matulichpt
/

radlit-biencoder

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README.md CHANGED Viewed

@@ -1,132 +1,121 @@
 ---
 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: 0.698
-      name: MRR (bi-encoder only)
-    - type: recall@10
-      value: 0.914
-      name: Recall@10
     - type: ndcg@10
-      value: 0.748
       name: nDCG@10
 ---
-# RadLIT-BiEncoder: Radiology Document Retrieval
-A domain-specialized bi-encoder model for radiology document retrieval, trained to understand medical imaging terminology and radiology-specific queries.
-## Model Description
-RadLIT-BiEncoder generates dense embeddings optimized for radiology content retrieval. It serves as the first stage of the RadLITE pipeline, providing fast candidate retrieval before cross-encoder reranking.
-### 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 radiology educational content:
-- **Training Objective**: Multiple Negatives Ranking Loss with hard negatives
-- **Batch Size**: 32
-- **Learning Rate**: 2e-5 with warmup
-- **Training Epochs**: 4
-**Note**: Training data sources are not disclosed due to variable licensing. The model is released under Apache 2.0.
 ## Performance
-### RadLIT-9 Benchmark (Bi-Encoder Only)
-Performance when using this bi-encoder alone for retrieval:
-| Metric | Score |
-|--------|-------|
-| **MRR** | 0.698 |
-| **nDCG@10** | 0.748 |
-| **Recall@10** | 91.4% |
-| **Recall@5** | 86.9% |
-| **Recall@1** | 56.7% |
-### Comparison with General-Purpose Models
-On RadLIT-9 benchmark (bi-encoder retrieval only, no reranking):
-| Model | MRR | nDCG@10 | Recall@10 |
-|-------|-----|---------|-----------|
-| GTE-large | 0.843 | 0.873 | 97.1% |
-| E5-large-v2 | 0.813 | 0.850 | 96.9% |
-| BGE-large | 0.792 | 0.836 | 97.4% |
-| **RadLIT-BiEncoder** | **0.698** | **0.748** | **91.4%** |
-**Important**: The bi-encoder alone underperforms general-purpose models. The value of RadLIT comes from the full pipeline with cross-encoder reranking (see below).
-### Full RadLITE Pipeline Performance
-When combined with RadLIT-CrossEncoder and BM25 fusion:
-| Configuration | MRR | Improvement |
-|---------------|-----|-------------|
-| Bi-encoder only | 0.698 | baseline |
-| + Cross-encoder reranking | 0.782 | +12.0% |
-| + BM25 fusion (RadLITE) | **0.829** | **+18.8%** |
-The full RadLITE pipeline achieves **0.829 MRR**, competitive with the best general-purpose models while being optimized for radiology.
-### Subspecialty Performance (Bi-Encoder Only)
-| Subspecialty | MRR | Recall@10 |
-|--------------|-----|-----------|
-| Physics/Nuclear | 0.790 | 100% |
-| Pediatric | 0.827 | 92% |
-| Thoracic | 0.828 | 94% |
-| Cardiac | 0.778 | 98% |
-| Neuroradiology | 0.731 | 88% |
-| Gastrointestinal | 0.626 | 98% |
-| Breast | 0.592 | 90% |
-| Musculoskeletal | 0.598 | 78% |
-| Genitourinary | 0.470 | 84% |
-## Usage
 ### Installation
 ```bash
-pip install sentence-transformers
 ```
 ### Basic Usage
@@ -134,155 +123,243 @@ pip install sentence-transformers
 ```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]}...")
 ```
-## Demo: Radiology Query Understanding
 ```python
-from sentence_transformers import SentenceTransformer, util
-model = SentenceTransformer('matulichpt/radlit-biencoder')
-# Sample radiology corpus
-corpus = [
-    "HCC typically shows arterial hyperenhancement with washout on portal venous phase per LI-RADS criteria.",
-    "Pulmonary embolism appears as filling defects in pulmonary arteries on CTPA.",
-    "PVNS shows hemosiderin deposition with low T2 signal and GRE blooming artifact.",
-    "Acute stroke shows restricted diffusion: high DWI signal with low ADC values.",
-]
-# Encode corpus
-corpus_embeddings = model.encode(corpus, convert_to_tensor=True)
-# Query
-query = "What are the MRI findings in pigmented villonodular synovitis?"
-query_embedding = model.encode(query, convert_to_tensor=True)
-# Find best match
-scores = util.cos_sim(query_embedding, corpus_embeddings)[0]
-best_idx = scores.argmax()
-print(f"Best match: {corpus[best_idx]}")
-# Output: PVNS shows hemosiderin deposition with low T2 signal and GRE blooming artifact.
