Sentence Similarity
sentence-transformers
Safetensors
qwen3
v0.5
experiment
feature-extraction
dense
Generated from Trainer
dataset_size:81326
loss:MSELoss
Eval Results (legacy)
text-embeddings-inference
Instructions to use ZirTech/QKoli-0.6B-Embedding-exp-v0.5 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- sentence-transformers
How to use ZirTech/QKoli-0.6B-Embedding-exp-v0.5 with sentence-transformers:
from sentence_transformers import SentenceTransformer model = SentenceTransformer("ZirTech/QKoli-0.6B-Embedding-exp-v0.5") sentences = [ "can eating all bran flakes for breakfast help me lose weight", "what is an embryo sac?", "definition of salary sacrifice", "how much do therapeutic recreation specialist make" ] embeddings = model.encode(sentences) similarities = model.similarity(embeddings, embeddings) print(similarities.shape) # [4, 4] - Notebooks
- Google Colab
- Kaggle
- Downloads last month
- 42
Model tree for ZirTech/QKoli-0.6B-Embedding-exp-v0.5
Evaluation results
- Pearson Cosine on valself-reported0.260
- Spearman Cosine on valself-reported0.289