| tags: | |
| - bertopic | |
| library_name: bertopic | |
| pipeline_tag: text-classification | |
| # BERTopic-transcripts | |
| 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("nataliecastro/BERTopic-transcripts") | |
| topic_model.get_topic_info() | |
| ``` | |
| ## Topic overview | |
| * Number of topics: 2 | |
| * Number of training documents: 4170 | |
| <details> | |
| <summary>Click here for an overview of all topics.</summary> | |
| | Topic ID | Topic Keywords | Topic Frequency | Label | | |
| |----------|----------------|-----------------|-------| | |
| | 0 | the - and - to - that - of | 4149 | 0_the_and_to_that | | |
| | 1 | music - and - to - the - of | 21 | 1_music_and_to_the | | |
| </details> | |
| ## Training hyperparameters | |
| * calculate_probabilities: False | |
| * language: english | |
| * 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: 1.24.3 | |
| * HDBSCAN: 0.8.29 | |
| * UMAP: 0.5.6 | |
| * Pandas: 1.5.3 | |
| * Scikit-Learn: 1.2.2 | |
| * Sentence-transformers: 3.1.0 | |
| * Transformers: 4.44.2 | |
| * Numba: 0.57.0 | |
| * Plotly: 5.9.0 | |
| * Python: 3.10.12 | |