xsum_123_3000_1500_test

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("KingKazma/xsum_123_3000_1500_test")

topic_model.get_topic_info()

Topic overview

  • Number of topics: 9
  • Number of training documents: 1500
Click here for an overview of all topics.
Topic ID Topic Keywords Topic Frequency Label
-1 yn - game - win - player - league 12 -1_yn_game_win_player
0 said - mr - would - people - also 142 0_said_mr_would_people
1 right - box - win - foul - half 1033 1_right_box_win_foul
2 race - world - sport - champion - team 118 2_race_world_sport_champion
3 film - prize - album - book - said 60 3_film_prize_album_book
4 league - season - appearance - club - transfer 49 4_league_season_appearance_club
5 cricket - england - test - wicket - captain 41 5_cricket_england_test_wicket
6 wales - rugby - side - ospreys - team 27 6_wales_rugby_side_ospreys
7 egypt - morocco - cup - uganda - football 18 7_egypt_morocco_cup_uganda

Training hyperparameters

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

Framework versions

  • Numpy: 1.22.4
  • HDBSCAN: 0.8.33
  • UMAP: 0.5.3
  • Pandas: 1.5.3
  • Scikit-Learn: 1.2.2
  • Sentence-transformers: 2.2.2
  • Transformers: 4.31.0
  • Numba: 0.57.1
  • Plotly: 5.13.1
  • Python: 3.10.12
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