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BERTopic_TheWellnessCompany
sdantonio/BERTopic_TheWellnessCompany
BERTopic_TheWellnessCompany is a text classification model from sdantonio. Use it when you need a label for a piece of text. It is set up for bertopic.
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.
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At a glance
- Task
- Text Classification
- Library
- bertopic
- Access
- Public
- Created
- Oct 8, 2024
- Updated
- Oct 8, 2024
- SHA
- 47167de5
Try a prompt
Model card
From the Hugging Face model README
BERTopic_TheWellnessCompany
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("sdantonio/BERTopic_TheWellnessCompany")
topic_model.get_topic_info()
Topic overview
- Number of topics: 9
- Number of training documents: 481
| Topic ID | Topic Keywords | Topic Frequency | Label |
|---|---|---|---|
| -1 | thewellnesscompany - shots - reprisal - serves - coulson | 12 | -1_thewellnesscompany_shots_reprisal_serves |
| 0 | thewellnesscompany - gessling - compelling - prolonged - daily_clout | 41 | 0_thewellnesscompany_gessling_compelling_prolonged |
| 1 | cardiologists - thewellnesscompany - myocarditis - epidemiologist - publications | 138 | 1_cardiologists_thewellnesscompany_myocarditis_epidemiologist |
| 2 | myocarditis - thewellnesscompany - prolonged - shots - reprisal | 96 | 2_myocarditis_thewellnesscompany_prolonged_shots |
| 3 | thewellnesscompany - packed - prolonged - dissolved - pomegranate | 80 | 3_thewellnesscompany_packed_prolonged_dissolved |
| 4 | thewellnesscompany - tedros - insights - marik - toxicity | 52 | 4_thewellnesscompany_tedros_insights_marik |
| 5 | ivermectin - thewellnesscompany - epidemiologist - hydroxychloroquine - misbehavior | 21 | 5_ivermectin_thewellnesscompany_epidemiologist_hydroxychloroquine |
| 6 | thewellnesscompany - hearts - concerns - reprisal - shots | 21 | 6_thewellnesscompany_hearts_concerns_reprisal |
| 7 | backtobasicsconference - unprepared - pregnancytalk - twcadventures - may9th | 20 | 7_backtobasicsconference_unprepared_pregnancytalk_twcadventures |
Training hyperparameters
- calculate_probabilities: False
- 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: False
- zeroshot_min_similarity: 0.7
- zeroshot_topic_list: None
Framework versions
- Numpy: 1.23.5
- HDBSCAN: 0.8.38.post1
- UMAP: 0.5.6
- Pandas: 2.2.2
- Scikit-Learn: 1.5.1
- Sentence-transformers: 3.0.1
- Transformers: 4.44.2
- Numba: 0.60.0
- Plotly: 5.24.0
- Python: 3.10.12
- Task
- Text Classification
- Library
- bertopic
- Created
- Oct 8, 2024
- Updated
- Oct 8, 2024