MARTINI_enrich_BERTopic_PascalNajadiNEWS

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_PascalNajadiNEWS")

topic_model.get_topic_info()

Topic overview

• Number of topics: 14
• Number of training documents: 1141

Click here for an overview of all topics.

Topic ID	Topic Keywords	Topic Frequency	Label
-1	trump - monde - commandant - democide - jetzt	22	-1_trump_monde_commandant_democide
0	fauci - mondiale - democide - jamais - biologiques	521	0_fauci_mondiale_democide_jamais
1	gaulle - republique - allez - presentateurs - hiver	103	1_gaulle_republique_allez_presentateurs
2	gendarmerie - republique - natale - bonjour - wahrheit	96	2_gendarmerie_republique_natale_bonjour
3	dɪsˈkloʊʒər - disclosure - desclos - definition - terbuka	85	3_dɪsˈkloʊʒər_disclosure_desclos_definition
4	impfschaden - injektionen - pfizer - strafanzeige - swiss	58	4_impfschaden_injektionen_pfizer_strafanzeige
5	geneve - documentaire - serpente - fauci - milioni	56	5_geneve_documentaire_serpente_fauci
6	usaf - luftstreitkrafte - nigthwatch - trump - commandant	49	6_usaf_luftstreitkrafte_nigthwatch_trump
7	pfizer - lawsuit - ny - scotus - mccarthy	29	7_pfizer_lawsuit_ny_scotus
8	biden - inauguration - ejecutado - treason - janvier	27	8_biden_inauguration_ejecutado_treason
9	ukraine - zelensky - schweizer - paradeplatz - mikhail	26	9_ukraine_zelensky_schweizer_paradeplatz
10	democratie - schweiz - militarisation - thedocuments - toujours	24	10_democratie_schweiz_militarisation_thedocuments
11	swissmedic - sworn - soldiers - verfassungsmaßige - gezwungen	23	11_swissmedic_sworn_soldiers_verfassungsmaßige
12	unabhangigkeitskrieges - federalize - delaware - decembre - 1776	22	12_unabhangigkeitskrieges_federalize_delaware_decembre

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