---
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("aaa961/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 | to - my - klarna - for - the | 12 | -1_to_my_klarna_for | 
| 0 | klarna - my - declined - in - for | 62 | 0_klarna_my_declined_in | 
| 1 | my - details - klarna - and - call | 34 | 1_my_details_klarna_and | 
| 2 | the - payment - for - to - pay | 24 | 2_the_payment_for_to | 
| 3 | the - store - it - for - ago | 19 | 3_the_store_it_for | 
| 4 | the - ago - sneakers - and - shoes | 17 | 4_the_ago_sneakers_and |

</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: 1.26.4
* HDBSCAN: 0.8.40
* UMAP: 0.5.7
* Pandas: 2.3.0+4.g1dfc98e16a
* Scikit-Learn: 1.6.1
* Sentence-transformers: 3.1.1
* Transformers: 4.42.2
* Numba: 0.60.0
* Plotly: 6.1.2
* Python: 3.9.22