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

About this lesson

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." UBIAI Website I https://ubiai.tools Facebook I https://www.facebook.com/ubiai.tools LinkedIn I https://www.facebook.com/ubiai.tools Twitter I https://twitter.com/UBIAI5 Youtube I https://www.youtube.com/channel/UCulBEok98-6xX8uGM0rn8Lw #UBIAI #Tutorial #training_models #spacy #BERT #Span_categorizer #ocr #DataExtraction #Annotation #NaturalLanguageProcessing #NLP #Tutorial #Unlimited #EntreprisePack #UnlimitedTeam #PDF #TextAnnotation #ML #speed #performance #simple #savetime #savetimeandmoney #deepLearningModelTraining #APIinference #SpansRelationsAnnotation #TextClassification #teammanagement #teamcollaboration #tracking #progress #TeamManagementTools

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