BERT, a cutting-edge transformer model, has proven its prowess in complex NLP tasks, such as NLU, NLG, Sentiment Analysis, and Text Classification. Its exceptional performance often surpasses human capabilities.
Conduct text classification on the AG News dataset using the state-of-the-art transformer model, BERT.
The AG News dataset (AG’s News Corpus) is a subset of AG's corpus, constructed by combining titles and description fields from articles in the four major classes: "World," "Sports," "Business," and "Sci/Tech" of AG’s Corpus.
-
Install the required packages specified in the
requirements.txt
file.For Anaconda:
conda create --name <yourenvname> conda activate <yourenvname> pip install -r requirements.txt
For Python Interpreter:
pip install -r requirements.txt
-
The entire repository is modularized into distinct sections, each handling specific tasks. Start by navigating to the
src
folder.Within
src
:ML_Pipeline
: Contains modules with function declarations for various Machine Learning tasks.engine.py
: The core of the project, orchestrating all function calls.
-
Run/Debug the
engine.py
file, and the necessary steps will be automated according to the defined logic. -
Input datasets are stored in the
input
folder. -
Predictions and models are saved in the
output
folder.