Labels

pipeline pipeline

The Labels pipeline uses a text classification model to apply labels to input text. This pipeline can classify text using either a zero shot model (dynamic labeling) or a standard text classification model (fixed labeling).

Example

The following shows a simple example using this pipeline.

  1. from txtai.pipeline import Labels
  2. # Create and run pipeline
  3. labels = Labels()
  4. labels(
  5. ["Great news", "That's rough"],
  6. ["positive", "negative"]
  7. )

See the link below for a more detailed example.

NotebookDescription
Apply labels with zero shot classificationUse zero shot learning for labeling, classification and topic modelingOpen In Colab

Configuration-driven example

Pipelines are run with Python or configuration. Pipelines can be instantiated in configuration using the lower case name of the pipeline. Configuration-driven pipelines are run with workflows or the API.

config.yml

  1. # Create pipeline using lower case class name
  2. labels:
  3. # Run pipeline with workflow
  4. workflow:
  5. labels:
  6. tasks:
  7. - action: labels
  8. args: [["positive", "negative"]]

Run with Workflows

  1. from txtai.app import Application
  2. # Create and run pipeline with workflow
  3. app = Application("config.yml")
  4. list(app.workflow("labels", ["Great news", "That's rough"]))

Run with API

  1. CONFIG=config.yml uvicorn "txtai.api:app" &
  2. curl \
  3. -X POST "http://localhost:8000/workflow" \
  4. -H "Content-Type: application/json" \
  5. -d '{"name":"labels", "elements": ["Great news", "Thats rough"]}'

Methods

Python documentation for the pipeline.

Source code in txtai/pipeline/text/labels.py

  1. def __init__(self, path=None, quantize=False, gpu=True, model=None, dynamic=True, **kwargs):
  2. super().__init__("zero-shot-classification" if dynamic else "text-classification", path, quantize, gpu, model, **kwargs)
  3. # Set if labels are dynamic (zero shot) or fixed (standard text classification)
  4. self.dynamic = dynamic

Applies a text classifier to text. Returns a list of (id, score) sorted by highest score, where id is the index in labels. For zero shot classification, a list of labels is required. For text classification models, a list of labels is optional, otherwise all trained labels are returned.

This method supports text as a string or a list. If the input is a string, the return type is a 1D list of (id, score). If text is a list, a 2D list of (id, score) is returned with a row per string.

Parameters:

NameTypeDescriptionDefault
text

text|list

required
labels

list of labels

None
multilabel

labels are independent if True, scores are normalized to sum to 1 per text item if False, raw scores returned if None

False
flatten

flatten output to a list of labels if present. Accepts a boolean or float value to only keep scores greater than that number.

None
workers

number of concurrent workers to use for processing data, defaults to None

0

Returns:

TypeDescription

list of (id, score) or list of labels depending on flatten parameter

Source code in txtai/pipeline/text/labels.py

  1. def __call__(self, text, labels=None, multilabel=False, flatten=None, workers=0):
  2. """
  3. Applies a text classifier to text. Returns a list of (id, score) sorted by highest score,
  4. where id is the index in labels. For zero shot classification, a list of labels is required.
  5. For text classification models, a list of labels is optional, otherwise all trained labels are returned.
  6. This method supports text as a string or a list. If the input is a string, the return
  7. type is a 1D list of (id, score). If text is a list, a 2D list of (id, score) is
  8. returned with a row per string.
  9. Args:
  10. text: text|list
  11. labels: list of labels
  12. multilabel: labels are independent if True, scores are normalized to sum to 1 per text item if False, raw scores returned if None
  13. flatten: flatten output to a list of labels if present. Accepts a boolean or float value to only keep scores greater than that number.
  14. workers: number of concurrent workers to use for processing data, defaults to None
  15. Returns:
  16. list of (id, score) or list of labels depending on flatten parameter
  17. """
  18. if self.dynamic:
  19. # Run zero shot classification pipeline
  20. results = self.pipeline(text, labels, multi_label=multilabel, truncation=True, num_workers=workers)
  21. else:
  22. # Set classification function based on inputs
  23. function = "none" if multilabel is None else "sigmoid" if multilabel or len(self.labels()) == 1 else "softmax"
  24. # Run text classification pipeline
  25. results = self.pipeline(text, top_k=None, function_to_apply=function, num_workers=workers)
  26. # Convert results to a list if necessary
  27. if isinstance(text, str):
  28. results = [results]
  29. # Build list of outputs and return
  30. outputs = self.outputs(results, labels, flatten)
  31. return outputs[0] if isinstance(text, str) else outputs