Sentence Similarity
sentence-transformers
Safetensors
gemma3_text
multilingual
model-compression
layer-pruning
vocab-pruning
knowledge-distillation
progressive-distillation
embeddinggemma-300m
text-embeddings-inference
Instructions to use gomyk/gemma-student-gemma_emb_compressed_distilled with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- sentence-transformers
How to use gomyk/gemma-student-gemma_emb_compressed_distilled with sentence-transformers:
from sentence_transformers import SentenceTransformer model = SentenceTransformer("gomyk/gemma-student-gemma_emb_compressed_distilled") sentences = [ "The weather is lovely today.", "It's so sunny outside!", "He drove to the stadium." ] embeddings = model.encode(sentences) similarities = model.similarity(embeddings, embeddings) print(similarities.shape) # [3, 3] - Notebooks
- Google Colab
- Kaggle
File size: 695 Bytes
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