MARTINI_enrich_BERTopic_Dr_DavidMartinWorld

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_Dr_DavidMartinWorld")

topic_model.get_topic_info()

Topic overview

• Number of topics: 14
• Number of training documents: 1148

Click here for an overview of all topics.

Topic ID	Topic Keywords	Topic Frequency	Label
-1	fauci - monkeypox - vaccinated - masks - misinformation	21	-1_fauci_monkeypox_vaccinated_masks
0	vaccinated - omicron - bivalent - reinfection - boostered	769	0_vaccinated_omicron_bivalent_reinfection
1	inspires - gratitude - mikkiwillis - souls - magnificent	42	1_inspires_gratitude_mikkiwillis_souls
2	vaccines - shots - 2021 - banned - 49	40	2_vaccines_shots_2021_banned
3	david - humanity - podcast - robinson - profiteering	36	3_david_humanity_podcast_robinson
4	malone - propaganda - tucker - breakthrough - mrna	33	4_malone_propaganda_tucker_breakthrough
5	pfizer - whistleblower - falsified - trials - jackson	30	5_pfizer_whistleblower_falsified_trials
6	globalist - totalitarian - communists - governments - takeover	29	6_globalist_totalitarian_communists_governments
7	fauci - disprove - unredacted - shawnfleetwood - megyn	27	7_fauci_disprove_unredacted_shawnfleetwood
8	vaccine - myocarditis - pacemaker - incidence - nucleocapsid	25	8_vaccine_myocarditis_pacemaker_incidence
9	environmentalists - emissions - justin - dicaprio - plane	25	9_environmentalists_emissions_justin_dicaprio
10	vaccine - military - rescinded - mandating - discharge	24	10_vaccine_military_rescinded_mandating
11	miscarriages - pfizer - placenta - antibodies - injection	24	11_miscarriages_pfizer_placenta_antibodies
12	wuhan - russia - covidselect - submarines - ssbn	23	12_wuhan_russia_covidselect_submarines

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