ONNX-converted models
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Models converted to ONNX for faster CPU inference on LLM Guard. • 20 items • Updated • 2
# Load model directly
from transformers import AutoTokenizer, AutoModelForSequenceClassification
tokenizer = AutoTokenizer.from_pretrained("protectai/MoritzLaurer-roberta-base-zeroshot-v2.0-c-onnx")
model = AutoModelForSequenceClassification.from_pretrained("protectai/MoritzLaurer-roberta-base-zeroshot-v2.0-c-onnx", device_map="auto")Quick Links
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This project and its associated code on GitHub are no longer under active development or maintained.
ONNX version of MoritzLaurer/roberta-base-zeroshot-v2.0-c
This model is a conversion of MoritzLaurer/roberta-base-zeroshot-v2.0-c to ONNX format using the 🤗 Optimum library.
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Model tree for protectai/MoritzLaurer-roberta-base-zeroshot-v2.0-c-onnx
Base model
MoritzLaurer/roberta-base-zeroshot-v2.0-c
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("zero-shot-classification", model="protectai/MoritzLaurer-roberta-base-zeroshot-v2.0-c-onnx")