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Fine-tuning pretrained NLP models with Huggingface's Trainer

#artificialintelligence

In the code above, the data used is a IMDB movie sentiments dataset. The data allows us to train a model to detect the sentiment of the movie review- 1 being positive while 0 being negative. This is a NLP task of sequence classification, as we want to classify each review(sequence of text) into positive or negative. There are many pretrained models which we can use to train our sentiment analysis model, let us use pretrained BERT as an example. There are many variants of pretrained BERT model, bert-base-uncased is just one of the variants.


BERT to the rescue!

#artificialintelligence

I assume that you're more or less familiar with what BERT is on a high level, and focus more on the practical side by showing you how to utilize it in your work. Roughly speaking, BERT is a model that knows to represent text. You give it some sequence as an input, it then looks left and right several times and produces a vector representation for each word as the output. In their paper, the authors describe two ways to work with BERT, one as with "feature extraction" mechanism. That is, we use the final output of BERT as an input to another model.


BERT to the rescue!

#artificialintelligence

In this post, I want to show how to apply BERT to a simple text classification problem. I assume that you're more or less familiar with what BERT is on a high level, and focus more on the practical side by showing you how to utilize it in your work. Roughly speaking, BERT is a model that knows to represent text. You give it some sequence as an input, it then looks left and right several times and produces a vector representation for each word as the output. In their paper, the authors describe two ways to work with BERT, one as with "feature extraction" mechanism.


How to Fine-Tune a Transformer Architecture NLP Model -- Visual Studio Magazine

#artificialintelligence

The goal is sentiment analysis -- accept the text of a movie review (such as, "This movie was a great waste of my time.") This article describes how to fine-tune a pretrained Transformer Architecture model for natural language processing. More specifically, this article explains how to fine-tune a condensed version of a pretrained BERT model to create binary classifier for a subset of the IMDB movie review dataset. The goal is sentiment analysis -- accept the text of a movie review (such as, "This movie was a great waste of my time.") You can think of a pretrained transformer architecture (TA) model as sort of an English language expert.