Sentence Similarity
sentence-transformers
Safetensors
gemma3_text
text-embeddings-inference
feature-extraction
text-embeddings
turkish
tr
multilingual
long-context
distillation
tokenizer-surgery
offline-distillation
semantic-search
retrieval
Instructions to use magibu/embeddingmagibu-200m with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- sentence-transformers
How to use magibu/embeddingmagibu-200m with sentence-transformers:
from sentence_transformers import SentenceTransformer model = SentenceTransformer("magibu/embeddingmagibu-200m") 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:
- e0fd0dd315cbaabc0549a824dda6bbd704efa3dc1cccda9f6b509e0a164930ba
- Size of remote file:
- 2.38 MB
- SHA256:
- 0f9dc7f7dd625c1a19c0aa85e11f1c4aeb6b3e986553e2c1f398e335560e2246
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