Feature Extraction
Transformers
Safetensors
sentence-transformers
multilingual
embedding_gemma2
embedding
multimodal-embedding
multimodal
vision
audio
video
image-feature-extraction
audio-feature-extraction
video-feature-extraction
sentence-similarity
Instructions to use google/embeddinggemma-2 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use google/embeddinggemma-2 with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("feature-extraction", model="google/embeddinggemma-2")# pip install -U transformers accelerate # Load model directly from transformers import AutoProcessor, AutoModel processor = AutoProcessor.from_pretrained("google/embeddinggemma-2") model = AutoModel.from_pretrained("google/embeddinggemma-2", device_map="auto") - sentence-transformers
How to use google/embeddinggemma-2 with sentence-transformers:
from sentence_transformers import SentenceTransformer model = SentenceTransformer("google/embeddinggemma-2") sentences = [ "The weather is lovely today.", "It's so sunny outside!", "He drove to the stadium." ] embeddings = model.encode(sentences) similarities = model.similarity(embeddings, embeddings) print(similarities.shape) # [3, 3] - Notebooks
- Google Colab
- Kaggle
Hindi fork: Bharat-Embed 270M (measured numbers inside)
#10 opened about 6 hours ago
by
GautamKishore
740 million parameters. 300 million for ears. 130 million for thoughts. Quelle inversion anatomique magnifique.
1
#9 opened about 9 hours ago
by
AdrienneNoctis
Update README.md
#8 opened 2 days ago
by
osanseviero
Update README.md
#7 opened 2 days ago
by
osanseviero
Built a local knowledge layer for macOS with EmbeddingGemma 2 + MCP
🚀 2
3
#6 opened 3 days ago
by
robyroy
V2 = V1 + multimodal
🔥👀 3
7
#5 opened 3 days ago
by
Duonglv
Official Skill por favor
#4 opened 4 days ago
by
Seryoger