Postprocessing

Postprocessing turns raw model outputs into human-friendly results and task artefacts. This is an optional final step in the data pipeline and is not required for all models.

info

Postprocessing is supported by the following algorithms:

• bitfount.ModelInference
• bitfount.HuggingFaceImageClassificationInference
• bitfount.HuggingFaceNERInference
• bitfount.HuggingFaceTextClassificationInference

Available postprocessors

Bitfount provides a suite of built-in postprocessors to handle common output-preparation needs. You can mix and match them, even chaining several together using the compound type.

Built-in postprocessor types

General postprocessors

These postprocessors can be used with any of the supported algorithms listed above.

Name	Description	Example Use Case
rename	Rename DataFrame columns.	Change "pred" column to "Prediction".
transform	Apply a transformation function from bitfount.transformations on output columns.	Apply softmax or custom transform to logits.
json_restructure	Move fields between levels in nested JSON structures.	Move a key from nested JSON upwards for flattening.
string_to_json	Parse columns containing JSON as strings into JSON objects.	Safely load prediction results stored as strings.
json_key_rename	Rename keys within JSON fields in columns.	Change "class1" to "cat" inside prediction JSON.
json_wrap_in_list	Wrap JSON fields in an additional list.	Ensure all predictions are in a JSON array format.
compound	Chain multiple postprocessors together in sequence.	Apply string_to_json followed by json_key_rename.

Hugging Face postprocessors

These postprocessors are designed specifically for use with Hugging Face algorithms.

Name	Description	Example Use Case
huggingface_apply_id_to_labels	Maps model output IDs to human-readable labels using a mapping file from the Hugging Face model repository.	Convert numeric class IDs to descriptive labels for multi-headed models.
ner_deidentification	De-identifies text by replacing named entities detected by NER models with placeholder tokens.	Remove patient names from clinical text after NER inference.

Example configuration in YAML

The postprocessors can be supplied as a list of dictionaries. The only required key is type which refers to the name of the postprocessor to use. The other keys are specific to the postprocessor and are passed as keyword arguments to the postprocessor.

More information about the available postprocessors and their arguments can be found in the API documentation:

• General postprocessors
• Hugging Face postprocessors
• Base classes and compound postprocessor

ModelInference example

algorithm:
  - name: bitfount.ModelInference
    model:
      bitfount_model:
        model_ref: MyModel
        model_version: 2
        username: amin-nejad
      hyperparameters:
        batch_size: 8
    arguments:
      postprocessors:
        - type: rename
          columns: ["logits"]
          mapping:
            logits: probabilities
        - type: transform
          columns: ["probabilities"]
          transform: softmax

Hugging Face image classification example

algorithm:
  - name: bitfount.HuggingFaceImageClassificationInference
    arguments:
      model_id: google/vit-base-patch16-224
      postprocessors:
        - type: huggingface_apply_id_to_labels
          model_id: google/vit-base-patch16-224
          filepath: config.json
          key: id2label