Lightweight NLP library in pure Python - currently implements a text classifier
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README.md

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.")

Checking individual words or ngrams

gptc check <compiled model file> <token or ngram>

This is very similar to gptc classify, except it takes the input as an argument, and it treats the input as a single token or ngram.

Compiling models

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

This will write the compiled model encoded in binary format to <compiled model file>.

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

Model.serialize(file)

Write binary data representing the model to file.

Model.deserialize(encoded_model)

Deserialize a Model from a file containing data from Model.serialize().

Model.confidence(text, max_ngram_length)

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.

For information about max_ngram_length, see section "Ngrams."

Model.get(token)

Return a confidence dict for the given token or ngram. This function is very similar to Model.confidence(), except it treats the input as a single token or ngram.

Model.compile(raw_model, max_ngram_length=1, min_count=1, hash_algorithm="sha256")

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.

The hash algorithm should be left as the default, which may change with a minor version update, but it can be changed by the application if needed. It is stored in the model, so changing the algorithm does not affect compatibility. The following algorithms are supported:

  • md5
  • sha1
  • sha224
  • sha256
  • sha384
  • sha512
  • sha3_224
  • sha3_384
  • sha3_256
  • sha3_512
  • shake_128
  • shake_256
  • blake2b
  • blake2s

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.

gptc.Classifier(model, max_ngram_length=1)

Classifier objects are deprecated starting with GPTC 3.1.0, and will be removed in 5.0.0. See the README from 3.0.2 if you need documentation.

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.