Sentence Similarity
sentence-transformers
Safetensors
bert
feature-extraction
Generated from Trainer
dataset_size:100
loss:TripletLoss
text-embeddings-inference
Instructions to use DariaaaS/models-moved with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- sentence-transformers
How to use DariaaaS/models-moved with sentence-transformers:
from sentence_transformers import SentenceTransformer model = SentenceTransformer("DariaaaS/models-moved") sentences = [ "What is the CBSA name and type in York, ME?", "average household income refers to avg_income_per_household; city known as \"Danzig\" refers to bad_alias = 'Danzig';", "coordinates refers to latitude, longitude; latitude = '18.090875; longitude = '-66.867756'", "\"York\" is the city; 'ME' is the state; type refers to CBSA_type" ] embeddings = model.encode(sentences) similarities = model.similarity(embeddings, embeddings) print(similarities.shape) # [4, 4] - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 5b1eebf28d51b425919503bde32f76a54f1055eb4b3e3f6a03946174a9a2060d
- Size of remote file:
- 17.1 MB
- SHA256:
- cad551d5600a84242d0973327029452a1e3672ba6313c2a3c3d69c4310e12719
·
Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.