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
readme-adjustments
#21
by jupyterjazz - opened
What I'm proposing to change and why:
- xlm-r languages from 89 to 100, both paper and model page say they trained it on 100 languages
- in the mean pooling example, model should receive adapter mask rather than task. task goes to encode() function.
- truncate dim can not be any integer, it should be one of the following (32, 64, 128, 256, 512, 768, 1024) which is specified in the top section of readme. also, it is not really related with sequence lengths.
jupyterjazz changed pull request status to open
LGTM
jupyterjazz changed pull request status to merged