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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("apostolosfilippas/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: 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 | for - klarna - to - my - the | 15 | -1_for_klarna_to_my | |
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| 0 | klarna - my - declined - for - ve | 51 | 0_klarna_my_declined_for | |
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| 1 | payment - to - the - my - for | 50 | 1_payment_to_the_my | |
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| 2 | my - klarna - and - details - account | 28 | 2_my_klarna_and_details | |
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| 3 | store - the - it - refund - back | 24 | 3_store_the_it_refund | |
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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: 2.2.3 |
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* Scikit-Learn: 1.6.1 |
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* Sentence-transformers: 3.4.1 |
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* Transformers: 4.50.1 |
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* Numba: 0.61.2 |
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* Plotly: 6.0.1 |
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* Python: 3.11.7 |
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