Feature Extraction
Transformers
PyTorch
ONNX
Safetensors
sentence-transformers
sentence-similarity
mteb
custom_code
Eval Results (legacy)
Instructions to use Qsevent77/jina-embeddings-v3 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use Qsevent77/jina-embeddings-v3 with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("feature-extraction", model="Qsevent77/jina-embeddings-v3", trust_remote_code=True)# Load model directly from transformers import AutoModel model = AutoModel.from_pretrained("Qsevent77/jina-embeddings-v3", trust_remote_code=True, device_map="auto") - sentence-transformers
How to use Qsevent77/jina-embeddings-v3 with sentence-transformers:
from sentence_transformers import SentenceTransformer model = SentenceTransformer("Qsevent77/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:
- d6d35d3ab4b505dd89240597f8a301dbcbc24fd13eafead0d0d2e8b0b14f06e4
- Size of remote file:
- 1.15 GB
- SHA256:
- 329c3ea03a1815cc98f6b97efcafb9000c6c780c2d89d40d4f541b9a88434c38
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