adamleeit/HIResBERT-sentence

Healthcare Interoperability Sentence Transformer based on PubMedBERT

This is a Sentence Transformer model derived from our domain-adapted adamleeit/pubmedbert-interop-mlm. It's specifically designed for generating embeddings for healthcare interoperability text.

Model Details

• Base model: Domain-adapted PubMedBERT-MS-MARCO
• Task: Generating document/sentence embeddings
• Output dimension: 768
• Optimal for: Healthcare interoperability topic modeling and semantic search

Usage

from sentence_transformers import SentenceTransformer

# Load model
model = SentenceTransformer("adamleeit/pubmedbert-interop-sentence")

# Generate embeddings for a single sentence or document
embedding = model.encode("FHIR enables healthcare information exchange between systems.")

# Generate embeddings for multiple documents
documents = [
    "FHIR is an HL7 standard for health information exchange.",
    "SNOMED CT provides a comprehensive clinical terminology.",
    "Interoperability frameworks enable secure data sharing between EHRs."
]
embeddings = model.encode(documents)

Recommended Use Cases

This model is optimized for:

• Topic modeling healthcare interoperability documents
• Semantic search of medical standards and terminology
• Document clustering for health information exchange literature
• Calculating semantic similarity between healthcare interoperability concepts

Related Models

• adamleeit/pubmedbert-interop-mlm: The base masked language model from which this sentence transformer was derived
• adamleeit/biolink-lg-interop-ner-model: Our NER model for identifying healthcare interoperability entities

Citation

If you use this model in your research, please cite:

@misc{pubmedbert-interop-sentence,
  author = {Lee AM},
  title = {Healthcare Interoperability Sentence Transformer based on PubMedBERT},
  year = {2025},
  publisher = {Hugging Face},
  howpublished = {\url{https://huggingface.co/adamleeit/pubmedbert-interop-sentence}}
}