Solution using adversarial training for the explainable detection of sexism in social networks (EDOS) task as part of SEMEVAL 2023
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Updated
Jun 14, 2024
Solution using adversarial training for the explainable detection of sexism in social networks (EDOS) task as part of SEMEVAL 2023
Human Language Technologies (HLT) project. Computer Science Master Degree, University of Pisa. A.Y 2023/2024
Hackathon for an NLP task involving sexism classification
Ghast farming addon for Meteor Client
Benchmark tool aimed at evaluating biases of large language models
Bengali Misogyny Identification with Deep Learning and LIME.
Analyze user comments through Natural Language Processing (NLP) techniques and Analyze sexism dataset
Smashing Sexism: Overcoming Bias in a Cross-Domain 5-Point Classification Challenge
A terminal based game about privileges. Build in the context of the Basic Programming M2 course at Systax Institute.
The project focuses on identifying signs of sexism in texts through three tasks: identifying sexism, categorizing sexism, and sub-categorizing sexism. The best model used for completing these tasks is RoBERTa pre-trained on hate speech with the addition of data augmentation and learning rate scheduler techniques.
Task 10: Explainable Detection of Online Sexism
Submission for SemEval 2023 Task 10 EDOS
Explainable Machine Learning in Linguistics and Applied NLP: Two Case Studies of Norwegian Dialectometry and Sexism Detection in French Tweets
Custom classifiers to detect sexist language.
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