MARTINI_enrich_BERTopic_Somosrealistas
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_Somosrealistas")
topic_model.get_topic_info()
Topic overview
- Number of topics: 11
- Number of training documents: 880
Click here for an overview of all topics.
| Topic ID | Topic Keywords | Topic Frequency | Label |
|---|---|---|---|
| -1 | vacunados - efectos - pandemia - somosrealistas - cientificos | 20 | -1_vacunados_efectos_pandemia_somosrealistas |
| 0 | vacunadas - polio - explicaciones - efectos - hijos | 518 | 0_vacunadas_polio_explicaciones_efectos |
| 1 | corona - pcr - diagnosticos - falsos - secuencias | 73 | 1_corona_pcr_diagnosticos_falsos |
| 2 | vacunadas - abortos - pfizer - efectos - coagulacion | 61 | 2_vacunadas_abortos_pfizer_efectos |
| 3 | inconsciencia - propagandistico - revolucion - manipulados - socialismo | 56 | 3_inconsciencia_propagandistico_revolucion_manipulados |
| 4 | mentiras - corrupciones - guerrera - presentadoras - palabras | 33 | 4_mentiras_corrupciones_guerrera_presentadoras |
| 5 | manifestacion - denunciaron - madrid - sabado - reporteros | 28 | 5_manifestacion_denunciaron_madrid_sabado |
| 6 | pandemia - elitistas - gobierno - confinamientos - rebelarse | 24 | 6_pandemia_elitistas_gobierno_confinamientos |
| 7 | negacionistas - seguimos - hospitales - prensa - ozonoterapia | 24 | 7_negacionistas_seguimos_hospitales_prensa |
| 8 | mascarillas - contaminados - respiracion - docentes - desobediencia | 22 | 8_mascarillas_contaminados_respiracion_docentes |
| 9 | vacunarse - constitucionales - obligatoria - discriminacion - europa | 21 | 9_vacunarse_constitucionales_obligatoria_discriminacion |
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
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