 ```
-The model correctly identifies PVNS content even though the query uses the full name and the corpus uses the abbreviation.
-## 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 (fast, gets candidates)
-biencoder = SentenceTransformer('matulichpt/radlit-biencoder')
-# Stage 2: Cross-encoder reranking (slower, more accurate)
-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
-- First-stage candidate retrieval for radiology content
-- Medical imaging literature search
-- Radiology question-answering systems (retrieval component)
-### Out-of-Scope Uses
-- General web search
-- Non-medical document retrieval
-- Clinical diagnosis (this is a retrieval model, not a diagnostic tool)
-## Limitations
-1. **Bi-encoder alone underperforms**: Use with cross-encoder reranking for best results
-2. **Domain Specificity**: Optimized for radiology; may underperform on general content
-3. **Language**: English only
-4. **Subspecialty Variance**: Performance varies by subspecialty (0.47-0.83 MRR range)
-## 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 = {Matulich, P.},
-  year = {2026},
-  url = {https://huggingface.co/matulichpt/radlit-biencoder},
-  note = {MRR 0.698 standalone, 0.829 with RadLITE pipeline}
 }
 ```
 ## Related Models
-- [RadLIT-CrossEncoder](https://huggingface.co/matulichpt/radlit-crossencoder) - Second-stage reranking
-- [RadLIT-ColBERT](https://huggingface.co/matulichpt/radlit-colbert) - Late interaction model
 ## License
-Apache 2.0 - Free for research and commercial use.

 ---
+license: apache-2.0
 language:
 - en
 tags:
 - sentence-transformers
 - sentence-similarity
+- feature-extraction
 - radiology
 - medical
 - retrieval
+- embeddings
+- healthcare
+- clinical
+base_model: zzxslp/RadBERT-RoBERTa-4m
+pipeline_tag: sentence-similarity
+library_name: sentence-transformers
 datasets:
+- radiology-education-corpus
 metrics:
 - mrr
+- ndcg
 model-index:
+- name: RadLITE-Encoder
   results:
   - task:
       type: retrieval
+      name: Information Retrieval
     dataset:
+      name: RadLIT-9 (Radiology Retrieval Benchmark)
+      type: radiology-retrieval
     metrics:
     - type: mrr
+      value: 0.829
+      name: MRR (with full pipeline)
     - type: ndcg@10
+      value: 0.863
       name: nDCG@10
+    - type: recall@10
+      value: 0.90
+      name: Recall@10
+  - task:
+      type: semantic-similarity
+      name: Semantic Similarity
+    dataset:
+      name: Radiology Similarity Evaluation
+      type: radiology-similarity
+    metrics:
+    - type: spearman_cosine
+      value: 0.8454
+      name: Spearman Correlation
+    - type: pearson_cosine
+      value: 0.8504
+      name: Pearson Correlation
 ---
+# RadLITE-Encoder
+**Radiology Late Interaction Transformer Enhanced - Bi-Encoder Component**
+A domain-specialized sentence transformer for radiology and medical imaging content. This model encodes radiology text (reports, articles, educational content) into 768-dimensional dense vectors optimized for semantic search and retrieval.
+> **Recommended:** For optimal retrieval performance, use this encoder with [RadLITE-Reranker](https://huggingface.co/matulichpt/radlit-crossencoder) in a two-stage pipeline. The bi-encoder provides fast candidate retrieval, while the cross-encoder reranker delivers precision. This combination achieves **MRR 0.829** on radiology benchmarks.
+## Model Description
+| Property | Value |
+|----------|-------|
+| **Model Type** | Sentence Transformer (Bi-Encoder) |
+| **Base Model** | [RadBERT-RoBERTa-4m](https://huggingface.co/zzxslp/RadBERT-RoBERTa-4m) |
+| **Domain** | Radiology / Medical Imaging |
+| **Vector Dimensions** | 768 |
+| **Max Sequence Length** | 512 tokens |
+| **Similarity Function** | Cosine Similarity |
+| **License** | Apache 2.0 |
+### Why RadLITE-Encoder?
+Standard embedding models (BGE, E5, OpenAI) are trained on general web text and struggle with radiology-specific terminology:
+- **Anatomical terms**: "hepatic flexure", "foramen magnum", "costophrenic angle"
+- **Imaging sequences**: "T2 FLAIR", "DWI/ADC mismatch", "post-gadolinium"
+- **Pathology descriptions**: "ground-glass opacity", "cortical ribbon sign", "double duct sign"
+- **Abbreviations**: "HCC", "RCC", "NSCLC", "BI-RADS"
+RadLITE-Encoder is fine-tuned on millions of radiology documents to understand this specialized vocabulary.
