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("ppuva1/rag-topic-model")
topic_model.get_topic_info()
Topic overview
- Number of topics: 3
- Number of training documents: 201
Click here for an overview of all topics.
| Topic ID | Topic Keywords | Topic Frequency | Label |
|---|---|---|---|
| -1 | charge - on - account - seeing - random | 75 | -1_charge_on_account_seeing |
| 0 | my - to - klarna - the - it | 7 | 0_my_to_klarna_the |
| 1 | refund - my - nike - for - store | 119 | 1_refund_my_nike_for |
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: 2.0.2
- HDBSCAN: 0.8.40
- UMAP: 0.5.7
- Pandas: 2.2.3
- Scikit-Learn: 1.6.1
- Sentence-transformers: 3.4.1
- Transformers: 4.48.2
- Numba: 0.60.0
- Plotly: 6.0.0
- Python: 3.9.21
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