Using Machine Learning to Aid Survivors and Race through Time

On February 6, 2023, earthquakes measuring 7.7 and 7.6 hit South Eastern Turkey, affecting 10 cities and resulting in more than 42,000 deaths and 120,000 injured as of February 21.

A few hours after the earthquake, a group of programmers started a Discord server to roll out an application called afetharita, literally meaning, disaster map. This application would serve search & rescue teams and volunteers to find survivors and bring them help. The need for such an app arose when survivors posted screenshots of texts with their addresses and what they needed (including rescue) on social media. Some survivors also tweeted what they needed so their relatives knew they were alive and that they need rescue. Needing to extract information from these tweets, we developed various applications to turn them into structured data and raced against time in developing and deploying these apps.

When I got invited to the discord server, there was quite a lot of chaos regarding how we (volunteers) would operate and what we would do. We decided to collaboratively train models so we needed a model and dataset registry. We opened a Hugging Face organization account and collaborated through pull requests as to build ML-based applications to receive and process information.

To collaborate and improve the application, we hosted it on Hugging Face Spaces and we've received a GPU grant to keep the application up and running. Hugging Face Hub team has set us up a CI bot for us to have an ephemeral environment, so we could see how a pull request would affect the Space, and it helped us during pull request reviews.

We wanted our NER model to be evaluated and crowd-sourced the effort because the data labelers were working to give us better and updated intent datasets. To evaluate the NER model, we’ve set up a labeling interface using Argilla and Gradio, where people could input a tweet and flag the output as correct/incorrect/ambiguous.