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
Add new SentenceTransformer model with an onnx backend
Browse files- config.json +1 -1
config.json
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{
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"_name_or_path": "/Volumes/Data/Projects/bookingcare/search/data/
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"architectures": [
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"XLMRobertaModel"
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],
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{
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"_name_or_path": "/Volumes/Data/Projects/bookingcare/search/data/BookingCare/multilingual-e5-base-v3-onnx-quantized",
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"architectures": [
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"XLMRobertaModel"
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],
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