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
File size: 205 Bytes
ba3b1a7 | 1 2 3 4 5 6 7 8 9 10 | {
"__version__": {
"sentence_transformers": "3.4.1",
"transformers": "4.51.3",
"pytorch": "2.6.0+cu124"
},
"prompts": {},
"default_prompt_name": null,
"similarity_fn_name": "cosine"
} |