This article discusses the use of pre-trained language models for text classification tasks, highlighting their ability to capture nuanced linguistic patterns and relationships.
A tutorial on using pre-trained language models like BERT and RoBERTa for text classification, including code examples and performance comparisons.
A comprehensive survey of pre-trained language models for text classification, covering their architecture, training objectives, and applications.
A tutorial on using the Hugging Face Transformers library to fine-tune pre-trained language models for text classification tasks, including sentiment analysis and topic modeling.
A research paper on adapting pre-trained language models for low-resource languages, including experiments on text classification and machine translation.
A video tutorial on using pre-trained language models for text classification, covering data preparation, model selection, and hyperparameter tuning.
A blog post on best practices for using pre-trained language models for text classification, including data preprocessing, model selection, and evaluation metrics.
An official course website from Stanford University, covering the use of pre-trained language models for text classification, sentiment analysis, and question answering.