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:
- 48d65a2ef0e152966c78edf9c3aea74341c2e34be7c7ced3e2f1f272fe1bb10a
- Size of remote file:
- 13.7 MB
- SHA256:
- a9302c1d1fffd21da5ca47b6f953f1960c0687cebc03e5fe8a0cd07621c21817
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