MARTINI_enrich_BERTopic_WilliamMakis

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_WilliamMakis")

topic_model.get_topic_info()

Topic overview

• Number of topics: 5
• Number of training documents: 208

Click here for an overview of all topics.

Topic ID	Topic Keywords	Topic Frequency	Label
-1	deaths - embolism - strep - ontario - 2022	20	-1_deaths_embolism_strep_ontario
0	deaths - cpr - myocarditis - jennifer - 2022	63	0_deaths_cpr_myocarditis_jennifer
1	metastatic - hodgkin - colon - merck - 2022	63	1_metastatic_hodgkin_colon_merck
2	nattokinase - clots - myocarditis - astrazeneca - mutated	34	2_nattokinase_clots_myocarditis_astrazeneca
3	alberta - unvaccinated - trudeau - deaths - pathologists	28	3_alberta_unvaccinated_trudeau_deaths

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