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AIDA-UPM/
MARTINI_enrich_BERTopic_TheWellnessCompany

Name: MARTINI_enrich_BERTopic_TheWellnessCompany
Availability: InStock

bertopic

text-classification

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MARTINI_enrich_BERTopic_TheWellnessCompany

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:

python

from bertopic import BERTopic
topic_model = BERTopic.load("AIDA-UPM/MARTINI_enrich_BERTopic_TheWellnessCompany")

topic_model.get_topic_info()

Topic overview

Number of topics: 6
Number of training documents: 450

Click here for an overview of all topics.

Topic ID	Topic Keywords	Topic Frequency	Label
-1	fauci - vaccinated - cancers - ivermectin - makis	21	-1_fauci_vaccinated_cancers_ivermectin
0	livestream - healthcare - supplements - tucker - amazing	228	0_livestream_healthcare_supplements_tucker
1	nattokinase - bromelain - spikesymposium - curcumin - supplement	87	1_nattokinase_bromelain_spikesymposium_curcumin
2	myocarditis - vaccinated - deaths - c19 - causally	69	2_myocarditis_vaccinated_deaths_c19
3	nattokinase - protease - fibrinolytic - neutralize - japan	23	3_nattokinase_protease_fibrinolytic_neutralize
4	cardiologists - suicide - prescribed - negligent - houston	22	4_cardiologists_suicide_prescribed_negligent

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