ONNX-converted models
Collection
Models converted to ONNX for faster CPU inference on LLM Guard.
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27 items
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Updated
This model is a conversion of valurank/distilroberta-bias to ONNX format. It is designed to detect biases in text using the distilled version of the RoBERTa model. The model was converted to ONNX using the ๐ค Optimum library.
Base Model: DistilRoBERTa, a distilled version of the RoBERTa model that is optimized for faster performance while maintaining similar accuracy.
Modifications: The model is converted to ONNX format with no additional changes.
Loading the model requires the ๐ค Optimum library installed.
from optimum.onnxruntime import ORTModelForSequenceClassification
from transformers import AutoTokenizer, pipeline
tokenizer = AutoTokenizer.from_pretrained("laiyer/distilroberta-bias-onnx")
model = ORTModelForSequenceClassification.from_pretrained("laiyer/distilroberta-bias-onnx")
classifier = pipeline(
task="text-classification",
model=model,
tokenizer=tokenizer,
)
classifier_output = classifier("Your text to analyze for bias.")
score = (classifier_output[0]["score"] if classifier_output[0]["label"] == "BIASED" else 1 - classifier_output[0]["score"])
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Base model
valurank/distilroberta-bias