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"])

LLM Guard

Bias scanner

Community

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