Feature Extraction
Transformers.js
ONNX
multilingual
embedding_gemma2
embedding
multimodal-embedding
multimodal
vision
audio
video
image-feature-extraction
audio-feature-extraction
video-feature-extraction
sentence-similarity
Instructions to use 0x3/embeddinggemma-2-ONNX with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers.js
How to use 0x3/embeddinggemma-2-ONNX with Transformers.js:
// npm i @huggingface/transformers import { pipeline } from '@huggingface/transformers'; // Allocate pipeline const pipe = await pipeline('feature-extraction', '0x3/embeddinggemma-2-ONNX');
File size: 560 Bytes
7c352bd | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 | {
"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,
"processor_class": "EmbeddingGemma2Processor"
}
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