metadata
language: en
license: mit
library_name: sentence-transformers
pipeline_tag: sentence-similarity
tags:
- sentence-transformers
- feature-extraction
- sentence-similarity
- transformers
widget:
- source_sentence: What are the key cybersecurity challenges in healthcare?
sentences:
- 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.
Cybersecurity Fine-Tuned Embedding Model
This model is fine-tuned on cybersecurity documents with a focus on healthcare contexts. It can be used to encode sentences and paragraphs into fixed-size embeddings for tasks like semantic search, clustering, or similarity detection.
Usage
from sentence_transformers import SentenceTransformer
model = SentenceTransformer('pratikmurali/finetuned_artic_ft')
# Sentences are encoded by calling model.encode()
embeddings = model.encode(['How can healthcare organizations improve cybersecurity?'])
Training Details
- Fine-tuned from Snowflake/snowflake-arctic-embed-l
- Training dataset: Healthcare and cybersecurity documents
- Used MatryoshkaLoss with MultipleNegativesRankingLoss
- Dimensions: [768, 512, 256, 128, 64]