---
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("ivanleomk/rag-topic-model")

topic_model.get_topic_info()
```

## Topic overview

* Number of topics: 5
* Number of training documents: 243

<details>
  <summary>Click here for an overview of all topics.</summary>
  
  | Topic ID | Topic Keywords | Topic Frequency | Label | 
|----------|----------------|-----------------|-------| 
| -1 | the - verification - my - for - code | 24 | -1_the_verification_my_for | 
| 0 | klarna - to - my - and - the | 19 | 0_klarna_to_my_and | 
| 1 | my - the - return - store - still | 98 | 1_my_the_return_store | 
| 2 | card - onetime - my - it - for | 69 | 2_card_onetime_my_it | 
| 3 | payment - my - due - date - the | 33 | 3_payment_my_due_date |
  
</details>

## Training hyperparameters

* calculate_probabilities: False
* language: None
* 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: 2.0.2
* HDBSCAN: 0.8.40
* UMAP: 0.5.7
* Pandas: 2.2.3
* Scikit-Learn: 1.5.2
* Sentence-transformers: 3.3.1
* Transformers: 4.46.3
* Numba: 0.60.0
* Plotly: 5.24.1
* Python: 3.9.6