Sentence Similarity
sentence-transformers
Safetensors
bert
feature-extraction
Generated from Trainer
dataset_size:2240
loss:TripletLoss
loss:CosineSimilarityLoss
text-embeddings-inference
Instructions to use HydroEmbed/HydroEmbed-FITB-MiniLM-DualLoss with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- sentence-transformers
How to use HydroEmbed/HydroEmbed-FITB-MiniLM-DualLoss with sentence-transformers:
from sentence_transformers import SentenceTransformer model = SentenceTransformer("HydroEmbed/HydroEmbed-FITB-MiniLM-DualLoss") sentences = [ "The groundwater residence time in the Chashma-Mianwali area was estimated to be in the range of _____ years based on Tracer Lump Parameter Model (LPM) and Chlorofluorocarbons (CFCs) data.", "14–59", "point scale; radar data", "dissolved oxygen" ] embeddings = model.encode(sentences) similarities = model.similarity(embeddings, embeddings) print(similarities.shape) # [4, 4] - Notebooks
- Google Colab
- Kaggle
File size: 368 Bytes
8260157 | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 | [
{
"idx": 0,
"name": "0",
"path": "",
"type": "sentence_transformers.models.Transformer"
},
{
"idx": 1,
"name": "1",
"path": "1_Pooling",
"type": "sentence_transformers.models.Pooling"
},
{
"idx": 2,
"name": "2",
"path": "2_Normalize",
"type": "sentence_transformers.models.Normalize"
}
] |