Sentence Similarity
sentence-transformers
Safetensors
English
mpnet
feature-extraction
dense
Generated from Trainer
dataset_size:3000
loss:BatchAllTripletLoss
text-embeddings-inference
Instructions to use bnoland/mpnet-base-clinc-subset with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- sentence-transformers
How to use bnoland/mpnet-base-clinc-subset with sentence-transformers:
from sentence_transformers import SentenceTransformer model = SentenceTransformer("bnoland/mpnet-base-clinc-subset") sentences = [ "what am i supposed to do if i lost my luggage", "do i need a visa if i go there", "why did you freeze my bank account", "tell my bank that i'm travelling to france in 2 days" ] embeddings = model.encode(sentences) similarities = model.similarity(embeddings, embeddings) print(similarities.shape) # [4, 4] - Notebooks
- Google Colab
- Kaggle
| [ | |
| { | |
| "idx": 0, | |
| "name": "0", | |
| "path": "", | |
| "type": "sentence_transformers.models.Transformer" | |
| }, | |
| { | |
| "idx": 1, | |
| "name": "1", | |
| "path": "1_Pooling", | |
| "type": "sentence_transformers.models.Pooling" | |
| } | |
| ] |