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