Instructions to use kais-radwan/gte-base-onnx with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use kais-radwan/gte-base-onnx with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("feature-extraction", model="kais-radwan/gte-base-onnx")# Load model directly from transformers import AutoTokenizer, AutoModel tokenizer = AutoTokenizer.from_pretrained("kais-radwan/gte-base-onnx") model = AutoModel.from_pretrained("kais-radwan/gte-base-onnx") - Notebooks
- Google Colab
- Kaggle
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Check out the documentation for more information.
gte-base
This is a fork from https://huggingface.co/thenlper/gte-base that was optimized using Optimum and ONNX for better performance.
Usage
from transformers import AutoTokenizer
from optimum.onnxruntime import ORTModelForFeatureExtraction
tokenizer = AutoTokenizer.from_pretrained("kais-radwan/gte-base-onnx")
model = ORTModelForFeatureExtraction.from_pretrained("kais-radwan/gte-base-onnx")
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