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---
tags:
- bertopic
library_name: bertopic
pipeline_tag: text-classification
---
# BERTopic_TheWellnessCompany
This is a [BERTopic](https://github.com/MaartenGr/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:
```python
from bertopic import BERTopic
topic_model = BERTopic.load("sdantonio/BERTopic_TheWellnessCompany")
topic_model.get_topic_info()
```
## Topic overview
* Number of topics: 9
* Number of training documents: 481
<details>
<summary>Click here for an overview of all topics.</summary>
| Topic ID | Topic Keywords | Topic Frequency | Label |
|----------|----------------|-----------------|-------|
| -1 | thewellnesscompany - shots - reprisal - serves - coulson | 12 | -1_thewellnesscompany_shots_reprisal_serves |
| 0 | thewellnesscompany - gessling - compelling - prolonged - daily_clout | 41 | 0_thewellnesscompany_gessling_compelling_prolonged |
| 1 | cardiologists - thewellnesscompany - myocarditis - epidemiologist - publications | 138 | 1_cardiologists_thewellnesscompany_myocarditis_epidemiologist |
| 2 | myocarditis - thewellnesscompany - prolonged - shots - reprisal | 96 | 2_myocarditis_thewellnesscompany_prolonged_shots |
| 3 | thewellnesscompany - packed - prolonged - dissolved - pomegranate | 80 | 3_thewellnesscompany_packed_prolonged_dissolved |
| 4 | thewellnesscompany - tedros - insights - marik - toxicity | 52 | 4_thewellnesscompany_tedros_insights_marik |
| 5 | ivermectin - thewellnesscompany - epidemiologist - hydroxychloroquine - misbehavior | 21 | 5_ivermectin_thewellnesscompany_epidemiologist_hydroxychloroquine |
| 6 | thewellnesscompany - hearts - concerns - reprisal - shots | 21 | 6_thewellnesscompany_hearts_concerns_reprisal |
| 7 | backtobasicsconference - unprepared - pregnancytalk - twcadventures - may9th | 20 | 7_backtobasicsconference_unprepared_pregnancytalk_twcadventures |
</details>
## Training hyperparameters
* calculate_probabilities: False
* 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: 1.23.5
* HDBSCAN: 0.8.38.post1
* UMAP: 0.5.6
* Pandas: 2.2.2
* Scikit-Learn: 1.5.1
* Sentence-transformers: 3.0.1
* Transformers: 4.44.2
* Numba: 0.60.0
* Plotly: 5.24.0
* Python: 3.10.12
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