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d4data/
bias-detection-model

Name: bias-detection-model
Availability: InStock

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About the Model

An English sequence classification model, trained on MBAD Dataset to detect bias and fairness in sentences (news articles). This model was built on top of distilbert-base-uncased model and trained for 30 epochs with a batch size of 16, a learning rate of 5e-5, and a maximum sequence length of 512.

Dataset : MBAD Data
Carbon emission 0.319355 Kg

Train Accuracy	Validation Accuracy	Train loss	Test loss
76.97	62.00	0.45	0.96

Usage

The easiest way is to load the inference api from huggingface and second method is through the pipeline object offered by transformers library.

python

from transformers import AutoTokenizer, TFAutoModelForSequenceClassification
from transformers import pipeline
tokenizer = AutoTokenizer.from_pretrained("d4data/bias-detection-model")
model = TFAutoModelForSequenceClassification.from_pretrained("d4data/bias-detection-model")

classifier = pipeline('text-classification', model=model, tokenizer=tokenizer) # cuda = 0,1 based on gpu availability
classifier("The irony, of course, is that the exhibit that invites people to throw trash at vacuuming Ivanka Trump lookalike reflects every stereotype feminists claim to stand against, oversexualizing Ivanka’s body and ignoring her hard work.")

Author

This model is part of the Research topic "Bias and Fairness in AI" conducted by Deepak John Reji, Shaina Raza. If you use this work (code, model or dataset), please star at:

Bias & Fairness in AI, (2022), GitHub repository, https://github.com/dreji18/Fairness-in-AI