Platform For AI:Machine reading comprehension predict

Limits

You can use the machine reading comprehension predict component based on Deep Learning Containers (DLC) resources.

Configure the component in Machine Learning Designer

• Input ports

  Input port	Data type	Recommended upstream component	Required
  input saved model	OSS	machine reading comprehension training	Yes
  Data for Prediction	OSS	Read File Data	Yes

• Component parameters

  Configure the component on the pipeline page of Machine Learning Designer. The following table describes the parameters.

  Tab	Parameter	Description
  Fields Setting	Language	The language of the input file. Default value: zh. Valid values: zh en
  	Input Schema	The data schema of each column in the input file. Separate multiple columns with commas (,). Default value: qas_id:str:1,context_text:str:1,question_text:str:1,answer_text:str:1,start_position_character:str:1,title:str:1.
  	Question Column	The name of the column that contains questions in the input file. Default value: question_text.
  	Context Column	The name of the column that contains text passages in the input file. Default value: context_text.
  	Answer Column	The name of the column that contains answers in the input file. Default value: answer_text.
  	Id Column	The name of the ID column in the input file. Default value: qas_id.
  	Start Position Column	The name of the column that contains the starting positions of answer spans in the input file. If the answer to a question can be found in the text passage, the starting position of the answer span is recorded in this column. Default value: start_position_character.
  	Output data file	The path of the Object Storage Service (OSS) bucket that stores the answer file used by this component.
  	Use User-defined Model	Specifies whether to use a custom model. Default value: no. Valid values: no yes
  	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 OSS bucket that stores the custom model.
  Parameters Setting	batchSize	The number of samples that you want to tprocess at the same time. If the model is trained on multiple servers that use multiple GPUs, this parameter specifies the number of samples that are processed by each GPU at the same time. The value must be of the INT type. Default value: 256.
  	Sequence Length	The maximum length of a text passage that can be handled. The value must be of the INT type. Default value: 384.
  	Max Query Length	The maximum length of a question that can be handled. The value must be of the INT type. Default value: 64.
  	Max Answer Length	The maximum length of an answer that can be handled. The value must be of the INT type. Default value: 30.
  	Doc Stride	The length of a sliding window for each sliced text passage. The value must be of the INT type. Default value: 128.
  	pretrainModelNameOrPath	The name or path of the pre-trained model provided by the system. Default value: hfl/macbert-base-zh. Valid values: User Defined hfl/macbert-base-zh hfl/macbert-large-zh bert-base-uncased bert-large-uncased
  	Additional Parameters	The custom parameters. You can fine-tune the model parameters based on your data. Format: {A: xxx, B: xxx}. Separate keys and values with colons (:). Separate multiple parameters with commas (,).
  Tuning	GPU Machine type	The instance type of the GPU-accelerated node that you want to use. The default value is gn5-c8g1.2xlarge, which indicates that the node uses 8 vCPUs, 80 GB memory, and a single P100 GPU.
  	num_GPU_worker	The number of GPUs for each worker. Default value: 1.

Example

The following figure shows a sample pipeline in which the machine reading comprehension predict component is used.

Perform the following steps to configure the component:

1. Prepare a prediction dataset and upload the dataset to an OSS bucket. For more information, see the "Upload an object" section in the Get started by using the OSS console topic.

   A dataset can be in the TSV or TEXT format and contains the following columns: ID column, text column, question column, answer column (optional), start position column (optional), and title column (optional).

   In this example, a TSV file is used to show how to train a model.

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

3. Connect the Read File Data-3 component to the machine reading comprehension predict component as an upstream node and configure the machine reading comprehension predict component. For more information, see the "Component parameters" section of this topic.

References

• You can use the machine reading comprehension predict component together with the machine reading comprehension training component. For more information, see machine reading comprehension training.

• For more information about Machine Learning Designer components, see Overview of Machine Learning Designer.

• Machine Learning Designer provides various preset algorithm components. You can select a component to process data based on your actual business scenario. For more information, see Component reference: Overview of all components.