MARTINI_enrich_BERTopic_nationalistesfr

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_nationalistesfr")

topic_model.get_topic_info()

Topic overview

Number of topics: 7
Number of training documents: 813

Click here for an overview of all topics.

Topic ID	Topic Keywords	Topic Frequency	Label
-1	russie - campagne - dombass - zemmour - europeenne	36	-1_russie_campagne_dombass_zemmour
0	novembre - vendredi - perpignan - mouvement - edouard	289	0_novembre_vendredi_perpignan_mouvement
1	sioniste - hollande - herve - avocats - jugement	207	1_sioniste_hollande_herve_avocats
2	bastille - abdelkader - septembre - patriotisme - tricolore	81	2_bastille_abdelkader_septembre_patriotisme
3	pandemie - publiques - vaccins - masques - perpignan	75	3_pandemie_publiques_vaccins_masques
4	gauchistes - reptilienne - accueil - migratoire - raciale	67	4_gauchistes_reptilienne_accueil_migratoire
5	gloire - souhaitent - vainqueurs - croix - mitraille	58	5_gloire_souhaitent_vainqueurs_croix

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