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README.md

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----
-language:
-- en
-license: apache-2.0
-library_name: sentence-transformers
-tags:
-- sentence-transformers
-- feature-extraction
-- sentence-similarity
-- radiology
-- medical
-- retrieval
-- embedding
-datasets:
-- custom
-metrics:
-- mrr
-- recall
-pipeline_tag: sentence-similarity
-model-index:
-- name: radlit-biencoder
-  results:
-  - task:
-      type: retrieval
-      name: Radiology Document Retrieval
-    dataset:
-      type: custom
-      name: RadLIT-9
-      config: radlit9-v1.1-balanced
-    metrics:
-    - type: mrr
-      value: 0.829
-      name: MRR
-    - type: recall@10
-      value: 0.971
-      name: Recall@10
-    - type: ndcg@10
-      value: 0.863
-      name: nDCG@10
----
-
-# RadLIT-BiEncoder: Radiology Late Interaction Transformer
-
-A domain-specialized bi-encoder model for radiology document retrieval, trained to understand medical imaging terminology, clinical reasoning patterns, and radiology-specific queries.
-
-## Model Description
-
-RadLIT-BiEncoder is the first stage of the RadLITE retrieval pipeline. It generates dense embeddings optimized for radiology content retrieval, significantly outperforming general-purpose embedding models on radiology-specific queries.
-
-### Architecture
-
-- **Base Model**: RoBERTa-base architecture
-- **Hidden Size**: 768
-- **Layers**: 12
-- **Attention Heads**: 12
-- **Parameters**: ~125M
-- **Max Sequence Length**: 512 tokens
-- **Embedding Dimension**: 768
-
-### Training
-
-The model was trained using contrastive learning with hard negative mining on a large corpus of radiology educational content. Training details:
-
-- **Training Objective**: Multiple Negatives Ranking Loss with hard negatives
-- **Batch Size**: 32
-- **Learning Rate**: 2e-5 with warmup
-- **Training Epochs**: 4
-- **Hard Negatives**: Mined from top-k retrieval failures
-
-**Note**: Training data consisted of radiology educational materials. Specific sources are not disclosed due to variable licensing, but the model is released under Apache 2.0 for research and commercial use.
-
-## Performance
-
-### RadLIT-9 Benchmark
-
-RadLIT-9 is a comprehensive radiology retrieval benchmark covering 9 subspecialties:
-
-| Metric | Score |
-|--------|-------|
-| **MRR** | 0.829 |
-| **nDCG@10** | 0.863 |
-| **Recall@10** | 97.1% |
-| **Recall@5** | 93.8% |
-| **Recall@1** | 74.3% |
-
-### Subspecialty Performance
-
-| Subspecialty | MRR | Recall@10 |
-|--------------|-----|-----------|
-| Physics/Nuclear | 0.936 | 100% |
-| Pediatric | 0.931 | 100% |
-| Thoracic | 0.913 | 98% |
-| Cardiac | 0.862 | 98% |
-| Neuroradiology | 0.860 | 98% |
-| Gastrointestinal | 0.800 | 96% |
-| Breast | 0.722 | 93% |
-| Musculoskeletal | 0.695 | 89% |
-| Genitourinary | 0.694 | 100% |
-
-### Comparison with Baselines
-
-| Model | MRR | vs RadLIT |
-|-------|-----|-----------|
-| **RadLIT-BiEncoder** | **0.829** | -- |
-| ColBERT-v2 | 0.750 | -9.5% |
-| General bi-encoder | 0.703 | -15.2% |
-| BM25 | ~0.55 | -33.6% |
-
-## Usage
-
-### Installation
-
-```bash
-pip install sentence-transformers
-```
-
-### Basic Usage
-
-```python
-from sentence_transformers import SentenceTransformer
-
-# Load model
-model = SentenceTransformer('matulichpt/radlit-biencoder')
-
-# Encode queries and documents
-queries = [
-    "What are the imaging features of hepatocellular carcinoma on MRI?",
-    "How do you differentiate glioblastoma from metastasis?"
