tailwiz 0.0.6

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Text Labeling AI Wizard (tailwiz)

tailwiz is an AI-powered tool for labeling text. It has three main capabilties: classifying text (tailwiz.classify), parsing text given context and prompts (tailwiz.parse), and generating text given prompts (tailwiz.generate).

Installation

Install tailwiz through pip:

python -m pip install tailwiz

Usage

In this section, we outline the three main functions of tailwiz and provide example workflows.

tailwiz.classify(text_to_label, prelabeled_text=None, output_metrics=False)

Given text, classify the text.

Parameters:

• text_to_label : pandas.DataFrame, List[str]]. Data structures containing text to classify. If in a pandas DataFrame, the column name must be text.
• prelabeled_text : pandas.DataFrame, List[List[str, Union[str, int]]], default None. Pre-labeled text to enhance the performance of the classification task. If in a pandas DataFrame, the text column must be named text and the label column must be named label.
• output_metrics : bool, default False. Whether to output performance_estimate together with results in a tuple.

Returns:

• results : List[List[str, Union[str, int]]. Classification results.
• performance_estimate : Dict[str, float]. Dictionary of metric name to metric value mappings. Included together with results in a tuple if output_metrics is True. Uses prelabeled_text to give an estimate of the accuracy of the classification. One vs. all metrics are given for multiclass classification.

Example:

>>> import tailwiz
>>> results = tailwiz.classify(
...     text_to_label=['You are the best!', 'You make me sick'],
...     prelabeled_text=[
...         ['Love you to the moon', 'nice'],
...         ['I hate you', 'mean'],
...         ['Have a great day', 'nice']
...     ]
... )
>>> results
['nice', 'mean']

tailwiz.parse(text_to_label, prelabeled_text=None, output_metrics=False)

Given a prompt and a context, parse the answer from the context.

Parameters:

• text_to_label : pandas.DataFrame, List[List[str, str]]. Data containing prompts and contexts from which answers will be parsed. If in a pandas DataFrame, the context column name must be context and the prompt column name must be prompt.
• prelabeled_text : pandas.DataFrame, List[List[str, str, str]], default None. Pre-labeled tuples to enhance the performance of the parsing task. If in a pandas DataFrame, the context column name must be context, the prompt column name must be prompt, and the label column must be label.
• output_metrics : bool, default False. Whether to output performance_estimate together with results in a tuple.

Returns:

• results : List[List[str, str, str]]. Parsed results.
• performance_estimate : Dict[str, float]. Dictionary of metric name to metric value mappings. Included together with results in a tuple if output_metrics is True. Uses prelabeled_text to give an estimate of the accuracy of the parsing job.

Example:

>>> import tailwiz
>>> results = tailwiz.parse(
...     text_to_label=[['Extract the number.', 'figure 8']],
...     prelabeled_text=[
...         ['Extract the number.', 'Noon is twelve oclock', 'twelve'],
...         ['Extract the number.', '10 jumping jacks', '10'],
...         ['Extract the number.', 'I have 3 eggs', '3'],
...     ]
... )
>>> results
['8']

tailwiz.generate(text_to_label, prelabeled_text=None, output_metrics=False)

Given a prompt, generate an answer.

Parameters:

• text_to_label : pandas.DataFrame, List[str]]. Data structures containing prompts for which answers will be generated. If in a pandas DataFrame, the prompt column name must be prompt.
• prelabeled_text : pandas.DataFrame, List[List[str, str, int]], default None. Pre-labeled text to enhance the performance of the text generation task. If in a pandas DataFrame, the prompt column must be named prompt and the label column must be named label.
• output_metrics : bool, default False. Whether to output performance_estimate together with results in a tuple.

Returns:

• results : List[List[str, Union[str, int]]. Generated prompt results.
• performance_estimate : Dict[str, float]. Dictionary of metric name to metric value mappings. Included together with results in a tuple if output_metrics is True. Uses prelabeled_text to give an estimate of the accuracy of the text generation job.

Example:

>>> import tailwiz
>>> results = tailwiz.generate(
...     text_to_label=['Is this sentence Happy or Sad? I am crying my eyes out.'],
...     prelabeled_text=[
...         ['Is this sentence Happy or Sad? I love puppies!', 'Happy'],
...         ['Is this sentence Happy or Sad? I do not like you at all.', 'Sad'],
...     ]
... )
>>> results
['Sad']

Templates (Notebooks)

Use these Jupyter Notebook examples as templates to help load your data and run any of the three tailwiz functions:

• For an example of tailwiz.classify, see examples/classify.ipynb
• For an example of tailwiz.parse, see examples/parse.ipynb
• For an example of tailwiz.generate, see examples/generate.ipynb