MARTINI_enrich_BERTopic_anto_boyle_channel

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_anto_boyle_channel")

topic_model.get_topic_info()

Topic overview

• Number of topics: 24
• Number of training documents: 3064

Click here for an overview of all topics.

Topic ID	Topic Keywords	Topic Frequency	Label
-1	ukraine - dublin - hold - saturday - antoboyle1	20	-1_ukraine_dublin_hold_saturday
0	migrants - galway - amnesty - ukrainians - protests	1436	0_migrants_galway_amnesty_ukrainians
1	fauci - pandemic - unvaccinated - illuminati - masks	233	1_fauci_pandemic_unvaccinated_illuminati
2	ukraine - warsaw - andrzej - farmers - brussels	173	2_ukraine_warsaw_andrzej_farmers
3	bundeswehr - putin - ukraine - missiles - leaked	135	3_bundeswehr_putin_ukraine_missiles
4	gaza - hamas - netanyahu - jerusalem - civilians	126	4_gaza_hamas_netanyahu_jerusalem
5	paedophiles - lgbtqia - schools - irish - transvestite	99	5_paedophiles_lgbtqia_schools_irish
6	russia - shoigu - superweapon - sarmat - satellites	91	6_russia_shoigu_superweapon_sarmat
7	ukraine - zelensky - biden - congressmen - pentagon	75	7_ukraine_zelensky_biden_congressmen
8	sanctions - gazprom - eu - slovakia - export	64	8_sanctions_gazprom_eu_slovakia
9	dublin - heuston - rathfarnham - landmark - 1916	59	9_dublin_heuston_rathfarnham_landmark
10	antoboyle1 - chat - donate - websites - epstein	57	10_antoboyle1_chat_donate_websites
11	donetsk - missiles - zaporizhzhya - paratroopers - drone	56	11_donetsk_missiles_zaporizhzhya_paratroopers
12	ukraine - macron - france - nato - soldiers	56	12_ukraine_macron_france_nato
13	chatroom - invite - hey - anthony - whatsapp	55	13_chatroom_invite_hey_anthony
14	trump - supreme - judges - elected - overturned	52	14_trump_supreme_judges_elected
15	navalny - litvinenko - kremlin - yeltsin - peskov	51	15_navalny_litvinenko_kremlin_yeltsin
16	sanctions - assets - yellen - confiscating - lipetsk	46	16_sanctions_assets_yellen_confiscating
17	yemen - tankers - airstrikes - suez - submarine	39	17_yemen_tankers_airstrikes_suez
18	irishfreedomradio - livestreaming - deirdre - 9pm - fitzsimmons	35	18_irishfreedomradio_livestreaming_deirdre_9pm
19	ukraine - munitions - eu - prague - dmytro	29	19_ukraine_munitions_eu_prague
20	avdeyevka - donetsk - semyonovka - kupyansk - regiment	29	20_avdeyevka_donetsk_semyonovka_kupyansk
21	raped - kidnapped - ostrava - arrested - police	25	21_raped_kidnapped_ostrava_arrested
22	kuzminov - spaniards - defector - murdered - helicopter	23	22_kuzminov_spaniards_defector_murdered

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