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BERT is an encoder-only Transformer model designed to produce contextual token representations.
BERT (Bidirectional Encoder Representations from Transformers) is a language model introduced by Google in October 2018. Its purpose is to learn contextual representations of text by converting input tokens into vectors using a Transformer encoder-only architecture. BERT is widely used as a baseline in NLP because it can be adapted to many tasks with relatively small amounts of task-specific data. BERT is trained using self-supervised objectives, primarily masked language modeling (predicting masked tokens from their surrounding context) and next sentence prediction (predicting whether one sentence follows another). This training enables BERT to capture bidirectional context—understanding how words relate to both the left and right sides of their usage—making it effective for natural language understanding tasks such as classification, question answering, and other text interpretation problems.
BERT is an encoder-only Transformer model designed to produce contextual token representations.
It is trained with masked language modeling and next sentence prediction to learn bidirectional context.
After pre-training, BERT can be fine-tuned for many downstream NLP tasks with fewer resources.
A Transformer-based language model that learns bidirectional contextual representations of tokens for NLP tasks.
A pre-training task where some tokens are masked and the model predicts the original tokens from surrounding context.
A pre-training task where the model predicts whether one sentence logically follows another.
A Transformer architecture variant that uses only the encoder stack (no decoder) to produce contextual embeddings.
The process of adapting a pre-trained BERT model to a specific downstream task by training task-specific layers on labeled data.
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