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:
- 3231519146e226f006eedf3a9ce08f18b107c6775deb2b8c56bcb85663925bf0
- Size of remote file:
- 17.1 MB
- SHA256:
- 6f6e88fcdbcafb90831892d5175459f7e85c8f84314f0ea2e67a901d594e8f3a
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