Feature Extraction
Transformers
PyTorch
ONNX
Safetensors
sentence-transformers
sentence-similarity
mteb
custom_code
Eval Results (legacy)
Eval Results
๐ช๐บ 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)# pip install -U transformers accelerate # Load model directly from transformers import AutoModel model = AutoModel.from_pretrained("jinaai/jina-embeddings-v3", trust_remote_code=True, device_map="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
Example for Fine-Tuning Models on Asymmetric Semantic Search with Hard Negatives.
#98
by wilfoderek - opened
Hi everyone!
I am currently working on a project focused on asymmetric semantic search involving hard negative sentences, and I would like to fine-tune a model using this approach. I am seeking a practical example to better understand the process.
Could you please provide an example or reference on:
- Preparing the dataset for fine-tuning, including how to structure pairs with hard negatives for asymmetric tasks?
- Implementing the fine-tuning process using Hugging Face tools (e.g., transformers or datasets)?
A step-by-step example or notebook reference would be extremely helpful!
Thank you so much for your guidance.
Hi, did you ever find the resources you were looking for?
Nothing