It comes with many “ recipes” which are workflows to perform certain task, check this flowchart to examine them. Prodigy can be used to tag named entities for NER, text for classification and even images, video and sound!.
It merges the labeling and training process so experts can label the data in an useful and meaningful way instead of outsourcing the labeling process and wasting a lot of time in labeling unnecessary text samples. With Prodigy, you label and train the model in a fast and iterative process removing a lot of manual work. Furthermore, it will suggest the best samples based on information gain, so you don’t waste time with samples that will not improve the model predictions. You can start by labeling few samples and the active learning model will try to learn and tag the rest of the data set for you, so you can only indicate if a sample is correct or not. Prodigy is powered by active learning which means it provides semi-automation. It is target to data scientists who has Python programming knowledge. It is more than an annotation tool, it is integrated with SpaCy and can be used to train models as well. It is a modern annotation tool for creating training and evaluation data for machine learning models. Prodigy was created by the same team behind SpaCy. In this case you can use Doccano to manually label the data in parallel by the members of the team who have the business knowledge. I recommend using Doccano if the people with the business domain knowledge are not coders and the complexity of the business domain is high. Doccano is great if you are not a programmer, anyone can get up to speed quickly and collaborate to speed up the labeling process.
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