> For the complete documentation index, see [llms.txt](https://docs.seldon.ai/seldon-enterprise-platform/llms.txt). Markdown versions of documentation pages are available by appending `.md` to page URLs; this page is available as [Markdown](https://docs.seldon.ai/seldon-enterprise-platform/demos/seldon-core-v2/explainer-anchor-text.md).

# Text Explanations

This demo helps you learn about:

* Launching a movie sentiment pipeline which takes text input
* Sending a request to get a sentiment prediction
* Creating an explainer for the model
* Sending the same request and then get an explanation

The explainer uses the [anchor technique](/seldon-enterprise-platform/product-tour/model-explanations.md) to provide insight into why a particular classification was made by the model. We'll see patterns in input text that are most relevant to the prediction outcome.

## Create a Seldon Deployment

1. In the **Overview** page, click **Create new deployment**.
2. Type the following deployment details and click **Next**:

   | Parameter | Value              |
   | --------- | ------------------ |
   | Name      | movie              |
   | Namespace | seldon             |
   | Type      | Seldon ML Pipeline |
3. Configure the default predictor as follows:

   | Parameter      | Value                                                        |
   | -------------- | ------------------------------------------------------------ |
   | Runtime        | Scikit Learn                                                 |
   | Model Project  | default                                                      |
   | Model URI      | `gs://seldon-models/scv2/examples/moviesentiment/classifier` |
   | Storage Secret | (leave blank/none)                                           |
4. Click **Next** for the remaining steps in the **Deployment Creation Wizard** and then click **Launch**.

![Predictor details](/files/nnzOlRKLE5KgG6SKTxLx)

{% hint style="info" %}

* The `seldon` and `seldon-gitops` namespaces are installed by default, which may not always be available. Select a namespace which best describes your environment.
* A secret may be required for private buckets.
* Additional steps may be required for your specific model.
  {% endhint %}

## Get Predictions

1. Click the **movie** deployment that you created.
2. In the **Deployment Dashboard**, click **Predict** in the left pane.
3. In the **Predict** page, click **Enter JSON** and paste the following text:

   ```JSON
   {
     "parameters": {
       "content_type": "str"
     },
     "inputs": [{
       "name": "text review",
       "shape": [1],
       "datatype": "BYTES",
       "data": ["this film has bad actors"]
     }]
   }
   ```
4. Click **Predict**.

![the Predict page with the textarea prepopulated](/files/bBHvX4SoJpKySIgCcM17)

## Add an Anchor Text Explainer

1. In the **Deployment Dashboard** page for the deployment `movie`, click **Add** inside the **MODEL EXPLANATION** card.
2. In the **Explainer Configuration Wizard**, choose **Text** and click **Next**.
3. In the **Explainer Types** step, choose the **Anchor** option for **Explainer Algorithms supported:** and click **Next**.
4. In the **Explainer URI** step, set the following details:

   ```
   - Explainer URI: gs://seldon-models/scv2/examples/moviesentiment/explainer
   - Explainer Project: default
   ```

   ![Next button clicked](/files/JcDBz4N9ETeDInhs9b5U)
5. Click **Next** in **Additional Parameters** step of the wizard.
6. In the **Memory** step of the wizard, set following details

   ```
   - Memory: 1Gi
   ```
7. Click **Next** for the remaining steps without changing any fields, and click **Launch**.

After sometime, the explainer should become available.

{% hint style="info" %}

* It is only possible to create one explainer for each deployment.
* You can also enter a comment here for a gitops enabled namespace.
  {% endhint %}

## Get Explanation for one Request

1. In the **Deployment Dashboard** for the deployment named `movie`, click **Predict** in the left pane.
2. In the **Predict** page, click **Enter JSON** and once again paste the following text and click **Predict**:

   ```JSON
   {
     "parameters": {
       "content_type": "str"
     },
     "inputs": [{
       "name": "text review",
       "shape": [1],
       "datatype": "BYTES",
       "data": ["this film has bad actors"]
     }]
   }
   ```
3. Click **Explain** to generate explanations for the request.

![Resending the prediction](/files/kW5aCIOwYZe3m6IHwT1g)

![Explaining the request](/files/a0MkwQTcp5Kc6w3wPfWm)

## Next Steps

Try the other [demos](/seldon-enterprise-platform/demos.md) or read our [operations guide](/seldon-enterprise-platform/operations.md) to learn more about how to use Seldon Enterprise Platform.