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

# transformers_issues_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("belenedgar/transformers_issues_topics")

topic_model.get_topic_info()
```

## Topic overview

* Number of topics: 5
* Number of training documents: 156

<details>
  <summary>Click here for an overview of all topics.</summary>
  
  | Topic ID | Topic Keywords | Topic Frequency | Label | 
|----------|----------------|-----------------|-------| 
| -1 | extremism - extremist - terrorism - radical - radicalization | 21 | -1_extremism_extremist_terrorism_radical | 
| 0 | phishing - theft - scammers - security - fraud | 17 | 0_phishing_theft_scammers_security | 
| 1 | addiction - violence - cyber - content - presence | 54 | 1_addiction_violence_cyber_content | 
| 2 | cyberbullying - bullying - cyber - cyberstalking - harassment | 39 | 2_cyberbullying_bullying_cyber_cyberstalking | 
| 3 | profanity - derogatory - vulgarity - hate - offensive | 25 | 3_profanity_derogatory_vulgarity_hate |
  
</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

## Framework versions

* Numpy: 1.24.4
* HDBSCAN: 0.8.33
* UMAP: 0.5.3
* Pandas: 2.0.3
* Scikit-Learn: 1.3.0
* Sentence-transformers: 2.2.2
* Transformers: 4.31.0
* Numba: 0.57.1
* Plotly: 5.15.0
* Python: 3.10.10