Datasaur Dinamic is a solution for you if you want to get the model directly rather than labeled data as an output. This built model can be deployed for your application and also can be used in ML-assisted labeling extension for your next labeling project. As an ML-assisted labeling provider, this model can return more accurate label because it was trained with your dataset previously.
Datasaur Dinamic extension with AWS SageMaker
Use Case
Let's use Datasaur Dinamic to train your NER model with text based dataset.
Label the data: Before utilizing Datasaur Dinamic, ensure the data is labeled. This can be done manually or through Datasaur's assisted labeling features, such as ML-assisted labeling or Predictive labeling.
Label the data
Enable Datasaur Dinamic: Click Manage extensions button (gear icon) in the extension panel on the right and turn on on the Datasaur Dinamic extension.
Manage extensions dialog
Deploy the model: Once labeling is complete, you can start configuring your selected Datasaur Dinamic providers. In this case, we'll use Hugging Face Auto Train for span-based tasks. To fill out the configuration, use your Username Account, which you can find in your Account Settings on Hugging Face. To get the API Token, you can use your existing Hugging Face Token or create a new access token.
Datasaur Dinamic Extension with Hugging Face (span labeling)
