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

# wolf_topic_model_custom

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_custom")

topic_model.get_topic_info()
```

## Topic overview

* Number of topics: 8
* Number of training documents: 2933

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

  | Topic ID | Topic Keywords | Topic Frequency | Label | 
|----------|----------------|-----------------|-------| 
| -1 | tier - like - just - milo - video | 114 | -1_tier_like_just_milo | 
| 0 | cable - exercise - lateral - like - delts | 979 | 0_cable_exercise_lateral_like | 
| 1 | squats - leg - squat - pistol - sissy | 986 | 1_squats_leg_squat_pistol | 
| 2 | mike - dr - dr mike - darth - like | 212 | 2_mike_dr_dr mike_darth | 
| 3 | deadlift - deadlifts - hypertrophy - stretch - strength | 212 | 3_deadlift_deadlifts_hypertrophy_stretch | 
| 4 | video - wolf - great - dr - channel | 151 | 4_video_wolf_great_dr | 
| 5 | exercises - video - studies - science - exercise | 149 | 5_exercises_video_studies_science | 
| 6 | week - protein - sets - fat - muscle | 130 | 6_week_protein_sets_fat |

</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