A sentence-transformers/all-MiniLM-L6-v2 model fine-tuned on medical text for the HealthMate-AI project (a medical RAG chatbot). This checkpoint is the ensemble (weight average) of 3 cross-validation folds trained on medical domain data.

Maps sentences and paragraphs to a 384-dimensional dense vector space for semantic search and retrieval over medical documents.

Usage

python

from sentence_transformers import SentenceTransformer

model = SentenceTransformer("yogvidwankhede/healthmate-minilm-l6-v2-medical-3fold")
embeddings = model.encode([
    "What are the symptoms of type 2 diabetes?",
    "Metformin is a first-line medication for type 2 diabetes.",
])
print(embeddings.shape)  # (2, 384)

Training

Base model: all-MiniLM-L6-v2 (384-dim, 6-layer MiniLM)
Fine-tuned with 3-fold cross-validation on medical corpus text extracted from medical reference books
This model averages the weights of all 3 fold checkpoints

Intended use and limitations

Embedding/retrieval component of a medical RAG system. Research/educational use. Not a substitute for professional medical advice.