Topic: disaster-tweets Goto Github
Some thing interesting about disaster-tweets
Some thing interesting about disaster-tweets
disaster-tweets,Kaggle real or not disaster tweets classification using FastText.
User: agrover112
Home Page: https://www.kaggle.com/c/nlp-getting-started
disaster-tweets,[FIUBA] 75.06/95.58 Organización de Datos 2020 - Trabajo Práctico 2 - Machine Learning
User: anichucai
disaster-tweets,Utilizing Natural Language Processing (NLP) to analyze and classify tweets for detecting disaster-related content.
User: ayoub-etoullali
disaster-tweets,A Multitask Framework for Present and Absent Keyphrase Generation using Knowledge Graphs
Organization: dice-group
disaster-tweets,An approach to solve the Kaggle Competition, Natural Language Processing with Disaster Tweets
User: egehanyorulmaz
disaster-tweets,🚀 Welcome to my Kaggle submission for "Natural Language Processing with Disaster Tweets." In this challenge, we explore tweets, using NLP to distinguish between those about real disasters and those that aren't. The goal is to build a robust model for accurate disaster-related tweet prediction. 🏆 Impressive F1 score of 0.79926 on the public leader
User: elmezianech
Home Page: https://www.kaggle.com/code/elmezianech/notebook86470c7043
disaster-tweets,This project was developed for the Natural Language Processing with Disaster Tweets Kaggle competition
User: explorermunchkin
disaster-tweets,Classification of Disaster Tweets as REAL or FAKE using Machine Learning
User: guptaharshnavin
disaster-tweets,My Kaggle submission notebook - 83.8% Accuracy 🤟 (Top 8%)
User: keivanipchihagh
disaster-tweets,Creating a model for the Kaggle competition: https://www.kaggle.com/competitions/nlp-getting-started/overview
User: manuel-2011
disaster-tweets,
User: mina-moeini
disaster-tweets,Classifier for predicting if a tweet is about real disasters or not.
User: mmedzin
disaster-tweets,(Re) Introduction to Tensorflow Natural Language Processing
User: mpolinowski
disaster-tweets,IDRISI is the largest-scale publicly-available Twitter Location Mention Prediction (LMP) datasets, in both English and Arabic languages. It contains 41 disaster events of different types (e.g., floods, fires). Annotations include tagged LMs in posts, location types (e.g., cities, streets), links to OSM toponyms, & usefulness of features for LMD.
User: rsuwaileh
disaster-tweets,Natural Language Processing with Disaster Tweets
User: trhgquan
Home Page: https://www.kaggle.com/competitions/nlp-getting-started
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