Labels

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.

from txtai.pipeline import Labels
# Create and run pipeline
labels = Labels()
labels(
    ["Great news", "That's rough"],
    ["positive", "negative"]
)

See the link below for a more detailed example.

Notebook	Description
Apply labels with zero shot classification	Use zero shot learning for labeling, classification and topic modeling

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

# Create pipeline using lower case class name
labels:
# Run pipeline with workflow
workflow:
  labels:
    tasks:
      - action: labels
        args: [["positive", "negative"]]

Run with Workflows

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

Run with API

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

Methods

Python documentation for the pipeline.

__init__(self, path=None, quantize=False, gpu=True, model=None, dynamic=True) special

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

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

__call__(self, text, labels=None, multilabel=False, flatten=None, workers=0) special

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:

Returns:

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

def __call__(self, text, labels=None, multilabel=False, flatten=None, workers=0):
    """
    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.
    Args:
        text: text|list
        labels: list of labels
        multilabel: labels are independent if True, scores are normalized to sum to 1 per text item if False, raw scores returned if None
        flatten: flatten output to a list of labels if present. Accepts a boolean or float value to only keep scores greater than that number.
        workers: number of concurrent workers to use for processing data, defaults to None
    Returns:
        list of (id, score) or list of labels depending on flatten parameter
    """
    if self.dynamic:
        # Run zero shot classification pipeline
        results = self.pipeline(text, labels, multi_label=multilabel, truncation=True, num_workers=workers)
    else:
        # Set classification function based on inputs
        function = "none" if multilabel is None else "sigmoid" if multilabel or len(self.labels()) == 1 else "softmax"
        # Run text classification pipeline
        results = self.pipeline(text, top_k=None, function_to_apply=function, num_workers=workers)
    # Convert results to a list if necessary
    if isinstance(text, str):
        results = [results]
    # Build list of outputs and return
    outputs = self.outputs(results, labels, flatten)
    return outputs[0] if isinstance(text, str) else outputs