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HumAID.html
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<!doctype html>
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<title>Benchmark Datasets for Machine Learning for Natural Disasters</title>
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<h3><b>HumAID: Human-Annotated Disaster Incidents Data from Twitter with Deep Learning Benchmarks</b></h3> <!-- Title -->
<h5> Text | General | Response </h5> <!-- Type | Topic | Disaster phase -->
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<div class="w3-container"> <!-- Data description -->
<p> HumAID is a general-disaster dataset for categratization of humanitarian tasks post disaster. It contains 77,196 annotated tweets and is introduced for multiclass classification.</p>
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<li class="list-group-item"><strong>ML task type:</strong> Multiclass classification </li>
<li class="list-group-item"><strong>Data Source:</strong> Social Media (Twitter)</li>
<li class="list-group-item"><strong>Size:</strong> 77,196 Tweets </li>
<li class="list-group-item"><strong>Timespan:</strong> 2016 - 2019</li>
<li class="list-group-item"> <strong>Geographical Coverage:</strong> Global </li>
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<li class="list-group-item"> <strong>Baseline Information</strong></li>
<li class="list-group-item"> <strong>Evaluated on:</strong> RF, SVM, FastText, BERT, D-BERT, RoBERTa, XLM-R</li>
<li class="list-group-item"> <strong>Metrics used:</strong> Weighted F1</li>
<li class="list-group-item"> <strong>Results as reported in original paper: </strong> Weighted F1: 0.781(RoBERTa) </li>
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<li class="list-group-item"> <strong>Find out more by:</strong></li>
<li class="list-group-item"> <a href="https://arxiv.org/abs/2104.03090" target="_blank">Reading the research paper</a></li>
<li class="list-group-item"> <a href="https://dataverse.harvard.edu/dataset.xhtml?persistentId=doi:10.7910/DVN/A7NVF7" target="_blank">Checking out the dataset!</a></li>
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Firoj Alam, Umair Qazi, Muhammad Imran, and Ferda Ofli. HumAID: Human-annotated disaster incidents data from
twitter with deep learning benchmarks. In International Conference on Web and Social Media, pages 933–942, 2021.
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