MARTINI_enrich_BERTopic_Daily_Clout

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_Daily_Clout")

topic_model.get_topic_info()

Topic overview

• Number of topics: 6
• Number of training documents: 643

Click here for an overview of all topics.

Topic ID	Topic Keywords	Topic Frequency	Label
-1	fauci - unvaccinated - dailyclout - mandates - millions	35	-1_fauci_unvaccinated_dailyclout_mandates
0	timeline - experts - dailyclout - biontech - findings	352	0_timeline_experts_dailyclout_biontech
1	vaers - miscarriages - stroke - 2021 - died	95	1_vaers_miscarriages_stroke_2021
2	bannon - dailyclout - scholar - freedom - complicit	75	2_bannon_dailyclout_scholar_freedom
3	yale - mandates - bivalent - pandemic - mitch	48	3_yale_mandates_bivalent_pandemic
4	nattokinase - spike - fauci - remedies - myocarditis	38	4_nattokinase_spike_fauci_remedies

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