wolf_topic_model_custom
This is a 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:
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
Click here for an overview of all topics.
| 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 |
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
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