Sentiment Analysis
Sentiment analysis neural network trained by fine-tuning BERT, ALBERT, or DistilBERT on the Stanford Sentiment Treebank.
Setup Sentiment Analysis
Clone Sentiment Analysis, update lists of packages, install Pyton 3.9
git clone https://github.com/barissayil/SentimentAnalysis.git
cd SentimentAnalysis
sudo apt-get update
sudo apt-get install python3.9
Verify that you have Python 3.9.10
python3.9 --version
Create virtual environment with Python 3.9.10 binary, activate it
python3.9 -m venv env
source env/bin/activate
Install necessary packages
pip install -r requirements.txt
Test it and verify that it passes all tests
python -m pytest
Use Sentiment Analysis with my model
Evaluate
python evaluate.py
Analyze your inputs
python analyze.py
Run server, and verify that you get back 99% positive
python server.py
curl localhost:5000 -G -d text=good
Train your own model and use Sentiment Analysis with it
Train (i.e.fine-tune) BERT
python train.py --model_name_or_path bert-base-uncased --output_dir XXX --num_eps 2
bert-base-uncased, albert-base-v2, distilbert-base-uncased, and other similar models are supported.
Evaluate
python evaluate.py --model_name_or_path XXX
Analyze your inputs
python analyze.py --model_name_or_path XXX
Run server
python server.py --model_name_or_path XXX