Sentence Similarity
sentence-transformers
Safetensors
gemma3_text
feature-extraction
text-embeddings-inference
Instructions to use noname002/embeddinggemma-300m-greennode-3fre with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- sentence-transformers
How to use noname002/embeddinggemma-300m-greennode-3fre with sentence-transformers:
from sentence_transformers import SentenceTransformer model = SentenceTransformer("noname002/embeddinggemma-300m-greennode-3fre") 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 sentence_bert_config.json from noname002/embeddinggemma-300m-greennode-3fre: direct link, hf CLI and curl.
- Browser
- Download file 241 Bytes
-
https://huggingface.co/noname002/embeddinggemma-300m-greennode-3fre/resolve/main/sentence_bert_config.json
- Command line
-
hf download hf://noname002/embeddinggemma-300m-greennode-3fre/sentence_bert_config.json
-
curl -L -o sentence_bert_config.json https://huggingface.co/noname002/embeddinggemma-300m-greennode-3fre/resolve/main/sentence_bert_config.json
241 Bytes
| { | |
| "transformer_task": "feature-extraction", | |
| "modality_config": { | |
| "text": { | |
| "method": "forward", | |
| "method_output_name": "last_hidden_state" | |
| } | |
| }, | |
| "module_output_name": "token_embeddings" | |
| } |