tailwiz 0.0.9

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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).

Quickstart

Install tailwiz by copying and pasting the following into command line:

python -m pip install tailwiz

Then run the following in a Python environment for a quick example of text classification:

import tailwiz
import pandas as pd

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

Installation

Install tailwiz through pip:

python -m pip install tailwiz

Usage

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

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

Given text, classify the text.

Parameters:

• text_to_label : pandas.DataFrame. Data structure containing text to classify. Must contain a string column named text.
• prelabeled_text : pandas.DataFrame, default None. Pre-labeled text to enhance the performance of the classification task. Must contain a string column for the classified text named text and a column for the labels named label.
• output_metrics : bool, default False. Whether to output performance_estimate together with results in a tuple.

Returns:

• results : pandas.DataFrame. A copy of text_to_label with a new column, label_from_tailwiz, containing 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
import pandas as pd

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

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. Data containing prompts and contexts from which answers will be parsed. Must contain a string column for the context named context and a string column for the prompt named prompt.
• prelabeled_text : pandas.DataFrame, default None. Pre-labeled text to enhance the performance of the parsing task. Must contain a string column for the context named context, a string column for the prompt named prompt, and a string column for the label named label.
• output_metrics : bool, default False. Whether to output performance_estimate together with results in a tuple.

Returns:

• results : pandas.DataFrame. A copy of text_to_label with a new column, label_from_tailwiz, containing 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
import pandas as pd

prelabeled_text = pd.DataFrame(
    [
        ['Extract the number.', 'Noon is twelve oclock', 'twelve'],
        ['Extract the number.', '10 jumping jacks', '10'],
        ['Extract the number.', 'I have 3 eggs', '3'],
    ],
    columns=['prompt', 'context', 'label'],
)
text_to_label = pd.DataFrame(
    [['Extract the number.', 'Figure 8']],
    columns=['prompt', 'context'],
)
results = tailwiz.parse(
    text_to_label=text_to_label,
    prelabeled_text=prelabeled_text,
)
print(results)

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

Given a prompt, generate an answer.

Parameters:

• text_to_label : pandas.DataFrame. Data structure containing prompts for which answers will be generated. Must contain a string column for the prompt named prompt.
• prelabeled_text : pandas.DataFrame, default None. Pre-labeled text to enhance the performance of the text generation task. Must contain a string column for the prompt named prompt and a string column for the label named label.
• output_metrics : bool, default False. Whether to output performance_estimate together with results in a tuple.

Returns:

• results : pandas.DataFrame. A copy of text_to_label with a new column, label_from_tailwiz, containing generated 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
import pandas as pd

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

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