MARTINI_enrich_BERTopic_Rus_truth

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_Rus_truth")

topic_model.get_topic_info()

Topic overview

• Number of topics: 9
• Number of training documents: 995

Click here for an overview of all topics.

Topic ID	Topic Keywords	Topic Frequency	Label
-1	donetsk - zakharova - sanctions - mercenaries - nazi	23	-1_donetsk_zakharova_sanctions_mercenaries
0	mariupol - azov - missiles - evacuated - battalion	604	0_mariupol_azov_missiles_evacuated
1	gazprombank - sanctions - euros - vladimir - poland	141	1_gazprombank_sanctions_euros_vladimir
2	zelensky - volodymyr - scholz - slovakia - suzdaltsev	73	2_zelensky_volodymyr_scholz_slovakia
3	kharkov - biolaboratories - pentagon - outbreak - borisovna	37	3_kharkov_biolaboratories_pentagon_outbreak
4	beijing - taiwan - ambassador - zhang - sino	36	4_beijing_taiwan_ambassador_zhang
5	marchers - nazis - victory - ivanovo - slovakia	29	5_marchers_nazis_victory_ivanovo
6	lavrov - sanctions - kissinger - baltic - aggressors	28	6_lavrov_sanctions_kissinger_baltic
7	missiles - howitzers - raytheon - supplied - cnn	24	7_missiles_howitzers_raytheon_supplied

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