Feature Extraction
sentence-transformers
Safetensors
bert
sentence-similarity
kubernetes
operators
text-embeddings-inference
Instructions to use mmwanje/kubeops-bge-large-ops-issues with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- sentence-transformers
How to use mmwanje/kubeops-bge-large-ops-issues with sentence-transformers:
from sentence_transformers import SentenceTransformer model = SentenceTransformer("mmwanje/kubeops-bge-large-ops-issues") sentences = [ "The weather is lovely today.", "It's so sunny outside!", "He drove to the stadium." ] embeddings = model.encode(sentences) similarities = model.similarity(embeddings, embeddings) print(similarities.shape) # [3, 3] - Notebooks
- Google Colab
- Kaggle
kubeops-bge-large-ops-issues
Fine-tuned BAAI/bge-large-en-v1.5 encoder for Kubernetes operator issue text (GitHub issues and Stack Overflow posts).
Used in the KubeOps study to embed operator-related support text for taxonomy classification (configuration, logical enhancements, observability, security, other).
Training
- Objective: MultipleNegativesRankingLoss (contrastive learning on same-category pairs)
- Data: Labeled GitHub operator issues from the KubeOps corpus
- Backbone: Last 2 transformer layers + pooler unfrozen; remaining layers frozen
- Max sequence length: 128 tokens
- Epochs: 5
Usage
from sentence_transformers import SentenceTransformer
model = SentenceTransformer("mmwanje/kubeops-bge-large-ops-issues")
embeddings = model.encode(["RBAC ClusterRole binding fails on upgrade"])
Citation
If you use this model, please cite the KubeOps / Suture paper.
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Model tree for mmwanje/kubeops-bge-large-ops-issues
Base model
BAAI/bge-large-en-v1.5