An NLP research project utilizing the "cardiffnlp/twitter-roberta-base-sentiment-latest" pre-trained transformer for tweet tokenization. The project includes an attention-based biLSTM model that predicts sentiment labels for tweets as negative (-1), neutral (0), or positive (1).

Welcome to our RoBERTa Sentiment Analysis project! In this repository, we explore the world of Natural Language Processing (NLP) by fine-tuning a RoBERTa Transformer for sentiment analysis.

Studies show that people are more depressed than ever after the pandemic, but is the way we are measuring depression even accurate?

BPE Tokenizer for Ropherta (subclass of GPT3Tokenizer)

Project begins with webscrapping Yelp reviews and rating with 'BeautifulSoup' in Python, followed by Natural Language Processing (text cleaning, removing stopwords, tokenization and lemmatization) using 'NLTK'. RoBERTa from 'HuggingFace' has been fine-tuned for text classification with early stopping and regularization using Pytorch.

Tokenizer and Roberta model for Nepali Language

Persian text simplification using lexical simplification

Finetuning Roberta on your own dataset

More and more people are exchanging text messages through the use of social media, and the analysis of the information can be used to make statistics in the behavior and in people's psychology. Using Natural Language Processing (NLP), we can extrapolate key words from each message that allow us to achieve the proposed goals.

LSTM models for text classification on character embeddings.