MARTINI_enrich_BERTopic_LA_BITACORA
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_LA_BITACORA")
topic_model.get_topic_info()
Topic overview
- Number of topics: 12
- Number of training documents: 1201
Click here for an overview of all topics.
| Topic ID | Topic Keywords | Topic Frequency | Label |
|---|---|---|---|
| -1 | vacunados - pfizer - plandemia - 2021 - australia | 20 | -1_vacunados_pfizer_plandemia_2021 |
| 0 | bitacora - blitzman - miercoles - twitch - transferencias | 601 | 0_bitacora_blitzman_miercoles_twitch |
| 1 | protesters - freedom - canberra - arrested - convoyreports | 116 | 1_protesters_freedom_canberra_arrested |
| 2 | siempre - asesinos - introducciones - experimentos - medicos | 92 | 2_siempre_asesinos_introducciones_experimentos |
| 3 | unvaccinated - mandates - qld - quaxinated - jab | 84 | 3_unvaccinated_mandates_qld_quaxinated |
| 4 | seguirnos - noviembre - argentina - libertad - invitados | 78 | 4_seguirnos_noviembre_argentina_libertad |
| 5 | vaccine - deaths - shots - mccullough - 2021 | 57 | 5_vaccine_deaths_shots_mccullough |
| 6 | vacunas - muertes - pfizer - abortos - miocarditis | 43 | 6_vacunas_muertes_pfizer_abortos |
| 7 | inoculados - gardasil - muertes - victimas - causas | 35 | 7_inoculados_gardasil_muertes_victimas |
| 8 | pandemias - constituciones - internacional - poderes - parlamento | 30 | 8_pandemias_constituciones_internacional_poderes |
| 9 | chemtrails - aviones - cielo - fumigando - climatico | 24 | 9_chemtrails_aviones_cielo_fumigando |
| 10 | plandemic - conspiracy - naturalnews - tucker - redacted | 21 | 10_plandemic_conspiracy_naturalnews_tucker |
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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