This paper introduces BigBERT, a modified version of BERT designed for large-scale text classification tasks. BigBERT achieves state-of-the-art results on several benchmarks.
This article provides a step-by-step guide to using BERT for text classification tasks, including data preparation, model fine-tuning, and hyperparameter tuning.
This dataset provides a collection of text classification datasets pre-processed for use with BERT-based models. The dataset includes a range of tasks, from sentiment analysis to topic modeling.
This paper explores the use of transfer learning and BERT for large-scale text classification tasks. The authors demonstrate the effectiveness of this approach on several real-world datasets.
This video tutorial demonstrates how to use BERT and scikit-learn for text classification tasks. The video covers data preparation, model training, and evaluation metrics.
This survey paper provides an overview of the current state of BERT-based text classification research. The authors discuss the strengths and limitations of BERT and identify areas for future research.
This report discusses the role of big data in text classification research from a government perspective. The report highlights the potential applications and challenges of using big data for text classification tasks.
This tutorial provides a comprehensive introduction to using BERT for text classification tasks. The tutorial covers the basics of BERT, data preparation, and model fine-tuning.