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Profanity-Classifier

Implemented a profanity detection algorithm in Python to statistically analyze datasets and classify written content according to profanity level. Compared models and achieved an accuracy of 95%, ablation study compared logistic regression, naïve bayes, SVM, MLP and transformer classifiers using SOTA word vector ELMo, BERT models.

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Implemented a profanity detection algorithm in Python to statistically analyze datasets and classify written content according to profanity level. Compared models and achieved an accuracy of 95%, ablation study compared logistic regression, naïve bayes, SVM, MLP and transformer classifiers using SOTA word vector ELMo, BERT models.

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