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BERT is an encoder-only Transformer language model introduced in 2018 to learn contextual text 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 self-supervised learning, enabling strong performance across many natural language processing (NLP) tasks. BERT is widely used as a baseline model in NLP experiments and is especially effective for understanding relationships between words and sentences in context. BERT’s core training objective is designed to produce bidirectional context-aware token embeddings. During pre-training, it learns from two tasks: masked language modeling (predicting masked words from surrounding context) and next sentence prediction (predicting whether one sentence follows another). Architecturally, BERT uses an encoder-only Transformer (no causal masking), and after pre-training it can be fine-tuned with fewer resources for specific downstream tasks such as sentiment classification, question answering, and other language understanding problems.
BERT is an encoder-only Transformer language model introduced in 2018 to learn contextual text representations.
Its purpose is to provide strong, general-purpose embeddings that can be fine-tuned for many NLP tasks.
BERT is pre-trained using masked language modeling and next sentence prediction to learn bidirectional context and sentence-level relationships.
A bidirectional, encoder-only Transformer language model that learns contextual token representations via self-supervised pre-training.
A pre-training task where BERT predicts randomly masked tokens using surrounding left and right context.
A pre-training task where BERT predicts whether one sentence logically follows another.
A Transformer architecture used by BERT that processes input with self-attention without causal (left-to-right) masking.
Adapting a pre-trained BERT model to a specific downstream task by training task-specific layers on smaller labeled datasets.
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