Text Explanations
Last updated
Last updated
In the Overview page, click Create new deployment.
Type the following deployment details and click Next:
Name
movie
Namespace
seldon
Type
Seldon Deployment
Configure the default predictor as follows:
Runtime
Scikit Learn
Model Project
default
Model URI
gs://seldon-models/scv2/examples/moviesentiment/classifier
Storage Secret
(leave blank/none)
Model Name
movies
Click Next for the remaining steps in the Deployment Creation Wizard and then click Launch.
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.
In the Deployment Dashboard page for the deployment movie
, click Add inside the MODEL EXPLANATION card.
In the Explainer Configuration Wizard, choose Text and click Next.
In the Explainer Types step, choose the Anchor option and click Next.
In the Explainer URI step, set the following details:
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.
Note: If the explainer is making use of parameters.content_type
field, View explanation
button from the Requests dashboard then it leads to the server error. The /explain
API works as expected if parameters.content_type
is included in the request input.
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.
Click the movie deployment that you created.
In the Deployment Dashboard, click Predict in the left pane.
In the Predict page, click Enter JSON and paste the following text:
Click Predict.
In the Deployment Dashboard for the deployment named movie
, click Predict in the left pane.
In the Predict page, click Enter JSON and once again paste the following text and click Predict:
Click Explain to generate explanations for the request.
The Model Name
is linked to the name described in the model-settings.json
file, located in the Google Cloud Storage location. Changing the name in the JSON file would also require changing the Model Name
, and vice versa.
Try the other demos or read our operations guide to learn more about how to use Seldon Enterprise Platform.