Sentence Similarity
sentence-transformers
Safetensors
gemma3_text
mirror
embeddinggemma
busco
text-embeddings-inference
Instructions to use busco-app/embeddinggemma-300m with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- sentence-transformers
How to use busco-app/embeddinggemma-300m with sentence-transformers:
from sentence_transformers import SentenceTransformer model = SentenceTransformer("busco-app/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
Mirror google/embeddinggemma-300m at 57c266a740f537b4dc058e1b0cda161fd15afa75 (weights unmodified)
80d1ba7 verified Download modules.json from busco-app/embeddinggemma-300m: direct link, hf CLI and curl.
- Browser
- Download file 573 Bytes
-
https://huggingface.co/busco-app/embeddinggemma-300m/resolve/main/modules.json
- Command line
-
hf download hf://busco-app/embeddinggemma-300m/modules.json
-
curl -L -o modules.json https://huggingface.co/busco-app/embeddinggemma-300m/resolve/main/modules.json
573 Bytes
| [ | |
| { | |
| "idx": 0, | |
| "name": "0", | |
| "path": "", | |
| "type": "sentence_transformers.models.Transformer" | |
| }, | |
| { | |
| "idx": 1, | |
| "name": "1", | |
| "path": "1_Pooling", | |
| "type": "sentence_transformers.models.Pooling" | |
| }, | |
| { | |
| "idx": 2, | |
| "name": "2", | |
| "path": "2_Dense", | |
| "type": "sentence_transformers.models.Dense" | |
| }, | |
| { | |
| "idx": 3, | |
| "name": "3", | |
| "path": "3_Dense", | |
| "type": "sentence_transformers.models.Dense" | |
| }, | |
| { | |
| "idx": 4, | |
| "name": "4", | |
| "path": "4_Normalize", | |
| "type": "sentence_transformers.models.Normalize" | |
| } | |
| ] |