Sentence Similarity
sentence-transformers
Safetensors
English
gemma3_text
feature-extraction
dense
Generated from Trainer
dataset_size:320
loss:CachedMultipleNegativesRankingLoss
Eval Results (legacy)
text-embeddings-inference
Instructions to use ttwin/embeddinggemma-300m-onlineshop with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- sentence-transformers
How to use ttwin/embeddinggemma-300m-onlineshop with sentence-transformers:
from sentence_transformers import SentenceTransformer model = SentenceTransformer("ttwin/embeddinggemma-300m-onlineshop") sentences = [ "၂ထုတ်ထည့်ပေးနော်", "item", "order", "order" ] embeddings = model.encode(sentences) similarities = model.similarity(embeddings, embeddings) print(similarities.shape) # [4, 4] - Notebooks
- Google Colab
- Kaggle
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