Project Fine-Tuning and Comparing performance of DistilBERT, BERT, and BERT Large (cased) models using Hugging Face
Table of Contents
Fine-tuning DistilBERT, BERT, and Large BERT cased models for sentiment analysis of financial data and creating a user interface that allows a user to select one of the models and enter a sentence for analysis.
Required Libraries/Frameworks:
HuggingFace, PyTorch, Scikit-Learn, Seaborn, Numpy, and Pandas
Recommended:
PyTorch built with CUDA and NVidia GPU
- Clone the Repo
git clone https://github.com/zjshermanburke/FineTuningBERT.git- Run all cells in the Jupyter Notebook
Run the Python script "Model_UI.py", enter a model (DistilBERT, BERT, or LargeBERT), and when prompted enter a statement. This will return a sentiment (Negative, Positive, or Neutral) and the BERT score.
Distributed under the Unlicense license. See LICENSE.txtfor more information.
Zachary Sherman-Burke - LinkedIn - zjshermanburke@gmail.com
Project Link: https://github.com/zjshermanburke/FineTuningBERT