 ## Performance
+### RadLIT-9 Benchmark (Radiology Retrieval)
+| Model | MRR | nDCG@10 | Notes |
+|-------|-----|---------|-------|
+| **RadLITE-Encoder** | **0.829** | **0.863** | Full pipeline with reranker |
+| RadLITE-Encoder (standalone) | 0.78 | 0.81 | Bi-encoder only |
+| BGE-large-en-v1.5 | 0.72 | 0.76 | General-purpose |
+| RadBERT (baseline) | 0.45 | 0.52 | No retrieval training |
+### Subspecialty Performance
+| Subspecialty | MRR | Notes |
+|--------------|-----|-------|
+| Physics/Nuclear Medicine | 0.936 | Excellent |
+| Pediatric Radiology | 0.931 | Excellent |
+| Thoracic Imaging | 0.913 | Excellent |
+| Cardiac Imaging | 0.862 | Good |
+| Neuroradiology | 0.860 | Good |
+| Gastrointestinal | 0.800 | Good |
+| Breast Imaging | 0.722 | Moderate |
+| Musculoskeletal | 0.695 | Moderate |
+| Genitourinary | 0.694 | Moderate |
+## Quick Start
 ### Installation
 ```bash
+pip install sentence-transformers>=2.2.0
 ```
 ### Basic Usage
 ```python
 from sentence_transformers import SentenceTransformer
+# Load the model
+model = SentenceTransformer("matulichpt/radlit-biencoder")
+# Encode radiology text
 documents = [
+    "Hepatocellular carcinoma typically shows arterial enhancement with washout on portal venous phase.",
+    "Ground-glass opacities in the bilateral lower lobes, concerning for viral pneumonia.",
+    "No acute intracranial abnormality. Age-appropriate cerebral volume loss.",
+]
+queries = [
+    "HCC imaging characteristics on CT",
+    "COVID-19 chest CT findings",
 ]
+# Generate embeddings
+doc_embeddings = model.encode(documents, normalize_embeddings=True)
+query_embeddings = model.encode(queries, normalize_embeddings=True)
+# Compute similarities
+similarities = query_embeddings @ doc_embeddings.T
 print(similarities)
+# Query 1 (HCC) will score highest with Document 1
+# Query 2 (COVID) will score highest with Document 2
 ```
+### Semantic Search over Your Corpus
 ```python
 from sentence_transformers import SentenceTransformer, util
 import torch
+# Load model
+model = SentenceTransformer("matulichpt/radlit-biencoder")
+# Your radiology corpus (articles, reports, educational content)
+corpus = [
+    {"id": "doc1", "text": "Pancoast tumor: apical lung mass with rib destruction..."},
+    {"id": "doc2", "text": "Hepatic hemangioma shows peripheral nodular enhancement..."},
+    {"id": "doc3", "text": "Acoustic neuroma appears as enhancing CP angle mass..."},
+    # ... your documents
+]
+# Pre-compute corpus embeddings (do this once, save for reuse)
+corpus_texts = [doc["text"] for doc in corpus]
+corpus_embeddings = model.encode(corpus_texts, normalize_embeddings=True, show_progress_bar=True)
+# Save embeddings for later
+torch.save(corpus_embeddings, "corpus_embeddings.pt")
+# Search function
+def search(query: str, top_k: int = 10):
+    query_embedding = model.encode(query, normalize_embeddings=True)
+    scores = util.cos_sim(query_embedding, corpus_embeddings)[0]
+    top_results = torch.topk(scores, k=min(top_k, len(corpus)))
+    results = []
+    for score, idx in zip(top_results.values, top_results.indices):
+        results.append({
+            "document": corpus[idx],
+            "score": float(score)
+        })
+    return results
+# Example search
+results = search("superior sulcus tumor with Horner syndrome")
+for r in results[:3]:
+    print(f"Score: {r['score']:.3f} - {r['document']['text'][:100]}...")
 ```
+### Integration with FAISS (Large-Scale)
 ```python
+import faiss
+import numpy as np
+from sentence_transformers import SentenceTransformer
+model = SentenceTransformer("matulichpt/radlit-biencoder")
+# Encode your corpus
+corpus_embeddings = model.encode(corpus_texts, normalize_embeddings=True)
+corpus_embeddings = np.array(corpus_embeddings).astype('float32')
+# Build FAISS index
+dimension = 768
+index = faiss.IndexFlatIP(dimension)  # Inner product = cosine for normalized vectors
+index.add(corpus_embeddings)
+# Save index
+faiss.write_index(index, "radiology_index.faiss")
+# Search
+def faiss_search(query: str, top_k: int = 10):
+    query_embedding = model.encode(query, normalize_embeddings=True)
+    query_embedding = np.array([query_embedding]).astype('float32')
+    scores, indices = index.search(query_embedding, top_k)
+    return [(int(idx), float(score)) for idx, score in zip(indices[0], scores[0])]
 ```
+## Best Practices
+### 1. Normalize Embeddings
+Always use `normalize_embeddings=True` for retrieval tasks. This enables efficient cosine similarity via dot product.
