# Datasaur Dinamic ## Overview **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. 1. 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](https://docs.datasaur.ai/assisted-labeling/ml-assisted-labeling) or [Predictive labeling](https://docs.datasaur.ai/assisted-labeling/predictive-labeling).

2. Enable **Datasaur Dinamic**: Click **Manage extensions** button (gear icon) in the extension panel on the right and turn on on the **Datasaur Dinamic** extension.

3. 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](https://huggingface.co/settings/account) on Hugging Face. To get the API Token, you can use your existing [Hugging Face Token](https://huggingface.co/settings/tokens) or create a new access token.

**Datasaur Dinamic** Extension with Hugging Face (span labeling)

For further details and supported providers, please visit the [Building Your Own Model - Datasaur Dinamic](https://docs.datasaur.ai/build-model/datasaur-dinamic).