Portfolio

Below is the list of projects I worked on. I believe that your work is as good as you can show it, that’s why I try to put each project in a demo-able format. For now I am using streamlit as a tool to demo the models i build.

Description

This is the simple model where you can analyze the sentiment of a single tweet by writing it in a text box, or you can also analyze a sentiment of hashtag.

The approach I used is utilizing transfer learning to fine-tune an existing Transformer based model ( specifically the distilbert-base-uncased). I fine-tuned the model on the Sentiment140 dataset.

Tech Stack

• Flair for NLP finetuning and data preprocessing.
• Huggingface for using the pipeline feature
• Tweepy for fetching the tweets from the twitter.
• Streamlit for building the demo app.

Quantized Neural Network for Object Detection

Description

This work was done as part of a project with a self driving car company. The aim was to take a pre-trained model and try to run it as fast as possible and as light as possible to be used for real-time object detection

The model we used is a MobileNetV2 pre-trained model on ImageNet with SSDLite object detector. The model was trained with Fp-32 data format.

We applied several model compression techniques to reduce the size of the model and monitor it’s performance. Some of the techniques we used are:

We got interesting results with the model. The model can reliably detect objects in images with same accuracy as the Fp-32 version while going as low as Int-8 data format.

Disclaimer: This work is not entirely my own. I was part of a team that worked on it.

Legal case classification

Description

This project was two folds, first we had to build a model that would classify a legal case based on the description entered into several categories. The aim was to provide a tool that will help lawyers to classify the cases faster and easier. Second, we had to extract the entities from the case description that were relevant to the classification.

Here’s a small sketch of how the system should behave.

This project was extremely interesting as we faced several technical challenges that I never encountered before. Some of the challenges:

• The data we got was very messy. It was a digitized version of PDF documents
• Each case in our training data was several hundred words long which made it difficult that we used some NLP models.