MARTINI_enrich_BERTopic_DrPaulMarik
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_DrPaulMarik")
topic_model.get_topic_info()
Topic overview
- Number of topics: 18
- Number of training documents: 1806
Click here for an overview of all topics.
| Topic ID | Topic Keywords | Topic Frequency | Label |
|---|---|---|---|
| -1 | fauci - vaccinated - pfizer - injections - symptoms | 21 | -1_fauci_vaccinated_pfizer_injections |
| 0 | myocarditis - gardasil - died - incidence - nattokinase | 1059 | 0_myocarditis_gardasil_died_incidence |
| 1 | novavax - paxlovid - vaccinated - reinfections - 2023 | 80 | 1_novavax_paxlovid_vaccinated_reinfections |
| 2 | deaths - dowd - 2022 - excess - millennials | 80 | 2_deaths_dowd_2022_excess |
| 3 | fauci - fbi - wuhan - coronaviruses - bioweapons | 77 | 3_fauci_fbi_wuhan_coronaviruses |
| 4 | ketogenic - supplements - dr_gazda - photobiomodulation - longcovid | 61 | 4_ketogenic_supplements_dr_gazda_photobiomodulation |
| 5 | thimerosal - vaccinated - autism - diagnosed - cnn | 55 | 5_thimerosal_vaccinated_autism_diagnosed |
| 6 | unvaccinated - mandates - lausd - exemptions - mask | 55 | 6_unvaccinated_mandates_lausd_exemptions |
| 7 | modrna - plasmid - genetically - contaminated - microbiologist | 53 | 7_modrna_plasmid_genetically_contaminated |
| 8 | vaccination - vaers - stillbirths - pfizer - placenta | 42 | 8_vaccination_vaers_stillbirths_pfizer |
| 9 | wuhan - lockdowns - shenzhen - millions - riots | 42 | 9_wuhan_lockdowns_shenzhen_millions |
| 10 | pandemic - wef - sovereignty - weaponize - cbdcs | 37 | 10_pandemic_wef_sovereignty_weaponize |
| 11 | drpaulmarik - janjekielek - epochtvus - earlytreatment - darkhorsepod | 29 | 11_drpaulmarik_janjekielek_epochtvus_earlytreatment |
| 12 | pfizer - adverse - falsehoods - rotavirus - mccullough | 24 | 12_pfizer_adverse_falsehoods_rotavirus |
| 13 | billgates - bioterrorist - malthusian - bmgf - cepi | 24 | 13_billgates_bioterrorist_malthusian_bmgf |
| 14 | fauci - censorship - misinformation - reclaimthenet - facebook | 24 | 14_fauci_censorship_misinformation_reclaimthenet |
| 15 | ivermectin - hydroxychloroquine - penicillin - longvax - miracle | 22 | 15_ivermectin_hydroxychloroquine_penicillin_longvax |
| 16 | remdesivir - ventilator - methylprednisolone - deadly - sedatives | 21 | 16_remdesivir_ventilator_methylprednisolone_deadly |
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
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