MARTINI_enrich_BERTopic_dr_judymikovitss

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_judymikovitss")

topic_model.get_topic_info()

Topic overview

• Number of topics: 13
• Number of training documents: 1039

Click here for an overview of all topics.

Topic ID	Topic Keywords	Topic Frequency	Label
-1	vaccinated - pfizer - rfk - misinformation - deaths	22	-1_vaccinated_pfizer_rfk_misinformation
0	hydroxychloroquine - doctors - misinformed - malhotra - suppression	540	0_hydroxychloroquine_doctors_misinformed_malhotra
1	fauci - anthony - lockdowns - funded - congress	91	1_fauci_anthony_lockdowns_funded
2	nattokinase - bromelain - supplements - spike - liposomal	67	2_nattokinase_bromelain_supplements_spike
3	pfizer - hpv - oncogenes - keytruda - plasmid	45	3_pfizer_hpv_oncogenes_keytruda
4	ivermectin - fenbendazole - hydroxychloroquine - penicillin - overdose	43	4_ivermectin_fenbendazole_hydroxychloroquine_penicillin
5	deaths - underreporting - suddenly - poisoned - 2023	42	5_deaths_underreporting_suddenly_poisoned
6	myocarditis - unvaxed - clots - guillain - aorta	41	6_myocarditis_unvaxed_clots_guillain
7	globalists - capitalism - zuckerberg - blackrock - wealth	38	7_globalists_capitalism_zuckerberg_blackrock
8	vaccinated - omicron - reinfection - backfired - cleveland	37	8_vaccinated_omicron_reinfection_backfired
9	pfizer - vaers - miscarriages - fainting - baby	25	9_pfizer_vaers_miscarriages_fainting
10	conspirators - fda - rockefeller - epidemic - founded	25	10_conspirators_fda_rockefeller_epidemic
11	unvaxed - mandates - novavax - hochul - lawsuit	23	11_unvaxed_mandates_novavax_hochul

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