imdb_bertopic_ten_topics

This is a BERTopic model. BERTopic is a flexible and modular topic modeling framework that allows for the generation of easily interpretable topics from large datasets.

Usage

To use this model, please install BERTopic:

pip install -U bertopic

You can use the model as follows:

from bertopic import BERTopic
topic_model = BERTopic.load("davanstrien/imdb_bertopic_ten_topics")

topic_model.get_topic_info()

Topic overview

  • Number of topics: 10
  • Number of training documents: 103062
Click here for an overview of all topics.
Topic ID Topic Keywords Topic Frequency Label
-1 film - movie - movies - character - characters 44 -1_film_movie_movies_character
0 film - movie - films - movies - too 25087 0_film_movie_films_movies
1 episodes - shows - watching - tv - episode 20955 1_episodes_shows_watching_tv
2 films - film - movies - godzilla - movie 2037 2_films_film_movies_godzilla
3 cinderella - disney - cartoon - animation - cartoons 895 3_cinderella_disney_cartoon_animation
4 gameplay - games - game - adventure - starcraft 465 4_gameplay_games_game_adventure
5 holmes - sherlock - watson - doyle - conan 228 5_holmes_sherlock_watson_doyle
6 panther - film - films - clouseau - movies 184 6_panther_film_films_clouseau
7 metallica - metal - genres - genre - headbanger 55 7_metallica_metal_genres_genre
8 che - ernesto - castro - biopic - film 50 8_che_ernesto_castro_biopic

Training hyperparameters

  • calculate_probabilities: False
  • language: None
  • low_memory: False
  • min_topic_size: 40
  • n_gram_range: (1, 1)
  • nr_topics: 10
  • seed_topic_list: None
  • top_n_words: 10
  • verbose: False

Framework versions

  • Numpy: 1.22.4
  • HDBSCAN: 0.8.29
  • UMAP: 0.5.3
  • Pandas: 1.5.3
  • Scikit-Learn: 1.2.2
  • Sentence-transformers: 2.2.2
  • Transformers: 4.29.2
  • Numba: 0.56.4
  • Plotly: 5.13.1
  • Python: 3.10.11
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