Sentence Similarity
sentence-transformers
Safetensors
static-embedding
feature-extraction
vocabulary-pruning
multilingual
Instructions to use flowercomputers/bekko-static-v1-slim-100k with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- sentence-transformers
How to use flowercomputers/bekko-static-v1-slim-100k with sentence-transformers:
from sentence_transformers import SentenceTransformer model = SentenceTransformer("flowercomputers/bekko-static-v1-slim-100k") 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
Ctrl+K