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("aaa961/rag-topic-model")
topic_model.get_topic_info()
Topic overview
- Number of topics: 6
- Number of training documents: 168
Click here for an overview of all topics.
| Topic ID | Topic Keywords | Topic Frequency | Label |
|---|---|---|---|
| -1 | to - my - klarna - for - the | 12 | -1_to_my_klarna_for |
| 0 | klarna - my - declined - in - for | 62 | 0_klarna_my_declined_in |
| 1 | my - details - klarna - and - call | 34 | 1_my_details_klarna_and |
| 2 | the - payment - for - to - pay | 24 | 2_the_payment_for_to |
| 3 | the - store - it - for - ago | 19 | 3_the_store_it_for |
| 4 | the - ago - sneakers - and - shoes | 17 | 4_the_ago_sneakers_and |
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: 0.5.7
- Pandas: 2.3.0+4.g1dfc98e16a
- Scikit-Learn: 1.6.1
- Sentence-transformers: 3.1.1
- Transformers: 4.42.2
- Numba: 0.60.0
- Plotly: 6.1.2
- Python: 3.9.22
- Downloads last month
- 2