Pre-training refers to the initial phase in training a machine learning model where it learns from a large dataset before being fine-tuned on a specific task. This approach allows models to acquire general language understanding and capture patterns in the data, which can be leveraged later for more focused applications. Pre-training is crucial in developing embeddings for sentences and documents, as well as enhancing the effectiveness of attention mechanisms and transformer architectures.
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