This repository contains a DistilBERT model fine-tuned using the Hugging Face Transformers library on the IMDb movie review dataset. The model is trained for sentiment analysis, enabling the determination of sentiment polarity (positive or negative) within text reviews.

This paper describes Humor Analysis using Ensembles of Simple Transformers, the winning submission at the Humor Analysis based on Human Annotation (HAHA) task at IberLEF 2021.

The official repository for the PSYCHIC model

Multiclass classification on tweets about the coronavirus

This repository contains my work on the prevention and anonymization of dox content on Twitter. It contains python code and demo of the proposed solution.

A Deep Learning Based Voice Analytics toolkit

Fine tuning pre-trained transformer models in TensorFlow and in PyTorch for question answering

This app searches reddit posts and comments to determine if a product or service has a positive or negative sentiment and predicts top product mentions using Named Entity Recognition

We explored recent studies in Question Answering System. Then tried out 3 different QA models(BERT and DistilBERT) for the sake of learning.

Finetune the Transformer model 'DistilBERT' with PyTorch framework . Then inference on a dataset by using this fine-tuned model with the help of Pipeline.

Developing a feedback theory-informed natural language processing (NLP) model to enable large-scale evaluation of written feedback, and analysing a large set of feedback extracted from Moodle using this model to understand the presence of student-centred feedback elements, the commonality and differences in feedback provision across disciplines.

Performing named entity extraction task using Huggingface Transformers

Advanced NLP with Contextual Question Answering: This notebook extracts, cleans, and processes text data from multiple files. It utilizes transformer models for contextual question answering and sentence generation. Perfect for exploring cutting-edge NLP techniques and comparing transformer model performances.

Positive/negative sentiment model on cleaned text data using Distilbert NLP pre-trained model from Hugging Face

Deep learning for Natural Language Processing

This project involves analyzing and classifying the BoolQ dataset from the SuperGLUE benchmark. We implemented various classifiers and techniques, including rules-based logic, BERT, RNN, and GPT-3/4 data augmentation, achieving performance improvements.

Successfully developed a fine-tuned DistilBERT transformer model which can accurately predict the overall sentiment of a piece of financial news up to an accuracy of nearly 81.5%.

Finetuning DistilBERT on IMDB Dataset