Feature Extraction
Transformers
Safetensors
sentence-transformers
multilingual
qwen3_vl_audio
embedding
qwen3
jina-embeddings-v5
multimodal
vision
audio
vllm
video
image-feature-extraction
audio-feature-extraction
video-feature-extraction
sentence-similarity
custom_code
🇪🇺 Region: EU
Instructions to use jinaai/jina-embeddings-v5-omni-small-text-matching 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-text-matching 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-text-matching", trust_remote_code=True)# Load model directly from transformers import AutoModel model = AutoModel.from_pretrained("jinaai/jina-embeddings-v5-omni-small-text-matching", trust_remote_code=True, dtype="auto") - sentence-transformers
How to use jinaai/jina-embeddings-v5-omni-small-text-matching with sentence-transformers:
from sentence_transformers import SentenceTransformer model = SentenceTransformer("jinaai/jina-embeddings-v5-omni-small-text-matching", 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
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