cnn_dailymail_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/cnn_dailymail_123_3000_1500_test")

topic_model.get_topic_info()

Topic overview

  • Number of topics: 12
  • Number of training documents: 1500
Click here for an overview of all topics.
Topic ID Topic Keywords Topic Frequency Label
-1 league - season - klopp - game - club 13 -1_league_season_klopp_game
0 said - one - year - also - people 73 0_said_one_year_also
1 liverpool - player - club - sterling - league 1088 1_liverpool_player_club_sterling
2 league - goal - madrid - barcelona - champions 91 2_league_goal_madrid_barcelona
3 manchester - united - city - van - gaal 51 3_manchester_united_city_van
4 world - first - woods - hamilton - win 38 4_world_first_woods_hamilton
5 england - cricket - test - captain - pietersen 35 5_england_cricket_test_captain
6 celtic - game - inverness - rangers - player 31 6_celtic_game_inverness_rangers
7 mayweather - fight - pacquiao - vegas - las 28 7_mayweather_fight_pacquiao_vegas
8 mccoy - national - lady - ride - race 23 8_mccoy_national_lady_ride
9 clermont - saracens - cup - england - northampton 16 9_clermont_saracens_cup_england
10 chelsea - mourinho - stoke - league - game 13 10_chelsea_mourinho_stoke_league

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: 0.56.4
  • Plotly: 5.13.1
  • Python: 3.10.6
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