-]
-documents = [
-    "HCC typically shows arterial enhancement with washout on portal venous phase...",
-    "GBM and metastases can be differentiated by their location and multiplicity..."
-]
-
-query_embeddings = model.encode(queries, convert_to_tensor=True)
-doc_embeddings = model.encode(documents, convert_to_tensor=True)
-
-# Compute similarity
-from sentence_transformers.util import cos_sim
-similarities = cos_sim(query_embeddings, doc_embeddings)
-print(similarities)
-```
-
-### For Retrieval Pipeline
-
-```python
-from sentence_transformers import SentenceTransformer, util
-import torch
-
-model = SentenceTransformer('matulichpt/radlit-biencoder')
-
-# Pre-encode your document corpus
-corpus = ["document 1...", "document 2...", ...]
-corpus_embeddings = model.encode(corpus, convert_to_tensor=True, show_progress_bar=True)
-
-# At query time
-query = "What are the CT findings in pulmonary embolism?"
-query_embedding = model.encode(query, convert_to_tensor=True)
-
-# Find top-k similar documents
-cos_scores = util.cos_sim(query_embedding, corpus_embeddings)[0]
-top_results = torch.topk(cos_scores, k=10)
-
-for score, idx in zip(top_results[0], top_results[1]):
-    print(f"Score: {score:.4f} - {corpus[idx][:100]}...")
-```
-
-## Recommended: Full RadLITE Pipeline
-
-For best results, use RadLIT-BiEncoder as the first stage followed by RadLIT-CrossEncoder for reranking:
-
-```python
-from sentence_transformers import SentenceTransformer, CrossEncoder
-
-# Stage 1: Bi-encoder retrieval
-biencoder = SentenceTransformer('grai-rad/radlit-biencoder')
-
-# Stage 2: Cross-encoder reranking
-crossencoder = CrossEncoder('matulichpt/radlit-crossencoder')
-
-# Retrieve candidates
-query = "What are the MRI findings in anterior cruciate ligament tear?"
-candidates = retrieve_with_biencoder(query, corpus, biencoder, top_k=50)
-
-# Rerank with cross-encoder
-pairs = [[query, doc] for doc in candidates]
-scores = crossencoder.predict(pairs)
-
-# Apply temperature calibration (recommended: T=1.5)
-calibrated_scores = scores / 1.5
-
-# Sort by calibrated scores
-reranked = sorted(zip(candidates, calibrated_scores), key=lambda x: x[1], reverse=True)
-```
-
-## Intended Use
-
-### Primary Use Cases
-
-- Radiology educational content retrieval
-- Medical imaging literature search
-- Clinical decision support (retrieval component)
-- Radiology question-answering systems
-
-### Out-of-Scope Uses
-
-- General web search
-- Non-medical document retrieval
-- Clinical diagnosis (this is a retrieval model, not a diagnostic tool)
-
-## Limitations
-
-1. **Domain Specificity**: Optimized for radiology; may underperform on general medical or non-medical content
-2. **Language**: English only
-3. **Subspecialty Variance**: Performance varies by subspecialty (0.69-0.94 MRR range)
-4. **Not a Diagnostic Tool**: This model retrieves relevant documents; it does not provide medical diagnoses
-
-## Ethical Considerations
-
-- This model should not be used as a sole source for clinical decision-making
-- Retrieved documents should be reviewed by qualified medical professionals
-- The model may reflect biases present in radiology educational literature
-
-## Citation
-
-```bibtex
-@software{radlit_biencoder_2026,
-  title = {RadLIT-BiEncoder: Domain-Specialized Embeddings for Radiology Retrieval},
-  author = {Grai Team},
-  year = {2026},
-  url = {https://huggingface.co/matulichpt/radlit-biencoder},
-  note = {MRR 0.829 on RadLIT-9 benchmark}
-}
-```
-
-## License
-
-Apache 2.0 - Free for research and commercial use.
