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wongzien2000/
wolf_topic_model_custom

Name: wolf_topic_model_custom
Availability: InStock

bertopic

text-classification

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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:

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

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