MARTINI_enrich_BERTopic_rassemblementnationalvaucluse

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_rassemblementnationalvaucluse")

topic_model.get_topic_info()

Topic overview

Number of topics: 6
Number of training documents: 486

Click here for an overview of all topics.

Topic ID	Topic Keywords	Topic Frequency	Label
-1	departement - campagne - metropolitain - jaouen - novembre	21	-1_departement_campagne_metropolitain_jaouen
0	republique - reformedesretraites - macron - rejoignez - laxisme	277	0_republique_reformedesretraites_macron_rejoignez
1	departement - gouvernement - macroniste - policiers - annonce	65	1_departement_gouvernement_macroniste_policiers
2	rnvaucluse - departementaux - europeennes - beaucaire - septembre	46	2_rnvaucluse_departementaux_europeennes_beaucaire
3	rnvaucluse - janvier - soiree - merci - dussausaye	41	3_rnvaucluse_janvier_soiree_merci
4	agriculteursencolere - montelimar - parlement - macronistes - onmarchesurlatete	36	4_agriculteursencolere_montelimar_parlement_macronistes

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