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A Beginner Guide to Incorporating Tabular Data via HuggingFace Transformersby@codekgu
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A Beginner Guide to Incorporating Tabular Data via HuggingFace Transformers

by Ken Gu7mNovember 11th, 2020
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Transformer-based models are a game-changer when it comes to using unstructured text data. The top-performing models in the General Language Understanding Evaluation (GLUE) benchmark are all BERT models. Transformer models can learn long-range dependencies between text and can be trained in parallel (as opposed to sequence to sequence models), meaning they can be pre-trained on large amounts of data. We set out to explore how text and tabular data could be used together to provide stronger signals in our projects.

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Ken Gu

Ken Gu

@codekgu

Passionate about NLP and Graph Deep Learning Research. Georgian. UCLA

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Ken Gu@codekgu
Passionate about NLP and Graph Deep Learning Research. Georgian. UCLA

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