MARTINI_enrich_BERTopic_Ryan_Cole

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_Ryan_Cole")

topic_model.get_topic_info()

Topic overview

Number of topics: 9
Number of training documents: 649

Click here for an overview of all topics.

Topic ID	Topic Keywords	Topic Frequency	Label
-1	pfizer - vaccinated - shots - mccullough - deaths	23	-1_pfizer_vaccinated_shots_mccullough
0	conspirators - cepi - rockefeller - doctors - world	371	0_conspirators_cepi_rockefeller_doctors
1	novavax - oncologist - mutated - biontech - makis	52	1_novavax_oncologist_mutated_biontech
2	myocarditis - clots - mrna - deadly - pathologist	51	2_myocarditis_clots_mrna_deadly
3	fauci - redfield - manipulated - reveal - ralph	44	3_fauci_redfield_manipulated_reveal
4	unvaccinated - rfk - toxoid - safer - sued	31	4_unvaccinated_rfk_toxoid_safer
5	ivermectin - hydroxychloroquine - penicillin - miracle - overdose	27	5_ivermectin_hydroxychloroquine_penicillin_miracle
6	vaccinated - deaths - lancet - c19 - australia	27	6_vaccinated_deaths_lancet_c19
7	reinfection - influenza - masks - chemoprophylaxis - researchers	23	7_reinfection_influenza_masks_chemoprophylaxis

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