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 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

• 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.

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:

   {
     "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

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
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.

• It is only possible to create one explainer for each deployment.

• You can also enter a comment here for a gitops enabled namespace.

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:

   {
     "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

Explaining the request

Next Steps

Try the other demos or read our operations guide to learn more about how to use Seldon Enterprise Platform.