Step by step tutorial to train a machine learning model on a custom dataset

https://ubiai.tools/your-most-powerful-annotation-tool-in-the-market-now/
When used correctly, model assisted labeling can save us up to 80% of the annotation time.
In this tutorial, we will train a machine learning model on our custom dataset and then use it to label our documents.

To get the most accurate results, follow the steps below!

1- Go to the “models” menu and add a “new model”.
2- Press on “train model” and then select a project to start the training process.
3- Specify the “type” of model you want to train, such as “spacy” or “BERT”, which can be either blank or an already trained model.
4- Select the “training” and “validation ratio”
5- Specify the “number of iterations” in the model.
6- Select the “dropout” and the “batch size”.
7- Run it to start the training.

At that point, you can begin evaluating your annotation for conflicts by autolabeling more datasets with the model.

1- Go to the project and upload a new dataset to test the model.
2- In the “model” menu, select the one you just trained.
3- Get back to the “annotation” interface and press “predict”.

And this way, you can efficiently verify your annotations and, once you’re done, press "validate."

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