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:
- c8dac4901167b56c108f1bfb995ec612a9ee2dc4141023452e913162082f114c
- Size of remote file:
- 1.14 GB
- SHA256:
- 9e83da37d1052fa1f9a6763199a5aa0fea1b5d3f290dc6b3d2e3ffb7c5356bcc
·
Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.