metadata
tags:
- bertopic
library_name: bertopic
pipeline_tag: text-classification
BERTopic-transcripts
This is a 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:
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
Click here for an overview of all topics.
| 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 |
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