Performing Question-Answering with the Fine-Tuned BERT
Explore how to apply a fine-tuned BERT model for question answering by learning data preprocessing, tokenization, and feeding inputs to generate accurate answers from text passages.
Let's learn how to perform question answering with a fine-tuned question-answering BERT model.
Importing the modules
First, let's import the necessary modules:
from transformers import BertForQuestionAnswering, BertTokenizer
Loading the model
Now, we download and load the model. We use the bert-large-uncased-whole-word-masking-fine-tuned-squad model, which is fine-tuned on the Stanford Question-Answering Dataset (SQUAD):
model = BertForQuestionAnswering.from_pretrained('bert-large-uncased-whole-word-masking-fine-tuned-squad')
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