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
unsloth
Instructions to use unsloth/embeddinggemma-2 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use unsloth/embeddinggemma-2 with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("feature-extraction", model="unsloth/embeddinggemma-2")# pip install -U transformers accelerate # Load model directly from transformers import AutoProcessor, AutoModel processor = AutoProcessor.from_pretrained("unsloth/embeddinggemma-2") model = AutoModel.from_pretrained("unsloth/embeddinggemma-2", device_map="auto") - sentence-transformers
How to use unsloth/embeddinggemma-2 with sentence-transformers:
from sentence_transformers import SentenceTransformer model = SentenceTransformer("unsloth/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
- Local Apps Settings
- Unsloth Desktop
File size: 511 Bytes
3c3221f | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 | {
"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
}
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