Feature Extraction
sentence-transformers
Safetensors
embedding_gemma2
sentence-similarity
embeddings
text-embeddings
multimodal
image-text-retrieval
audio-embeddings
semantic-search
retrieval
quantized
4-bit precision
int4
mixed-precision
low-memory
gemma
embeddinggemma
multimodal-embeddings
cross-modal-retrieval
clip-alternative
quantization
post-training-quantization
cpu
edge
8-bit precision
Instructions to use ewin-reg/embeddinggemma-2-Quantized with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- sentence-transformers
How to use ewin-reg/embeddinggemma-2-Quantized with sentence-transformers:
from sentence_transformers import SentenceTransformer model = SentenceTransformer("ewin-reg/embeddinggemma-2-Quantized") 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
File size: 511 Bytes
798b305 | 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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