MARTINI_enrich_BERTopic_efectosadversosvacuna

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_efectosadversosvacuna")

topic_model.get_topic_info()

Topic overview

• Number of topics: 11
• Number of training documents: 1969

Click here for an overview of all topics.

Topic ID	Topic Keywords	Topic Frequency	Label
-1	vacunarse - miocarditis - repentinamente - sintomas - hijos	20	-1_vacunarse_miocarditis_repentinamente_sintomas
0	futbolistas - repentinamente - atleta - infarto - diego	1165	0_futbolistas_repentinamente_atleta_infarto
1	genocidas - transgenico - argentina - vacunen - laboratorios	188	1_genocidas_transgenico_argentina_vacunen
2	efectos - sintomas - convulsiones - guillainbarre - justyna	174	2_efectos_sintomas_convulsiones_guillainbarre
3	fallecimiento - richard - marzo - prometida - miocarditis	108	3_fallecimiento_richard_marzo_prometida
4	vacunadas - vaers - fallecidos - eudravigilance - reportes	72	4_vacunadas_vaers_fallecidos_eudravigilance
5	falecimento - janeiro - oliveira - abogado - paulo	67	5_falecimento_janeiro_oliveira_abogado
6	vacunadas - explosificadas - zombificado - inmunoglobulinas - autocombustion	57	6_vacunadas_explosificadas_zombificado_inmunoglobulinas
7	michela - repentinamente - sicilia - dormido - villanterio	48	7_michela_repentinamente_sicilia_dormido
8	covidvaccinevictims - pfizers - brasilena - hospitalizaron - ivermectina	47	8_covidvaccinevictims_pfizers_brasilena_hospitalizaron
9	filipinasvictima - pangilinan - cagayan - fallecidos - sinovacde	23	9_filipinasvictima_pangilinan_cagayan_fallecidos

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