Sentence Similarity
sentence-transformers
PyTorch
Safetensors
bert
feature-extraction
text-embeddings-inference
Instructions to use Suva/bge-large-finetuned with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- sentence-transformers
How to use Suva/bge-large-finetuned with sentence-transformers:
from sentence_transformers import SentenceTransformer model = SentenceTransformer("Suva/bge-large-finetuned") 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:
- 3952e5bda64b3797cdd1b9fd11c59a4d3be4204010ac83fd1a782c9ec9f797ec
- Size of remote file:
- 1.34 GB
- SHA256:
- 901a41f228477665040b4623e2ebe0cc58149945f519137bfa52266bd2dcb8cc
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