MARTINI_enrich_BERTopic_taylorhudak

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_taylorhudak")

topic_model.get_topic_info()

Topic overview

• Number of topics: 8
• Number of training documents: 564

Click here for an overview of all topics.

Topic ID	Topic Keywords	Topic Frequency	Label
-1	vaccine - pandemic - deaths - lies - 2021	29	-1_vaccine_pandemic_deaths_lies
0	wikileaks - extradited - julian - supreme - belmarsh	168	0_wikileaks_extradited_julian_supreme
1	fauci - symposium - orsolya - catherine - cardiologist	106	1_fauci_symposium_orsolya_catherine
2	vaccine - modrna - myocarditis - autopsies - autoimmune	67	2_vaccine_modrna_myocarditis_autopsies
3	amtsgericht - bhakdi - conviction - holocaust - vaccination	56	3_amtsgericht_bhakdi_conviction_holocaust
4	fed - monetary - coup - ecb - catherine	51	4_fed_monetary_coup_ecb
5	mariupol - lugansk - zelensky - vladimir - propaganda	49	5_mariupol_lugansk_zelensky_vladimir
6	eu - disinformation - censor - regulatory - headlines	38	6_eu_disinformation_censor_regulatory

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