|
|
| ---
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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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|
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| topic_model.get_topic_info()
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| ```
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|
|
| ## Topic overview
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|
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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 |
|
| |----------|----------------|-----------------|-------|
|
| | -1 | to - my - klarna - for - the | 12 | -1_to_my_klarna_for |
|
| | 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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| |
