MARTINI_enrich_BERTopic_returnofthesacred

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_returnofthesacred")

topic_model.get_topic_info()

Topic overview

Number of topics: 10
Number of training documents: 1098

Click here for an overview of all topics.

Topic ID	Topic Keywords	Topic Frequency	Label
-1	humanity - become - sun - years - mercola	24	-1_humanity_become_sun_years
0	monday - meme - moments - loves - blessing	666	0_monday_meme_moments_loves
1	vaers - vaccinated - deaths - injections - 2021	71	1_vaers_vaccinated_deaths_injections
2	fireweed - schisandra - herbal - mullein - tincture	71	2_fireweed_schisandra_herbal_mullein
3	spiritually - consciousness - negentropy - energeticsynthesis - become	52	3_spiritually_consciousness_negentropy_energeticsynthesis
4	dawn - darkness - becoming - whispers - bright	50	4_dawn_darkness_becoming_whispers
5	vaccinations - unvaccinated - exemptions - mandatory - employers	49	5_vaccinations_unvaccinated_exemptions_mandatory
6	solstice - capricorn - january - chiron - rebirth	48	6_solstice_capricorn_january_chiron
7	prayers - mercy - sanctity - responders - heroic	40	7_prayers_mercy_sanctity_responders
8	agrihood - sovereignty - empowering - gmo - mufi	27	8_agrihood_sovereignty_empowering_gmo

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