Sentence Similarity
sentence-transformers
Safetensors
English
nvomniembed
text
image
video
audio
vidore
multimodal-embedding
Text-to-Video retrieval
Text-to-Audio retrieval
Visual Document Retrieval
feature-extraction
custom_code
Instructions to use nvidia/omni-embed-nemotron-3b with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- sentence-transformers
How to use nvidia/omni-embed-nemotron-3b with sentence-transformers:
from sentence_transformers import SentenceTransformer model = SentenceTransformer("nvidia/omni-embed-nemotron-3b", trust_remote_code=True) sentences = [ "That is a happy person", "That is a happy dog", "That is a very happy person", "Today is a sunny day" ] embeddings = model.encode(sentences) similarities = model.similarity(embeddings, embeddings) print(similarities.shape) # [4, 4] - Notebooks
- Google Colab
- Kaggle
| { | |
| "chunk_length": 300, | |
| "dither": 0.0, | |
| "feature_extractor_type": "WhisperFeatureExtractor", | |
| "feature_size": 128, | |
| "hop_length": 160, | |
| "image_mean": [ | |
| 0.48145466, | |
| 0.4578275, | |
| 0.40821073 | |
| ], | |
| "image_processor_type": "Qwen2VLImageProcessor", | |
| "image_std": [ | |
| 0.26862954, | |
| 0.26130258, | |
| 0.27577711 | |
| ], | |
| "max_pixels": 12845056, | |
| "merge_size": 2, | |
| "min_pixels": 3136, | |
| "n_fft": 400, | |
| "n_samples": 4800000, | |
| "nb_max_frames": 30000, | |
| "padding_side": "right", | |
| "padding_value": 0.0, | |
| "patch_size": 14, | |
| "processor_class": "Qwen2_5OmniProcessor", | |
| "return_attention_mask": true, | |
| "sampling_rate": 16000, | |
| "temporal_patch_size": 2 | |
| } | |