Introduction to Transformers and BERT
The transformer architecture has become a foundational model in natural language processing (NLP), powering many state-of-the-art approaches for transfer learning. At its core, the transformer is built around the concept of self-attention, which allows the model to weigh the importance of each word in an input sequence relative to every other word. Unlike traditional recurrent neural networks (RNNs) that process tokens sequentially, transformers process all tokens in parallel, enabling much faster training and better handling of long-range dependencies. The architecture is divided into two main parts: the encoder, which processes the input sequence, and the decoder, which generates output sequences. In many NLP tasks, only the encoder or the decoder is used, depending on the task requirements.
BERT, which stands for bidirectional encoder representations from transformers, is pre-trained using two main objectives: masked language modeling (predicting masked words in a sentence) and next sentence prediction (determining if one sentence logically follows another). These objectives enable BERT to learn deep, contextual representations of language that transfer well to downstream tasks.
BERT's design makes it highly effective for transfer learning in NLP. After pre-training on large text corpora, you can adapt BERT to specific tasks such as sentiment analysis, named entity recognition, or question answering. This adaptation is typically done by adding a small task-specific layer (like a classifier) on top of BERT's encoder output, then fine-tuning the entire model or just the new layer on your labeled dataset. Because BERT has already learned rich language features, you often need much less task-specific data and training time to achieve strong results.
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The transformer architecture has become a foundational model in natural language processing (NLP), powering many state-of-the-art approaches for transfer learning. At its core, the transformer is built around the concept of self-attention, which allows the model to weigh the importance of each word in an input sequence relative to every other word. Unlike traditional recurrent neural networks (RNNs) that process tokens sequentially, transformers process all tokens in parallel, enabling much faster training and better handling of long-range dependencies. The architecture is divided into two main parts: the encoder, which processes the input sequence, and the decoder, which generates output sequences. In many NLP tasks, only the encoder or the decoder is used, depending on the task requirements.
BERT, which stands for bidirectional encoder representations from transformers, is pre-trained using two main objectives: masked language modeling (predicting masked words in a sentence) and next sentence prediction (determining if one sentence logically follows another). These objectives enable BERT to learn deep, contextual representations of language that transfer well to downstream tasks.
BERT's design makes it highly effective for transfer learning in NLP. After pre-training on large text corpora, you can adapt BERT to specific tasks such as sentiment analysis, named entity recognition, or question answering. This adaptation is typically done by adding a small task-specific layer (like a classifier) on top of BERT's encoder output, then fine-tuning the entire model or just the new layer on your labeled dataset. Because BERT has already learned rich language features, you often need much less task-specific data and training time to achieve strong results.
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