MARTINI_enrich_BERTopic_diedsuddenlyworldwide
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_diedsuddenlyworldwide")
topic_model.get_topic_info()
Topic overview
- Number of topics: 7
- Number of training documents: 680
Click here for an overview of all topics.
| Topic ID | Topic Keywords | Topic Frequency | Label |
|---|---|---|---|
| -1 | diagnosed - mum - stroke - ambulance - sepsis | 25 | -1_diagnosed_mum_stroke_ambulance |
| 0 | vaccinated - pfizer - injection - myocarditis - nattokinase | 321 | 0_vaccinated_pfizer_injection_myocarditis |
| 1 | russell - cancer - aged - jenna - 65 | 147 | 1_russell_cancer_aged_jenna |
| 2 | paramedics - collapsed - tillman - schoolboy - stadium | 69 | 2_paramedics_collapsed_tillman_schoolboy |
| 3 | grief - unexpectedly - matt - yesterday - nephew | 52 | 3_grief_unexpectedly_matt_yesterday |
| 4 | godhra - igatpuri - pandey - jamnagar - arvind | 37 | 4_godhra_igatpuri_pandey_jamnagar |
| 5 | paramedics - sheriff - mcdaniel - debra - dallas | 29 | 5_paramedics_sheriff_mcdaniel_debra |
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
- Downloads last month
- 4
Inference Providers
NEW
This model is not currently available via any of the supported third-party Inference Providers, and
the model is not deployed on the HF Inference API.