Fine-Tuning BERT for Downstream Tasks
Explore how to fine-tune pre-trained BERT models for various NLP downstream tasks including sentiment analysis, named entity recognition, question answering, and natural language inference. Understand the process of updating BERT weights during fine-tuning versus using it as a feature extractor, and learn practical methods for adapting BERT to specific classification challenges.
Let's learn how to fine-tune the pre-trained BERT model for downstream tasks. Note that fine-tuning implies that we are not training BERT from scratch; instead, we are using the pre-trained BERT and updating its weights according to our task.
Downstream tasks
We will learn how to fine-tune the pre-trained BERT model for the following downstream tasks:
Text classification
Natural language inference (NLI)
Named entity recognition (NER)
Question-answering
Text classification
Let's learn how to fine-tune the pre-trained BERT ...