Feature Extraction
sentence-transformers
Safetensors
gemma3_text
text-embeddings
compression
depth-pruning
embedding-width-reduction
retrieval
semantic-search
text-embeddings-inference
Instructions to use Pieces/embeddinggemma-300m-distilled-depth10pct-10-768dim-best with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- sentence-transformers
How to use Pieces/embeddinggemma-300m-distilled-depth10pct-10-768dim-best with sentence-transformers:
from sentence_transformers import SentenceTransformer model = SentenceTransformer("Pieces/embeddinggemma-300m-distilled-depth10pct-10-768dim-best") 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: 992 Bytes
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"model_type": "SentenceTransformer",
"__version__": {
"sentence_transformers": "5.2.0",
"transformers": "4.52.4",
"pytorch": "2.7.0+cu126"
},
"prompts": {
"query": "task: search result | query: ",
"document": "title: none | text: ",
"BitextMining": "task: search result | query: ",
"Clustering": "task: clustering | query: ",
"Classification": "task: classification | query: ",
"InstructionRetrieval": "task: code retrieval | query: ",
"MultilabelClassification": "task: classification | query: ",
"PairClassification": "task: sentence similarity | query: ",
"Reranking": "task: search result | query: ",
"Retrieval": "task: search result | query: ",
"Retrieval-query": "task: search result | query: ",
"Retrieval-document": "title: none | text: ",
"STS": "task: sentence similarity | query: ",
"Summarization": "task: summarization | query: "
},
"default_prompt_name": null,
"similarity_fn_name": "cosine"
} |