matulichpt
/

radlit-crossencoder

+                                 Apache License
+                           Version 2.0, January 2004
+                        http://www.apache.org/licenses/
+   TERMS AND CONDITIONS FOR USE, REPRODUCTION, AND DISTRIBUTION
+   1. Definitions.
+      "License" shall mean the terms and conditions for use, reproduction,
+      and distribution as defined by Sections 1 through 9 of this document.
+      "Licensor" shall mean the copyright owner or entity authorized by
+      the copyright owner that is granting the License.
+      "Legal Entity" shall mean the union of the acting entity and all
+      other entities that control, are controlled by, or are under common
+      control with that entity. For the purposes of this definition,
+      "control" means (i) the power, direct or indirect, to cause the
+      direction or management of such entity, whether by contract or
+      otherwise, or (ii) ownership of fifty percent (50%) or more of the
+      outstanding shares, or (iii) beneficial ownership of such entity.
+      "You" (or "Your") shall mean an individual or Legal Entity
+      exercising permissions granted by this License.
+      "Source" form shall mean the preferred form for making modifications,
+      including but not limited to software source code, documentation
+      source, and configuration files.
+      "Object" form shall mean any form resulting from mechanical
+      transformation or translation of a Source form, including but
+      not limited to compiled object code, generated documentation,
+      and conversions to other media types.
+      "Work" shall mean the work of authorship, whether in Source or
+      Object form, made available under the License, as indicated by a
+      copyright notice that is included in or attached to the work
+      (an example is provided in the Appendix below).
+      "Derivative Works" shall mean any work, whether in Source or Object
+      form, that is based on (or derived from) the Work and for which the
+      editorial revisions, annotations, elaborations, or other modifications
+      represent, as a whole, an original work of authorship. For the purposes
+      of this License, Derivative Works shall not include works that remain
+      separable from, or merely link (or bind by name) to the interfaces of,
+      the Work and Derivative Works thereof.
+      "Contribution" shall mean any work of authorship, including
+      the original version of the Work and any modifications or additions
+      to that Work or Derivative Works thereof, that is intentionally
+      submitted to the Licensor for inclusion in the Work by the copyright owner
+      or by an individual or Legal Entity authorized to submit on behalf of
+      the copyright owner. For the purposes of this definition, "submitted"
+      means any form of electronic, verbal, or written communication sent
+      to the Licensor or its representatives, including but not limited to
+      communication on electronic mailing lists, source code control systems,
+      and issue tracking systems that are managed by, or on behalf of, the
+      Licensor for the purpose of discussing and improving the Work, but
+      excluding communication that is conspicuously marked or otherwise
+      designated in writing by the copyright owner as "Not a Contribution."
+      "Contributor" shall mean Licensor and any individual or Legal Entity
+      on behalf of whom a Contribution has been received by Licensor and
+      subsequently incorporated within the Work.
+   2. Grant of Copyright License. Subject to the terms and conditions of
+      this License, each Contributor hereby grants to You a perpetual,
+      worldwide, non-exclusive, no-charge, royalty-free, irrevocable
+      copyright license to reproduce, prepare Derivative Works of,
+      publicly display, publicly perform, sublicense, and distribute the
+      Work and such Derivative Works in Source or Object form.
+   3. Grant of Patent License. Subject to the terms and conditions of
+      this License, each Contributor hereby grants to You a perpetual,
+      worldwide, non-exclusive, no-charge, royalty-free, irrevocable
+      (except as stated in this section) patent license to make, have made,
+      use, offer to sell, sell, import, and otherwise transfer the Work,
+      where such license applies only to those patent claims licensable
+      by such Contributor that are necessarily infringed by their
+      Contribution(s) alone or by combination of their Contribution(s)
+      with the Work to which such Contribution(s) was submitted. If You
+      institute patent litigation against any entity (including a
+      cross-claim or counterclaim in a lawsuit) alleging that the Work
+      or a Contribution incorporated within the Work constitutes direct
+      or contributory patent infringement, then any patent licenses
+      granted to You under this License for that Work shall terminate
+      as of the date such litigation is filed.
