How to Use the Generative Text Infilling Model based on Artificial Intelligence
The following snippet shows how Artificial Intelligence was used in the fielf of NLP. The neural net was trained for infilling different sentences for a given context. Text infilling is the task of predicting missing spans of text which are consistent with the preceding and subsequent text.
This article is based on the works of the Infilling Language Model (ILM) framework outlined in the ACL 2020 paper Enabling language models to fill in the blanks by Donahue et al. 2020. In this article, it is explained how to use this simple microservice. Use the resulting API here.
You can use this model by following a few simple steps:
1. Go to the micro service web page, and click the infilling you want to use.
2. Next, press the button: Try it out. In the upper right corner.
3. Edit the input part of the request. Make sure you add a little underscore sign (‘_’) where you want to infill.
4. Click the button execute in the lower part of the service.
5. Check the response body for different infillings.
Additional Perks: Test the other two services for infilling only words or multiple words (so called n-grams).
There is also the possibility to test the service locally or to get a console-based output. Drop me a line if you are interested in any of these.
Thanks to Javier and the team of Narrativa who suggested me to find a solution to this problem. Also a big thanks to HuggingFace, the Team from Stanford around Mr. Donahue, and WifiTribe with which I am currently living as a Digital Nomad. I am doing my research and education remotely.
Who am I?
I am Sebastian an NLP Deep Learning Research Scientist (M.Sc. in IT and Business). In my former life, I was a manager at Austria’s biggest bank. In the future, I want to work remotely whenever I want to & in the field of NLP.
Drop me a message on LinkedIn if you want to get in touch.