Topic classification of news articles using machine learning and deep learning

Natural Language Processing Tutorial

Question answering system - research work [4 semester]

We augmented an already existing BERT Tiny Transformer network designed to train the Google NQ dataset to randomly sample some of the tokens in a question with its synonyms. The idea comes from the process of image data augmentation used in computer vision pipelines. This experiment directly tackles the concepts of Natural Language Inference and…

PAN @ CLEF 2021 shared Task: Detection of hate speach spreaders in tweets with the help of ML-Methods and transformer models.

Applying different ML & Neural Network algorithms to analyze MBTI Dataset.

Reimplementation of BerConvoNet: A deep learning framework for fake news classification

Document summary evaluation model using Hugging Face transformer library.

Final project tech review - BERT

The project is all about predicting the Twitter's tweets as reliable or unreliable

In this project, we have proposed a meta-learning approach for text classification using a combination of a base model and a meta-learner model. The base model, based on the BERT architecture, is used to extract contextualized representations of text.

Implemented pre-trained Transformer-based distilBERT and BERT multilingual model to classify sentiments in positive or negative class and ranked them on scale of 1 to 5

Implementation of the link identification task in BERT.

Here we leverage a subset of the amazon_polarity dataset to train two machine learning models: an LSTM model with GloVe embeddings and a fine-tuned DistilBERT model. The LSTM model achieved an accuracy of 80.40%, while the DistilBERT model outperformed with an impressive 90.75% accuracy. Predictions can made in real time via our streamlit app

Multi class classification of tweets using BERT

This project compares the performance of a Naive Bayes model and fine-tuned BERT models on emotion classification from text.

Example of usage several NLP algorithms to create summary of political topic articles from web.

A solution to the Kaggle competition ‘Contradictory, My Dear Watson’

Repository of the Machine Learning course at Sapienza University