+   4. Redistribution. You may reproduce and distribute copies of the
+      Work or Derivative Works thereof in any medium, with or without
+      modifications, and in Source or Object form, provided that You
+      meet the following conditions:
+      (a) You must give any other recipients of the Work or
+          Derivative Works a copy of this License; and
+      (b) You must cause any modified files to carry prominent notices
+          stating that You changed the files; and
+      (c) You must retain, in the Source form of any Derivative Works
+          that You distribute, all copyright, patent, trademark, and
+          attribution notices from the Source form of the Work,
+          excluding those notices that do not pertain to any part of
+          the Derivative Works; and
+      (d) If the Work includes a "NOTICE" text file as part of its
+          distribution, then any Derivative Works that You distribute must
+          include a readable copy of the attribution notices contained
+          within such NOTICE file, excluding those notices that do not
+          pertain to any part of the Derivative Works, in at least one
+          of the following places: within a NOTICE text file distributed
+          as part of the Derivative Works; within the Source form or
+          documentation, if provided along with the Derivative Works; or,
+          within a display generated by the Derivative Works, if and
+          wherever such third-party notices normally appear. The contents
+          of the NOTICE file are for informational purposes only and
+          do not modify the License. You may add Your own attribution
+          notices within Derivative Works that You distribute, alongside
+          or as an addendum to the NOTICE text from the Work, provided
+          that such additional attribution notices cannot be construed
+          as modifying the License.
+      You may add Your own copyright statement to Your modifications and
+      may provide additional or different license terms and conditions
+      for use, reproduction, or distribution of Your modifications, or
+      for any such Derivative Works as a whole, provided Your use,
+      reproduction, and distribution of the Work otherwise complies with
+      the conditions stated in this License.
+   5. Submission of Contributions. Unless You explicitly state otherwise,
+      any Contribution intentionally submitted for inclusion in the Work
+      by You to the Licensor shall be under the terms and conditions of
+      this License, without any additional terms or conditions.
+      Notwithstanding the above, nothing herein shall supersede or modify
+      the terms of any separate license agreement you may have executed
+      with Licensor regarding such Contributions.
+   6. Trademarks. This License does not grant permission to use the trade
+      names, trademarks, service marks, or product names of the Licensor,
+      except as required for reasonable and customary use in describing the
+      origin of the Work and reproducing the content of the NOTICE file.
+   7. Disclaimer of Warranty. Unless required by applicable law or
+      agreed to in writing, Licensor provides the Work (and each
+      Contributor provides its Contributions) on an "AS IS" BASIS,
+      WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or
+      implied, including, without limitation, any warranties or conditions
+      of TITLE, NON-INFRINGEMENT, MERCHANTABILITY, or FITNESS FOR A
+      PARTICULAR PURPOSE. You are solely responsible for determining the
+      appropriateness of using or redistributing the Work and assume any
+      risks associated with Your exercise of permissions under this License.
+   8. Limitation of Liability. In no event and under no legal theory,
+      whether in tort (including negligence), contract, or otherwise,
+      unless required by applicable law (such as deliberate and grossly
+      negligent acts) or agreed to in writing, shall any Contributor be
+      liable to You for damages, including any direct, indirect, special,
+      incidental, or consequential damages of any character arising as a
+      result of this License or out of the use or inability to use the
+      Work (including but not limited to damages for loss of goodwill,
+      work stoppage, computer failure or malfunction, or any and all
+      other commercial damages or losses), even if such Contributor
+      has been advised of the possibility of such damages.
+   9. Accepting Warranty or Additional Liability. While redistributing
+      the Work or Derivative Works thereof, You may choose to offer,
+      and charge a fee for, acceptance of support, warranty, indemnity,
+      or other liability obligations and/or rights consistent with this
+      License. However, in accepting such obligations, You may act only
+      on Your own behalf and on Your sole responsibility, not on behalf
+      of any other Contributor, and only if You agree to indemnify,
+      defend, and hold each Contributor harmless for any liability
+      incurred by, or claims asserted against, such Contributor by reason
+      of your accepting any such warranty or additional liability.
