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KingKazma/
xsum_123_3000_1500_test

Name: xsum_123_3000_1500_test
Availability: InStock

bertopic

text-classification

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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:

python

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