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
Download preprocessor_config.json from google/embeddinggemma-2: direct link, hf CLI and curl.
- Browser
- Download file 511 Bytes
-
https://huggingface.co/google/embeddinggemma-2/resolve/main/preprocessor_config.json
- Command line
-
hf download hf://google/embeddinggemma-2/preprocessor_config.json
-
curl -L -o preprocessor_config.json https://huggingface.co/google/embeddinggemma-2/resolve/main/preprocessor_config.json
511 Bytes
| { | |
| "dither": 0.0, | |
| "feature_extractor_type": "Gemma4AudioFeatureExtractor", | |
| "feature_size": 128, | |
| "fft_length": 512, | |
| "fft_overdrive": false, | |
| "frame_length": 320, | |
| "hop_length": 160, | |
| "input_scale_factor": 1.0, | |
| "max_frequency": 8000.0, | |
| "mel_floor": 0.001, | |
| "min_frequency": 0.0, | |
| "padding_side": "right", | |
| "padding_value": 0.0, | |
| "per_bin_mean": null, | |
| "per_bin_stddev": null, | |
| "preemphasis": 0.0, | |
| "preemphasis_htk_flavor": true, | |
| "return_attention_mask": true, | |
| "sampling_rate": 16000 | |
| } | |