MARTINI_enrich_BERTopic_TheBigConspiracy

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_TheBigConspiracy")

topic_model.get_topic_info()

Topic overview

• Number of topics: 6
• Number of training documents: 615

Click here for an overview of all topics.

Topic ID	Topic Keywords	Topic Frequency	Label
-1	unvaccinated - bbc - pfizer - midazolam - 2022	29	-1_unvaccinated_bbc_pfizer_midazolam
0	brilliant - yep - oversees - videos - guys	426	0_brilliant_yep_oversees_videos
1	pfizer - deaths - 2021 - injection - australia	47	1_pfizer_deaths_2021_injection
2	unvaxed - jabbed - booster - everybody - jocavic	38	2_unvaxed_jabbed_booster_everybody
3	pandemic - globalists - illuminati - remdesivir - agenda	38	3_pandemic_globalists_illuminati_remdesivir
4	hiv - viroligist - undetectable - pcr - fearmongering	37	4_hiv_viroligist_undetectable_pcr

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