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

# model_wolf

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_wolf")

topic_model.get_topic_info()
```

## Topic overview

* Number of topics: 19
* Number of training documents: 2933

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

  | Topic ID | Topic Keywords | Topic Frequency | Label | 
|----------|----------------|-----------------|-------| 
| -1 | split - great - creatine - best - exercise | 37 | -1_split_great_creatine_best | 
| 0 | cable - just - exercise - exercises - lateral | 468 | 0_cable_just_exercise_exercises | 
| 1 | mike - dr - dr mike - darth - sith | 1267 | 1_mike_dr_dr mike_darth | 
| 2 | sets - protein - week - muscle - volume | 166 | 2_sets_protein_week_muscle | 
| 3 | deadlift - deadlifts - strength - hypertrophy - legs | 107 | 3_deadlift_deadlifts_strength_hypertrophy | 
| 4 | tier - list - accent - tier list - pencil | 95 | 4_tier_list_accent_tier list | 
| 5 | pistol - squats - pistol squats - squat - reverse | 80 | 5_pistol_squats_pistol squats_squat | 
| 6 | tier - deadlift tier - deadlift - sticky ricky - ricky | 79 | 6_tier_deadlift tier_deadlift_sticky ricky | 
| 7 | uncles - stamps - time stamps - comment - timestamps | 77 | 7_uncles_stamps_time stamps_comment | 
| 8 | sound - audio - ai - milo - sound effects | 75 | 8_sound_audio_ai_milo | 
| 9 | curl - incline - curls - preacher - preacher curl | 74 | 9_curl_incline_curls_preacher | 
| 10 | milo - dr milo - hear - ending - miew | 73 | 10_milo_dr milo_hear_ending | 
| 11 | leg - leg extension - extension - quads - quad | 54 | 11_leg_leg extension_extension_quads | 
| 12 | calf - calf raise - seated calf - seated - calves | 54 | 12_calf_calf raise_seated calf_seated | 
| 13 | partials - lengthened partials - lengthened - song - grandma | 50 | 13_partials_lengthened partials_lengthened_song | 
| 14 | wolf - dr wolf - meadows - meadows row - dr | 46 | 14_wolf_dr wolf_meadows_meadows row | 
| 15 | squats - squat - hack - sissy - sissy squats | 45 | 15_squats_squat_hack_sissy | 
| 16 | app - myoadapt - december - waiting - coming | 43 | 16_app_myoadapt_december_waiting | 
| 17 | mike - interviewing mike - bomb - interviewing - love mike | 43 | 17_mike_interviewing mike_bomb_interviewing |

</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