|
|
| --- |
| tags: |
| - bertopic |
| library_name: bertopic |
| pipeline_tag: text-classification |
| --- |
| |
| # TopicModel_StoreReviews |
| |
| 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("shantanudave/TopicModel_StoreReviews") |
| |
| topic_model.get_topic_info() |
| ``` |
| |
| ## Topic overview |
| |
| * Number of topics: 10 |
| * Number of training documents: 14747 |
| |
| <details> |
| <summary>Click here for an overview of all topics.</summary> |
| |
| | Topic ID | Topic Keywords | Topic Frequency | Label | |
| |----------|----------------|-----------------|-------| |
| | 0 | clothing - clothes - fashion - clothe - clothing store | 2672 | Fashionable Clothing Selection | |
| | 1 | shopping - shop - price - cheap - store | 1864 | Diverse Shopping Experiences | |
| | 2 | tidy - clean - branch - range - renovation | 1807 | Clean Retail Space | |
| | 3 | quality - offer - use - stop - good | 1793 | Quality Offer Search | |
| | 4 | selection - choice - large - large selection - size | 1459 | Large Size Selection | |
| | 5 | advice - saleswoman - service - friendly - competent | 1447 | Friendly Saleswoman Service | |
| | 6 | staff - friendly staff - staff staff - staff friendly - friendly | 1177 | Friendly Staff Selection | |
| | 7 | wow - waw - oh - yeah - | 1108 | Expressive Words Discovery | |
| | 8 | voucher - money - return - exchange - cash | 933 | Customer Return Experience | |
| | 9 | super - friendly super - super friendly - pleasure - super service | 487 | super friendly service | |
| |
| </details> |
| |
| ## Training hyperparameters |
| |
| * calculate_probabilities: True |
| * language: None |
| * low_memory: False |
| * min_topic_size: 10 |
| * n_gram_range: (1, 1) |
| * nr_topics: None |
| * seed_topic_list: None |
| * top_n_words: 10 |
| * verbose: True |
| * zeroshot_min_similarity: 0.7 |
| * zeroshot_topic_list: None |
| |
| ## Framework versions |
| |
| * Numpy: 1.23.5 |
| * HDBSCAN: 0.8.33 |
| * UMAP: 0.5.5 |
| * Pandas: 1.3.5 |
| * Scikit-Learn: 1.4.1.post1 |
| * Sentence-transformers: 2.6.1 |
| * Transformers: 4.39.3 |
| * Numba: 0.59.1 |
| * Plotly: 5.21.0 |
| * Python: 3.10.13 |
| |
