Sentence Similarity
sentence-transformers
Safetensors
gemma3_text
feature-extraction
text-embeddings-inference
Instructions to use noname002/embeddinggemma-300m-greennode-5fre with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- sentence-transformers
How to use noname002/embeddinggemma-300m-greennode-5fre with sentence-transformers:
from sentence_transformers import SentenceTransformer model = SentenceTransformer("noname002/embeddinggemma-300m-greennode-5fre") 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
Download 2_Dense/config.json from noname002/embeddinggemma-300m-greennode-5fre: direct link, hf CLI and curl.
- Browser
- Download file 229 Bytes
-
https://huggingface.co/noname002/embeddinggemma-300m-greennode-5fre/resolve/main/2_Dense/config.json
- Command line
-
hf download hf://noname002/embeddinggemma-300m-greennode-5fre/2_Dense/config.json
-
curl -L -o config.json https://huggingface.co/noname002/embeddinggemma-300m-greennode-5fre/resolve/main/2_Dense/config.json
229 Bytes
| { | |
| "in_features": 768, | |
| "out_features": 3072, | |
| "bias": false, | |
| "activation_function": "torch.nn.modules.linear.Identity", | |
| "module_input_name": "sentence_embedding", | |
| "module_output_name": "sentence_embedding" | |
| } |