MARTINI_enrich_BERTopic_HarveyRisch

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_HarveyRisch")

topic_model.get_topic_info()

Topic overview

Number of topics: 8
Number of training documents: 715

Click here for an overview of all topics.

Topic ID	Topic Keywords	Topic Frequency	Label
-1	vaccinated - pfizer - deaths - misinformation - kennedy	28	-1_vaccinated_pfizer_deaths_misinformation
0	vaccinated - pfizer - antibodies - plasmid - injection	394	0_vaccinated_pfizer_antibodies_plasmid
1	vax - myocarditis - cordis - deaths - mri	75	1_vax_myocarditis_cordis_deaths
2	doctors - harvey - marik - integrative - webinar	74	2_doctors_harvey_marik_integrative
3	ivermectin - hydroxychloroquine - remdesivir - penicillin - prescribing	46	3_ivermectin_hydroxychloroquine_remdesivir_penicillin
4	unvaccinated - polio - children - allergies - cnbc	36	4_unvaccinated_polio_children_allergies
5	masks - omicron - hospitals - mandatory - texas	31	5_masks_omicron_hospitals_mandatory
6	fauci - wuhan - coronaviruses - bioweapons - conspired	31	6_fauci_wuhan_coronaviruses_bioweapons

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