In this article, we have discussed some of the most popular datasets that are used in Question Classification. Further, we implemented these text corpus using Pytorch and TensorFlow.
Questions Classification assumes a significant part in question answering systems, with one of the most important steps in the enhancement of the classification process being the identification of question types. The main aim of question classification is to anticipate the substance kind of the appropriate response of a natural language processing. Question order is regularly done using machine learning procedures.
For example, Naive Bayes, k-Nearest Neighbors, and SVM calculation can be utilized to actualize the question classification. In some cases, Bag-of-Words and n-grams are the highlights used in the AI approach.
The recent development in deep learning has demonstrated its ability in question classification. The CNN architecture models are equipped for extricating the elevated level highlights from the local text by window filters. Distinctive lexical, grammatical, and semantic highlights can be extracted from a question.
Here, we will discuss some of the popular datasets and their code implementation using TensorFlow and Pytorch. Further, we will discuss some of the benchmark models that gave high accuracy on these datasets.
Most Benchmarked Datasets for Question Answering in NLP with implementation in PyTorch, Keras, and TensorFlow. Question Answering is a technique that consequently answers the addresses presented by people in natural language processing.
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A collection of datasets ready to use with TensorFlow or other Python ML frameworks, such as Jax, enabling easy-to-use and high-performance input pipelines.
A Comprehensive Guide To Fine-Tuning BERT For Text Classification And SQuAD Tasks. Fine Tuning BERT for Text Classification and Question Answering using TensorFlow Framework
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