Sentence Similarity
sentence-transformers
Safetensors
bert
feature-extraction
dataset_size:10K<n<100K
loss:BatchAllTripletLoss
text-embeddings-inference
Instructions to use abideen/router-embedding with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- sentence-transformers
How to use abideen/router-embedding with sentence-transformers:
from sentence_transformers import SentenceTransformer model = SentenceTransformer("abideen/router-embedding") sentences = [ "How do bees make honey?", "How do plants make their food?", "How do the themes of transience and human triumph over it manifest in the story?", "Discuss the significance of the mentorship program in Sarah's professional growth within the company." ] embeddings = model.encode(sentences) similarities = model.similarity(embeddings, embeddings) print(similarities.shape) # [4, 4] - Notebooks
- Google Colab
- Kaggle
File size: 129 Bytes
9a63e25 | 1 2 3 4 | version https://git-lfs.github.com/spec/v1
oid sha256:dc577c89383bafeaaa07fc49f77ea09e37f10794f08732bfe7f5ce9aa95d9375
size 5368
|