An NLP Java Application that detects Names, organizations, and locations in a text by running Hugging face's Roberta NER model using ONNX runtime and Deep Java Library.

predict movie's genres based on overview

Sentiment Sense is a Python project that combines VADER sentiment analysis with fine-tuned RoBERTa models to predict sentiment scores for textual data. It provides a streamlined way to analyze sentiment across various texts using state-of-the-art natural language processing techniques.

GPT and BERT on Sentiment analysis

Analysis of a sample of comments taken from r/Politics on June 28, 2024.

# Project--Sentiment_Analysis Developed Python script to extract comments data from Amazon and Official site. Performed NLP based Tokenization, Lemmatization, vectorization and processed data in Machine understandable language Have used VADERS, ROBERTA and BERT models to find the sentiment of the reviews and used the ratings on the source to chec

text security audit 安全审核-语义模型过滤 敏感内容检测系统

This project was developed for a Kaggle competition focused on detecting Personally Identifiable Information (PII) in student writing. The primary objective was to build a robust model capable of identifying PII with high recall. The DeBERTa v3 transformer model was chosen for this task after comparing its performance with other transformer models.

Transformers 3rd Edition

Fine Tuning text classification NLP models from huggingface with Covid-19 tweet data to build a model that classifies text based on Covid-19 sentiment

Finetuning Roberta on your own dataset

This project uses the stsb-roberta-large sentence transformer model (deprecated) to check whether a set of given phrases match a certain phrase in meaning.

Natural Language Processing Python Project creating a Sentiment Analysis Classifier with NLTK's VADER and Huggingface's Roberta Transformers

🙂🙃 Being happy :) being sad :( with this tool, you become sentiment GIGA chad!

OnnxRT based Inference Optimization of Roberta model trained for Sentiment Analysis On Twitter Dataset

The project involves developing a proof-of-concept system for classifying financial excerpts into predefined categories using Natural Language Processing (NLP) techniques.

Sentilyze aims to analyze sentiment in text from social media, news, and websites. Real-time analysis, granular classification, customizable settings.

For this project, machine learning algorithms are used on amazon fine food reviews dataset to analyze if the given review is a positive review or a negative review.