75 lines
2.1 KiB
Markdown
75 lines
2.1 KiB
Markdown
# GPTC
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General-purpose text classifier in Python
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GPTC provides both a CLI tool and a Python library.
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## CLI Tool
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### Classifying text
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python -m gptc classify <compiled model file>
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This will prompt for a string and classify it, then print (in JSON) a dict of
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the format `{category: probability, category:probability, ...}` to stdout.
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Alternatively, if you only need the most likely category, you can use this:
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python -m gptc classify [-c|--category] <compiled model file>
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This will prompt for a string and classify it, outputting the category on
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stdout (or "None" if it cannot determine anything).
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### Compiling models
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python -m gptc compile <raw model file>
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This will print the compiled model in JSON to stdout.
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## Library
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### `gptc.Classifier(model)`
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Create a `Classifier` object using the given *compiled* model (as a dict, not
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JSON).
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#### `Classifier.confidence(text)`
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Classify `text`. Returns a dict of the format `{category: probability,
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category:probability, ...}`
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#### `Classifier.classify(text)`
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Classify `text`. Returns the category into which the text is placed (as a
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string), or `None` when it cannot classify the text.
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### `gptc.compile(raw_model)`
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Compile a raw model (as a list, not JSON) and return the compiled model (as a
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dict).
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## Model format
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This section explains the raw model format, which is how you should create and
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edit models.
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Raw models are formatted as a list of dicts. See below for the format:
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[
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{
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"text": "<text in the category>",
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"category": "<the category>"
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}
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]
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GPTC handles models as Python `list`s of `dict`s of `str`s (for raw models) or
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`dict`s of `str`s and `float`s (for compiled models), and they can be stored
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in any way these Python objects can be. However, it is recommended to store
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them in JSON format for compatibility with the command-line tool.
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## Example model
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An example model, which is designed to distinguish between texts written by
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Mark Twain and those written by William Shakespeare, is available in `models`.
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The raw model is in `models/raw.json`; the compiled model is in
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`models/compiled.json`.
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