Sentence Similarity
sentence-transformers
Safetensors
Transformers
bert
feature-extraction
text-embeddings-inference
Instructions to use basic-go/math-ru-sbert with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- sentence-transformers
How to use basic-go/math-ru-sbert with sentence-transformers:
from sentence_transformers import SentenceTransformer model = SentenceTransformer("basic-go/math-ru-sbert") 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] - Transformers
How to use basic-go/math-ru-sbert with Transformers:
# Load model directly from transformers import AutoTokenizer, AutoModel tokenizer = AutoTokenizer.from_pretrained("basic-go/math-ru-sbert") model = AutoModel.from_pretrained("basic-go/math-ru-sbert") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- c080ed6d98c89a294d68d39fb73ad062cc8cccf772b33b00cd25778a929c520f
- Size of remote file:
- 711 MB
- SHA256:
- 65be405b4404cb1258250c51be6c320da835e336adf3a3a2bb686a04797b8c4f
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