GPTC

General-purpose text classifier in Python

GPTC provides both a CLI tool and a Python library.

Installation

pip install gptc

CLI Tool

Classifying text

gptc classify [-n <max_ngram_length>] <compiled model file>

This will prompt for a string and classify it, then print (in JSON) a dict of the format {category: probability, category:probability, ...} to stdout. (For information about -n <max_ngram_length>, see section "Ngrams.")

Alternatively, if you only need the most likely category, you can use this:

gptc classify [-n <max_ngram_length>] <-c|--category> <compiled model file>

This will prompt for a string and classify it, outputting the category on stdout (or "None" if it cannot determine anything).

Compiling models

gptc compile [-n <max_ngram_length>] [-c <min_count>] <raw model file>

This will print the compiled model encoded in binary format to stdout.

If -c is specified, words and ngrams used less than min_count times will be excluded from the compiled model.

Packing models

gptc pack <dir>

This will print the raw model in JSON to stdout. See models/unpacked/ for an example of the format. Any exceptions will be printed to stderr.

Library

gptc.Classifier(model, max_ngram_length=1)

Create a Classifier object using the given compiled model (as a gptc.Model object, not as a serialized byte string).

For information about max_ngram_length, see section "Ngrams."

Classifier.confidence(text)

Classify text. Returns a dict of the format {category: probability, category:probability, ...}

Note that this may not include values for all categories. If there are no common words between the input and the training data (likely, for example, with input in a different language from the training data), an empty dict will be returned.

Classifier.classify(text)

Classify text. Returns the category into which the text is placed (as a string), or None when it cannot classify the text.

Classifier.model

The classifier's model.

gptc.compile(raw_model, max_ngram_length=1, min_count=1)

Compile a raw model (as a list, not JSON) and return the compiled model (as a gptc.Model object).

For information about max_ngram_length, see section "Ngrams."

Words or ngrams used less than min_count times throughout the input text are excluded from the model.

gptc.Model.serialize()

Returns a bytes representing the model.

gptc.deserialize(encoded_model)

Deserialize a Model from a bytes returned by Model.serialize().

`gptc.pack(directory, print_exceptions=False)

Pack the model in directory and return a tuple of the format:

(raw_model, [(exception,),(exception,)...])

Note that the exceptions are contained in single-item tuples. This is to allow more information to be provided without breaking the API in future versions of GPTC.

See models/unpacked/ for an example of the format.

Ngrams

GPTC optionally supports using ngrams to improve classification accuracy. They are disabled by default (maximum length set to 1) for performance reasons. Enabling them significantly increases the time required both for compilation and classification. The effect seems more significant for compilation than for classification. Compiled models are also much larger when ngrams are enabled. Larger maximum ngram lengths will result in slower performance and larger files. It is a good idea to experiment with different values and use the highest one at which GPTC is fast enough and models are small enough for your needs.

Once a model is compiled at a certain maximum ngram length, it cannot be used for classification with a higher value. If you instantiate a Classifier with a model compiled with a lower max_ngram_length, the value will be silently reduced to the one used when compiling the model.

Model format

This section explains the raw model format, which is how models are created and edited.

Raw models are formatted as a list of dicts. See below for the format:

[
    {
        "text": "<text in the category>",
        "category": "<the category>"
    }
]

GPTC handles raw models as lists of dicts of strs (List[Dict[str, str]]), and they can be stored in any way these Python objects can be. However, it is recommended to store them in JSON format for compatibility with the command-line tool.

Emoji

GPTC treats individual emoji as words.

Example model

An example model, which is designed to distinguish between texts written by Mark Twain and those written by William Shakespeare, is available in models. The raw model is in models/raw.json; the compiled model is in models/compiled.json.

The example model was compiled with max_ngram_length=10.

Benchmark

A benchmark script is available for comparing performance of GPTC between different Python versions. To use it, run benchmark.py with all of the Python installations you want to test. It tests both compilation and classification. It uses the default Twain/Shakespeare model for both, and for classification it uses Mark Antony's "Friends, Romans, countrymen" speech from Shakespeare's Julius Caesar.