Twitter has become an important communication channel in times of emergency. The ubiquitousness of smartphones enables people to announce an emergency they’re observing in real-time. Because of this, more agencies are interested in programatically monitoring Twitter (i.e. disaster relief organizations and news agencies). But, it’s not always clear whether a person’s words are actually announcing a disaster. In this competition, you’re challenged to build a machine learning model that predicts which Tweets are about real disasters and which one’s aren’t. You’ll have access to a dataset of 10,000 tweets that were hand classified.
Competition details and dataset can be found in kaggle: https://www.kaggle.com/c/nlp-getting-started
- Performed text pre-processing and replaced constactions (e.g. wouldn't to would not) in dataset.
- Used BERT pre-trainted model bert-base-uncased with maxlength 512.
- Identified optimal learning rate (3e-5) and fine-tuned using one cycle policy and the optimal learning rate.
- Evaluated on Test dataset. Confusion matrix is as below:
- Performed prediction on final dataset and submitted. Got Public Score: 0.81428