A pipeline component that wraps inference model and set up inference related work.


  • initialize(): Pipeline will call it at the start of processing. The processor will be initialized with configs, and register global resources into forte.common.Resources. The implementation should set up the states of the component.

    • default_configs is a class method that returns default configuration in a dictionary format. Parent reader class configuration will be merged or overwritten by child class.

      • default_configs usage example

        • To use an existing processor, User should check configurations from method default_configs of the particular processor used to find out what configurations can be customized. For example, suppose after checking processor API we decide to use BaseProcessor. Then we need to check the source of forte.processors.base.base_processor.BaseProcessor.default_configs() and found that "overwrite" is a boolean configuration and we can set it to False in our customized configuration when we don’t want the default configuration. The default configuration will be overwritten when we initialize the processor with our customized configuration.

        • To implement a new processor, User should check the appropriate processor to inherit from. One consideration is whether User wants to process a DataPack or a DataPack batch for each processing iteration. If it’s the DataPack, then User should inherit from PackProcessor. If it’s the DataPack batch, then User should inherit from BaseBatchProcessor For example, in the implementation of VocabularyProcessor, it inherits from PackProcessor because it builds vocabulary from DataPack. Then User can consider adding a new configuration field in default_configs() based on the needs or overwrite the configuration field from its parent class. It’s just a simple consideration to explain the process of choosing the right processor, there are many other processors with more features that User can inherit from. User can refer to Processors API for more information.

    • resource is for advanced developer. It’s an shared object that stores data accessible by all PipelineComponent in the pipeline.

  • _process(): The main function of the processor. The implementation should process the input_pack, and conduct operations such as adding entries into the pack, or produce some side-effect such as writing data into the disk.

We also have plenty of written processors available to use. If you don’t find one suitable in your case, you can refer to pipeline examples, API or tutorials to customize a new processor.