xsum_22457_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_22457_3000_1500_test")

topic_model.get_topic_info()

Topic overview

Number of topics: 10
Number of training documents: 1500

Click here for an overview of all topics.

Topic ID	Topic Keywords	Topic Frequency	Label
-1	said - club - player - game - football	10	-1_said_club_player_game
0	said - mr - people - would - year	167	0_said_mr_people_would
1	kick - shot - free - goal - right	1066	1_kick_shot_free_goal
2	midfielder - loan - season - club - league	69	2_midfielder_loan_season_club
3	race - sport - gold - medal - olympic	53	3_race_sport_gold_medal
4	celtic - club - aberdeen - cup - player	39	4_celtic_club_aberdeen_cup
5	england - cricket - wicket - test - captain	33	5_england_cricket_wicket_test
6	cup - wales - rugby - game - fa	33	6_cup_wales_rugby_game
7	wimbledon - player - murray - im - atp	18	7_wimbledon_player_murray_im
8	armstrong - banned - antidoping - rugby - sky	12	8_armstrong_banned_antidoping_rugby

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