---
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("3lv27/rag-topic-model")

topic_model.get_topic_info()
```

## Topic overview

* Number of topics: 5
* Number of training documents: 201

<details>
  <summary>Click here for an overview of all topics.</summary>
  
  | Topic ID | Topic Keywords | Topic Frequency | Label | 
|----------|----------------|-----------------|-------| 
| -1 | my - for - was - payment - it | 17 | -1_my_for_was_payment | 
| 0 | refund - nike - my - store - for | 41 | 0_refund_nike_my_store | 
| 1 | my - the - payment - app - balance | 72 | 1_my_the_payment_app | 
| 2 | to - email - my - account - the | 37 | 2_to_email_my_account | 
| 3 | card - klarna - details - to - do | 34 | 3_card_klarna_details_to |
  
</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: 1.6.1
* Sentence-transformers: 3.1.1
* Transformers: 4.45.2
* Numba: 0.60.0
* Plotly: 6.0.0
* Python: 3.9.6