Instructions to use wongzien2000/wolf_topic_model_custom with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- BERTopic
How to use wongzien2000/wolf_topic_model_custom with BERTopic:
from bertopic import BERTopic model = BERTopic.load("wongzien2000/wolf_topic_model_custom") - Notebooks
- Google Colab
- Kaggle
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Download README.md from wongzien2000/wolf_topic_model_custom: direct link, hf CLI and curl.
- Browser
- Download file 2.05 kB
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https://huggingface.co/wongzien2000/wolf_topic_model_custom/resolve/main/README.md
- Command line
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hf download hf://wongzien2000/wolf_topic_model_custom/README.md
-
curl -L -o README.md https://huggingface.co/wongzien2000/wolf_topic_model_custom/resolve/main/README.md
2.05 kB
| tags: | |
| - bertopic | |
| library_name: bertopic | |
| pipeline_tag: text-classification | |
| # wolf_topic_model_custom | |
| 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/wolf_topic_model_custom") | |
| topic_model.get_topic_info() | |
| ``` | |
| ## Topic overview | |
| * Number of topics: 8 | |
| * Number of training documents: 2933 | |
| <details> | |
| <summary>Click here for an overview of all topics.</summary> | |
| | 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 | | |
| </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: 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 | |