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
Request for MMEBv2 evaluation configuration
Hello team,
I'm trying to evaluate the public checkpoint on MMEBv2 for reproducing the scores published on the MMEB leaderboard. I tried the model card's general guidance but observed score differences on some tasks.
While investigating these differences, I found the MTEB evaluation code, which seems to contain several settings that differ from the general guide (e.g., ArguAna used fact-checking prefix, rather than the general search prefix). This makes me wonder whether the reported MMEB metrics similarly used task-specific settings.
Would you be willing to share the configuration used for the MMEB evaluation? In particular, the following details would be helpful:
- Query/target prefixes
- Context length and truncation policy
- Visual token budget for images, document pages, and individual frames.
- (For video tasks) number of frames, FPS, and the policy used when a video exceeds the frame limit
Thank you for your time and consideration!