MARTINI_enrich_BERTopic_RogerHodkinson
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_RogerHodkinson")
topic_model.get_topic_info()
Topic overview
- Number of topics: 23
- Number of training documents: 2203
Click here for an overview of all topics.
| Topic ID | Topic Keywords | Topic Frequency | Label |
|---|---|---|---|
| -1 | pfizer - fauci - vaccinated - lockdowns - published | 20 | -1_pfizer_fauci_vaccinated_lockdowns |
| 0 | fauci - virologists - conspiracy - laboratories - whistleblower | 1174 | 0_fauci_virologists_conspiracy_laboratories |
| 1 | pandemics - ghebreyesus - trudeau - sovereignty - iran | 98 | 1_pandemics_ghebreyesus_trudeau_sovereignty |
| 2 | vaccinated - twindemic - bivalent - booster - updated | 93 | 2_vaccinated_twindemic_bivalent_booster |
| 3 | vaccinations - unvaccinated - dtap - rotavirus - infant | 78 | 3_vaccinations_unvaccinated_dtap_rotavirus |
| 4 | masks - washington - vaccination - stanford - exemptions | 66 | 4_masks_washington_vaccination_stanford |
| 5 | myopericarditis - nuvaxovid - physicians - lymphocytic - fatal | 64 | 5_myopericarditis_nuvaxovid_physicians_lymphocytic |
| 6 | coroners - cv19 - died - worldwide - 2021 | 61 | 6_coroners_cv19_died_worldwide |
| 7 | newsom - misinformation - physicians - inoculated - astrazeneca | 59 | 7_newsom_misinformation_physicians_inoculated |
| 8 | infodemic - reclaimthenet - censored - zuckerberg - agencies | 54 | 8_infodemic_reclaimthenet_censored_zuckerberg |
| 9 | longcovid - lingering - vax - symptoms - sufferers | 50 | 9_longcovid_lingering_vax_symptoms |
| 10 | lockdown - china - zhengzhou - sars - wechat | 43 | 10_lockdown_china_zhengzhou_sars |
| 11 | pregnant - miscarriages - pfizer - placental - multiparous | 42 | 11_pregnant_miscarriages_pfizer_placental |
| 12 | ivermectin - fda - penicillin - cuomo - miracle | 40 | 12_ivermectin_fda_penicillin_cuomo |
| 13 | plasmidgate - modrna - polio - snapgene - contaminated | 36 | 13_plasmidgate_modrna_polio_snapgene |
| 14 | pfizer - whistleblower - paxton - quillivant - lawsuit | 33 | 14_pfizer_whistleblower_paxton_quillivant |
| 15 | fluoxetine - drugmaker - lilly - shortages - mandrola | 33 | 15_fluoxetine_drugmaker_lilly_shortages |
| 16 | masks - plastic - waste - expose - diapers | 31 | 16_masks_plastic_waste_expose |
| 17 | military - mandated - discharged - exemptions - whistleblowers | 30 | 17_military_mandated_discharged_exemptions |
| 18 | oncologists - brca - leukemias - p53 - lymphocytes | 27 | 18_oncologists_brca_leukemias_p53 |
| 19 | therealanthonyfauci - rfk - joe - shootings - debaters | 27 | 19_therealanthonyfauci_rfk_joe_shootings |
| 20 | clots - hypercoagulation - vaccinated - pegylated - embalmed | 23 | 20_clots_hypercoagulation_vaccinated_pegylated |
| 21 | euthanasia - remdesivir - midazolam - ventilator - murdered | 21 | 21_euthanasia_remdesivir_midazolam_ventilator |
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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