It's Smart-Question Answering System on short as well as long documents. It can automatically find answers to matching questions directly from documents. The deep learning language model converts the questions and documents to semantic vectors to find the matching answer.

❓Fine-tuning BERT for extractive QA on SQuAD 2.0

WSD for Word-in-Context (WiC) disambiguation, experimenting with BERT feature-based and fine-tuning approaches (GlossBERT)

Successfully developed a resume classification model which can accurately classify the resume of any person into its corresponding job with a tremendously high accuracy of more than 99%.

Turkish Hate Speech Detection

Initially implement Document-Retrieval-System with SBERT embeddings and evaluate it in CORD-19 dataset. Afterwards, fine tune BERT model with SQuAD.v2 dataset so as to evaluate it in Question Answering task.

Successfully developed a fine-tuned BERT transformer model which can effectively perform emotion classification on any given piece of texts to identify a suitable human emotion based on semantic meaning of the text.

Using BERT models to perform sentiment analysis on women's clothing

A comprehensive guide for beginners looking to start fine-tuning BERT models for sentiment analysis on Arabic text. This project walks through the complete process of data preprocessing, model training, and evaluation, providing a beginner-friendly tutorial on how to fine-tune and deploy machine learning models for real-world applications.

External Knowledge Infusion using INCIDecoder into BERT for Chemical Mapping

Notebooks for fine-tuning and evaluating pre-trained BERT models to tasks for semantic search in Web API documentation

Successfully developed a news category classification model using fine-tuned BERT which can accurately classify any news text into its respective category i.e. Politics, Business, Technology and Entertainment.

Successfully developed a resume classification model which can accurately classify the resume of any person into its corresponding job with a tremendously high accuracy of more than 99%.

A PyTorch Lightning Implementation of Multi-Language Identification using a SentenceTransformer model pre-trained on English. Work done while interning at ByteFuse.

Question Answering with a Fine-Tuned BERT

Notebooks for fine-tuning and evaluating a pre-trained BERT model to the task of semantic parameter matching in Web APIs

Final project for class Data Mining and Text Mining; University of Illinois at Chicago (Fall 2023); 1st year of Master's Degree in Computer Science coursework.

Successfully established a Seq2Seq with attention model which can perform English to Spanish language translation up to an accuracy of almost 97%.