BERTopic_generationidentitaire

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("sdantonio/BERTopic_generationidentitaire")

topic_model.get_topic_info()

Topic overview

  • Number of topics: 6
  • Number of training documents: 1102
Click here for an overview of all topics.
Topic ID Topic Keywords Topic Frequency Label
-1 france - lyonnaise - grenoble - peines - militants 23 -1_france_lyonnaise_grenoble_peines
0 identitaires - france - lyonnais - montpellier - migrants 12 0_identitaires_france_lyonnais_montpellier
1 identitaires - france - turquie - imposture - dubois 847 1_identitaires_france_turquie_imposture
2 identitaires - france - lyonnais - musulmans - migrants 96 2_identitaires_france_lyonnais_musulmans
3 identitaires - france - banderole - djihadistes - montpellier 92 3_identitaires_france_banderole_djihadistes
4 identitaires - vraie - dubois - victoires - publications 32 4_identitaires_vraie_dubois_victoires

Training hyperparameters

  • calculate_probabilities: False
  • 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: 1.23.5
  • HDBSCAN: 0.8.38.post1
  • UMAP: 0.5.6
  • Pandas: 2.2.2
  • Scikit-Learn: 1.5.1
  • Sentence-transformers: 3.0.1
  • Transformers: 4.44.2
  • Numba: 0.60.0
  • Plotly: 5.24.0
  • Python: 3.10.12
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