Sentence Similarity
sentence-transformers
Safetensors
mpnet
feature-extraction
Generated from Trainer
dataset_size:100000
loss:CosineSimilarityLoss
text-embeddings-inference
Instructions to use milnico/Personality_Cross_Encoder with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- sentence-transformers
How to use milnico/Personality_Cross_Encoder with sentence-transformers:
from sentence_transformers import SentenceTransformer model = SentenceTransformer("milnico/Personality_Cross_Encoder") sentences = [ "Believe that unfortunate events occur because of bad luck.", "Had someone over for dinner.", "Avoid difficult reading material.", "Bought or picked flowers." ] embeddings = model.encode(sentences) similarities = model.similarity(embeddings, embeddings) print(similarities.shape) # [4, 4] - Notebooks
- Google Colab
- Kaggle
Update model metadata to set pipeline tag to the new `text-ranking`
#1 opened over 1 year ago
by
tomaarsen