This shirt is very flattering to all due to th. I had such high hopes for this dress and reall. Download & ParseĪbsolutely wonderful - silky and sexy and comf. You can find the Colab Notebook version of this post here. In our weekly discussion group, I talked through this Notebook and we also met with Ken Gu, the author of the Multi-Modal Toolkit! You can watch the recording here.
The toolkit implements a number of more complicated techniques, but their benchmarks (as well as our results below) show that this simple features-to-text strategy works best for this dataset. There is a GitHub project called the Multimodal-Toolkit which is how I learned about this clothing review dataset. Specifically, I make text out of the additional features, and prepend this text to the review. Then, in Section 3, I’ve implemented a simple strategy to combine everything and feed it through BERT. In Section 2 of this Notebook, I’ve implemented four different “baseline” strategies which score fairly well, but which don’t incorporate all of the features together. This dataset was scraped from an (un-specified) e-commerce website by Nick Brooks and made available on Kaggle here. The department and category of the clothing item (categorical features)įor each review, we also have a binary label, which is whether or not the reviewer ultimately recommends the item.The number of upvotes on the review (numerical feature).The age of the reviewer (numerical feature).For each review, we have the review text, but also additional information such as:
To help motivate our discussion, we’ll be working with a dataset of about 23k clothing reviews. In this tutorial we’ll look at the topic of classifying text with BERT, but where we also have additional numerical or categorical features that we want to use to improve our predictions.
Combining personal image plus text for poster archive#
Chris McCormick About Membership Blog Archive Become an NLP expert with videos & code for BERT and beyond → Join NLP Basecamp now! Combining Categorical and Numerical Features with Text in BERT
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