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
| { | |
| "word_embedding_dimension": 768, | |
| "pooling_mode_cls_token": false, | |
| "pooling_mode_mean_tokens": true, | |
| "pooling_mode_max_tokens": false, | |
| "pooling_mode_mean_sqrt_len_tokens": false, | |
| "pooling_mode_weightedmean_tokens": false, | |
| "pooling_mode_lasttoken": false, | |
| "include_prompt": true | |
| } |