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---
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("przadka/rag-topic-model")
topic_model.get_topic_info()
```
## Topic overview
* Number of topics: 4
* Number of training documents: 203
<details>
<summary>Click here for an overview of all topics.</summary>
| Topic ID | Topic Keywords | Topic Frequency | Label |
|----------|----------------|-----------------|-------|
| -1 | on - card - my - charge - account | 54 | -1_on_card_my_charge |
| 0 | refund - my - nike - for - store | 16 | 0_refund_my_nike_for |
| 1 | to - my - klarna - email - and | 77 | 1_to_my_klarna_email |
| 2 | my - the - payment - klarna - for | 56 | 2_my_the_payment_klarna |
</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: 2.1.3
* 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: 0.61.0
* Plotly: 6.0.0
* Python: 3.10.12
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