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:

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