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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 textBlob library for processing textual data.
TOSRoberta: AI-powered Terms of Service analyzer. Instantly assess fairness of clauses in ToS documents. Upload PDFs or text, get color-coded results. Powered by fine-tuned RoBERTa model with 89% accuracy. Streamline contract review and protect your rights effortlessly.
The programming environment »Open Roberta Lab« by Fraunhofer IAIS enables children and adolescents to program robots. A variety of different programming blocks are provided to program motors and sensors of the robot. Open Roberta Lab uses an approach of graphical programming so that beginners can seamlessly start coding. As a cloud-based applica…
This repository contains a project focused on performing sentiment analysis on the Amazon Fine Food Reviews dataset. The goal is to analyze customer reviews to determine their sentiment, whether positive or negative, and to gain insights into customer opinions to improve product offerings.
BERT classification model for processing texts longer than 512 tokens. Text is first divided into smaller chunks and after feeding them to BERT, intermediate results are pooled. The implementation allows fine-tuning.