Successfully developed a multiclass text classification model by fine-tuning pretrained DistilBERT transformer model to classify various distinct types of luxury apparels into their respective categories i.e. pants, accessories, underwear, shoes, etc.

In this project we have tried to do multi-label hate-speech classification in Bengali and Hindi language using fill-mask transformer models.

Code for a comparative analysis of the performance of fine-tuned transformer models on climate change data. The transformer models used were BERT, DistilBERT and RoBERTa.

Fine-tuned DistilBERT for binary sentiment analysis on IMDB movie reviews with token-level interpretability using LayerIntegratedGradients

The project involves developing a proof-of-concept system for classifying financial excerpts into predefined categories using Natural Language Processing (NLP) techniques.

Successfully established a multiclass text classification model by fine-tuning pretrained DistilBERT transformer model to classify several distinct types of mental health statuses such as anxiety, stress, personality disorder, etc. with an accuracy of 77%.