Feature Extraction
Transformers
PyTorch
ONNX
Safetensors
sentence-transformers
sentence-similarity
mteb
custom_code
Eval Results (legacy)
Eval Results
🇪🇺 Region: EU
Instructions to use jinaai/jina-embeddings-v3 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use jinaai/jina-embeddings-v3 with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("feature-extraction", model="jinaai/jina-embeddings-v3", trust_remote_code=True)# Load model directly from transformers import AutoModel model = AutoModel.from_pretrained("jinaai/jina-embeddings-v3", trust_remote_code=True, dtype="auto") - sentence-transformers
How to use jinaai/jina-embeddings-v3 with sentence-transformers:
from sentence_transformers import SentenceTransformer model = SentenceTransformer("jinaai/jina-embeddings-v3", 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
- Xet hash:
- 0abd02c8ae3c446b4b196b4041ef6b8dc3dce7d172091fa5ca5ce17b4eb7d627
- Size of remote file:
- 17.1 MB
- SHA256:
- f59925fcb90c92b894cb93e51bb9b4a6105c5c249fe54ce1c704420ac39b81af
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