Feature Extraction
sentence-transformers
PyTorch
Safetensors
English
bert
sentence-similarity
mteb
custom_code
Eval Results (legacy)
text-embeddings-inference
Instructions to use TitanML/jina-v2-base-en-embed with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- sentence-transformers
How to use TitanML/jina-v2-base-en-embed with sentence-transformers:
from sentence_transformers import SentenceTransformer model = SentenceTransformer("TitanML/jina-v2-base-en-embed", 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
Download pytorch_model.bin from TitanML/jina-v2-base-en-embed: direct link, hf CLI and curl.
- Browser
- Download file 275 MB
-
https://huggingface.co/TitanML/jina-v2-base-en-embed/resolve/main/pytorch_model.bin
- Command line
-
hf download hf://TitanML/jina-v2-base-en-embed/pytorch_model.bin
-
curl -L -o pytorch_model.bin https://huggingface.co/TitanML/jina-v2-base-en-embed/resolve/main/pytorch_model.bin
275 MB
- Xet hash:
- 02ee562c79a490b88c3051ab178559c050c9369353d4c67ee005fa7e20dd1147
- Size of remote file:
- 275 MB
- SHA256:
- 6cd5a65131aa1db04c4146f474bdf68fac06417cba56789f4e6aaabd190e2818
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