MARTINI_enrich_BERTopic_VaccineInjuriesca

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_VaccineInjuriesca")

topic_model.get_topic_info()

Topic overview

• Number of topics: 10
• Number of training documents: 930

Click here for an overview of all topics.

Topic ID	Topic Keywords	Topic Frequency	Label
-1	died - vaxgenocide - pilot - 2022 - sudden	20	-1_died_vaxgenocide_pilot_2022
0	died - cpr - sudden - collapses - chelan	539	0_died_cpr_sudden_collapses
1	vaccinated - clots - died - amputated - quack	65	1_vaccinated_clots_died_amputated
2	rash - psoriasis - hives - pfizer - injections	65	2_rash_psoriasis_hives_pfizer
3	vaccineinjuries - symptoms - numbness - needles - isabelle	54	3_vaccineinjuries_symptoms_numbness_needles
4	vaxgenocide - gofundme - sheila - funeral - 2023	45	4_vaxgenocide_gofundme_sheila_funeral
5	bitchute - illuminati - unvaccinated - genocidio - pandemic	40	5_bitchute_illuminati_unvaccinated_genocidio
6	vaccinated - died - brandon - rostrevor - 2022	34	6_vaccinated_died_brandon_rostrevor
7	pfizer - immunized - died - myocarditis - mazatlan	34	7_pfizer_immunized_died_myocarditis
8	sinovac - 2021020092 - died - january - 48yrs	34	8_sinovac_2021020092_died_january

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