---
tags:
- bertopic
library_name: bertopic
pipeline_tag: text-classification
---

# model_sbs

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("wongzien2000/model_sbs")

topic_model.get_topic_info()
```

## Topic overview

* Number of topics: 7
* Number of training documents: 1396

<details>
  <summary>Click here for an overview of all topics.</summary>

  | Topic ID | Topic Keywords | Topic Frequency | Label | 
|----------|----------------|-----------------|-------| 
| -1 | training - just - greg - videos - people | 57 | -1_training_just_greg_videos | 
| 0 | sets - muscle - volume - week - training | 151 | 0_sets_muscle_volume_week | 
| 1 | bench - press - shoulder - ohp - bar | 583 | 1_bench_press_shoulder_ohp | 
| 2 | greg - great - answer - thanks - thank | 222 | 2_greg_great_answer_thanks | 
| 3 | mike - dr - dr mike - eric - dr pak | 187 | 3_mike_dr_dr mike_eric | 
| 4 | science - based - science based - studies - brad | 135 | 4_science_based_science based_studies | 
| 5 | music - background - background music - audio - merrily | 61 | 5_music_background_background music_audio |

</details>

## 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: True
* zeroshot_min_similarity: 0.7
* zeroshot_topic_list: None

## Framework versions

* Numpy: 2.0.2
* HDBSCAN: 0.8.40
* UMAP: 0.5.7
* Pandas: 2.2.2
* Scikit-Learn: 1.6.1
* Sentence-transformers: 3.4.1
* Transformers: 4.50.2
* Numba: 0.60.0
* Plotly: 5.24.1
* Python: 3.11.11