Instructions to use flavour/vtde-dinov2-small-jina-embedding-t-en-v1 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use flavour/vtde-dinov2-small-jina-embedding-t-en-v1 with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("feature-extraction", model="flavour/vtde-dinov2-small-jina-embedding-t-en-v1")# pip install -U transformers accelerate # Load model directly from transformers import AutoProcessor, AutoModel processor = AutoProcessor.from_pretrained("flavour/vtde-dinov2-small-jina-embedding-t-en-v1") model = AutoModel.from_pretrained("flavour/vtde-dinov2-small-jina-embedding-t-en-v1", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download pytorch_model.bin from flavour/vtde-dinov2-small-jina-embedding-t-en-v1: direct link, hf CLI and curl.
- Browser
- Download file 147 MB
-
https://huggingface.co/flavour/vtde-dinov2-small-jina-embedding-t-en-v1/resolve/main/pytorch_model.bin
- Command line
-
hf download hf://flavour/vtde-dinov2-small-jina-embedding-t-en-v1/pytorch_model.bin
-
curl -L -o pytorch_model.bin https://huggingface.co/flavour/vtde-dinov2-small-jina-embedding-t-en-v1/resolve/main/pytorch_model.bin
147 MB
- Xet hash:
- 237f48747648828fe8394d0779b60e86025e5df373d43da3acc7b8cee298757d
- Size of remote file:
- 147 MB
- SHA256:
- 122ed6b3647aba3eb34884797aa201d2a1480e7aad1d57b2b39e45186f9099a0
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