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README.md

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+---
+license: apache-2.0
+language:
+- en
+tags:
+- cross-encoder
+- reranker
+- radiology
+- medical
+- retrieval
+- sentence-similarity
+- healthcare
+- clinical
+base_model: cross-encoder/ms-marco-MiniLM-L-12-v2
+pipeline_tag: text-classification
+library_name: sentence-transformers
+datasets:
+- radiology-education-corpus
+metrics:
+- mrr
+- ndcg
+model-index:
+- name: RadLITE-Reranker
+  results:
+  - task:
+      type: reranking
+      name: Document Reranking
+    dataset:
+      name: RadLIT-9 (Radiology Retrieval Benchmark)
+      type: radiology-retrieval
+    metrics:
+    - type: mrr
+      value: 0.829
+      name: MRR (with bi-encoder)
+    - type: mrr_improvement
+      value: 0.303
+      name: MRR Improvement on ACR Core Exam (+30.3%)
+---
+
+# RadLITE-Reranker
+
+**Radiology Late Interaction Transformer Enhanced - Cross-Encoder Reranker**
+
+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.
+
+> **Recommended:** Use this reranker together with [RadLITE-Encoder](https://huggingface.co/matulichpt/RadLITE-Encoder) 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 benchmarks (+30% on board exam questions).
+
+## Model Description
+
+| Property | Value |
+|----------|-------|
+| **Model Type** | Cross-Encoder (Reranker) |
+| **Base Model** | [ms-marco-MiniLM-L-12-v2](https://huggingface.co/cross-encoder/ms-marco-MiniLM-L-12-v2) |
+| **Domain** | Radiology / Medical Imaging |
+| **Hidden Size** | 384 |
+| **Max Sequence Length** | 512 tokens |
+| **Output** | Single relevance score |
+| **License** | Apache 2.0 |
+
+### Why Use a Reranker?
+
+Bi-encoders (like RadLITE-Encoder) are fast but encode query and document independently. Cross-encoders process them together, capturing fine-grained interactions:
+
+| Approach | Speed | Accuracy | Use Case |
+|----------|-------|----------|----------|
+| Bi-Encoder | Fast (1000s docs/sec) | Good | First-stage retrieval |
+| Cross-Encoder | Slow (10s docs/sec) | Excellent | Reranking top candidates |
+
+**Two-stage pipeline**: Use bi-encoder to get top 50-100 candidates, then rerank with cross-encoder for best results.
+
+## Performance
+
+### Impact on RadLIT-9 Benchmark
+
+| Configuration | MRR | Improvement |
+|---------------|-----|-------------|
+| Bi-Encoder only | 0.78 | baseline |
+| **Bi-Encoder + Reranker** | **0.829** | **+6.3%** |
+
+### ACR Core Exam (Board-Style Questions)
+
+| Dataset | With Reranker | Without | Improvement |
+|---------|---------------|---------|-------------|
+| Core Exam Chest | 0.533 | 0.409 | **+30.3%** |
+| Core Exam Combined | 0.466 | 0.381 | **+22.5%** |
+
+The reranker is especially valuable for complex, multi-part queries typical of board exam questions.
+
+## Quick Start
+
+### Installation
+
+```bash
+pip install sentence-transformers>=2.2.0
+```
+
+### Basic Usage
+
+```python
+from sentence_transformers import CrossEncoder
+
+# Load the reranker
+reranker = CrossEncoder("matulichpt/RadLITE-Reranker", max_length=512)
+
+# Query and candidate documents
+query = "What are the imaging features of hepatocellular carcinoma?"
+documents = [
+    "HCC typically shows arterial enhancement with portal venous washout on CT.",
+    "Fatty liver disease presents as decreased attenuation on non-contrast CT.",
+    "Hepatic hemangiomas show peripheral nodular enhancement.",
+]
+
+# Create query-document pairs
+pairs = [[query, doc] for doc in documents]
+
+# Get relevance scores
+scores = reranker.predict(pairs)
+
+# Apply temperature calibration (RECOMMENDED)
+calibrated_scores = scores / 1.5
