Task 10: Explainable Detection of Online Sexism
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Updated
Apr 16, 2023 - Jupyter Notebook
Task 10: Explainable Detection of Online Sexism
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.
Submission for SemEval 2023 Task 10 EDOS
Bengali Misogyny Identification with Deep Learning and LIME.
Text classification with ngrams for Natural Language Processing case study @ Università degli Studi di Bari Aldo Moro, AY 2023/24
A terminal based game about privileges. Build in the context of the Basic Programming M2 course at Systax Institute.
Smashing Sexism: Overcoming Bias in a Cross-Domain 5-Point Classification Challenge
Hackathon for an NLP task involving sexism classification
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
Benchmark tool aimed at evaluating biases of large language models
Explainable Machine Learning in Linguistics and Applied NLP: Two Case Studies of Norwegian Dialectometry and Sexism Detection in French Tweets
Analyze user comments through Natural Language Processing (NLP) techniques and Analyze sexism dataset
Ghast farming addon for Meteor Client
Custom classifiers to detect sexist language.
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