Similarity Detection in English Language Using RoBERTa

RoBERTa (Robustly Optimized BERT Approach) is a transformer-based language model introduced by Liu et al. in 2019. It is a variant of BERT (Bidirectional Encoder Representations from Transformers) that achieves state-of-the-art performance on various natural language processing (NLP) tasks. RoBERTa uses a larger training dataset and longer training duration compared to BERT, and removes the next sentence prediction (NSP) task from the pretraining process. These optimizations make RoBERTa more robust and effective for a variety of tasks such as sentence classification, named entity recognition, and question answering, etc.

In this project, we'll use the RoBERTa model from the Transformers library with the Webis Crowd Paraphrase Corpus 2011 dataset for similarity detection in English language texts. We'll preprocess the data by tokenizing the text using RobertaTokenizer, split it into training, validation, and test sets, and create PyTorch datasets. We'll then fine-tune a pretrained model using the Trainer class from the Transformers library with specified training arguments such as number of epochs, batch size, learning rate, etc. Finally, we'll evaluate the trained model on the test set, calculate accuracy, compute the confusion matrix, and display it using Matplotlib.