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
| { | |
| "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" | |
| } |