<jupyter_start><jupyter_text>Gamma-Poisson mixture topic model for short textThis notebook gives basic usage examples of the Gamma-Poisson mixture (GPM) topic model for short text. GPM is implemented in a Python package `GPyM_TM` and is available [online](https://pypi.org/project/GPyM-TM/) for use within Python 3 enviroments. The installation can be performed through the use of a standard `pip` install command:`pip install GPyM-TM` The reference for the associated article is: <NAME>, <NAME>, <NAME>, ["A Gamma-Poisson Mixture Topic Model for Short Text"](https://doi.org/10.1155/2020/4728095 ), Mathematical Problems in Engineering, vol. 2020, Article ID 4728095, 17 pages, 2020.  1. Prepare the environment<jupyter_code>from GPyM_TM import GPM<jupyter_output><empty_output>
{'fraction_non_alphanumeric': 0.08857509627727857, 'fraction_numerical': 0.04621309370988447, 'mean_word_length': 5.610169491525424, 'pattern_counts': {'":': 0, '<': 8, '<?xml version=': 0, '>': 8, 'https://': 2, 'lorem ipsum': 0, 'www.': 0, 'xml': 0}, 'pii_count': 0, 'substrings_counts': 0, 'word_list_counts': {'cursed_substrings.json': 0, 'profanity_word_list.json': 0, 'sexual_word_list.json': 0, 'zh_pornsignals.json': 0}}
{'chain_length': '1', 'dir': 'jupyter-structured-clean-dedup', 'id': '583375', 'license': 'MIT', 'n_tokens_mistral': 261, 'n_tokens_neox': 225, 'n_words': 93, 'path': 'examples/GPM_basic.ipynb', 'repo_name': 'CAIR-ZA/GPM', 'size': '807'}