MARTINI_enrich_BERTopic_DrSimoneGold

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_DrSimoneGold")

topic_model.get_topic_info()

Topic overview

  • Number of topics: 14
  • Number of training documents: 1094
Click here for an overview of all topics.
Topic ID Topic Keywords Topic Frequency Label
-1 lockdowns - fda - vaccine - aflds - disinformation 20 -1_lockdowns_fda_vaccine_aflds
0 citizencorps - freedom - reawaken - aflds - live 625 0_citizencorps_freedom_reawaken_aflds
1 unvaccinated - mandates - upheld - unconstitutional - nyc 78 1_unvaccinated_mandates_upheld_unconstitutional
2 newsom - fda - ivermectin - lawsuit - vetoed 53 2_newsom_fda_ivermectin_lawsuit
3 freedom - dissent - dictatorship - merry - protesters 49 3_freedom_dissent_dictatorship_merry
4 pfizer - unvaccinated - regulatory - shots - disclose 45 4_pfizer_unvaccinated_regulatory_shots
5 lockdowns - tyranny - mandated - ukraine - masked 45 5_lockdowns_tyranny_mandated_ukraine
6 imprisonment - prosecuted - freedrgold - doj - misdemeanor 38 6_imprisonment_prosecuted_freedrgold_doj
7 masks - skeptics - respiratory - dioxide - toxicity 30 7_masks_skeptics_respiratory_dioxide
8 vaccine - pfizer - toddlers - shots - c19 25 8_vaccine_pfizer_toddlers_shots
9 arrestfauci - ebola - accusations - investigated - kickbacks 23 9_arrestfauci_ebola_accusations_investigated
10 frontlineflash_ - unvaxxed - presented - maddow - fearless 22 10_frontlineflash__unvaxxed_presented_maddow
11 doctor - pharma - communism - revolutionizing - epidemic 21 11_doctor_pharma_communism_revolutionizing
12 simone - honored - freedom - flynn - congressman 20 12_simone_honored_freedom_flynn

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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