.
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> **Recommended:** Use this reranker together with [RadLITE-Encoder](https://huggingface.co/matulichpt/radlit-biencoder) in a two-stage pipeline for optimal performance. The bi-encoder handles fast retrieval over large corpora, then this cross-encoder reranks the top candidates for precision. This combination achieves **MRR 0.829** on radiology retrieval benchmarks.
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## Model Description
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| Bi-Encoder only | 0.78 | baseline |
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| **Bi-Encoder + Reranker** | **0.829** | **+6.3%** |
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### ABR Core Exam (Board-Style Questions)
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Comparing two-stage pipeline (bi-encoder + reranker) vs bi-encoder alone:
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| Dataset | Two-Stage MRR | Bi-Encoder Only | Improvement |
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|---------|---------------|-----------------|-------------|
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| Core Exam Chest | 0.533 | 0.409 | +30.3% |
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| Core Exam Combined | 0.466 | 0.381 | +22.5% |
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The reranker provides significant gains on complex, multi-part queries typical of board exam questions.
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## Quick Start
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year = {2026},
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month = {January},
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url = {https://huggingface.co/matulichpt/radlit-crossencoder},
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note = {MRR 0.829 on RadLIT-9 benchmark}
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}
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```
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