<jupyter_start><jupyter_text>Test one with a fake review about history<jupyter_code># Test with a fake review
history_post = [ """
                History if my favorite subject.  I love to read historical accounts about ancient Rome and Greece.
                I'm also a big World War 2 buff and I collect objects with historical significance.
                """]
get_predictions(history_post)<jupyter_output><empty_output><jupyter_text>Test two with a fake review about pokemon<jupyter_code># Try again and mention pokemon to see if the model correctly guesses pokemon:
pokemon_post = [ """
                My favorite pokemon are pikachu and charizard. I love pokemon.  Pokemon is great.
                """]
get_predictions(pokemon_post)<jupyter_output><empty_output><jupyter_text>Test three with a fake post about android<jupyter_code># Try a fake prediction to see if android gets predicted:
android_post = [ """
                I use a galaxy note 5.  My favorite opperating system version was oreo.
                Android phones are better than iphones. I like to create apps for the app store.
                """]
get_predictions(android_post)<jupyter_output><empty_output><jupyter_text>Test four with a fake post about music<jupyter_code># Try a fake prediction to see if music gets predicted:
music_post = [ """
                I love to listen to music.  My favorite singer/songwriter is <NAME>.  Every so often
                I like to listen to <NAME>.  I have a large vinyl music collection but more recently I've
                been listening to everything on Spotify.
                """]
get_predictions(music_post)<jupyter_output><empty_output><jupyter_text>Test five with fake post about politics<jupyter_code>politics_post = ["""
                    <NAME> and <NAME>.  Democrats, republicans and the tea party.
                """]
get_predictions(politics_post)<jupyter_output><empty_output>
{'fraction_non_alphanumeric': 0.06875, 'fraction_numerical': 0.0010416666666666667, 'mean_word_length': 3.0785562632696393, 'pattern_counts': {'":': 0, '<': 25, '<?xml version=': 0, '>': 25, 'https://': 0, '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': '5', 'dir': 'jupyter-structured-clean-dedup', 'id': '649873', 'license': 'MIT', 'n_tokens_mistral': 548, 'n_tokens_neox': 522, 'n_words': 200, 'path': 'Unit_4_Build_NLP_Model2.ipynb', 'repo_name': 'BW-Post-Here-01/DS', 'size': '1939'}