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PROJECT


Similarity Detection in English Language Using RoBERTa

Learn to use RoBERTa to detect similarity in English language texts.

Similarity Detection in English Language Using RoBERTa

You will learn to:

Load and preprocess the Webis Crowd Paraphrase Corpus 2011 dataset.

Use the Hugging Face Transformers library to download the pretrained RoBERTa model.

Train, validate, and test the model for similarity detection in English language texts.

Evaluate the model and display performance metrics using the scikit-learn library.

Skills

Natural Language Processing

Deep Learning

Machine Learning

Deep Neural Networks

Prerequisites

Basic understanding of deep learning concepts

Familiarity with natural language processing (NLP) concepts

Intermediate knowledge of Python programming and libraries

Familiarity with BERT or RoBERTa architecture

Technologies

NumPy

Pandas

PyTorch

Matplotlib

Scikit-learn

Project Description

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.

Project Tasks

1

Get Started

Task 0: Introduction

Task 1: Import Libraries

2

Data Preprocessing

Task 2: Load and Preprocess the Data

Task 3: Split the Dataset

3

Implementing RoBERTa

Task 4: Tokenize Datasets

Task 5: Generate Tensors

Task 6: Load Pretrained RoBERTa Model and Set Device

Task 7: Prepare Training Arguments and Create Trainer Object

Task 8: Train the Model

Task 9: Test the Model and Calculate Accuracy

4

Generating Performance Metrics

Task 10: Compute and Display the Confusion Matrix

Congratulations!