Thanks! I'll answer this, since Ines doesn't have an account here (I'm Matt). We're really happy with how Prodigy's being received. It's only been on sale two

Thanks! I'll answer this, since Ines doesn't have an account here (I'm Matt). We're really happy with how Prodigy's being received. It's only been on sale two months, so I'm looking forward to hearing more success stories as people finish their projects (and of course, feedback to change what needs to be changed!). You can read how FullFact used it to train claim identification models for fact-checking here: https://fullfact.org/blog/2018/feb/how-we-customised-prodigy-ai/ https://fullfact.org/blog/2018/feb/how-we-customised-prodigy... Probably the best place to follow the progress is the support forum: https://support.prodi.gy/ https://support.prodi.gy/ We're also working on more tutorial videos. This video shows the workflow for training a new entity type: https://www.youtube.com/watch?v=l4scwf8KeIA https://www.youtube.com/watch?v=l4scwf8KeIA . This is one of the bits of the tool we're particularly proud of --- you can start off with a couple of seed terms, use word vectors to build up a larger terminology list, and then turn that list into a set of pattern rules to start boot-strapping a classifier. Prodigy will suggest phrases that match the patterns as entities, and your answers are used to train the statistical model. As you keep annotating, the model will start suggesting phrases too, which you'll say yes or no to. Eventually the model basically takes over, and you're mostly correcting its suggestions.