What is masked lm in BERT?

What is masked lm in BERT?

Masked LM (MLM) Before feeding word sequences into BERT, 15% of the words in each sequence are replaced with a [MASK] token. The model then attempts to predict the original value of the masked words, based on the context provided by the other, non-masked, words in the sequence.

Why MASK is used in BERT?

In the original paper of BERT it is said: Note that the purpose of the masking strategies is to reduce the mismatch between pre-training and fine-tuning, as the [MASK] symbol never appears during the fine-tuning stage.

What is MASK in BERT?

MLM consists of giving BERT a sentence and optimizing the weights inside BERT to output the same sentence on the other side. So we input a sentence and ask that BERT outputs the same sentence. However, before we actually give BERT that input sentence — we mask a few tokens.

What is BERT good for?

BERT is designed to help computers understand the meaning of ambiguous language in text by using surrounding text to establish context. The BERT framework was pre-trained using text from Wikipedia and can be fine-tuned with question and answer datasets.

What is next sentence prediction BERT?

Next Sentence Prediction (NSP) In the BERT training process, the model receives pairs of sentences as input and learns to predict if the second sentence in the pair is the subsequent sentence in the original document.

What can you use Bert for in NLP?

Typical uses would be fine tuning BERT for a particular task or for feature extraction. BERT generates multiple, contextual, bidirectional word representations, as opposed to its predecessors ( word2vec, GLoVe ).

How does Bert work to train specific models?

In the general run of things, to train task-specific models, we add an extra output layer to existing BERT and fine-tune the resultant model — all parameters, end to end.

How is Bert fine tuned for MRPC task?

BERT language model is fine tuned for MRPC task ( sentence pairs semantic equivalence ). For example, if input sentences are: Ranko Mosic is one of the world foremost experts in Natural Language Processing arena. In a world where there aren’t that many NLP experts, Ranko is the one.

How are parameter reduction techniques used in Bert?

ALBERT by Google and more — This paper describes parameter reduction techniques to lower memory reduction and increase the training speed of BERT models. RoBERTa by Facebook — This paper for FAIR believes the original BERT models were under-trained and shows with more training/tuning it can outperform the initial results.