Sentence Similarity
sentence-transformers
Safetensors
bert
feature-extraction
compliance
nist-800-53
hipaa
cybersecurity
text-embeddings-inference
Instructions to use stetteh/regmap-embedder with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- sentence-transformers
How to use stetteh/regmap-embedder with sentence-transformers:
from sentence_transformers import SentenceTransformer model = SentenceTransformer("stetteh/regmap-embedder") sentences = [ "That is a happy person", "That is a happy dog", "That is a very happy person", "Today is a sunny day" ] embeddings = model.encode(sentences) similarities = model.similarity(embeddings, embeddings) print(similarities.shape) # [4, 4] - Notebooks
- Google Colab
- Kaggle
| """Quick start: map a few NIST SP 800-53 controls to HIPAA provisions with RegMap.""" | |
| from regmap_map import map_control | |
| CONTROLS = [ | |
| "The organization enforces multi-factor authentication for remote access.", | |
| "Employ integrity verification tools to detect unauthorized changes to software and firmware.", | |
| "Retain audit records for a defined period to support after-the-fact investigations.", | |
| ] | |
| for control in CONTROLS: | |
| print("\nNIST control:", control) | |
| for r in map_control(control, top_k=3): | |
| print(f" {r['score']:.3f} {r['hipaa_citation']}") | |