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