Contextual Brand Safety -II

Sooraj Subrahmannian
May 19 · 7 min read
Contextual Brand Safety Cover picture
Contextual Brand Safety Cover picture

1. Introduction

2. Experimental setup

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Model training ecosystem

3. Experiments with ULMFiT

3.1 Language model experiments:

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ULMFiT approach towards language modeling

Drop out Multiplier

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Variation of brand safety performance with a change in drop out, classifier hidden layer size and encoder

Characteristics of the dataset, encoder and classifier hyperparameter need to be tuned together because even though we develop the classifier in 3 steps. Each step is dependent on the outcomes of previous steps.

4. Comparison of ULMFiT Vs BERT

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dataset-1: Multilabel threat
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dataset-2: Binary threat
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Future work:

Conclusion


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