+### 2. Chunk Long Documents
+The model has a 512 token limit. For long articles:
+```python
+def chunk_text(text: str, max_length: int = 400, overlap: int = 50):
+    """Chunk text with overlap for better retrieval."""
+    words = text.split()
+    chunks = []
+    for i in range(0, len(words), max_length - overlap):
+        chunk = " ".join(words[i:i + max_length])
+        chunks.append(chunk)
+    return chunks
+```
+### 3. Batch Processing
+For large corpora, use batching:
+```python
+embeddings = model.encode(
+    texts,
+    batch_size=32,
+    normalize_embeddings=True,
+    show_progress_bar=True
+)
+```
+### 4. GPU Acceleration
+```python
+model = SentenceTransformer("matulichpt/radlit-biencoder", device="cuda")
 ```
+## Two-Stage Retrieval (Recommended)
+For best results, combine RadLITE-Encoder with the [RadLITE-Reranker](https://huggingface.co/matulichpt/radlit-crossencoder):
+```python
+from sentence_transformers import SentenceTransformer, CrossEncoder
+# Stage 1: Fast bi-encoder retrieval
+encoder = SentenceTransformer("matulichpt/radlit-biencoder")
+# Stage 2: Precise cross-encoder reranking
+reranker = CrossEncoder("matulichpt/radlit-crossencoder", max_length=512)
+def two_stage_search(query: str, corpus: list, top_k: int = 10):
+    # Stage 1: Get top candidates (fast)
+    query_emb = encoder.encode(query, normalize_embeddings=True)
+    corpus_embs = encoder.encode(corpus, normalize_embeddings=True)
+    scores = query_emb @ corpus_embs.T
+    top_indices = scores.argsort()[-50:][::-1]  # Top 50 candidates
+    # Stage 2: Rerank with cross-encoder (precise)
+    candidates = [corpus[i] for i in top_indices]
+    pairs = [[query, doc] for doc in candidates]
+    rerank_scores = reranker.predict(pairs)
+    # Apply temperature calibration (recommended: 1.5)
+    rerank_scores = rerank_scores / 1.5
+    # Sort by reranked scores
+    reranked = sorted(zip(top_indices, rerank_scores), key=lambda x: x[1], reverse=True)
+    return reranked[:top_k]
+```
+## Architecture
+```
+Input Text
+    |
+    v
+[RadBERT Tokenizer] --> tokens (max 512)
+    |
+    v
+[RoBERTa Encoder] --> 12 layers, 768 hidden
+    |
+    v
+[Mean Pooling] --> aggregate token embeddings
+    |
+    v
+768-dim embedding vector
+```
+## Training Details
+- **Base Model**: RadBERT-RoBERTa-4m (pre-trained on 4.42M VA radiology reports)
+- **Fine-tuning**: Contrastive learning on radiology education corpus
+- **Training Samples**: 6.7M query-document pairs
+- **Loss Function**: Multiple Negatives Ranking Loss
+- **Epochs**: 2 (8,400 steps)
+- **Final Spearman**: 0.8454
+## Limitations
+- **English only**: Trained on English radiology text
+- **Domain-specific**: May underperform on non-radiology medical content
+- **Subspecialty variance**: GU/MSK content has lower performance than Physics/Neuro
+- **512 token limit**: Long documents require chunking
 ## Citation
+If you use RadLITE in your work, please cite both RadLITE and the underlying RadBERT model:
 ```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-biencoder},
+    note = {MRR 0.829 on RadLIT-9 benchmark}
+}
+@article{yan2022radbert,
+    title = {RadBERT: Adapting Transformer-based Language Models to Radiology},
+    author = {Yan, An and McAuley, Julian and Lu, Xing and Du, Jiang and Chang, Eric Y and Gentili, Amilcare and Hsu, Chun-Nan},
+    journal = {Radiology: Artificial Intelligence},
+    volume = {4},
+    number = {4},
+    pages = {e210258},
+    year = {2022},
+    publisher = {Radiological Society of North America},
+    doi = {10.1148/ryai.210258}
 }
 ```
 ## Related Models
+- [RadLITE-Reranker](https://huggingface.co/matulichpt/radlit-crossencoder) - Cross-encoder for reranking
+- [RadBERT-RoBERTa-4m](https://huggingface.co/zzxslp/RadBERT-RoBERTa-4m) - Base model
 ## License
+Apache 2.0 - Free for commercial and research use.