Sentence Similarity
sentence-transformers
ONNX
xlm-roberta
mteb
Sentence Transformers
Eval Results (legacy)
text-embeddings-inference
Instructions to use BookingCare/multilingual-e5-base-v3-onnx-quantized with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- sentence-transformers
How to use BookingCare/multilingual-e5-base-v3-onnx-quantized with sentence-transformers:
from sentence_transformers import SentenceTransformer model = SentenceTransformer("BookingCare/multilingual-e5-base-v3-onnx-quantized") 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 BookingCare/multilingual-e5-base-v3-onnx-quantized: direct link, hf CLI and curl.
- Browser
- Download file 17.1 MB
-
https://huggingface.co/BookingCare/multilingual-e5-base-v3-onnx-quantized/resolve/main/tokenizer.json
- Command line
-
hf download hf://BookingCare/multilingual-e5-base-v3-onnx-quantized/tokenizer.json
-
curl -L -o tokenizer.json https://huggingface.co/BookingCare/multilingual-e5-base-v3-onnx-quantized/resolve/main/tokenizer.json
17.1 MB
- Xet hash:
- 900d575674d5bfcd5c2b9059c9221a42f92c957ed7230f7e096d3e9580c3e4c2
- Size of remote file:
- 17.1 MB
- SHA256:
- 883b037111086fd4dfebbbc9b7cee11e1517b5e0c0514879478661440f137085
·
Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.