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
| { | |
| "__version__":{ | |
| "sentence_transformers":"3.1.0", | |
| "transformers":"4.41.2", | |
| "pytorch":"2.3.1+cu121" | |
| }, | |
| "prompts":{ | |
| "retrieval.query":"Represent the query for retrieving evidence documents: ", | |
| "retrieval.passage":"Represent the document for retrieval: ", | |
| "separation": "", | |
| "classification": "", | |
| "text-matching": "" | |
| }, | |
| "default_prompt_name":null, | |
| "similarity_fn_name":"cosine" | |
| } |