+
+print("Scores:", calibrated_scores)
+# Document about HCC will have highest score
+```
+
+### Temperature Calibration
+
+**Important**: This model outputs scores with high variance. Apply temperature scaling for better fusion with other signals:
+
+```python
+# Raw scores might be: [4.2, -1.5, 0.8]
+# After calibration:   [2.8, -1.0, 0.53]
+
+TEMPERATURE = 1.5  # Recommended value
+
+def calibrated_predict(reranker, pairs):
+    raw_scores = reranker.predict(pairs)
+    return raw_scores / TEMPERATURE
+```
+
+### Full Two-Stage Search Pipeline
+
+```python
+from sentence_transformers import SentenceTransformer, CrossEncoder
+import numpy as np
+
+class RadLITESearch:
+    def __init__(self, device="cuda"):
+        # Stage 1: Fast bi-encoder
+        self.encoder = SentenceTransformer(
+            "matulichpt/RadLITE-Encoder",
+            device=device
+        )
+        # Stage 2: Precise reranker
+        self.reranker = CrossEncoder(
+            "matulichpt/RadLITE-Reranker",
+            max_length=512,
+            device=device
+        )
+        self.temperature = 1.5
+        self.corpus_embeddings = None
+        self.corpus = None
+
+    def index_corpus(self, documents: list):
+        """Pre-compute embeddings for your corpus."""
+        self.corpus = documents
+        self.corpus_embeddings = self.encoder.encode(
+            documents,
+            normalize_embeddings=True,
+            show_progress_bar=True,
+            batch_size=32
+        )
+
+    def search(self, query: str, top_k: int = 10, candidates: int = 50):
+        """Two-stage search: retrieve then rerank."""
+
+        # Stage 1: Bi-encoder retrieval
+        query_emb = self.encoder.encode(query, normalize_embeddings=True)
+        scores = query_emb @ self.corpus_embeddings.T
+        top_indices = np.argsort(scores)[-candidates:][::-1]
+
+        # Stage 2: Cross-encoder reranking
+        candidate_docs = [self.corpus[i] for i in top_indices]
+        pairs = [[query, doc] for doc in candidate_docs]
+        rerank_scores = self.reranker.predict(pairs) / self.temperature
+
+        # Sort by reranked scores
+        sorted_indices = np.argsort(rerank_scores)[::-1]
+
+        results = []
+        for idx in sorted_indices[:top_k]:
+            results.append({
+                "document": candidate_docs[idx],
+                "corpus_index": int(top_indices[idx]),
+                "score": float(rerank_scores[idx]),
+                "biencoder_score": float(scores[top_indices[idx]])
+            })
+        return results
+
+
+# Usage
+searcher = RadLITESearch()
+searcher.index_corpus(your_radiology_documents)
+results = searcher.search("pneumothorax CT findings")
+```
+
+## Integration with Any Corpus
+
+### Radiopaedia / Educational Content
+
+```python
+import json
+
+# Load your content (e.g., Radiopaedia articles)
+with open("radiopaedia_articles.json") as f:
+    articles = json.load(f)
+
+corpus = [article["content"] for article in articles]
+
+# Initialize search
+searcher = RadLITESearch()
+searcher.index_corpus(corpus)
+
+# Search
+results = searcher.search("classic findings of pulmonary embolism on CTPA")
+
+for r in results[:5]:
+    print(f"Score: {r['score']:.3f}")
+    print(f"Content: {r['document'][:200]}...")
+    print()
+```
+
+### Integration with Elasticsearch/OpenSearch
+
+```python
+from sentence_transformers import CrossEncoder
+
+reranker = CrossEncoder("matulichpt/RadLITE-Reranker", max_length=512)
+
+def rerank_elasticsearch_results(query: str, es_results: list, top_k: int = 10):
+    """Rerank Elasticsearch BM25 results."""
+    documents = [hit["_source"]["content"] for hit in es_results]
