Sentence Similarity
sentence-transformers
Safetensors
bert
feature-extraction
text-embeddings-inference
Instructions to use yahyaabd/sbert-bps-custom-tokenizer-en with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- sentence-transformers
How to use yahyaabd/sbert-bps-custom-tokenizer-en with sentence-transformers:
from sentence_transformers import SentenceTransformer model = SentenceTransformer("yahyaabd/sbert-bps-custom-tokenizer-en") sentences = [ "That is a happy person", "That is a happy dog", "That is a very happy person", "Today is a sunny day" ] embeddings = model.encode(sentences) similarities = model.similarity(embeddings, embeddings) print(similarities.shape) # [4, 4] - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 1a256a1581afeede5aa67d90a8d29792af3a0a40c602c06cca62cb527e95bddf
- Size of remote file:
- 17.2 MB
- SHA256:
- 9af2b71168e2ba0d1d86576f118aa7750854932138943d33bd0769a0d6b61637
·
Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.