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:
- 945917fb5377c0fa2707ae13c564f92197c6675aefeaa75dadb5e426363b6f4d
- Size of remote file:
- 471 MB
- SHA256:
- 059ab35ddd3a9058fe711ac5a2d72b6db2ce45d653659a97354cd9a4113abc7a
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