Tweet Sentiment Extraction with Bert - Serving the app with Flask

Repo with code for training your own Bert model fine tuned on imdb dataset to serve it on a webapp using Flask.

Demo

Getting Started

Clone repository, install requirements, download datasets and pretrained bert model.

Prerequisites

Must have a GPU for training and inference. You can train on CPU but this will very likely burn up your machine :). Inference can be done through CPU, yet will not be very efficient.

Download following datasets and put them in input file (see config.py for paths I used):

• https://www.kaggle.com/lakshmi25npathi/imdb-dataset-of-50k-movie-reviews
• https://www.kaggle.com/abhishek/bert-base-uncased

Requirements

• boto3==1.13.9
• botocore==1.16.9
• certifi==2020.4.5.1
• chardet==3.0.4
• click==7.1.2
• docutils==0.15.2
• filelock==3.0.12
• flask==1.1.2
• future==0.18.2
• idna==2.9
• itsdangerous==1.1.0
• jinja2==2.11.2
• jmespath==0.10.0
• joblib==0.14.1
• markupsafe==1.1.1
• numpy==1.18.4
• pandas==0.25.3
• python-dateutil==2.8.1
• pytz==2020.1
• regex==2020.5.13
• requests==2.23.0
• s3transfer==0.3.3
• sacremoses==0.0.43
• scikit-learn==0.22.2.post1
• scipy==1.4.1
• sentencepiece==0.1.90
• six==1.14.0
• sklearn==0.0
• tokenizers==0.5.2
• torch==1.5.0
• tqdm==4.46.0
• transformers==2.5.1
• urllib3==1.25.9 ; python_version != '3.4'
• werkzeug==1.0.1

Acknowledgments

Thanks to Abhishek Thakur (x3 GM on Kaggle) for providing awesome tutorials.