Copyright [2022] [AI Engineer: Ahmed]
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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).
This dataset was created by the company figure-eight and originally shared on their ‘Data For Everyone’ website here.
Tweet source: https://twitter.com/AnyOtherAnnaK/status/629195955506708480
I decided to use the Transfer Learning in this project and Fine-tune it since I believe in not to re-invent the wheels. We're going to use one of my favourite libraries; it's Hugging Face. It is the library – you need to use to inject your model with a pre-trained model that has trained on billions of examples.
| Variables | Definition |
|---|---|
| id | a unique identifier for each tweet |
| text | the text of the tweet |
| location | the location the tweet was sent from (may be blank) |
| keyword | a particular keyword from the tweet (may be blank) |
| target | in train.csv only, this denotes whether a tweet is about a real disaster (1) or not (0) |