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

# wolf_topic_model_repKB

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/wolf_topic_model_repKB")

topic_model.get_topic_info()
```

## Topic overview

* Number of topics: 4
* Number of training documents: 2933

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

  | Topic ID | Topic Keywords | Topic Frequency | Label | 
|----------|----------------|-----------------|-------| 
| -1 | myoadapt app - myoadapt launch - myoadapt coming - myoadapt - myoadapt compare | 99 | -1_myoadapt app_myoadapt launch_myoadapt coming_myoadapt | 
| 0 | deadlifts - deadlift - pull ups - exercises - lateral raises | 116 | 0_deadlifts_deadlift_pull ups_exercises | 
| 1 | squats - squats gym - sissy squats - pistol squats - squat | 2512 | 1_squats_squats gym_sissy squats_pistol squats | 
| 2 | calf raises - calf raise - protein intake - seated calf - lean mass | 206 | 2_calf raises_calf raise_protein intake_seated calf |

</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: 5
* 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