Feature Extraction
Model2Vec
Safetensors
sentence-transformers
embeddings
static-embeddings
sentence-similarity
Instructions to use CISCai/jina-embeddings-v3-classification-distilled with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Model2Vec
How to use CISCai/jina-embeddings-v3-classification-distilled with Model2Vec:
from model2vec import StaticModel model = StaticModel.from_pretrained("CISCai/jina-embeddings-v3-classification-distilled") embeddings = model.encode(["It's dangerous to go alone!", "It's a secret to everybody."]) print(embeddings.shape) - sentence-transformers
How to use CISCai/jina-embeddings-v3-classification-distilled with sentence-transformers:
from sentence_transformers import SentenceTransformer model = SentenceTransformer("CISCai/jina-embeddings-v3-classification-distilled") 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 tokenizer.json from CISCai/jina-embeddings-v3-classification-distilled: direct link, hf CLI and curl.
- Browser
- Download file 17.1 MB
-
https://huggingface.co/CISCai/jina-embeddings-v3-classification-distilled/resolve/main/tokenizer.json
- Command line
-
hf download hf://CISCai/jina-embeddings-v3-classification-distilled/tokenizer.json
-
curl -L -o tokenizer.json https://huggingface.co/CISCai/jina-embeddings-v3-classification-distilled/resolve/main/tokenizer.json
17.1 MB
- Xet hash:
- fd8b244b613f7189b1547758d849817b284f0f3ae52113a915789f927ee4b164
- Size of remote file:
- 17.1 MB
- SHA256:
- 3a56def25aa40facc030ea8b0b87f3688e4b3c39eb8b45d5702b3a1300fe2a20
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