Feature Extraction
sentence-transformers
ONNX
English
bert
sentence-similarity
text-embeddings-inference
Instructions to use technology-123/e5-base-v2-onnx with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- sentence-transformers
How to use technology-123/e5-base-v2-onnx with sentence-transformers:
from sentence_transformers import SentenceTransformer model = SentenceTransformer("technology-123/e5-base-v2-onnx") 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
File size: 674 Bytes
22a2b37 | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 | {
"per_channel": true,
"reduce_range": true,
"per_model_config": {
"model": {
"op_types": [
"Mul",
"Sub",
"Softmax",
"Sqrt",
"Erf",
"Slice",
"Cast",
"Div",
"Gather",
"Constant",
"Unsqueeze",
"Add",
"Shape",
"Reshape",
"Transpose",
"Pow",
"ReduceMean",
"Concat",
"MatMul"
],
"weight_type": "QInt8"
}
}
} |