Zero-shot text classification using trnasformers and TARSclassifier.
In this piece, we will delve into the fascinating realm of zero-shot learning, and explore how it can effectively address the challenge of text classification. Zero-Shot Learning Let us begin by establishing a clear distinction between traditional machine learning and zero-shot learning. Traditional machine learning involves training models using extensive datasets and making predictions based on the acquired knowledge during the training phase.
Sclassifier. Selecting the appropriate transformer model or Sclassifier for a comprehensive understanding. Before getting into the implementation, we will first install the required packages. !pip3 install transformers Having installed the packages, we will now start with the transformer implementation of zero-shot text classification. facebook/bart-large-mnli The bart-large-mnli model, a creation of Facebook researchers, stands as a testament to advancements in natural language processing.
Sclassifier In addition to transformers, zero-shot text classification can be effectively implemented using the Flair library, leveraging the power of the
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