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rag-topic-model
przadka/rag-topic-model
rag-topic-model is a text classification model from przadka. 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
- Feb 22, 2025
- Updated
- Feb 22, 2025
- SHA
- b235340a
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("przadka/rag-topic-model")
topic_model.get_topic_info()
Topic overview
- Number of topics: 4
- Number of training documents: 203
| Topic ID | Topic Keywords | Topic Frequency | Label |
|---|---|---|---|
| -1 | on - card - my - charge - account | 54 | -1_on_card_my_charge |
| 0 | refund - my - nike - for - store | 16 | 0_refund_my_nike_for |
| 1 | to - my - klarna - email - and | 77 | 1_to_my_klarna_email |
| 2 | my - the - payment - klarna - for | 56 | 2_my_the_payment_klarna |
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: 2.1.3
- HDBSCAN: 0.8.40
- UMAP: 0.5.7
- Pandas: 2.2.3
- Scikit-Learn: 1.6.1
- Sentence-transformers: 3.1.1
- Transformers: 4.45.2
- Numba: 0.61.0
- Plotly: 6.0.0
- Python: 3.10.12
- Task
- Text Classification
- Library
- bertopic
- Created
- Feb 22, 2025
- Updated
- Feb 22, 2025