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

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.

This project classifies Internet Hinglish memes using multimodal learning. It combines text and image analysis to categorize memes by sentiment and emotion, leveraging the Memotion 3.0 dataset.

Multiclass classification on tweets about the coronavirus

Deep learning for Natural Language Processing

Analyzes emotions in text chunks per chapter using a sentiment analysis model, visualizing scores across chunks as line graphs. Includes pie charts showing dominant emotions per chapter, enhancing understanding of emotional variations in text chunks. Developed using Transformers library.

Sentiment analysis using the distilbert-base-uncased model using the movies dataset.

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

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

A Deep Learning Based Voice Analytics toolkit

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

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

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.