MARTINI_enrich_BERTopic_childcovidvaccineinjuriesuk
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_childcovidvaccineinjuriesuk")
topic_model.get_topic_info()
Topic overview
- Number of topics: 23
- Number of training documents: 2102
Click here for an overview of all topics.
| Topic ID | Topic Keywords | Topic Frequency | Label |
|---|---|---|---|
| -1 | vaccinated - astrazeneca - reports - 2022 - mrna | 20 | -1_vaccinated_astrazeneca_reports_2022 |
| 0 | lockdown - totalitarianism - cepi - bbc - everyone | 1141 | 0_lockdown_totalitarianism_cepi_bbc |
| 1 | myopericarditis - carditis - vaers - tachycardia - inflammation | 104 | 1_myopericarditis_carditis_vaers_tachycardia |
| 2 | clots - amyloid - vials - nanoparticles - injected | 72 | 2_clots_amyloid_vials_nanoparticles |
| 3 | victims - evidence - ccviuk - officer - warwickshire | 67 | 3_victims_evidence_ccviuk_officer |
| 4 | miscarriages - vaers - trimester - caesarean - pfizer | 65 | 4_miscarriages_vaers_trimester_caesarean |
| 5 | vaccinated - doctors - schizophrenia - dangerous - roger | 64 | 5_vaccinated_doctors_schizophrenia_dangerous |
| 6 | died - soccer - cardiac - sudden - 27 | 56 | 6_died_soccer_cardiac_sudden |
| 7 | vaers - underreporting - killed - 35 - 2022 | 54 | 7_vaers_underreporting_killed_35 |
| 8 | polio - guillain - encephalopathy - strokes - eudravigilance | 54 | 8_polio_guillain_encephalopathy_strokes |
| 9 | vaers - died - reportedlink - 2022 - injected | 45 | 9_vaers_died_reportedlink_2022 |
| 10 | lockdowns - mask - headteachers - billionaires - abuse | 39 | 10_lockdowns_mask_headteachers_billionaires |
| 11 | sars - bioweapon - lies - origins - corbett | 39 | 11_sars_bioweapon_lies_origins |
| 12 | mortality - 2022 - millennials - cohort - 40 | 38 | 12_mortality_2022_millennials_cohort |
| 13 | soldiers - embolisms - unwell - boeing - discharge | 33 | 13_soldiers_embolisms_unwell_boeing |
| 14 | novavax - pneumococcal - toddlers - doses - bivalent | 29 | 14_novavax_pneumococcal_toddlers_doses |
| 15 | pfizer - pertussis - died - billions - 2009 | 29 | 15_pfizer_pertussis_died_billions |
| 16 | newsom - minors - kentucky - consent - disallows | 29 | 16_newsom_minors_kentucky_consent |
| 17 | gardasil - died - 17 - injection - boy | 28 | 17_gardasil_died_17_injection |
| 18 | injuries - mhra - booster - reports - 229 | 27 | 18_injuries_mhra_booster_reports |
| 19 | unvaxed - deadlier - england - 495 - quadruple | 25 | 19_unvaxed_deadlier_england_495 |
| 20 | metastasizing - brca - toxshot - interferon - micrornas | 24 | 20_metastasizing_brca_toxshot_interferon |
| 21 | hpv - bibfertility - ovaries - postmenopausal - antibodies | 20 | 21_hpv_bibfertility_ovaries_postmenopausal |
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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