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README.md

@@ -5,121 +5,63 @@ tags:
 - feature-extraction
 - sentence-similarity
 - transformers
+- neuroradiology
+- medical
+license: apache-2.0
 ---
 
-# {MODEL_NAME}
+# NeuroBERT
 
-This is a [sentence-transformers](https://www.SBERT.net) model: It maps sentences & paragraphs to a 768 dimensional dense vector space and can be used for tasks like clustering or semantic search.
+A sentence-transformers model optimized for neuroradiology reports. Maps sentences to 768-dimensional embeddings for semantic similarity tasks.
 
-<!--- Describe your model here -->
+## Overview
 
-## Usage (Sentence-Transformers)
+NeuroBERT is a RoBERTa-based model with a **custom 10,000-word neuroradiology vocabulary** trained from scratch. Standard BERT tokenization fragments medical terms (e.g., "hemorrhage" → "he", "morr", "hage"), so we trained a domain-specific WordPiece vocabulary to preserve neuroradiologic terminology.
 
-Using this model becomes easy when you have [sentence-transformers](https://www.SBERT.net) installed:
+**Training:**
+1. **Masked language modeling** on neuroradiology reports (next sentence prediction omitted as adjacent sentences are often unrelated)
+2. **Radiology section matching** using a SentenceBERT twin-network architecture to align Findings and Summary sections from the same report
 
-```
-pip install -U sentence-transformers
-```
+## Usage
 
-Then you can use the model like this:
-
-```python
-from sentence_transformers import SentenceTransformer
-sentences = ["This is an example sentence", "Each sentence is converted"]
-
-model = SentenceTransformer('{MODEL_NAME}')
-embeddings = model.encode(sentences)
-print(embeddings)
+```bash
+pip install -U sentence-transformers
 ```
 
-
-
-## Usage (HuggingFace Transformers)
-Without [sentence-transformers](https://www.SBERT.net), you can use the model like this: First, you pass your input through the transformer model, then you have to apply the right pooling-operation on-top of the contextualized word embeddings.
-
 ```python
-from transformers import AutoTokenizer, AutoModel
-import torch
-
-
-#Mean Pooling - Take attention mask into account for correct averaging
-def mean_pooling(model_output, attention_mask):
-    token_embeddings = model_output[0] #First element of model_output contains all token embeddings
-    input_mask_expanded = attention_mask.unsqueeze(-1).expand(token_embeddings.size()).float()
-    return torch.sum(token_embeddings * input_mask_expanded, 1) / torch.clamp(input_mask_expanded.sum(1), min=1e-9)
-
-
-# Sentences we want sentence embeddings for
-sentences = ['This is an example sentence', 'Each sentence is converted']
-
-# Load model from HuggingFace Hub
-tokenizer = AutoTokenizer.from_pretrained('{MODEL_NAME}')
-model = AutoModel.from_pretrained('{MODEL_NAME}')
-
-# Tokenize sentences
-encoded_input = tokenizer(sentences, padding=True, truncation=True, return_tensors='pt')
-
-# Compute token embeddings
-with torch.no_grad():
-    model_output = model(**encoded_input)
-
-# Perform pooling. In this case, mean pooling.
-sentence_embeddings = mean_pooling(model_output, encoded_input['attention_mask'])
-
-print("Sentence embeddings:")
-print(sentence_embeddings)
-```
-
-
-
-## Evaluation Results
-
-<!--- Describe how your model was evaluated -->
-
-For an automated evaluation of this model, see the *Sentence Embeddings Benchmark*: [https://seb.sbert.net](https://seb.sbert.net?model_name={MODEL_NAME})
-
-
-## Training
-The model was trained with the parameters:
-
-**DataLoader**:
-
-`torch.utils.data.dataloader.DataLoader` of length 3101 with parameters:
-```
-{'batch_size': 14, 'sampler': 'torch.utils.data.sampler.RandomSampler', 'batch_sampler': 'torch.utils.data.sampler.BatchSampler'}
-```
-
-**Loss**:
-
-`sentence_transformers.losses.CosineSimilarityLoss.CosineSimilarityLoss` 
-
-Parameters of the fit()-Method:
-```
-{
-    "epochs": 10,
-    "evaluation_steps": 1000,
-    "evaluator": "sentence_transformers.evaluation.EmbeddingSimilarityEvaluator.EmbeddingSimilarityEvaluator",
-    "max_grad_norm": 1,
-    "optimizer_class": "<class 'transformers.optimization.AdamW'>",
-    "optimizer_params": {
-        "lr": 2e-05
-    },
-    "scheduler": "WarmupLinear",
-    "steps_per_epoch": null,
-    "warmup_steps": 100,
-    "weight_decay": 0.01
-}
+from sentence_transformers import SentenceTransformer, util
+
+model = SentenceTransformer('davvwood/NeuroBERT')
+
+# Reference templates for normal findings
+templates = [
+    'normal study',
+    'normal appearances of the brain',
+    'no intracranial abnormality identified'
+]
+template_embeddings = model.encode(templates)
+
+# Example reports
+reports = [
+    "mri head: there is restricted diffusion in the left paramedian ventral pons at the level of the middle cerebellar peduncle in keeping with an acute infarct.",
+    "mri head: the ventricles and extra cerebral csf spaces are of normal size. no focal intracranial abnormality has been identified. conclusion: normal intracranial appearances"
+]
+
+for report in reports:
+    report_embedding = model.encode(report)
+    similarities = [util.cos_sim(t_emb, report_embedding).item() for t_emb in template_embeddings]
+    print(f"Max similarity to normal templates: {max(similarities):.3f}")
 ```
 
+## Model Architecture
 
-## Full Model Architecture
 ```
 SentenceTransformer(
-  (0): Transformer({'max_seq_length': 512, 'do_lower_case': False}) with Transformer model: RobertaModel 
-  (1): Pooling({'word_embedding_dimension': 768, 'pooling_mode_cls_token': False, 'pooling_mode_mean_tokens': True, 'pooling_mode_max_tokens': False, 'pooling_mode_mean_sqrt_len_tokens': False})
+  (0): Transformer({'max_seq_length': 512, 'do_lower_case': False}) with RobertaModel
+  (1): Pooling({'word_embedding_dimension': 768, 'pooling_mode_mean_tokens': True})
 )
 ```
 
-## Citing & Authors
+## Citation
 
-<!--- Describe where people can find more information -->
+If you use NeuroBERT, please cite the associated paper.