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Determine the polarity of amazon fine food reviews using ULMFiT, BERT, XLNet and RoBERTa

Jupyter Notebook 100.00%
fastai nlp-machine-learning sentiment-analysis pytorch torchtext bert pytorch-transformers xlnet-pytorch xlnet roberta

amazon-fine-food-reviews's Introduction

Amazon Fine Food Reviews

Determine the polarity of amazon fine food reviews. This dataset consists of reviews of fine foods from amazon. The data span a period of more than 10 years, including all ~500,000 reviews up to October 2012. Reviews include product and user information, ratings, and a plain text review. It also includes reviews from all other Amazon categories.
Dataset can be downloaded from here.

Models used

  • TFIDF Vectorizer with Logistic Regression.
  • ULMFiT with FastAI
  • LSTM using Pytorch and Torchtext.
  • BERT using TensorFlow and Keras.
  • BERT using Pytorch-Transformers.
  • XLNet using Pytorch-Transformers.
  • RoBERTa using Pytorch-Transformers.

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