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Frequently Asked Questions
How do you optimize a BERT?
We can optimize BERT using the below steps:
We should write content for people to understand and not for the bots.
We should try to understand our audience before writing the content.
We should make our language simple and easy to understand.
On-Page SEO is a must when we want our content to work.
What is the output of the BERT model?
The output of the BERT model is a vector with a hidden size. If we want the output to be a classifier from this model, we can take the output corresponding to the CLS token.
How does BERT make predictions?
The BERT NLP model uses Masked LM(MLM). It is a powerful training mechanism where BERT randomly masks words and tries to predict them in the sentence. Because BERT is a bi-directional model, it will look from both directions.
How do BERT models train?
We can train the BERT models using the following steps:
We should install pip transformers.
We must initialize a pre-trained transformer model from _pretrained.
We should test it with some random data.
We can fine-tune the model and then train the model again.
Is the BERT model supervised or unsupervised?
The BERT model is an unsupervised language representation. It is a deep bidirectional, pre-trained model which uses plain text corpus.
Can BERT be used for any language?
BERT is multilingual. It was pre-trained on mono-lingual text in 104 languages. We can use a WordPiece Tokenizer for mapping these texts into a shared vocabulary. Because of its multi-lingual pre-training phase, BERT can be fine-tuned into any language and can perform any task.