Sentence Similarity
sentence-transformers
Safetensors
English
distilbert
feature-extraction
Generated from Trainer
dataset_size:100000
loss:MultipleNegativesRankingLoss
loss:CachedMultipleNegativesRankingLoss
Eval Results (legacy)
text-embeddings-inference
Instructions to use kwondw/quora-mnrl with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- sentence-transformers
How to use kwondw/quora-mnrl with sentence-transformers:
from sentence_transformers import SentenceTransformer model = SentenceTransformer("kwondw/quora-mnrl") sentences = [ "What is the main circuit board of a computer? How is it built and what is its function?", "Information systems are too important to be left to computer specialist. Do you agree?", "What is the main circuit board of a computer? What are its functions?", "Has reading a book ever changed your life? Which one?" ] embeddings = model.encode(sentences) similarities = model.similarity(embeddings, embeddings) print(similarities.shape) # [4, 4] - Notebooks
- Google Colab
- Kaggle
| { | |
| "embedding_dimension": 768, | |
| "pooling_mode": "mean", | |
| "include_prompt": true | |
| } |