MARTINI_enrich_BERTopic_shadowman2020

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_shadowman2020")

topic_model.get_topic_info()

Topic overview

• Number of topics: 8
• Number of training documents: 656

Click here for an overview of all topics.

Topic ID	Topic Keywords	Topic Frequency	Label
-1	antifa - britain - veterans - boris - invasion	27	-1_antifa_britain_veterans_boris
0	coronavirus - pandemic - pfizer - nhs - masks	274	0_coronavirus_pandemic_pfizer_nhs
1	protesters - palestine - barricades - dublin - stabbed	103	1_protesters_palestine_barricades_dublin
2	illegals - migrant - britain - dover - invaded	71	2_illegals_migrant_britain_dover
3	biden - votes - recount - michigan - republicans	60	3_biden_votes_recount_michigan
4	syria - airstrikes - mercenaries - karabakh - pentagon	44	4_syria_airstrikes_mercenaries_karabakh
5	london - veterans - poppies - cenotaph - doherty	39	5_london_veterans_poppies_cenotaph
6	telegram - patrioticcrusader - youtube - subscribed - banned	38	6_telegram_patrioticcrusader_youtube_subscribed

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