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] - Notebooks
- Google Colab
- Kaggle
Download rng_state_0.pth from cisco-ai/SecureBERT2.0-cross_encoder: direct link, hf CLI and curl.
- Browser
- Download file 16.4 kB
-
https://huggingface.co/cisco-ai/SecureBERT2.0-cross_encoder/resolve/main/rng_state_0.pth
- Command line
-
hf download hf://cisco-ai/SecureBERT2.0-cross_encoder/rng_state_0.pth
-
curl -L -o rng_state_0.pth https://huggingface.co/cisco-ai/SecureBERT2.0-cross_encoder/resolve/main/rng_state_0.pth
16.4 kB
- Xet hash:
- 025c078882364ada68ec4ecca97502aa7e93f62fb8ac282bdfce1ebaf7061a15
- Size of remote file:
- 16.4 kB
- SHA256:
- 243a4925489c190ee58a88c7361972d99c4762450fc884f52e3e46de42986d61
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