Feature Extraction
Transformers
Safetensors
sentence-transformers
multilingual
jina_embeddings_v5
mteb
custom_code
🇪🇺 Region: EU
Instructions to use jinaai/jina-embeddings-v5-text-small with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use jinaai/jina-embeddings-v5-text-small with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("feature-extraction", model="jinaai/jina-embeddings-v5-text-small", trust_remote_code=True)# Load model directly from transformers import AutoModel model = AutoModel.from_pretrained("jinaai/jina-embeddings-v5-text-small", trust_remote_code=True, device_map="auto") - sentence-transformers
How to use jinaai/jina-embeddings-v5-text-small with sentence-transformers:
from sentence_transformers import SentenceTransformer model = SentenceTransformer("jinaai/jina-embeddings-v5-text-small", 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:
- 5d82c788538b8a39772cbb90230fdea33913db5764b535af30a8dc3409d50069
- Size of remote file:
- 1.19 GB
- SHA256:
- 045fa75ff963a528cda2589fb1ca0a9ad848b53511780ed4f08f6fe10f6167c3
·
Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.