metadata
tags:
- bertopic
library_name: bertopic
pipeline_tag: text-classification
bbc_news_topics
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("CarlosMorales/bbc_news_topics")
topic_model.get_topic_info()
Topic overview
- Number of topics: 3
- Number of training documents: 100
Click here for an overview of all topics.
| Topic ID | Topic Keywords | Topic Frequency | Label |
|---|---|---|---|
| -1 | the - of - to - and - eu | 28 | -1_the_of_to_and |
| 0 | the - of - to - and - in | 6 | 0_the_of_to_and |
| 1 | the - to - of - and - in | 66 | 1_the_to_of_and |
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: False
- zeroshot_min_similarity: 0.7
- zeroshot_topic_list: None
Framework versions
- Numpy: 1.26.4
- HDBSCAN: 0.8.33
- UMAP: 0.5.6
- Pandas: 2.2.1
- Scikit-Learn: 1.4.1.post1
- Sentence-transformers: 2.6.1
- Transformers: 4.39.3
- Numba: 0.59.1
- Plotly: 5.20.0
- Python: 3.11.6