---
license: apache-2.0
datasets:
- imdb
language:
- en
metrics:
- f1
pipeline_tag: text-classification
widget:
  - text: First off this movie is for kids and fans of Nintendo and the Mario franchise.cI still think an adult who isnt a fan could still enjoy it but this movie is so full of fan service that it will have you smiling the whole time. The voice acting I was skeptical but they all work and work well too. Jack Black is the star here. I love how they kept the story simple like all of the games. Truly felt like a video game on screen. This movie felt like a beautifully animated amusement park ride. The audio in the movie was amazing too. The sounds and the score with reimagined iconic music was perfect. Some of the songs in the movie felt unnecessary but they worked. I think they should've bumped the run time to 105-120 min. 90 min felt too short as it goes by quick. I havent had this much wholesome fun at the movies in a long time. If youre a fan you HAVE to see it.
    example_title: Positive Example
  - text: Flat, visual noise. Fundamentally incurious. Potentially injurious. The mystique generated by the characters in the games is here raked over and presented haphazardly by hacks. A hobbled attempt to explain a long and random evolution of characters who were never meant to be narratised fails. Doing it well is near impossible when you insist on EVERY LITTLE BIT OF LORE, from the last forty years being shoehorned into 90 minutes. Makes little sense, shamelessly leans on member berries to stimulate older viewers but offers nothing else. I feel sad for the animators who did a sterling job, but to no end as this movie has no soul.
    example_title: Negative Example
---

First posted in my [Kaggle](https://www.kaggle.com/code/wesleyacheng/movie-review-sentiment-classifier-with-bert).

I was just reading the IMDb reviews of [The Super Mario Bros. Movie](https://www.imdb.com/title/tt6718170/), I thought why don't we make a sentiment classifier to categorize movie reviews!

I made a Movie Review Sentiment Classifier using [transfer learning](https://en.wikipedia.org/wiki/Transfer_learning) on [BERT](https://ai.googleblog.com/2018/11/open-sourcing-bert-state-of-art-pre.html) with the [IMDb Movie Review Dataset](https://huggingface.co/datasets/imdb).