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wongzien2000/
model_sbs

Name: model_sbs
Availability: InStock

bertopic

text-classification

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model_sbs

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/model_sbs")

topic_model.get_topic_info()

Topic overview

Number of topics: 7
Number of training documents: 1396

Click here for an overview of all topics.

Topic ID	Topic Keywords	Topic Frequency	Label
-1	training - just - greg - videos - people	57	-1_training_just_greg_videos
0	sets - muscle - volume - week - training	151	0_sets_muscle_volume_week
1	bench - press - shoulder - ohp - bar	583	1_bench_press_shoulder_ohp
2	greg - great - answer - thanks - thank	222	2_greg_great_answer_thanks
3	mike - dr - dr mike - eric - dr pak	187	3_mike_dr_dr mike_eric
4	science - based - science based - studies - brad	135	4_science_based_science based_studies
5	music - background - background music - audio - merrily	61	5_music_background_background music_audio

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