In this blog, we discuss various types of learning paradigms present in NLP, notations often used in the prompt-based learning paradigm, demo applications of prompt-based learning, and discuss some design considerations to make while designing a prompting environment.
We couldn't pass up the chance to talk to Rohan Tondulkar, a Senior Research Scientist at SciSpace using machine learning to make scientific literature more approachable and collaboration more accessible.
In this article, we looked at the basic elements of an end-to-end Automatic Speech Recognition pipeline, the major challenges encountered with these pipelines, and some of the potential solutions.
In this blog post, we learn how to build an unsupervised NLP pipeline for automatically extracting/generating glossaries and associated definitions from a given text document like a book/chapter/essay.
Follow this blog post to learn about several of the best metrics used for evaluating the quality of generated text, including: BLEU, ROUGE, BERTscore, METEOR, Self-BLEU, and Word Mover's Distance. We then show how to use them in a Gradient Notebook.
In this tutorial, we look at and implement the pipeline for running zero-shot text classification with Hugging Face on a Gradient Notebook.
This article explores, suggests, and breaks down four popular techniques for augmenting data meant to be used with NLP.
In this tutorial, we examine how the BERT language model works in detail before jumping into a coding demo. We then showed how to fine-tune the model for a particular text classification task.
Follow this guide to learn how to set up and use GPT-NeoX-20B within Paperspace Gradient to generate text in response to an inputted prompt.
