Transfer Learning in Natural Language Processing: Leveraging Pretrained Models for Improved Performance on Specific Tasks
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Abstract
Transfer learning has emerged as a powerful technique in natural language processing (NLP), allowing pretrained models to be leveraged for improved performance on specific tasks. This paper provides an overview of transfer learning in NLP, highlighting its benefits, challenges, and applications. Pretrained models, such as BERT, GPT, and RoBERTa, have been trained on large-scale corpora and demonstrate strong performance across a wide range of NLP tasks. By fine-tuning these models on task-specific data, researchers and practitioners can achieve state-of-the-art results with minimal computational resources. However, challenges such as domain adaptation, dataset size, and model selection remain areas of active research. various transfer learning techniques in NLP, including feature-based methods, fine-tuning approaches, and multitask learning. Additionally, it explores applications of transfer learning in sentiment analysis, named entity recognition, question answering, and other NLP tasks. Overall, transfer learning offers a promising avenue for advancing NLP research and applications, enabling models to learn from large-scale datasets and generalize to diverse tasks and domains.
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