README.md

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

# model_jeff

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_jeff")

topic_model.get_topic_info()
```

## Topic overview

* Number of topics: 26
* Number of training documents: 36727

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

  | Topic ID | Topic Keywords | Topic Frequency | Label | 
|----------|----------------|-----------------|-------| 
| -1 | jeff - just - great - love - thank | 256 | -1_jeff_just_great_love | 
| 0 | protein - fat - weight - eat - calories | 8978 | 0_protein_fat_weight_eat | 
| 1 | sets - reps - exercise - week - sets reps | 9577 | 1_sets_reps_exercise_week | 
| 2 | content - great - thank - science - informative | 5017 | 2_content_great_thank_science | 
| 3 | breaking bad - breaking - bad - better - better breaking | 1473 | 3_breaking bad_breaking_bad_better | 
| 4 | level - ready - ready level - level ready - level level | 1425 | 4_level_ready_ready level_level ready | 
| 5 | bench - press - chest - bench press - grip | 1234 | 5_bench_press_chest_bench press | 
| 6 | thank - great - man - amazing - thanks | 951 | 6_thank_great_man_amazing | 
| 7 | fitness - videos - science - thank - jeff | 818 | 7_fitness_videos_science_thank | 
| 8 | pro - elite - average - lost - level | 741 | 8_pro_elite_average_lost | 
| 9 | app - macros - macrofactor - macro - fat | 641 | 9_app_macros_macrofactor_macro | 
| 10 | jeff - jeff jeff - bro jeff - bro - love jeff | 578 | 10_jeff_jeff jeff_bro jeff_bro | 
| 11 | videos - love - love videos - youtube - quality | 524 | 11_videos_love_love videos_youtube | 
| 12 | kiwi - kiwi juice - juice - snort - snorting | 468 | 12_kiwi_kiwi juice_juice_snort | 
| 13 | steroids - testosterone - steroid - anabolic - use steroids | 440 | 13_steroids_testosterone_steroid_anabolic | 
| 14 | book - program - bought - books - ordered | 395 | 14_book_program_bought_books | 
| 15 | roids - women - habits - atomic - atomic habits | 393 | 15_roids_women_habits_atomic | 
| 16 | tall - short - look - height - dude | 382 | 16_tall_short_look_height | 
| 17 | mask - wearing - wearing mask - hoodie - shirt | 340 | 17_mask_wearing_wearing mask_hoodie | 
| 18 | shapiro - ben - ben shapiro - jacked - nerd | 336 | 18_shapiro_ben_ben shapiro_jacked | 
| 19 | mom - chili - macros - recipe - plastic | 314 | 19_mom_chili_macros_recipe | 
| 20 | jeff - thanks jeff - thanks - thank jeff - thank | 308 | 20_jeff_thanks jeff_thanks_thank jeff | 
| 21 | sleep - hours - sleeping - work - job | 301 | 21_sleep_hours_sleeping_work | 
| 22 | steph - bro steph - ber - bro - ber ber | 288 | 22_steph_bro steph_ber_bro | 
| 23 | voice - sound - match - voice doesn - sounds | 282 | 23_voice_sound_match_voice doesn | 
| 24 | thumbnail - picture - look - pic - photo | 267 | 24_thumbnail_picture_look_pic |

</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