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
by GautamKishore - opened
We forked the text-only 270M path for Hinglish/Hindi retrieval over at eulogik/bharat-embed-270m-gemma2 (Apache-2.0). Measured on Hindi IndicQA: base 0.7279 vs ours 0.7324 NDCG@10, STS tie. Truncation table measured too: 512d -0.008, 256d -0.030, 128d -0.103, so we default 256d. ONNX INT8 parity drift 0.0066, GGUF Q4 cosine 0.75 on a Hindi spot check. Misses stated plainly on the card. Feedback welcome, especially Tamil/Telugu eval help.