sentimentizer

Introduction

Beta release, api subject to change. Install with:

pip install sentimentizer

This repo contains Neural Nets written with the pytorch framework for sentiment analysis. Small models can be pretty effective for classification tasks at a much smaller cost to deploy. This package focuses on sentiment analysis and all models were trained on a single 2080Ti gpu in minutes. Deploying models for inference requires less than 1GB of memory which makes creating multiple containers relatively efficient.

Usage

# where 0 is very negative and 1 is very positive
from sentimentizer.tokenizer import get_trained_tokenizer
from sentimentizer.models.rnn import get_trained_model

model = get_trained_model(64, 'cpu')
tokenizer = get_trained_tokenizer()
review_text = "greatest pie ever, best in town!"
positive_ids = tokenizer.tokenize_text(review_text)
model.predict(positive_ids)
  
>> tensor(0.9701)

Install for development with miniconda:

conda create -n {env}  
conda install pip  
pip install -e .  

Retrain model

To rerun the model:

• get the yelp dataset,
• get the glove 6B 100D dataset
• place both files in the package data directory
• run the training script in workflows