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rag-topic-model
Maximgolubov/rag-topic-model
rag-topic-model is a text classification model from Maximgolubov. 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
- Apr 16, 2026
- Updated
- Apr 16, 2026
- SHA
- f314234d
Try a prompt
Model card
From the Hugging Face model README
rag-topic-model
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("Maximgolubov/rag-topic-model")
topic_model.get_topic_info()
Topic overview
- Number of topics: 5
- Number of training documents: 168
| Topic ID | Topic Keywords | Topic Frequency | Label |
|---|---|---|---|
| -1 | for - my - to - klarna - the | 11 | -1_for_my_to_klarna |
| 0 | the - klarna - my - for - to | 38 | 0_the_klarna_my_for |
| 1 | samsung - the - it - for - and | 76 | 1_samsung_the_it_for |
| 2 | my - details - klarna - and - call | 23 | 2_my_details_klarna_and |
| 3 | my - to - time - you - one | 20 | 3_my_to_time_you |
Training hyperparameters
- calculate_probabilities: False
- language: None
- low_memory: False
- min_topic_size: 10
- n_gram_range: (1, 1)
- nr_topics: auto
- seed_topic_list: None
- top_n_words: 10
- verbose: False
- zeroshot_min_similarity: 0.7
- zeroshot_topic_list: None
Framework versions
- Numpy: 1.26.4
- HDBSCAN: 0.8.40
- UMAP: 0.5.8
- Pandas: 2.3.0
- Scikit-Learn: 1.7.0
- Sentence-transformers: 5.0.0
- Transformers: 4.45.2
- Numba: 0.61.2
- Plotly: 6.2.0
- Python: 3.11.9
- Task
- Text Classification
- Library
- bertopic
- Created
- Apr 16, 2026
- Updated
- Apr 16, 2026