wolf_topic_model_repKB
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_repKB")
topic_model.get_topic_info()
Topic overview
- Number of topics: 4
- Number of training documents: 2933
Click here for an overview of all topics.
| Topic ID | Topic Keywords | Topic Frequency | Label |
|---|---|---|---|
| -1 | myoadapt app - myoadapt launch - myoadapt coming - myoadapt - myoadapt compare | 99 | -1_myoadapt app_myoadapt launch_myoadapt coming_myoadapt |
| 0 | deadlifts - deadlift - pull ups - exercises - lateral raises | 116 | 0_deadlifts_deadlift_pull ups_exercises |
| 1 | squats - squats gym - sissy squats - pistol squats - squat | 2512 | 1_squats_squats gym_sissy squats_pistol squats |
| 2 | calf raises - calf raise - protein intake - seated calf - lean mass | 206 | 2_calf raises_calf raise_protein intake_seated calf |
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
- Downloads last month
- -