Sentence Similarity
sentence-transformers
PyTorch
TensorFlow
Core ML
ONNX
Safetensors
OpenVINO
English
bert
mteb
Sentence Transformers
Eval Results (legacy)
text-embeddings-inference
Instructions to use novelcore/model15 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- sentence-transformers
How to use novelcore/model15 with sentence-transformers:
from sentence_transformers import SentenceTransformer model = SentenceTransformer("novelcore/model15") sentences = [ "That is a happy person", "That is a happy dog", "That is a very happy person", "Today is a sunny day" ] embeddings = model.encode(sentences) similarities = model.similarity(embeddings, embeddings) print(similarities.shape) # [4, 4] - Notebooks
- Google Colab
- Kaggle
File size: 617 Bytes
b769a64 | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 | {
"fileFormatVersion": "1.0.0",
"itemInfoEntries": {
"63B0B880-B145-44C1-9871-506E9D0C9935": {
"author": "com.apple.CoreML",
"description": "CoreML Model Specification",
"name": "model.mlmodel",
"path": "com.apple.CoreML/model.mlmodel"
},
"98B220AA-0669-4838-B6C4-38F80D1282C2": {
"author": "com.apple.CoreML",
"description": "CoreML Model Weights",
"name": "weights",
"path": "com.apple.CoreML/weights"
}
},
"rootModelIdentifier": "63B0B880-B145-44C1-9871-506E9D0C9935"
}
|