Training Data Generation

Programmatically build training datasets by defining heuristic rules which are used in functions for labeling training data.

Boost your performance by creating data out of data, instead of new data.

Creating training data is a labor-intensive task. Fine-tune the training data definition yourself and then scale-up by outsourcing to remote workers.

Gold data contains the ground truth. Re-use available resources, but be careful that the dataset matches your purpose.

Use an annotation tool that benefits from active learning to enforce a robust annotion process and balanced annotations.

Nobody wants to do the manual labor of tagging. Everybody wants to build language models with annotated training data.