Sentence Similarity
sentence-transformers
Safetensors
English
German
gemma3_text
feature-extraction
text-embeddings-inference
Instructions to use headwAI/embeddinggemma-300m with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- sentence-transformers
How to use headwAI/embeddinggemma-300m with sentence-transformers:
from sentence_transformers import SentenceTransformer model = SentenceTransformer("headwAI/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
Download .gitattributes from headwAI/embeddinggemma-300m: direct link, hf CLI and curl.
- Browser
- Download file 281 Bytes
-
https://huggingface.co/headwAI/embeddinggemma-300m/resolve/main/.gitattributes
- Command line
-
hf download hf://headwAI/embeddinggemma-300m/.gitattributes
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curl -L -o .gitattributes https://huggingface.co/headwAI/embeddinggemma-300m/resolve/main/.gitattributes
281 Bytes
| 2_Dense/model.safetensors filter=lfs diff=lfs merge=lfs -text | |
| 3_Dense/model.safetensors filter=lfs diff=lfs merge=lfs -text | |
| model.safetensors filter=lfs diff=lfs merge=lfs -text | |
| tokenizer.json filter=lfs diff=lfs merge=lfs -text | |
| tokenizer.model filter=lfs diff=lfs merge=lfs -text | |