MARTINI_enrich_BERTopic_QAnon17_Awakening

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_QAnon17_Awakening")

topic_model.get_topic_info()

Topic overview

  • Number of topics: 7
  • Number of training documents: 468
Click here for an overview of all topics.
Topic ID Topic Keywords Topic Frequency Label
-1 soros - thepentagonpapers - ukraine - global - banned 25 -1_soros_thepentagonpapers_ukraine_global
0 fauci - vaccinated - clots - ivermectin - deadly 209 0_fauci_vaccinated_clots_ivermectin
1 432hz - illuminati - pyramids - frequencies - resonate 79 1_432hz_illuminati_pyramids_frequencies
2 republic - liberty - indivisible - flag - salvation 50 2_republic_liberty_indivisible_flag
3 hillary - kamala - leaked - epstein - videos 38 3_hillary_kamala_leaked_epstein
4 trump - raffle - 2500 - millionaire - bitcoin 36 4_trump_raffle_2500_millionaire
5 supplements - liver - antioxidants - detoxification - retina 31 5_supplements_liver_antioxidants_detoxification

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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