PAI BERT Model Inference
The PAI BERT Model Inference component of Platform for AI (PAI) is used to perform offline inference based on a Bidirectional Encoder Representations from Transformers (BERT) model. The component classifies text in an input table by using a trained BERT-based classification model.
Limits
You can use the PAI BERT Model Inference component based only on the resources of Deep Learning Containers (DLC).
Algorithm
BERT is a pretrained language model (PLM) based on Transformer in the field of natural language processing (NLP). BERT is trained on a large amount of raw text data to learn bidirectional context understanding of the input text. BERT understands each word based on the preceding and succeeding words and can be fine-tuned for various NLP tasks. The PAI BERT Model Inference component uses a trained BERT-based classification model to classify text in the input table into a category that is specified during training.
Configure the component in the PAI console
You can configure the parameters of the PAI BERT Model Inference component in Machine Learning Designer.
Input ports
Input port (left to right)
Data type
Recommended upstream component
Required
Inference Data Table
MaxCompute Table
Yes
Component parameters
Tab
Parameter
Required
Description
Default value
Fields Setting
OSS model path
Yes
The Object Storage Service (OSS) path of the BERT-based classification model that is trained in QuickStart.
N/A
Selected Column Name
Yes
Select the text columns that you want to predict.
N/A
Lifecycle
No
The lifecycle of the output table.
28
Tuning
GPU
No
Select a GPU-accelerated Elastic Compute Service (ECS) instance type for the training.
N/A
Maximum Running Duration
No
The maximum duration for which the component runs.
N/A
Output ports
Output port (left to right)
Data type
Downstream component
Output Table
MaxCompute table
References
For information about Machine Learning Designer components, see Designer overview.