+   END OF TERMS AND CONDITIONS
+   Copyright 2026 Grai Team
+   Licensed under the Apache License, Version 2.0 (the "License");
+   you may not use this file except in compliance with the License.
+   You may obtain a copy of the License at
+       http://www.apache.org/licenses/LICENSE-2.0
+   Unless required by applicable law or agreed to in writing, software
+   distributed under the License is distributed on an "AS IS" BASIS,
+   WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+   See the License for the specific language governing permissions and
+   limitations under the License.

README.md CHANGED Viewed

@@ -1,111 +1,97 @@
 ---
 language:
 - en
-license: apache-2.0
-library_name: sentence-transformers
 tags:
-- sentence-transformers
 - cross-encoder
-- text-classification
 - radiology
 - medical
-- reranking
 datasets:
-- custom
 metrics:
 - mrr
-- recall
-pipeline_tag: text-classification
 model-index:
-- name: radlit-crossencoder
   results:
   - task:
       type: reranking
-      name: Radiology Document Reranking
     dataset:
-      type: custom
-      name: RadLIT-9
-      config: radlit9-v1.1-balanced
     metrics:
     - type: mrr
       value: 0.829
       name: MRR (with bi-encoder)
     - type: mrr_improvement
-      value: 0.30
-      name: MRR Improvement on Complex Queries
 ---
-# RadLIT-CrossEncoder: Radiology Reranking Model
-A cross-encoder model fine-tuned for reranking radiology document retrieval results. Designed to work as the second stage of the RadLITE pipeline, providing significant improvements on complex clinical queries.
-## Model Description
-RadLIT-CrossEncoder takes a query-document pair and outputs a relevance score. Unlike bi-encoders that encode queries and documents separately, cross-encoders process them jointly, enabling more nuanced relevance judgments at the cost of higher latency.
-### Architecture
-- **Base Model**: BERT architecture (medical-initialized)
-- **Hidden Size**: 384
-- **Layers**: 12
-- **Attention Heads**: 12
-- **Parameters**: ~33M (optimized for inference speed)
-- **Max Sequence Length**: 512 tokens
-- **Output**: Single relevance score (regression)
-### Training
-The model was fine-tuned on radiology query-document pairs with relevance labels:
-- **Training Objective**: Binary Cross-Entropy with soft labels
-- **Training Data**: Expert-labeled query-document pairs from radiology education
-- **Hard Negatives**: Mined from bi-encoder retrieval failures
-- **Batch Size**: 16
-- **Learning Rate**: 2e-5
-- **Epochs**: 3
-**Note**: Training data sources are not disclosed due to variable licensing. The model is released under Apache 2.0.
 ## Performance
-### Impact on RadLITE Pipeline
-When combined with RadLIT-BiEncoder:
 | Configuration | MRR | Improvement |
 |---------------|-----|-------------|
-| Bi-encoder only | 0.698 | baseline |
-| + Cross-encoder reranking | 0.782 | +12.0% |
-| + BM25 fusion (RadLITE) | **0.829** | **+18.8%** |
-### Performance on Complex Queries
-The cross-encoder shows largest improvements on complex clinical reasoning queries:
-| Query Type | Improvement |
-|------------|-------------|
-| Board exam questions | **+30.3%** |
-| Differential diagnosis | +22.5% |
-| Staging/classification | +18.0% |
-| Simple factual | +5.0% |
-### Subspecialty Impact
-Greatest improvements on subspecialties requiring clinical reasoning:
-| Subspecialty | Improvement with CE |
-|--------------|---------------------|
-| Physics | +33.9% |
-| Genitourinary | +20.1% |
-| Neuroradiology | +18.0% |
-| Gastrointestinal | +16.6% |
-## Usage
 ### Installation
 ```bash
-pip install sentence-transformers
 ```
 ### Basic Usage
@@ -113,206 +99,279 @@ pip install sentence-transformers
 ```python
 from sentence_transformers import CrossEncoder
-# Load model
-model = CrossEncoder('matulichpt/radlit-crossencoder')
-# Score query-document pairs
-pairs = [
-    ["What are the CT findings in pulmonary embolism?",
-     "CT pulmonary angiography shows filling defects in the pulmonary arteries..."],
-    ["What are the CT findings in pulmonary embolism?",
-     "MRI of the knee shows ACL tear with bone bruise pattern..."]
