Sentence Similarity
sentence-transformers
TensorBoard
Safetensors
bert
feature-extraction
dense
Generated from Trainer
dataset_size:42459
loss:TripletLoss
Eval Results (legacy)
text-embeddings-inference
Instructions to use jjstuart/rego-retrieval with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- sentence-transformers
How to use jjstuart/rego-retrieval with sentence-transformers:
from sentence_transformers import SentenceTransformer model = SentenceTransformer("jjstuart/rego-retrieval") sentences = [ "policy for how can i verify if a tekton task version is still supported by checking for the build.appstudio.redhat.com/expires-on annotation?", "Helper: lib.to_array\nSignature: to_array(s)\nDescription: ", "Helper: lib.pipelinerun_attestations\nSignature: pipelinerun_attestations\nDescription: ", "Helper: lib.k8s.name\nSignature: name(resource)\nDescription: " ] embeddings = model.encode(sentences) similarities = model.similarity(embeddings, embeddings) print(similarities.shape) # [4, 4] - Notebooks
- Google Colab
- Kaggle
File too large to display, you can check the raw version instead.