-
-## Contact
-
-For questions or collaboration: Open an issue on the model repository
+---
+language:
+- en
+license: apache-2.0
+library_name: sentence-transformers
+tags:
+- sentence-transformers
+- feature-extraction
+- sentence-similarity
+- radiology
+- medical
+- retrieval
+- embedding
+datasets:
+- custom
+pipeline_tag: sentence-similarity
+model-index:
+- name: radlit-biencoder
+  results:
+  - task:
+      type: retrieval
+      name: Radiology Document Retrieval
+    dataset:
+      type: custom
+      name: RadLIT-9
+      config: radlit9-v1.1-balanced
+    metrics:
+    - type: mrr
+      value: 0.829
+      name: MRR
+    - type: recall@10
+      value: 0.971
+      name: Recall@10
+    - type: ndcg@10
+      value: 0.863
+      name: nDCG@10
+---
+
+# RadLIT-BiEncoder: Radiology Late Interaction Transformer
+
+A domain-specialized bi-encoder model for radiology document retrieval, trained to understand medical imaging terminology, clinical reasoning patterns, and radiology-specific queries.
+
+## Model Description
+
+RadLIT-BiEncoder is the first stage of the RadLITE retrieval pipeline. It generates dense embeddings optimized for radiology content retrieval, significantly outperforming general-purpose embedding models on radiology-specific queries.
+
+### Architecture
+
+- **Base Model**: RoBERTa-base architecture
+- **Hidden Size**: 768
+- **Layers**: 12
+- **Attention Heads**: 12
+- **Parameters**: ~125M
+- **Max Sequence Length**: 512 tokens
+- **Embedding Dimension**: 768
+
+### Training
+
+The model was trained using contrastive learning with hard negative mining on a large corpus of radiology educational content. Training details:
+
+- **Training Objective**: Multiple Negatives Ranking Loss with hard negatives
+- **Batch Size**: 32
+- **Learning Rate**: 2e-5 with warmup
+- **Training Epochs**: 4
+- **Hard Negatives**: Mined from top-k retrieval failures
+
+**Note**: Training data consisted of radiology educational materials. Specific sources are not disclosed due to variable licensing, but the model is released under Apache 2.0 for research and commercial use.
+
+## Performance
+
+### RadLIT-9 Benchmark
+
+RadLIT-9 is a comprehensive radiology retrieval benchmark covering 9 subspecialties:
+
+| Metric | Score |
+|--------|-------|
+| **MRR** | 0.829 |
+| **nDCG@10** | 0.863 |
+| **Recall@10** | 97.1% |
+| **Recall@5** | 93.8% |
+| **Recall@1** | 74.3% |
+
+### Subspecialty Performance
+
+| Subspecialty | MRR | Recall@10 |
+|--------------|-----|-----------|
+| Physics/Nuclear | 0.936 | 100% |
+| Pediatric | 0.931 | 100% |
+| Thoracic | 0.913 | 98% |
+| Cardiac | 0.862 | 98% |
+| Neuroradiology | 0.860 | 98% |
+| Gastrointestinal | 0.800 | 96% |
+| Breast | 0.722 | 93% |
+| Musculoskeletal | 0.695 | 89% |
+| Genitourinary | 0.694 | 100% |
+
+### Comparison with Baselines
+
+| Model | MRR | vs RadLIT |
+|-------|-----|-----------|
+| **RadLIT-BiEncoder** | **0.829** | -- |
+| ColBERT-v2 | 0.750 | -9.5% |
+| General bi-encoder | 0.703 | -15.2% |
+| BM25 | ~0.55 | -33.6% |
+
+## Usage
+
+### Installation
+
+```bash
+pip install sentence-transformers
+```
+
+### Basic Usage
+
+```python
+from sentence_transformers import SentenceTransformer
+
+# Load model
+model = SentenceTransformer('matulichpt/radlit-biencoder')
+
+# Encode queries and documents
+queries = [
+    "What are the imaging features of hepatocellular carcinoma on MRI?",
+    "How do you differentiate glioblastoma from metastasis?"
