Platform For AI:Use the Text Summarization Predict component

You can configure parameters of the Text Summarization Predict component in the Machine Learning Designer.

• Input ports

  Input port (from left to right)	Type	Recommended upstream component	Required
  Data for prediction	OSS	Read File Data	Yes
  Prediction model	Component output	Text Summarization	No

• Configure the component

  Tab	Parameter	Description
  Fields Setting	Input Schema	The text columns in the input table. Default value: target:str:1,source:str:1.
  	TextColumn	The name of the column that stores the source text in the input table. Default value: source.
  	AppendColumn	The names of the text columns to be appended to the output table from the input table. Separate multiple column names with commas (,). Default value: source.
  	Output Schema	The names of the columns that store the text summarization results in the output table. Default value: predictions,beams.
  	Output data file	The path of the output table in an Object Storage Service (OSS) bucket.
  	Use User-defined Model	Specifies whether to use the default model of PAI to perform prediction. Valid values: yes no (default)
  	Whether the Model is a Megatron One	Only pre-trained text summarization models that are prefixed with mg are supported. Valid values: yes no (default)
  	OSS Directory for Alink Model	This parameter is required only if you set the Use User-defined Model parameter to yes. The path of the custom model in an OSS bucket.
  Parameters Setting	batchSize	The number of samples to be processed at a time. The value must be of the INT type. Default value: 8. If the model is trained on multiple servers with multiple GPUs, this parameter indicates the number of samples to be processed by each GPU at a time.
  	sequenceLength	The maximum length of a sequence. The value must be of the INT type. Valid values: 1 to 512. Default value: 512.
  	The model language	The language that is used. Valid values: zh: Chinese en: English
  	Whether to copy text from input while decoding	Specify whether to copy the text. Valid values: false (default) true
  	The Minimal Length of the Predicted Sequence	The minimum length of the output text, which is of the INT type. Default value: 12. The text output by the model must be longer than the specified value.
  	The Maximal Length of the Predicted Sequence	The maximum length of the output text, which is of the INT type. Default value: 32. The text output by the model must be shorter than the specified value.
  	The Minimal Non-Repeated N-gram Size	The minimum size of a non-repeated n-gram phrase, which is of the INT type. Default value: 2.
  	The Number of Beam Search Scope	The beam search scope, which is of the INT type. Default value: 5.
  	The Number of Returned Candidate Sequences	The number of results to be returned, which is of the INT type. Default value: 5. Important This parameter must be set to the value of The Number of Beam Search Scope.
  Execution Tuning	GPU Machine type	The GPU-accelerated instance type of the computing resource. Default value: gn5-c8g1.2xlarge.

You can use the Text Summarization Predict component to build a pipeline by using one of the following methods:

• Method 1: Fine-tune a model that is trained by using the Text Summarization component.

• Method 2: Use a custom model.

In this example, perform the following operations to configure the component and run the pipeline:

1. Build a pipeline based on the instructions of building a Text Summarization pipeline. For more information, see the "Examples" section in the Use the Text Summarization Training component topic.

2. Prepare the dataset (predict_data.txt) for which you want to generate a summary and upload the dataset to an OSS bucket. The test dataset used in this example is a tab-delimited TXT file.

   You can also upload CSV files to MaxCompute by running the Tunnel commands on a MaxCompute client. For more information about how to install and configure a MaxCompute client, see MaxCompute client (odpscmd). For more information about Tunnel commands, see Tunnel commands.

3. Use the Read File Data - 3 component in Method 1 or Read File Data - 1 component in Method 2 to read the test dataset. Set the OSS Data Path parameter of the Read File Data component to the OSS path in which the test dataset is stored.

4. Connect the model file and test dataset to the Text Summarization Predict component and set the required parameters. For more information, see Configure the component in the PAI console.

   • If you want to use a model fine-tuned by the Text Summarization component, configure the output of the Text Summarization component as the input of the Text Summarization Predict component.

   • If you want to use a custom model, set the Use User-defined Model parameter to yes on the Fields Setting tab and set the ModelSavePath parameter to the OSS path where the model is stored.

5. Click to run the pipeline. After you run the pipeline, you can view the output in the OSS path that you configured in the Output data file parameter of the Text Summarization Predict component.