MARTINI_enrich_BERTopic_covidvaccinevictims

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_covidvaccinevictims")

topic_model.get_topic_info()

Topic overview

• Number of topics: 8
• Number of training documents: 474

Click here for an overview of all topics.

Topic ID	Topic Keywords	Topic Frequency	Label
-1	vaers - pfizer - stroke - injection - 2021	23	-1_vaers_pfizer_stroke_injection
0	covid - friday - victims - joining - podcast	283	0_covid_friday_victims_joining
1	vaccines - victims - march - goldstein - attending	33	1_vaccines_victims_march_goldstein
2	vaccinated - died - dr - 2021 - eduardo	31	2_vaccinated_died_dr_2021
3	died - theworldwidewakeup - allison - footballer - 2023	27	3_died_theworldwidewakeup_allison_footballer
4	covidvaccinevictimsunite - videos - ivermectin - testimonials - telegram	27	4_covidvaccinevictimsunite_videos_ivermectin_testimonials
5	died - 2022 - sinovac - doses - katie	26	5_died_2022_sinovac_doses
6	fauci - rfk - link - documentary - watched	24	6_fauci_rfk_link_documentary

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