In this work, we build an Artificial Intelligence model that understands inference relations between English and Italian sentences. To do so, we leverage pre-trained Transformer-based sentence Encoders to understand and encode sentences into high-dimensional vectors. Then, we build and test different algorithms for comparing the encoded vectors and inferring relations between texts. We start from the simple and fast dot-product (that obtains an accuracy of 50.13% on the SNLI validation set), we fine-tune the encoder (obtaining a 15.26% increment) and we move to more complex algorithms: Support Vector Machines (that obtain an accuracy of 84.13% on the same set). At the end, we study 4 different Deep Learnig end-to-end models with 4 different attention heads (Fully Connected, Convolution, Convolution generalization and dot-product generalization) We detail the theory behind Transformers and why they outperformed state-of-the-art architectures on Natural Language Processing tasks.