Idea is to develop an approach that given a sample will identify the sub themes along with their respective sentiments.

QA Chat-bot for Faculty of Computer Science NaUKMA.

Creation of a web app where users can take pictures of their receipts and receive information on the calories of the items in the receipt. Project for HackNYU_2020.

Machine Translation

Jupyter Notebooks consisting of various nlp tasks. (NLP role based interviews)

🏷️ Classificação multi-label com BERT.

Application of the BERT model for text classification

A large-scale datasets for session-based recommendation and sequential recommendation

My Kaggle submission notebook - 83.8% Accuracy 🤟 (Top 8%)

Use BERT pre-trained model to solve text classification task

BERT-Sentiment-Analysis AI Model

Twitter Users Geolocation

This analytical project allows you to determine the mood of users by text. The sentimentality 140 dataset is used as a dataset. It contains 1,600,000 tweets extracted using the twitter api. The streets have been labeled (0 = negative, 4 = positive), and they can be used to determine moods. The following works were carried out: research data anal…

Hate Speech classification in Italian using XLM (fine-tuning). Published at the WOAH workshop (NAACL2022).

Sentiment Analysis on the Corona Tweet Dataset. Classification of tweets into classes: Positive, Negative and Neutral using various Machine Learning Models and Pre-Trained Models such as BERT and RoBERTa.

4th place project made for Ohio State University's HackAI hackathon 2022. Uses Bert Vectorization/Tfidf to classify text segments to determine if a text selection contains opinionated of false information

A BERT model built with PyTorch.

One of the main point that AT&T users are facing is constant exposure to SPAM messages🕵️‍♀️. AT&T has been able to manually flag spam messages for a time, but they are looking for an automated way of detecting spams to protect their users.📱

Model that demonstrates in forecasting the Length dimension of a Product

This is the official implementation of our paper Robust Hate Speech Detection via Mitigating Spurious Correlations (Tiwari et al., AACL-IJCNLP 2022)