MARTINI_enrich_BERTopic_covid
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_covid")
topic_model.get_topic_info()
Topic overview
- Number of topics: 12
- Number of training documents: 1269
Click here for an overview of all topics.
| Topic ID | Topic Keywords | Topic Frequency | Label |
|---|---|---|---|
| -1 | fauci - vaccinated - injections - monkeypox - misinformation | 21 | -1_fauci_vaccinated_injections_monkeypox |
| 0 | vaccinated - deaths - pfizer - lancet - pericarditis | 676 | 0_vaccinated_deaths_pfizer_lancet |
| 1 | omicron - booster - antibodies - shots - harvard | 135 | 1_omicron_booster_antibodies_shots |
| 2 | fauci - coronaviruses - darpa - funded - origins | 78 | 2_fauci_coronaviruses_darpa_funded |
| 3 | pandemic - gates - bannon - misinformation - klaus | 66 | 3_pandemic_gates_bannon_misinformation |
| 4 | vaccinate - mrna - microbiologist - plasmid - bhakdi | 61 | 4_vaccinate_mrna_microbiologist_plasmid |
| 5 | pfizer - whistleblower - davos - lawsuits - stephane | 55 | 5_pfizer_whistleblower_davos_lawsuits |
| 6 | quarantine - china - shenzhen - passport - red | 48 | 6_quarantine_china_shenzhen_passport |
| 7 | nattokinase - pfizer - bromelain - bleeding - exosomes | 39 | 7_nattokinase_pfizer_bromelain_bleeding |
| 8 | ivermectin - remdesivir - hydroxychloroquine - penicillin - medications | 37 | 8_ivermectin_remdesivir_hydroxychloroquine_penicillin |
| 9 | strokes - encephalitis - neurologists - aphasia - astrazeneca | 31 | 9_strokes_encephalitis_neurologists_aphasia |
| 10 | australia - unjabbed - djokovic - banned - andrews | 22 | 10_australia_unjabbed_djokovic_banned |
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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