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MARTINI_enrich_BERTopic_dr_judymikovitss
AIDA-UPM/MARTINI_enrich_BERTopic_dr_judymikovitss
MARTINI_enrich_BERTopic_dr_judymikovitss is a text classification model from AIDA-UPM. Use it when you need a label for a piece of text. It is set up for bertopic.
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.
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At a glance
- Task
- Text Classification
- Library
- bertopic
- Access
- Public
- Created
- Jan 14, 2025
- Updated
- Jan 14, 2025
- SHA
- be086fd9
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Model card
From the Hugging Face model README
MARTINI_enrich_BERTopic_dr_judymikovitss
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_dr_judymikovitss")
topic_model.get_topic_info()
Topic overview
- Number of topics: 13
- Number of training documents: 1039
| Topic ID | Topic Keywords | Topic Frequency | Label |
|---|---|---|---|
| -1 | vaccinated - pfizer - rfk - misinformation - deaths | 22 | -1_vaccinated_pfizer_rfk_misinformation |
| 0 | hydroxychloroquine - doctors - misinformed - malhotra - suppression | 540 | 0_hydroxychloroquine_doctors_misinformed_malhotra |
| 1 | fauci - anthony - lockdowns - funded - congress | 91 | 1_fauci_anthony_lockdowns_funded |
| 2 | nattokinase - bromelain - supplements - spike - liposomal | 67 | 2_nattokinase_bromelain_supplements_spike |
| 3 | pfizer - hpv - oncogenes - keytruda - plasmid | 45 | 3_pfizer_hpv_oncogenes_keytruda |
| 4 | ivermectin - fenbendazole - hydroxychloroquine - penicillin - overdose | 43 | 4_ivermectin_fenbendazole_hydroxychloroquine_penicillin |
| 5 | deaths - underreporting - suddenly - poisoned - 2023 | 42 | 5_deaths_underreporting_suddenly_poisoned |
| 6 | myocarditis - unvaxed - clots - guillain - aorta | 41 | 6_myocarditis_unvaxed_clots_guillain |
| 7 | globalists - capitalism - zuckerberg - blackrock - wealth | 38 | 7_globalists_capitalism_zuckerberg_blackrock |
| 8 | vaccinated - omicron - reinfection - backfired - cleveland | 37 | 8_vaccinated_omicron_reinfection_backfired |
| 9 | pfizer - vaers - miscarriages - fainting - baby | 25 | 9_pfizer_vaers_miscarriages_fainting |
| 10 | conspirators - fda - rockefeller - epidemic - founded | 25 | 10_conspirators_fda_rockefeller_epidemic |
| 11 | unvaxed - mandates - novavax - hochul - lawsuit | 23 | 11_unvaxed_mandates_novavax_hochul |
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
- Task
- Text Classification
- Library
- bertopic
- Created
- Jan 14, 2025
- Updated
- Jan 14, 2025