+    pairs = [[query, doc] for doc in documents]
+
+    scores = reranker.predict(pairs) / 1.5  # Temperature calibration
+
+    # Combine with ES scores (optional)
+    for i, hit in enumerate(es_results):
+        hit["rerank_score"] = float(scores[i])
+        hit["combined_score"] = 0.3 * hit["_score"] + 0.7 * scores[i]
+
+    # Sort by combined score
+    reranked = sorted(es_results, key=lambda x: x["combined_score"], reverse=True)
+    return reranked[:top_k]
+```
+
+## Optimal Fusion Weights
+
+When combining multiple signals (bi-encoder, cross-encoder, BM25), use these weights:
+
+```python
+# Optimal weights from grid search on RadLIT-9
+FUSION_WEIGHTS = {
+    "biencoder": 0.5,    # RadLITE-Encoder similarity
+    "crossencoder": 0.2, # RadLITE-Reranker (after temp calibration)
+    "bm25": 0.3          # Lexical matching (if available)
+}
+
+def fused_score(bienc_score, ce_score, bm25_score=0):
+    return (
+        FUSION_WEIGHTS["biencoder"] * bienc_score +
+        FUSION_WEIGHTS["crossencoder"] * ce_score +
+        FUSION_WEIGHTS["bm25"] * bm25_score
+    )
+```
+
+## Architecture
+
+```
+[Query] + [SEP] + [Document]
+           |
+           v
+    [BERT Tokenizer]
+           |
+           v
+    [MiniLM Encoder] (12 layers, 384 hidden)
+           |
+           v
+    [Classification Head]
+           |
+           v
+    Relevance Score (float)
+```
+
+## Training Details
+
+- **Base Model**: ms-marco-MiniLM-L-12-v2 (trained on MS MARCO passage ranking)
+- **Fine-tuning**: Radiology query-document relevance pairs
+- **Training Steps**: 5,626
+- **Best Validation Loss**: 0.691
+- **Learning Rate**: 2e-5
+- **Batch Size**: 32
+- **Category Weighting**: Yes (balanced across radiology subspecialties)
+
+## Best Practices
+
+### 1. Always Use Temperature Calibration
+
+Raw cross-encoder scores can be extreme. Temperature scaling (1.5) produces better fusion:
+
+```python
+calibrated = raw_score / 1.5
+```
+
+### 2. Limit Candidates for Reranking
+
+Cross-encoders are slow. Only rerank top 50-100 candidates from bi-encoder:
+
+```python
+# Good: Rerank top 50
+rerank_candidates = 50
+
+# Bad: Rerank entire corpus
+rerank_candidates = len(corpus)  # Too slow!
+```
+
+### 3. Batch Predictions
+
+```python
+# Efficient: Single batch call
+pairs = [[query, doc] for doc in candidates]
+scores = reranker.predict(pairs, batch_size=32)
+
+# Inefficient: Individual calls
+scores = [reranker.predict([[query, doc]])[0] for doc in candidates]
+```
+
+### 4. GPU Acceleration
+
+```python
+reranker = CrossEncoder(
+    "matulichpt/RadLITE-Reranker",
+    max_length=512,
+    device="cuda"  # Use GPU
+)
+```
+
+## Limitations
+
+- **English only**: Trained on English radiology text
+- **Speed**: ~10-50 pairs/second (use for reranking, not full corpus)
+- **512 token limit**: Long documents are truncated
+- **Domain-specific**: Optimized for radiology, may underperform on general medical content
+
+## Citation
+
+If you use RadLITE in your work, please cite:
+
+```bibtex
+@software{radlite_2026,
+    title = {RadLITE: Calibrated Multi-Stage Retrieval for Radiology Education},
+    author = {Grai Team},
+    year = {2026},
+    month = {January},
+    url = {https://huggingface.co/matulichpt/RadLITE-Reranker},
+    note = {+30% MRR improvement on ACR Core Exam questions}
+}
+```
+
+## Related Models
+
+- [RadLITE-Encoder](https://huggingface.co/matulichpt/RadLITE-Encoder) - Bi-encoder for first-stage retrieval
+- [RadBERT-RoBERTa-4m](https://huggingface.co/zzxslp/RadBERT-RoBERTa-4m) - Base radiology language model
+
+## License
+
+Apache 2.0 - Free for commercial and research use.

