---
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("jkdamilola/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 - klarna - for - my - the | 10 | -1_to_klarna_for_my | 
| 0 | klarna - declined - my - in - ive | 63 | 0_klarna_declined_my_in | 
| 1 | payment - the - to - for - pay | 33 | 1_payment_the_to_for | 
| 2 | my - details - klarna - and - call | 27 | 2_my_details_klarna_and | 
| 3 | store - refund - back - the - credit | 23 | 3_store_refund_back_the | 
| 4 | the - shoes - ago - havent - sneakers | 12 | 4_the_shoes_ago_havent |

</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: 2.2.3
* Scikit-Learn: 1.6.1
* Sentence-transformers: 3.1.1
* Transformers: 4.42.2
* Numba: 0.60.0
* Plotly: 6.1.2
* Python: 3.9.6