Sentence Similarity
sentence-transformers
Safetensors
English
modernbert
IR
reranking
securebert
docembedding
text-embeddings-inference
Instructions to use cisco-ai/SecureBERT2.0-cross_encoder with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- sentence-transformers
How to use cisco-ai/SecureBERT2.0-cross_encoder with sentence-transformers:
from sentence_transformers import SentenceTransformer model = SentenceTransformer("cisco-ai/SecureBERT2.0-cross_encoder") 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] - Inference
- Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 094cd1559f4f8c4262b305248b033ee7e13efdba6abf1acab7f293e5cc8d74de
- Size of remote file:
- 16.4 kB
- SHA256:
- f59970869f06ddf9a28a0736381e30ba7b2bfb9cba0beb65c41a0e81c4751064
·
Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.