MARTINI_enrich_BERTopic_TheWellnessCompany
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_TheWellnessCompany")
topic_model.get_topic_info()
Topic overview
- Number of topics: 6
- Number of training documents: 450
Click here for an overview of all topics.
| Topic ID | Topic Keywords | Topic Frequency | Label |
|---|---|---|---|
| -1 | fauci - vaccinated - cancers - ivermectin - makis | 21 | -1_fauci_vaccinated_cancers_ivermectin |
| 0 | livestream - healthcare - supplements - tucker - amazing | 228 | 0_livestream_healthcare_supplements_tucker |
| 1 | nattokinase - bromelain - spikesymposium - curcumin - supplement | 87 | 1_nattokinase_bromelain_spikesymposium_curcumin |
| 2 | myocarditis - vaccinated - deaths - c19 - causally | 69 | 2_myocarditis_vaccinated_deaths_c19 |
| 3 | nattokinase - protease - fibrinolytic - neutralize - japan | 23 | 3_nattokinase_protease_fibrinolytic_neutralize |
| 4 | cardiologists - suicide - prescribed - negligent - houston | 22 | 4_cardiologists_suicide_prescribed_negligent |
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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