Large Scale benchmarking of state of the art text vectorizers
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Updated
Nov 21, 2022 - Python
Large Scale benchmarking of state of the art text vectorizers
Extractive summarizationof medical transcriptions
Course Project of Information Retrieval.
Application of Machine Learning Techniques for Text Classification and Topic Modelling on CrisisLexT26 dataset.
A web application that detects aggression and misogyny in text using BERT augmentation, sentiment analysis, XGBoost, TF-IDF vectorization, LIME explainability. [Paper accepted at ICON 2021]
Implementation of a search engine using a vector space model.
SMS Spam prediction using classification algorithms.
A software for extracting key facts from a redundant paragraph to provide the users with the necessary information in lesser span of time.
Retrieve themes from a user inputted query and semantically connect them to lyrical data from songs.
ML model for spam detection using Naive Bayes & TF-IDF. Achieved 0.98 accuracy. Utilized Scikit-learn, Numpy, nltk. Implements NLP concepts. Explore precise spam classification effortlessly. #MachineLearning #SpamDetection 🚀✉️📱
Project showing the sentiment analysis of text data using NLP and Dash.
Data Augmentation for Improved Generalizability of Natural Language Processing Models
This is a basic implementation of a resume screening model using machine learning techniques
Fake reviews detection using SGD Classifier , with an flexible user interface
Fake news detection
Practice how to perform text classification using a machine learning classification model and the results of tf-idf as a feature vector
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