In this paper we present our approach and system description on Task 5 A in MAMI: Multimedia Automatic Misogyny Identification. In our experiments we compared several architectures based on deep learning algorithms with various other approaches to binary classification using Transformers, combined with a nudity image detection algorithm to provide better results. With this approach, we achieved an F1-score of 0.665 in the evaluation process

In this paper we present our approach and system description on iSarcasmEval: a SemEval task for intended sarcasm detection on social networks. This derives from our participation in SubTask A: Given a text, determine whether it is sarcastic or non-sarcastic. In our approach to complete the task, a comparison of several machine learning and deep learning algorithms using two datasets was conducted. The model which obtained the highest values of F1-score was a BERT-base-cased model. With this one, an F1-score of 0.2451 for the sarcastic class in the evaluation process was achieved. Finally, our team reached the 30th position.