Sentence Similarity
sentence-transformers
Safetensors
mpnet
ontology-embedding
hyperbolic-space
hierarchical-reasoning
biomedical-ontology
Generated from Trainer
dataset_size:150000
loss:HierarchyTransformerLoss
text-embeddings-inference
Instructions to use Hui97/OnT-MPNet-go with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- sentence-transformers
How to use Hui97/OnT-MPNet-go with sentence-transformers:
from sentence_transformers import SentenceTransformer model = SentenceTransformer("Hui97/OnT-MPNet-go") sentences = [ "cellular response to stimulus", "response to stimulus", "medial transverse frontopolar gyrus", "biological regulation" ] embeddings = model.encode(sentences) similarities = model.similarity(embeddings, embeddings) print(similarities.shape) # [4, 4] - Notebooks
- Google Colab
- Kaggle
File size: 229 Bytes
f90701e | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 | [
{
"idx": 0,
"name": "0",
"path": "",
"type": "sentence_transformers.models.Transformer"
},
{
"idx": 1,
"name": "1",
"path": "1_Pooling",
"type": "sentence_transformers.models.Pooling"
}
] |