---
tags:
- bertopic
library_name: bertopic
pipeline_tag: text-classification
---

# bbc_news_topics

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("CarlosMorales/bbc_news_topics")

topic_model.get_topic_info()
```

## Topic overview

* Number of topics: 3
* Number of training documents: 100

<details>
  <summary>Click here for an overview of all topics.</summary>
  
  | 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 |
  
</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: 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