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MARTINI_enrich_BERTopic_DiedSuddenlyConnecticut
AIDA-UPM/MARTINI_enrich_BERTopic_DiedSuddenlyConnecticut
MARTINI_enrich_BERTopic_DiedSuddenlyConnecticut 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 13, 2025
- Updated
- Jan 13, 2025
- SHA
- 94eb94bb
Try a prompt
Model card
From the Hugging Face model README
MARTINI_enrich_BERTopic_DiedSuddenlyConnecticut
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_DiedSuddenlyConnecticut")
topic_model.get_topic_info()
Topic overview
- Number of topics: 15
- Number of training documents: 1737
| Topic ID | Topic Keywords | Topic Frequency | Label |
|---|---|---|---|
| -1 | danbury - stamford - vaccinated - rn - july | 21 | -1_danbury_stamford_vaccinated_rn |
| 0 | naugatuck - mary - carolee - terryville - nurse | 1012 | 0_naugatuck_mary_carolee_terryville |
| 1 | chemotherapy - lymphoblastic - diagnosed - megan - marrow | 116 | 1_chemotherapy_lymphoblastic_diagnosed_megan |
| 2 | danbury - robert - steven - navy - moynihan | 91 | 2_danbury_robert_steven_navy |
| 3 | norwalk - teachers - kristin - stafford - vaccinated | 81 | 3_norwalk_teachers_kristin_stafford |
| 4 | uconn - vaccinated - snhu - colleges - unitedhealth | 61 | 4_uconn_vaccinated_snhu_colleges |
| 5 | vaers - vaccinated - myocarditis - deaths - 65 | 59 | 5_vaers_vaccinated_myocarditis_deaths |
| 6 | firefighters - norwalk - responder - lieutenant - tracy | 54 | 6_firefighters_norwalk_responder_lieutenant |
| 7 | micheal - larose - dahill - wajnowski - eversource | 47 | 7_micheal_larose_dahill_wajnowski |
| 8 | defibrillator - icu - strokes - blockage - cody | 46 | 8_defibrillator_icu_strokes_blockage |
| 9 | vaccinated - pfizer - shots - darien - toddlers | 40 | 9_vaccinated_pfizer_shots_darien |
| 10 | vaccinating - clinic - pratt - hhc - fema | 33 | 10_vaccinating_clinic_pratt_hhc |
| 11 | david - wethersfield - buckley - eisenberg - idaho | 32 | 11_david_wethersfield_buckley_eisenberg |
| 12 | glioblastoma - neurosurgeon - migraines - stamford - andrew | 23 | 12_glioblastoma_neurosurgeon_migraines_stamford |
| 13 | quinnipiac - hamden - dr - sheila - vaccination | 21 | 13_quinnipiac_hamden_dr_sheila |
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 13, 2025
- Updated
- Jan 13, 2025