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xsum_6789_3000_1500_train

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_6789_3000_1500_train")

topic_model.get_topic_info()

Topic overview

  • Number of topics: 16
  • Number of training documents: 3000
Click here for an overview of all topics.
Topic ID Topic Keywords Topic Frequency Label
-1 league - club - game - win - player 5 -1_league_club_game_win
0 said - mr - would - people - year 382 0_said_mr_would_people
1 sport - medal - gold - team - olympic 2143 1_sport_medal_gold_team
2 cricket - wicket - test - england - match 72 2_cricket_wicket_test_england
3 arsenal - league - liverpool - chelsea - kick 56 3_arsenal_league_liverpool_chelsea
4 world - open - round - mcilroy - golf 55 4_world_open_round_mcilroy
5 foul - town - half - kick - win 52 5_foul_town_half_kick
6 season - club - dedicated - transfer - appearance 46 6_season_club_dedicated_transfer
7 celtic - game - aberdeen - rangers - player 42 7_celtic_game_aberdeen_rangers
8 madrid - atltico - win - real - barcelona 36 8_madrid_atltico_win_real
9 race - hamilton - team - prix - grand 26 9_race_hamilton_team_prix
10 rugby - wales - game - coach - england 22 10_rugby_wales_game_coach
11 fight - champion - boxing - amateur - world 19 11_fight_champion_boxing_amateur
12 yn - wedi - ei - ar - bod 17 12_yn_wedi_ei_ar
13 fan - club - bet - stadium - standing 14 13_fan_club_bet_stadium
14 connacht - ronaldson - blade - penalty - ulster 13 14_connacht_ronaldson_blade_penalty

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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