metadata
pipeline_tag: text-classification
language: en
datasets:
- valurank/wikirev-bias
inference: false
tags:
- bias
- distilroberta
base_model: valurank/distilroberta-bias
ONNX version of valurank/distilroberta-bias
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 🤗 Optimum library.
Model Architecture
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.
Usage
Optimum
Loading the model requires the 🤗 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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