MARTINI_enrich_BERTopic_covidbc
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_covidbc")
topic_model.get_topic_info()
Topic overview
- Number of topics: 41
- Number of training documents: 5063
Click here for an overview of all topics.
| Topic ID | Topic Keywords | Topic Frequency | Label |
|---|---|---|---|
| -1 | vaxgenocide - pfizer - mercury - brain - dose | 20 | -1_vaxgenocide_pfizer_mercury_brain |
| 0 | clots - pneumonia - thrombectomy - transfusions - vaccinated | 3038 | 0_clots_pneumonia_thrombectomy_transfusions |
| 1 | vaccines - pandemic - chemtrails - mask - irrefutable | 168 | 1_vaccines_pandemic_chemtrails_mask |
| 2 | cancerous - chemotherapy - metastatic - lymphoma - vaccinated | 152 | 2_cancerous_chemotherapy_metastatic_lymphoma |
| 3 | strokes - vaccinated - ischemic - subarachnoid - hemorrhagic | 134 | 3_strokes_vaccinated_ischemic_subarachnoid |
| 4 | vaccineinjuries - collapses - convulsing - screams - videos | 104 | 4_vaccineinjuries_collapses_convulsing_screams |
| 5 | vaxgenocide - hallie - chambliss - passed - shingles | 91 | 5_vaxgenocide_hallie_chambliss_passed |
| 6 | steven - funeral - corey - vaxgenocide - dave | 85 | 6_steven_funeral_corey_vaxgenocide |
| 7 | died - vaccinated - vinson - officer - luzardo | 82 | 7_died_vaccinated_vinson_officer |
| 8 | footballer - coulibaly - teammates - swimmer - collapsing | 63 | 8_footballer_coulibaly_teammates_swimmer |
| 9 | connecticut - waterbury - wallingford - donnelly - bethel | 61 | 9_connecticut_waterbury_wallingford_donnelly |
| 10 | mortem - sajeevan - salman - lakshmika - gujarat | 56 | 10_mortem_sajeevan_salman_lakshmika |
| 11 | vaxxidents - crashed - bus - hubei - tiktok | 54 | 11_vaxxidents_crashed_bus_hubei |
| 12 | astrazeneca - death - sathong - 18th - bovornkittipaisal | 52 | 12_astrazeneca_death_sathong_18th |
| 13 | doctors - died - saskatchewan - comox - mcmaster | 52 | 13_doctors_died_saskatchewan_comox |
| 14 | ribeirao - santos - pacatuba - serranopolis - isabel | 51 | 14_ribeirao_santos_pacatuba_serranopolis |
| 15 | cpr - defibrillator - suddenly - schoolboy - dead | 51 | 15_cpr_defibrillator_suddenly_schoolboy |
| 16 | kathy - natalie - momma - monica - november | 49 | 16_kathy_natalie_momma_monica |
| 17 | troponin - angioplasty - blockages - nhs - jonanovic | 47 | 17_troponin_angioplasty_blockages_nhs |
| 18 | pfizer - headaches - symptoms - dizzieness - victim | 44 | 18_pfizer_headaches_symptoms_dizzieness |
| 19 | truthsocial - channels - uploading - deleted - gab | 42 | 19_truthsocial_channels_uploading_deleted |
| 20 | paralyzed - guillain - pfizer - spasms - penticton | 41 | 20_paralyzed_guillain_pfizer_spasms |
| 21 | stickers - eggshell - destructible - vancouver - scratching | 39 | 21_stickers_eggshell_destructible_vancouver |
| 22 | pfizer - oaxaca - vanessa - 14 - boy | 39 | 22_pfizer_oaxaca_vanessa_14 |
| 23 | aquino - filipino - paglinawan - villafuerte - paulina | 38 | 23_aquino_filipino_paglinawan_villafuerte |
| 24 | pangasinan - 2021 - 39yrs - nelvin - victims | 37 | 24_pangasinan_2021_39yrs_nelvin |
| 25 | injured - doctors - mrna - jenni - warning | 36 | 25_injured_doctors_mrna_jenni |
| 26 | richie - everett - condolences - chastain - yiannis | 35 | 26_richie_everett_condolences_chastain |
| 27 | normies - shit - deaths - followers - depressed | 28 | 27_normies_shit_deaths_followers |
| 28 | vaxed - benadryl - dupixent - nonanaphylactic - cutaneous | 26 | 28_vaxed_benadryl_dupixent_nonanaphylactic |
| 29 | tremor - spasming - vertigo - debilitating - alzheimers | 25 | 29_tremor_spasming_vertigo_debilitating |
| 30 | michael - reeves - caleb - courtenay - survivors | 25 | 30_michael_reeves_caleb_courtenay |
| 31 | anne - vaxgenocide - mum - england - nikolina | 24 | 31_anne_vaxgenocide_mum_england |
| 32 | sister - sondra - isabella - cathy - valladares | 23 | 32_sister_sondra_isabella_cathy |
| 33 | pericarditis - pfizer - pleurisy - diagnosed - mickael | 23 | 33_pericarditis_pfizer_pleurisy_diagnosed |
| 34 | canada - antivaxx - chilliwack - mandatory - enforcers | 23 | 34_canada_antivaxx_chilliwack_mandatory |
| 35 | amputated - sinovac - thrombosis - necrotic - penicillin | 22 | 35_amputated_sinovac_thrombosis_necrotic |
| 36 | stillbirths - abortions - vaers - trimesters - shots | 21 | 36_stillbirths_abortions_vaers_trimesters |
| 37 | thecovidblog - booster - shot - matej - ronnie | 21 | 37_thecovidblog_booster_shot_matej |
| 38 | metastasized - tammy - glioblastoma - roslyn - surviving | 21 | 38_metastasized_tammy_glioblastoma_roslyn |
| 39 | ivermectin - injection - valentina - mrna - 41 | 20 | 39_ivermectin_injection_valentina_mrna |
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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