## 4 Results
### Model Performance: Sexism and Racism Detection  
Table 8 summarizes the classification results obtained from our models and compares their performance against the latest models from Hugging Face. Domain-specific models, fine-tuned using the SpaCy (v3.8.7) pipeline on the Mediavida corpus, exhibited varying effectiveness based on the use of contextual information. \begin{table}
\begin{tabular}{l c c c c c c} \hline \hline
**Model** & **Train/FT** & **Evaluate** & **Precision** & **Recall** & **F1 Score** & **ROC-AUC** \\ \hline \multicolumn{6}{c}{Sexism Detection} \\ \hline LogReg baseline & ST & ST & 0.52 & 0.24 & 0.33 & 0.77 \\ LogReg baseline & CTX & CTX & 0.72 & 0.43 & 0.54 & 0.82 \\ TextCatBOW (SpaCy) & ST & ST & 0.69 & 0.20 & 0.31 & 0.76 \\ TextCatBOW (SpaCy) & CTX & CTX & 0.64 & 0.51 & 0.57 & 0.81 \\ HF BETO (SpaCy) & ST & ST & 0.59 & 0.76 & 0.67 & 0.85 \\ HF BETO (SpaCy) & CTX & CTX & 0.64 & 0.71 & 0.67 & 0.87 \\ \hline HF PIUBA-contextualized & – & ST & 0.86 & 0.13 & 0.23 & 0.84 \\ HF PIUBA-contextualized & – & CTX & 1.00 & 0.08 & 0.15 & 0.82 \\ HF multilingual-toxic-XLm-roberta & – & ST & 0.30 & 0.07 & 0.11 & 0.72 \\ HF multilingual-toxic-XLm-roberta & – & CTX & 0.50 & 0.12 & 0.20 & 0.62 \\ \hline \multicolumn{6}{c}{Racism Detection} \\ \hline LogReg baseline & ST & ST & 0.86 & 0.53 & 0.66 & 0.87 \\ LogReg baseline & CTX & CTX & 0.93 & 0.46 & 0.61 & 0.89 \\ TextCatBOW (SpaCy) & ST & ST & 0.91 & 0.50 & 0.64 & 0.88 \\ TextCatBOW (SpaCy) & CTX & CTX & 0.89 & 0.60 & 0.72 & 0.88 \\ HF BETO (SpaCy) & ST & ST & 0.84 & 0.79 & 0.81 & 0.94 \\ HF BETO (SpaCy) & CTX & CTX & 0.75 & 0.75 & 0.75 & 0.90 \\ \hline HF PIUBA-contextualized & – & ST & 1.00 & 0.07 & 0.13 & 0.82 \\ HF PIUBA-contextualized & – & CTX & 0.50 & 0.02 & 0.04 & 0.79 \\ HF multilingual-toxic-XLm-roberta & – & ST & 0.40 & 0.10 & 0.16 & 0.70 \\ HF multilingual-toxic-XLm-roberta & – & CTX & 0.79 & 0.20 & 0.32 & 0.65 \\ \hline \hline \end{tabular}
\end{table} 
Table 8: Evaluation results * for sexism and racism detection across multiple models. LogReg baseline, TextCatBOW, and transformer (BETO) models were fine-tuned on the Mediavida corpus using SpaCy, with single-turn (ST) and contextual (CTX) variants. For sexism detection, the single-turn TextCatBOW (SpaCy) model yielded a precision of 69%, a recall of 20%, an F1 score of 31%, and a ROC-AUC of 0.76. The contextualized variant of TextCatBOW improved significantly in recall (51%) and F1 score (57%), despite slightly reduced precision (64%), and exhibited a higher ROC-AUC of 0.81. The Transformer-based BETO model trained within the SpaCy pipeline delivered strong overall performance, particularly notable in its single-turn variant, achieving a precision of 59%, a high recall of 76%, an F1 score of 67%, and a robust ROC-AUC of 0.85. Its contextual variant similarly performed well, with balanced precision (64%), recall (71%), F1 score (67%), and the highest ROC-AUC among our models at 0.87. For racism detection, the single-turn TextCatBOW model demonstrated high precision (91%) but moderate recall (50%), resulting in an F1 score of 64% and ROC-AUC of 0.88. The contextualized TextCatBOW variant showed slightly lower precision (89%) but higher recall (60%), thus improving the F1 score to 72%, maintaining a ROC-AUC of 0.88. The Transformer-based BETO models significantly outperformed the other variants, particularly in the single-turn setting, obtaining a precision of 84%, a recall of 79%, an F1 score of 81%, and the highest ROC-AUC at 0.94. The contextualized BETO variant had a balanced precision and recall of 75%, resulting in an F1 score of 75% and ROC-AUC of 0.90. The Logistic Regression baseline, trained with TF-IDF features, presented competitive but generally lower performance. For sexism detection, the single-turn variant showed limited effectiveness with precision at 52%, recall at 24%, and F1 at 33% (ROC-AUC of 0.77). The contextual variant showed improvement across metrics (precision 72%, recall 43%, F1 54%, ROC-AUC 0.82).