MARTINI_enrich_BERTopic_OdeToPower2

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_OdeToPower2")

topic_model.get_topic_info()

Topic overview

• Number of topics: 5
• Number of training documents: 492

Click here for an overview of all topics.

Topic ID	Topic Keywords	Topic Frequency	Label
-1	telegram - freemasonry - anyone - prigozhin - hitler	24	-1_telegram_freemasonry_anyone_prigozhin
0	jews - freud - netanyahu - ukrainians - gaza	208	0_jews_freud_netanyahu_ukrainians
1	whites - fuckers - racism - goddamn - soul	108	1_whites_fuckers_racism_goddamn
2	men - libido - whore - smokers - daoist	101	2_men_libido_whore_smokers
3	meme - normies - banned - subscribers - videos	51	3_meme_normies_banned_subscribers

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