NLP with LLMs: Fine-tuning Models for Language Translation, & Summarization

Skillsoft issued completion badges are earned based on viewing the percentage required or receiving a passing score when assessment is required. Causal language modeling (CLM), text translation, and summarization demonstrate the versatility and depth of language understanding and generation by artificial intelligence (AI). Fine-tuning models help improve the performance of models for these specific tasks. ​In this course, you will explore CLM with DistilGPT-2 and masked language modeling (MLM) with DistilRoBERTa, learning how to prepare, process, and fine-tune models for generating and predicting text. Next, you will dive into the nuances of language translation, focusing on translating English to Spanish. You will prepare and evaluate training data and learn to use BLEU scores for assessing translation quality. You will fine-tune a pre-trained T5-small model, enhancing its accuracy and broadening its linguistic capabilities. Finally, you will explore the intricacies of text summarization. Starting with data loading and visualization, you will establish a benchmark using the pre-trained T5-small model. You will then fine-tune this model for summarization tasks, learning to condense extensive texts into succinct summaries.