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AIDA-UPM/
MARTINI_enrich_BERTopic_COVID19VACCINEVICTIMSANDFAMILIES

Name: MARTINI_enrich_BERTopic_COVID19VACCINEVICTIMSANDFAMILIES
Availability: InStock

bertopic

text-classification

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MARTINI_enrich_BERTopic_COVID19VACCINEVICTIMSANDFAMILIES

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_COVID19VACCINEVICTIMSANDFAMILIES")

topic_model.get_topic_info()

Topic overview

Number of topics: 6
Number of training documents: 544

Click here for an overview of all topics.

Topic ID	Topic Keywords	Topic Frequency	Label
-1	vaers - pfizer - polio - myocarditis - lymphadenopathy	28	-1_vaers_pfizer_polio_myocarditis
0	globalist - tyranny - pandemic - cyber - everything	268	0_globalist_tyranny_pandemic_cyber
1	vaers - pfizer - clots - symptoms - overdose	71	1_vaers_pfizer_clots_symptoms
2	pfizer - deaths - 2023 - injected - dna	67	2_pfizer_deaths_2023_injected
3	fauci - sars - lockdowns - misinformation - laboratory	56	3_fauci_sars_lockdowns_misinformation
4	vax - hydroxychloroquine - zelenko - vladimir - surgeons	54	4_vax_hydroxychloroquine_zelenko_vladimir

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