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
File size: 229 Bytes
58deb94 | 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"
}
] |