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

# rag-topic-model

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("paulperry/rag-topic-model")

topic_model.get_topic_info()
```

## Topic overview

* Number of topics: 6
* Number of training documents: 168

<details>
  <summary>Click here for an overview of all topics.</summary>
  
  | Topic ID | Topic Keywords | Topic Frequency | Label | 
|----------|----------------|-----------------|-------| 
| -1 | klarna - for - my - to - the | 11 | -1_klarna_for_my_to | 
| 0 | klarna - declined - my - in - ve | 58 | 0_klarna_declined_my_in | 
| 1 | to - payment - the - my - for | 33 | 1_to_payment_the_my | 
| 2 | my - details - klarna - and - call | 25 | 2_my_details_klarna_and | 
| 3 | it - store - the - for - ago | 23 | 3_it_store_the_for | 
| 4 | the - sneakers - they - shoes - ago | 18 | 4_the_sneakers_they_shoes |
  
</details>

## Training hyperparameters

* calculate_probabilities: False
* language: None
* low_memory: False
* min_topic_size: 10
* n_gram_range: (1, 1)
* nr_topics: auto
* seed_topic_list: None
* top_n_words: 10
* verbose: False
* zeroshot_min_similarity: 0.7
* zeroshot_topic_list: None

## Framework versions

* Numpy: 2.0.2
* HDBSCAN: 0.8.40
* UMAP: 0.5.7
* Pandas: 2.2.3
* Scikit-Learn: 1.6.1
* Sentence-transformers: 3.1.1
* Transformers: 4.45.2
* Numba: 0.60.0
* Plotly: 6.0.0
* Python: 3.9.6