---
tags:
- bertopic
library_name: bertopic
pipeline_tag: text-classification
---

# bertopic_openai_emb_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("MaximSIMO/bertopic_openai_emb_model")

topic_model.get_topic_info()
```

## Topic overview

* Number of topics: 3
* Number of training documents: 100

<details>
  <summary>Click here for an overview of all topics.</summary>

  | Topic ID | Topic Keywords | Topic Frequency | Label | 
|----------|----------------|-----------------|-------| 
| -1 | Evening TV Programming | 13 | -1_Evening TV Programming | 
| 0 | Elettrodotti e ambiente | 15 | 0_Elettrodotti e ambiente | 
| 1 | Political Tensions | 72 | 1_Political Tensions |

</details>

## Training hyperparameters

* calculate_probabilities: False
* language: multilingual
* low_memory: False
* min_topic_size: 10
* n_gram_range: (1, 1)
* nr_topics: None
* seed_topic_list: None
* top_n_words: 10
* verbose: True
* zeroshot_min_similarity: 0.7
* zeroshot_topic_list: None

## Framework versions

* Numpy: 2.2.6
* HDBSCAN: 0.8.41
* UMAP: 0.5.11
* Pandas: 2.3.3
* Scikit-Learn: 1.7.2
* Sentence-transformers: 5.2.2
* Transformers: 5.1.0
* Numba: 0.63.1
* Plotly: 6.5.2
* Python: 3.10.19