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---
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tags:
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- bertopic
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library_name: bertopic
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pipeline_tag: text-classification
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---
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# rag-topic-model
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This is a [BERTopic](https://github.com/MaartenGr/BERTopic) model.
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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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## Usage
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To use this model, please install BERTopic:
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```
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pip install -U bertopic
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```
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You can use the model as follows:
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```python
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from bertopic import BERTopic
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topic_model = BERTopic.load("aaa961/rag-topic-model")
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topic_model.get_topic_info()
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```
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## Topic overview
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* Number of topics: 6
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* Number of training documents: 168
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<details>
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<summary>Click here for an overview of all topics.</summary>
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| Topic ID | Topic Keywords | Topic Frequency | Label |
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|----------|----------------|-----------------|-------|
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| -1 | to - my - klarna - for - the | 12 | -1_to_my_klarna_for |
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| 0 | klarna - my - declined - in - for | 62 | 0_klarna_my_declined_in |
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| 1 | my - details - klarna - and - call | 34 | 1_my_details_klarna_and |
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| 2 | the - payment - for - to - pay | 24 | 2_the_payment_for_to |
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| 3 | the - store - it - for - ago | 19 | 3_the_store_it_for |
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| 4 | the - ago - sneakers - and - shoes | 17 | 4_the_ago_sneakers_and |
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</details>
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## Training hyperparameters
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* calculate_probabilities: False
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* language: None
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* low_memory: False
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* min_topic_size: 10
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* n_gram_range: (1, 1)
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* nr_topics: auto
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* seed_topic_list: None
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* top_n_words: 10
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* verbose: False
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* zeroshot_min_similarity: 0.7
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* zeroshot_topic_list: None
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## Framework versions
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* Numpy: 1.26.4
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* HDBSCAN: 0.8.40
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* UMAP: 0.5.7
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* Pandas: 2.3.0+4.g1dfc98e16a
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* Scikit-Learn: 1.6.1
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* Sentence-transformers: 3.1.1
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* Transformers: 4.42.2
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* Numba: 0.60.0
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* Plotly: 6.1.2
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* Python: 3.9.22
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