Sentence Similarity
sentence-transformers
Joblib
Safetensors
modernbert
security
intrusion-detection
behavior-analytics
intent-recognition
linux
kubernetes
audit-log
text-embeddings-inference
Instructions to use willchen0011/SecEBL with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- sentence-transformers
How to use willchen0011/SecEBL with sentence-transformers:
from sentence_transformers import SentenceTransformer model = SentenceTransformer("willchen0011/SecEBL") 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
| SecEBL-Rev20 | |
| Copyright 2026 SecEBL contributors. | |
| This Hugging Face repository contains SecEBL-Rev20 model artifacts, schema | |
| metadata, public benchmark-subset examples, public documentation, and an | |
| experimental L2 artifact. These files are licensed under Apache License, | |
| Version 2.0. See LICENSE. | |
| SecEBL-Rev20 is based on Alibaba-NLP/gte-modernbert-base, which is licensed | |
| under Apache License, Version 2.0: | |
| https://huggingface.co/Alibaba-NLP/gte-modernbert-base | |
| Source code, schemas, public examples, documentation, and helper scripts in the | |
| companion GitHub repository are also licensed under Apache-2.0 unless a file | |
| explicitly states otherwise. | |
| The names SecEBL and SecEBL-Rev20 are not licensed as trademarks except for | |
| reasonable and customary attribution. | |