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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("3lv27/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: 5 |
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* Number of training documents: 201 |
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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 | my - for - was - payment - it | 17 | -1_my_for_was_payment | |
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| 0 | refund - nike - my - store - for | 41 | 0_refund_nike_my_store | |
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| 1 | my - the - payment - app - balance | 72 | 1_my_the_payment_app | |
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| 2 | to - email - my - account - the | 37 | 2_to_email_my_account | |
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| 3 | card - klarna - details - to - do | 34 | 3_card_klarna_details_to | |
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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: None |
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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: 2.0.2 |
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* HDBSCAN: 0.8.40 |
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* UMAP: 0.5.7 |
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* Pandas: 2.2.3 |
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* Scikit-Learn: 1.6.1 |
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* Sentence-transformers: 3.1.1 |
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* Transformers: 4.45.2 |
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* Numba: 0.60.0 |
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* Plotly: 6.0.0 |
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* Python: 3.9.6 |
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