Sentence Similarity
sentence-transformers
Safetensors
bert
feature-extraction
text-embeddings-inference
Instructions to use yahyaabd/sbert-bps-custom-tokenizer 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 with sentence-transformers:
from sentence_transformers import SentenceTransformer model = SentenceTransformer("yahyaabd/sbert-bps-custom-tokenizer") 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:
- 91b499084bebcbd3cb5a76f334e852c81ff0e852e963ee9a98d8dbb309605d8b
- Size of remote file:
- 17.1 MB
- SHA256:
- f06fd16b7adf807bcc94c51b56698fe959bf5132eb451727e81eb999f3a11aa8
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