Masked Word Prediction Using Transformer Models

I think therefore I [MASK]

Implementing a Transformer model can be daunting. To make this process easier, my team published a package called Happy Transformer that allows programmers to implement Transformer models for various tasks with just a few lines of code. In this article, we’ll be discussing how to implement popular Transformer models to perform masked word prediction using Happy Transformer.

Simply install Happy Transformer from PyPi using pip.

Prediction

text (required): A string that contains a single “[MASK]” substring.

targets: Visit the “Prediction With Targets” section to learn more.

Output:

[WordPredictionResult(token=’health’, score=0.12805598974227905), WordPredictionResult(token=’science’, score=0.0797640010714531)]

The method returns a list of dataclass objects with variables token and score. The token variable contains the prediction in the form of a string, while the score variable contains the probability for the answer. Answers are returned in descending order by score.

Extracting Results

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