Sentence Similarity
sentence-transformers
Safetensors
bert
feature-extraction
dense
Generated from Trainer
dataset_size:32
loss:CosineSimilarityLoss
text-embeddings-inference
Instructions to use yagosys/cloudinit-embedding with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- sentence-transformers
How to use yagosys/cloudinit-embedding with sentence-transformers:
from sentence_transformers import SentenceTransformer model = SentenceTransformer("yagosys/cloudinit-embedding") sentences = [ "cloud init", "EC2 instance user data", "CFT parameters", "user data" ] embeddings = model.encode(sentences) similarities = model.similarity(embeddings, embeddings) print(similarities.shape) # [4, 4] - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 3e7f284cfd9ee09f2ffd4744fed9620ec0e91a33b1d19c3271cd1da82ee005b8
- Size of remote file:
- 90.9 MB
- SHA256:
- f13147cd7edef87394ef4d8f7f8b203651cca52a567577316ba6d64b993eb209
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