MARTINI_enrich_BERTopic_ActiveClubXIV

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("AIDA-UPM/MARTINI_enrich_BERTopic_ActiveClubXIV")

topic_model.get_topic_info()

Topic overview

• Number of topics: 15
• Number of training documents: 1103

Click here for an overview of all topics.

Topic ID	Topic Keywords	Topic Frequency	Label
-1	antifa - patriot - fight - zealandia - live	20	-1_antifa_patriot_fight_zealandia
0	commemoration - comrades - kosovo - marched - athens	564	0_commemoration_comrades_kosovo_marched
1	motto - nietzsche - legion - struggle - victory	79	1_motto_nietzsche_legion_struggle
2	clubs - activities - brotherhood - canadian - accomplish	57	2_clubs_activities_brotherhood_canadian
3	will2rise - shopw2r - shirts - asgardbrand - website	54	3_will2rise_shopw2r_shirts_asgardbrand
4	activitist - active_club_latvia - protonmail - mayday - stencil	48	4_activitist_active_club_latvia_protonmail_mayday
5	indiana - nationalists - protesters - white - gathered	41	5_indiana_nationalists_protesters_white
6	nationalistes - bulgarie - groupe - musculaire - activites	37	6_nationalistes_bulgarie_groupe_musculaire
7	kampf - antifa - legiohungaria - madrid - apoel	37	7_kampf_antifa_legiohungaria_madrid
8	mindset - strive - warrior - exercise - stronger	34	8_mindset_strive_warrior_exercise
9	media2rise - upcoming - documentary - patriot - journalist	33	9_media2rise_upcoming_documentary_patriot
10	australians - tribe - eam - qld - organisations	26	10_australians_tribe_eam_qld
11	activists - patriot - rallies - marched - illinois	26	11_activists_patriot_rallies_marched
12	antifa - rioters - arrests - robert - defendants	25	12_antifa_rioters_arrests_robert
13	nationalist - activists - brotherhood - guerilla - edge	22	13_nationalist_activists_brotherhood_guerilla

Training hyperparameters

• calculate_probabilities: True
• language: None
• 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
• zeroshot_min_similarity: 0.7
• zeroshot_topic_list: None

Framework versions

• Numpy: 1.26.4
• HDBSCAN: 0.8.40
• UMAP: 0.5.7
• Pandas: 2.2.3
• Scikit-Learn: 1.5.2
• Sentence-transformers: 3.3.1
• Transformers: 4.46.3
• Numba: 0.60.0
• Plotly: 5.24.1
• Python: 3.10.12