MARTINI_enrich_BERTopic_joinmiFight

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_joinmiFight")

topic_model.get_topic_info()

Topic overview

Number of topics: 9
Number of training documents: 1033

Click here for an overview of all topics.

Topic ID	Topic Keywords	Topic Frequency	Label
-1	pfizer - vaxx - bioweapons - biden - 2021	21	-1_pfizer_vaxx_bioweapons_biden
0	god - trump - freedoms - unrighteousness - warmongers	586	0_god_trump_freedoms_unrighteousness
1	vaccinated - pfizer - injections - hospitalizations - 2021	120	1_vaccinated_pfizer_injections_hospitalizations
2	vaccine - mrna - injections - nanotechnologies - dr	70	2_vaccine_mrna_injections_nanotechnologies
3	transhumanism - darpa - neuralink - chatgpt - blinken	60	3_transhumanism_darpa_neuralink_chatgpt
4	pfizer - biowarfare - dod - prosecuting - liability	55	4_pfizer_biowarfare_dod_prosecuting
5	fauci - karen - whistleblower - livestream - mikovits	51	5_fauci_karen_whistleblower_livestream
6	sars - bioweapons - vaccine - darpa - aerosolized	38	6_sars_bioweapons_vaccine_darpa
7	fauci - sarscov - funded - wuhan - huff	32	7_fauci_sarscov_funded_wuhan

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