config.json

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+{
+  "architectures": [
+    "BertForSequenceClassification"
+  ],
+  "attention_probs_dropout_prob": 0.1,
+  "classifier_dropout": null,
+  "dtype": "float32",
+  "gradient_checkpointing": false,
+  "hidden_act": "gelu",
+  "hidden_dropout_prob": 0.1,
+  "hidden_size": 384,
+  "id2label": {
+    "0": "relevance"
+  },
+  "initializer_range": 0.02,
+  "intermediate_size": 1536,
+  "label2id": {
+    "relevance": 0
+  },
+  "layer_norm_eps": 1e-12,
+  "max_position_embeddings": 512,
+  "model_type": "bert",
+  "num_attention_heads": 12,
+  "num_hidden_layers": 12,
+  "pad_token_id": 0,
+  "position_embedding_type": "absolute",
+  "sbert_ce_default_activation_function": "torch.nn.modules.linear.Identity",
+  "transformers_version": "4.56.0",
+  "type_vocab_size": 2,
+  "use_cache": true,
+  "vocab_size": 30522
+}

model.safetensors

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+oid sha256:3dfc8832e0d99ed4c39d357bd5be9ea2552eab7107daa09b30db39a43f741a73
+size 133464836

special_tokens_map.json

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+{
+  "cls_token": {
+    "content": "[CLS]",
+    "lstrip": false,
+    "normalized": false,
+    "rstrip": false,
+    "single_word": false
+  },
+  "mask_token": {
+    "content": "[MASK]",
+    "lstrip": false,
+    "normalized": false,
+    "rstrip": false,
+    "single_word": false
+  },
+  "pad_token": {
+    "content": "[PAD]",
+    "lstrip": false,
+    "normalized": false,
+    "rstrip": false,
+    "single_word": false
+  },
+  "sep_token": {
+    "content": "[SEP]",
+    "lstrip": false,
+    "normalized": false,
+    "rstrip": false,
+    "single_word": false
+  },
+  "unk_token": {
+    "content": "[UNK]",
+    "lstrip": false,
+    "normalized": false,
+    "rstrip": false,
+    "single_word": false
+  }
+}

tokenizer_config.json

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+{
+  "added_tokens_decoder": {
+    "0": {
+      "content": "[PAD]",
+      "lstrip": false,
+      "normalized": false,
+      "rstrip": false,
+      "single_word": false,
+      "special": true
+    },
+    "100": {
+      "content": "[UNK]",
+      "lstrip": false,
+      "normalized": false,
+      "rstrip": false,
+      "single_word": false,
+      "special": true
+    },
+    "101": {
+      "content": "[CLS]",
+      "lstrip": false,
+      "normalized": false,
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+      "single_word": false,
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+      "lstrip": false,
+      "normalized": false,
+      "rstrip": false,
+      "single_word": false,
+      "special": true
+    },
+    "103": {
+      "content": "[MASK]",
+      "lstrip": false,
+      "normalized": false,
+      "rstrip": false,
+      "single_word": false,
+      "special": true
+    }
+  },
+  "clean_up_tokenization_spaces": true,
+  "cls_token": "[CLS]",
+  "do_basic_tokenize": true,
+  "do_lower_case": true,
+  "extra_special_tokens": {},
+  "mask_token": "[MASK]",
+  "model_max_length": 512,
+  "never_split": null,
+  "pad_token": "[PAD]",
+  "sep_token": "[SEP]",
+  "strip_accents": null,
+  "tokenize_chinese_chars": true,
+  "tokenizer_class": "BertTokenizer",
+  "unk_token": "[UNK]"
+}