📄 Topic Classification¶

This is a special case of multi class text classification task, in which we classify a given text to a set of topics. The only difference from tasks.classification.multi_class, is that the the ‘type_of_class’ is set to ‘topic’.

tasks.classification.multi_class.topic_classification

type: Task
input_fields: 
  text: str
  text_type: str
  classes: List[str]
  type_of_class: str
reference_fields: 
  label: str
prediction_type: str
metrics: 
  - metrics.f1_micro
  - metrics.accuracy
  - metrics.f1_macro
augmentable_inputs: 
  - text
defaults: 
  text_type: text
  type_of_class: topic
[source]

Explanation about Task¶

Task packs the different instance fields into dictionaries by their roles in the task.

Args:

input_fields (Union[Dict[str, str], List[str]]):
Dictionary with string names of instance input fields and types of respective values. In case a list is passed, each type will be assumed to be Any.

reference_fields (Union[Dict[str, str], List[str]]):
Dictionary with string names of instance output fields and types of respective values. In case a list is passed, each type will be assumed to be Any.

metrics (List[str]):
List of names of metrics to be used in the task.

prediction_type (Optional[str]):
Need to be consistent with all used metrics. Defaults to None, which means that it will be set to Any.

defaults (Optional[Dict[str, Any]]):
An optional dictionary with default values for chosen input/output keys. Needs to be consistent with names and types provided in ‘input_fields’ and/or ‘output_fields’ arguments. Will not overwrite values if already provided in a given instance.

The output instance contains three fields:

“input_fields” whose value is a sub-dictionary of the input instance, consisting of all the fields listed in Arg ‘input_fields’.

“reference_fields” – for the fields listed in Arg “reference_fields”.

“metrics” – to contain the value of Arg ‘metrics’

References: metrics.accuracy, metrics.f1_micro, metrics.f1_macro