/BERT_XLNet-sentiment

Primary LanguagePython

BERT/XLNet Sentiment Classification with Flask

  1. To use fine-tune BERT for sentiment classification with HuggingFace's BERTForSequenceClassification on the IMDB dataset and then to serve the fine-tuned BERT model using Flask.

  2. To experiment, understand and compare XLNet with BERT.

Dataset

The motivation to using this dataset because it's already quite clean and has balanced classes: 50% positive and 50% negative reviews.

Web application

review_page

results_page

thankyou_page

XLNet Fine-tuning

Using a single P100 GPU, these are the parameters that are used in order to fine-tune XLNet.

In train.py:

    train_dataset = IMDBXLNet(review=df_train.review.values, 
                          label=df_train.sentiment.values,
                          max_len=768)

    valid_dataset = IMDBXLNet(review=df_valid.review.values, 
                          label=df_valid.sentiment.values,
                          max_len=768)

    model = XLNetSentiment(train_dset=train_dataset, 
                           eval_dset=valid_dataset, 
                           train_batch_size = 6,
                           eval_batch_size = 6)