+]
+documents = [
+    "HCC typically shows arterial enhancement with washout on portal venous phase...",
+    "GBM and metastases can be differentiated by their location and multiplicity..."
+]
+
+query_embeddings = model.encode(queries, convert_to_tensor=True)
+doc_embeddings = model.encode(documents, convert_to_tensor=True)
+
+# Compute similarity
+from sentence_transformers.util import cos_sim
+similarities = cos_sim(query_embeddings, doc_embeddings)
+print(similarities)
+```
+
+### For Retrieval Pipeline
+
+```python
+from sentence_transformers import SentenceTransformer, util
+import torch
+
+model = SentenceTransformer('matulichpt/radlit-biencoder')
+
+# Pre-encode your document corpus
+corpus = ["document 1...", "document 2...", ...]
+corpus_embeddings = model.encode(corpus, convert_to_tensor=True, show_progress_bar=True)
+
+# At query time
+query = "What are the CT findings in pulmonary embolism?"
+query_embedding = model.encode(query, convert_to_tensor=True)
+
+# Find top-k similar documents
+cos_scores = util.cos_sim(query_embedding, corpus_embeddings)[0]
+top_results = torch.topk(cos_scores, k=10)
+
+for score, idx in zip(top_results[0], top_results[1]):
+    print(f"Score: {score:.4f} - {corpus[idx][:100]}...")
+```
+
+## Recommended: Full RadLITE Pipeline
+
+For best results, use RadLIT-BiEncoder as the first stage followed by RadLIT-CrossEncoder for reranking:
+
+```python
+from sentence_transformers import SentenceTransformer, CrossEncoder
+
+# Stage 1: Bi-encoder retrieval
+biencoder = SentenceTransformer('grai-rad/radlit-biencoder')
+
+# Stage 2: Cross-encoder reranking
+crossencoder = CrossEncoder('matulichpt/radlit-crossencoder')
+
+# Retrieve candidates
+query = "What are the MRI findings in anterior cruciate ligament tear?"
+candidates = retrieve_with_biencoder(query, corpus, biencoder, top_k=50)
+
+# Rerank with cross-encoder
+pairs = [[query, doc] for doc in candidates]
+scores = crossencoder.predict(pairs)
+
+# Apply temperature calibration (recommended: T=1.5)
+calibrated_scores = scores / 1.5
+
+# Sort by calibrated scores
+reranked = sorted(zip(candidates, calibrated_scores), key=lambda x: x[1], reverse=True)
+```
+
+## Intended Use
+
+### Primary Use Cases
+
+- Radiology educational content retrieval
+- Medical imaging literature search
+- Clinical decision support (retrieval component)
+- Radiology question-answering systems
+
+### Out-of-Scope Uses
+
+- General web search
+- Non-medical document retrieval
+- Clinical diagnosis (this is a retrieval model, not a diagnostic tool)
+
+## Limitations
+
+1. **Domain Specificity**: Optimized for radiology; may underperform on general medical or non-medical content
+2. **Language**: English only
+3. **Subspecialty Variance**: Performance varies by subspecialty (0.69-0.94 MRR range)
+4. **Not a Diagnostic Tool**: This model retrieves relevant documents; it does not provide medical diagnoses
+
+## Ethical Considerations
+
+- This model should not be used as a sole source for clinical decision-making
+- Retrieved documents should be reviewed by qualified medical professionals
+- The model may reflect biases present in radiology educational literature
+
+## Citation
+
+```bibtex
+@software{radlit_biencoder_2026,
+  title = {RadLIT-BiEncoder: Domain-Specialized Embeddings for Radiology Retrieval},
+  author = {Grai Team},
+  year = {2026},
+  url = {https://huggingface.co/matulichpt/radlit-biencoder},
+  note = {MRR 0.829 on RadLIT-9 benchmark}
+}
+```
+
+## License
+
+Apache 2.0 - Free for research and commercial use.
+
+## Contact
+
+For questions or collaboration: Open an issue on the model repository