metadata
language:
- en
tags:
- Text Classification
This model is part of the Research topic "Bias and Fairness in AI" conducted by Shaina Raza, Deepak John Reji, Chen Ding
- Dataset : MBAD Data
- datasize : 17775 entries
- Label distribution : Biased - 10651, Non-Biased - 7124
- Train-Test split : 90 : 10
- tokenizer : distilbert-base-uncased
- model : distilbert-base-uncased
- optimizer : adam
- Learning rate : 5e-5
- Model parameters : 66,955,010
- epochs : 30
- Train accuracy : 0.7697
- Test accuracy : 0.62
- Train loss : 0.4506
- Test loss : 0.9644
- Carbon emission 0.319355 Kg
widget:
- text: "Is this review positive or negative? Review: Best cast iron skillet you will every buy." example_title: "Sentiment analysis"
- text: "Barack Obama nominated Hilary Clinton as his secretary of state on Monday. He chose her because she had ..." example_title: "Coreference resolution"
- text: "On a shelf, there are five books: a gray book, a red book, a purple book, a blue book, and a black book ..." example_title: "Logic puzzles"
- text: "The two men running to become New York City's next mayor will face off in their first debate Wednesday night ..." example_title: "Reading comprehension"