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