Sentence Similarity
sentence-transformers
PyTorch
Transformers
xlm-roberta
feature-extraction
text-embeddings-inference
Instructions to use BlueAvenir/sti_cyber_security_model_updated with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- sentence-transformers
How to use BlueAvenir/sti_cyber_security_model_updated with sentence-transformers:
from sentence_transformers import SentenceTransformer model = SentenceTransformer("BlueAvenir/sti_cyber_security_model_updated") 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] - Transformers
How to use BlueAvenir/sti_cyber_security_model_updated with Transformers:
# Load model directly from transformers import AutoTokenizer, AutoModel tokenizer = AutoTokenizer.from_pretrained("BlueAvenir/sti_cyber_security_model_updated") model = AutoModel.from_pretrained("BlueAvenir/sti_cyber_security_model_updated", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- a7ec1dc2f02fc8618bb28ad91185099806fca243804090d68d0a5bd665602326
- Size of remote file:
- 1.11 GB
- SHA256:
- f1bca080f67cb5bef64d87640f65355617f07f96a78a5644bac2149fada85288
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