 ]
-scores = model.predict(pairs)
-print(scores)  # [0.92, 0.08] - higher score = more relevant
-```
-### Reranking Pipeline
-```python
-from sentence_transformers import SentenceTransformer, CrossEncoder
-import numpy as np
-# Load models
-biencoder = SentenceTransformer('matulichpt/radlit-biencoder')
-crossencoder = CrossEncoder('matulichpt/radlit-crossencoder')
-def retrieve_and_rerank(query, corpus, corpus_embeddings, top_k=10, rerank_k=50):
-    # Stage 1: Bi-encoder retrieval
-    query_embedding = biencoder.encode(query, convert_to_tensor=True)
-    cos_scores = util.cos_sim(query_embedding, corpus_embeddings)[0]
-    top_indices = torch.topk(cos_scores, k=rerank_k)[1].tolist()
-    # Stage 2: Cross-encoder reranking
-    candidates = [corpus[i] for i in top_indices]
-    pairs = [[query, doc] for doc in candidates]
-    ce_scores = crossencoder.predict(pairs)
-    # Apply temperature calibration (IMPORTANT: use T=1.5)
-    calibrated_scores = ce_scores / 1.5
-    # Sort and return top-k
-    sorted_indices = np.argsort(calibrated_scores)[::-1][:top_k]
-    return [(candidates[i], calibrated_scores[i]) for i in sorted_indices]
-# Example
-results = retrieve_and_rerank(
-    "What are the imaging features of hepatocellular carcinoma?",
-    corpus, corpus_embeddings
-)
 ```
-## Demo: Cross-Encoder Reranking
 ```python
-from sentence_transformers import CrossEncoder
 import numpy as np
-model = CrossEncoder('matulichpt/radlit-crossencoder')
-query = "What causes ring-enhancing brain lesions in AIDS patients?"
-# Candidates from bi-encoder retrieval (simulated)
-candidates = [
-    "In AIDS, toxoplasmosis shows ring-enhancing lesions in basal ganglia. CNS lymphoma is typically periventricular.",
-    "Brain metastases occur at gray-white junction and may show ring enhancement.",
-    "Glioblastoma is the most common primary brain malignancy.",
-]
-# Score each candidate
-pairs = [[query, doc] for doc in candidates]
-scores = model.predict(pairs)
-# Rank by relevance
-ranked = sorted(zip(candidates, scores), key=lambda x: x[1], reverse=True)
-print(f"Top result: {ranked[0][0][:80]}...")
-print(f"Score: {ranked[0][1]:.2f}")
-# The AIDS-specific answer ranks first despite shorter text
-```
-The cross-encoder correctly prioritizes the clinically relevant answer about AIDS-specific differentials.
-### Temperature Calibration
-**Important**: For optimal performance in score fusion, apply temperature scaling:
 ```python
-# Raw CE scores have higher variance than bi-encoder scores
-raw_scores = crossencoder.predict(pairs)
-# Temperature calibration aligns score distributions
-# T=1.5 found optimal through grid search
-calibrated_scores = raw_scores / 1.5
 ```
-This is critical when combining cross-encoder scores with bi-encoder scores.
-### Full RadLITE Fusion
 ```python
-def radlite_score(query, document, biencoder, crossencoder, bm25_score):
-    """
-    Full RadLITE scoring with optimal weights.
