|
|
| --- |
| tags: |
| - bertopic |
| library_name: bertopic |
| pipeline_tag: text-classification |
| --- |
| |
| # rag-topic-model |
|
|
| This is a [BERTopic](https://github.com/MaartenGr/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: |
|
|
| ```python |
| from bertopic import BERTopic |
| topic_model = BERTopic.load("Maximgolubov/rag-topic-model") |
| |
| topic_model.get_topic_info() |
| ``` |
|
|
| ## Topic overview |
|
|
| * Number of topics: 5 |
| * Number of training documents: 168 |
|
|
| <details> |
| <summary>Click here for an overview of all topics.</summary> |
|
|
| | Topic ID | Topic Keywords | Topic Frequency | Label | |
| |----------|----------------|-----------------|-------| |
| | -1 | for - my - to - klarna - the | 11 | -1_for_my_to_klarna | |
| | 0 | the - klarna - my - for - to | 38 | 0_the_klarna_my_for | |
| | 1 | samsung - the - it - for - and | 76 | 1_samsung_the_it_for | |
| | 2 | my - details - klarna - and - call | 23 | 2_my_details_klarna_and | |
| | 3 | my - to - time - you - one | 20 | 3_my_to_time_you | |
|
|
| </details> |
|
|
| ## 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: 0.5.8 |
| * Pandas: 2.3.0 |
| * Scikit-Learn: 1.7.0 |
| * Sentence-transformers: 5.0.0 |
| * Transformers: 4.45.2 |
| * Numba: 0.61.2 |
| * Plotly: 6.2.0 |
| * Python: 3.11.9 |
| |
