XLM-RoBERTa Sexism Classifier (Trained: ES / Evaluated: EN)

Experiment Details: cross_lingual_transfer

• Training Data: The model was fine-tuned exclusively on the Spanish (ES) subset of the EXIST 2023 dataset.
• Evaluation Data: The model was evaluated exclusively on the English (EN) test set.
• Objective: This experiment measures the base model's zero-shot cross-lingual transfer capabilities—specifically, its ability to learn the conceptual nuances of sexism and reporting in Spanish and successfully map those concepts to English text without explicit English fine-tuning.
• Loss Function: Trained using a weighted Cross-Entropy loss to account for class imbalances in the Spanish training split.

Intended Use

1. DIRECT (Directly sexist messages)
2. JUDGEMENTAL (Messages condemning sexist behaviors)
3. REPORTED (Messages reporting a sexist situation)

Preprocessing

• Replace user mentions (@user) with the token @user
• Replace URLs with the token http

Evaluation Results (English Test Set)

• Macro F1: 0.4547
• Precision: 0.4596
• Recall: 0.4938

How to Use