How to use pratikmurali/pratik_cybersecurity_emb_ft with sentence-transformers:
from sentence_transformers import SentenceTransformer model = SentenceTransformer("pratikmurali/pratik_cybersecurity_emb_ft") sentences = [ "What are the key cybersecurity challenges in healthcare?", "Healthcare organizations face numerous security threats.", "Improving digital hygiene is important for medical devices.", "IoT security is critical for medical equipment.", "HIPAA regulations require strong data protection measures.", "Security breaches can lead to patient data exposure." ] embeddings = model.encode(sentences) similarities = model.similarity(embeddings, embeddings) print(similarities.shape) # [6, 6]
How to use pratikmurali/pratik_cybersecurity_emb_ft with Transformers:
# Load model directly from transformers import AutoTokenizer, AutoModel tokenizer = AutoTokenizer.from_pretrained("pratikmurali/pratik_cybersecurity_emb_ft") model = AutoModel.from_pretrained("pratikmurali/pratik_cybersecurity_emb_ft")
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