Feature Extraction
Transformers
Safetensors
sentence-transformers
multilingual
jina_embeddings_v5_omni
image-feature-extraction
embedding
qwen3
jina-embeddings-v5
multimodal
vision
audio
vllm
video
audio-feature-extraction
video-feature-extraction
sentence-similarity
custom_code
🇪🇺 Region: EU
Instructions to use jinaai/jina-embeddings-v5-omni-small with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use jinaai/jina-embeddings-v5-omni-small with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("feature-extraction", model="jinaai/jina-embeddings-v5-omni-small", trust_remote_code=True)# Load model directly from transformers import AutoModel model = AutoModel.from_pretrained("jinaai/jina-embeddings-v5-omni-small", trust_remote_code=True, dtype="auto") - sentence-transformers
How to use jinaai/jina-embeddings-v5-omni-small with sentence-transformers:
from sentence_transformers import SentenceTransformer model = SentenceTransformer("jinaai/jina-embeddings-v5-omni-small", trust_remote_code=True) 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
| { | |
| "add_prefix_space": false, | |
| "backend": "tokenizers", | |
| "bos_token": null, | |
| "clean_up_tokenization_spaces": false, | |
| "eos_token": "<|im_end|>", | |
| "errors": "replace", | |
| "is_local": true, | |
| "model_max_length": 131072, | |
| "pad_token": "<|endoftext|>", | |
| "processor_class": "Qwen3VLProcessor", | |
| "split_special_tokens": false, | |
| "tokenizer_class": "Qwen2Tokenizer", | |
| "unk_token": null | |
| } | |