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- Explain what the task is generally
- Explain how it is done
- What is an ontology - importance
Pre-training and fine-tuning BERT
BERT, Bidirectional Encoder Representations from Transformers, is a widely used transformer-based language model designed for various natural language processing tasks, including classification. It consists of two types of training procedures:
During pre-training, BERT is trained on a large corpus of English text in a self-supervised manner. This means it is trained on large-scale, raw, unlabeled text without human annotations, using an automatic process to generate input-output pairs from the text.
During fine-tuning, BERT is first initialized with its pre-trained parameters,
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and then all parameters are fine-tuned using labeled data from downstream tasks, allowing it to adapt to specific applications.
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BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding
LLMs and Hugging Face
- Explain Pretrained LLMs like BERT used
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