Sentence Similarity
sentence-transformers
Safetensors
gemma3_text
feature-extraction
text-embeddings-inference
Instructions to use confamnode/embeddinggemma-300m with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- sentence-transformers
How to use confamnode/embeddinggemma-300m with sentence-transformers:
from sentence_transformers import SentenceTransformer model = SentenceTransformer("confamnode/embeddinggemma-300m") 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
File size: 997 Bytes
24f1d83 | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 | {
"model_type": "SentenceTransformer",
"__version__": {
"sentence_transformers": "5.1.0",
"transformers": "4.57.0.dev0",
"pytorch": "2.8.0+cu128"
},
"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"
} |