DistilBERT is a compact and efficient Transformer model that inherits the architecture of BERT. It is designed to be smaller, faster, and more cost-effective compared to its predecessor, making it ideal for tasks where computational resources are limited or speed is crucial. Despite its reduced size, DistilBERT retains much of the original BERT model's performance, making it suitable for various natural language processing tasks, such as text classification, sentiment analysis, and question answering.
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