MARTINI_enrich_BERTopic_eleccionestransparentes_APET

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_eleccionestransparentes_APET")

topic_model.get_topic_info()

Topic overview

• Number of topics: 7
• Number of training documents: 365

Click here for an overview of all topics.

Topic ID	Topic Keywords	Topic Frequency	Label
-1	electorales - necesitamos - derechos - contacto - espana	22	-1_electorales_necesitamos_derechos_contacto
0	ballots - mandamus - supremo - invalidar - maricopa	188	0_ballots_mandamus_supremo_invalidar
1	electorales - audiencia - pruebas - sumarlas - asturias	38	1_electorales_audiencia_pruebas_sumarlas
2	bolsonaro - brasilia - senado - infiltrados - manifestacion	34	2_bolsonaro_brasilia_senado_infiltrados
3	correos - melilla - votes - gobierno - septiembre	32	3_correos_melilla_votes_gobierno
4	invitamos - noviembre - manifestacion - entrevistado - jesusarojo	28	4_invitamos_noviembre_manifestacion_entrevistado
5	documentos - procurador - presentada - reenviado - subsiguiente	23	5_documentos_procurador_presentada_reenviado

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