MARTINI_enrich_BERTopic_diedsuddenlyworldwide

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_diedsuddenlyworldwide")

topic_model.get_topic_info()

Topic overview

• Number of topics: 7
• Number of training documents: 680

Click here for an overview of all topics.

Topic ID	Topic Keywords	Topic Frequency	Label
-1	diagnosed - mum - stroke - ambulance - sepsis	25	-1_diagnosed_mum_stroke_ambulance
0	vaccinated - pfizer - injection - myocarditis - nattokinase	321	0_vaccinated_pfizer_injection_myocarditis
1	russell - cancer - aged - jenna - 65	147	1_russell_cancer_aged_jenna
2	paramedics - collapsed - tillman - schoolboy - stadium	69	2_paramedics_collapsed_tillman_schoolboy
3	grief - unexpectedly - matt - yesterday - nephew	52	3_grief_unexpectedly_matt_yesterday
4	godhra - igatpuri - pandey - jamnagar - arvind	37	4_godhra_igatpuri_pandey_jamnagar
5	paramedics - sheriff - mcdaniel - debra - dallas	29	5_paramedics_sheriff_mcdaniel_debra

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