-    Optimal weights (found via grid search on RadLIT-9):
-    - Bi-encoder: 0.5
-    - Cross-encoder: 0.2
-    - BM25: 0.3
-    """
-    # Bi-encoder score
-    q_emb = biencoder.encode(query, convert_to_tensor=True)
-    d_emb = biencoder.encode(document, convert_to_tensor=True)
-    biencoder_score = float(util.cos_sim(q_emb, d_emb)[0][0])
-    # Cross-encoder score (calibrated)
-    ce_score = crossencoder.predict([[query, document]])[0] / 1.5
-    # Fusion
-    final_score = (
-        0.5 * biencoder_score +
-        0.2 * ce_score +
-        0.3 * bm25_score  # Normalized BM25
     )
-    return final_score
 ```
-## Technical Details
-### Why Temperature Calibration?
-Cross-encoder scores tend to be more extreme than bi-encoder similarity scores:
-| Score Type | Typical Range | Variance |
-|------------|---------------|----------|
-| Bi-encoder cosine | [0.3, 0.9] | Low |
-| Raw CE score | [-2, 3] | High |
-| Calibrated CE (T=1.5) | [-1.3, 2] | Medium |
-Without calibration, the CE dominates the fusion and degrades overall performance. Temperature 1.5 achieves ~0.7 correlation between score distributions.
-### Latency Considerations
-| Operation | Latency |
-|-----------|---------|
-| Single pair scoring | ~4ms |
-| 50 pairs (batch) | ~200-300ms |
-| Bi-encoder (50 docs) | ~80-120ms |
-For production use, consider:
-- Limiting rerank candidates (50 is optimal)
-- Batch processing
-- GPU acceleration
-## Intended Use
-### Primary Use Cases
-- Second-stage reranking for radiology retrieval
-- Relevance scoring for radiology Q&A
-- Fine-grained document ranking
-### Out-of-Scope Uses
-- First-stage retrieval (too slow for large corpora)
-- Non-radiology content
-- Clinical diagnosis
-## Limitations
-1. **Latency**: ~4ms per pair; not suitable for first-stage retrieval
-2. **Domain**: Optimized for radiology; limited generalization
-3. **Context Length**: 512 tokens max; long documents need truncation
-4. **Score Interpretation**: Requires calibration for fusion
-## Ethical Considerations
-- Not a diagnostic tool
-- Should be used to surface relevant educational content, not replace clinical judgment
-- May reflect biases in radiology literature
 ## Citation
 ```bibtex
-@software{radlit_crossencoder_2026,
-  title = {RadLIT-CrossEncoder: Radiology Reranking Model},
-  author = {Grai Team},
-  year = {2026},
-  url = {https://huggingface.co/matulichpt/radlit-crossencoder},
-  note = {+30% improvement on complex radiology queries}
 }
 ```
 ## Related Models
-- [RadLIT-BiEncoder](https://huggingface.co/matulichpt/radlit-biencoder) - First-stage retrieval
-- RadLITE Pipeline - Full retrieval system documentation
 ## License
-Apache 2.0 - Free for research and commercial use.
-## Contact
-For questions or collaboration: Open an issue on the model repository

 ---
+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/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 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/radlit-crossencoder", 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/radlit-biencoder",
+            device=device
+        )
+        # Stage 2: Precise reranker
+        self.reranker = CrossEncoder(
+            "matulichpt/radlit-crossencoder",
+            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/radlit-crossencoder", 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/radlit-crossencoder",
+    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/radlit-crossencoder},
+    note = {+30% MRR improvement on ACR Core Exam questions}
 }
 ```
 ## Related Models
+- [RadLITE-Encoder](https://huggingface.co/matulichpt/radlit-biencoder) - 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 CHANGED Viewed

@@ -10,12 +10,12 @@
   "hidden_dropout_prob": 0.1,
   "hidden_size": 384,
   "id2label": {
-    "0": "LABEL_0"
   },
   "initializer_range": 0.02,
   "intermediate_size": 1536,
   "label2id": {
-    "LABEL_0": 0
   },
   "layer_norm_eps": 1e-12,
   "max_position_embeddings": 512,

   "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,