txtai - Labels (original) (raw)
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 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__(path=None, quantize=False, gpu=True, model=None, dynamic=True, **kwargs)
Source code in txtai/pipeline/text/labels.py
| def __init__(self, path=None, quantize=False, gpu=True, model=None, dynamic=True, **kwargs): super().__init__("zero-shot-classification" if dynamic else "text-classification", path, quantize, gpu, model, **kwargs) # Set if labels are dynamic (zero shot) or fixed (standard text classification) self.dynamic = dynamic |
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__call__(text, labels=None, multilabel=False, flatten=None, workers=0, **kwargs)
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:
| Name | Type | Description | Default |
|---|---|---|---|
| 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 | |
| kwargs | additional keyword args | {} |
Returns:
| Type | Description |
|---|---|
| list of (id, score) or list of labels depending on flatten parameter |
Source code in txtai/pipeline/text/labels.py
| 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 | def __call__(self, text, labels=None, multilabel=False, flatten=None, workers=0, **kwargs): """ 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 kwargs: additional keyword args 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, **kwargs) 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, **kwargs) # 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) # Stream outputs into list, if necessary outputs = list(outputs) if isinstance(results, list) else outputs # Return format that matches input format return outputs[0] if isinstance(text, str) else outputs |
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