cnn_dailymail_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/cnn_dailymail_22457_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	mccoy - jockey - ap - champion - winner	15	-1_mccoy_jockey_ap_champion
0	said - one - year - also - told	9	0_said_one_year_also
1	league - season - player - goal - game	994	1_league_season_player_goal
2	labour - mr - said - miliband - leader	290	2_labour_mr_said_miliband
3	race - hamilton - rosberg - mercedes - marathon	84	3_race_hamilton_rosberg_mercedes
4	england - cricket - test - pietersen - anderson	32	4_england_cricket_test_pietersen
5	ncaa - first - game - college - basketball	30	5_ncaa_first_game_college
6	masters - spieth - augusta - hole - round	28	6_masters_spieth_augusta_hole
7	mayweather - fight - pacquiao - boxing - vegas	18	7_mayweather_fight_pacquiao_boxing

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