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

# BERTopic_mincevicius

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("sdantonio/BERTopic_mincevicius")

topic_model.get_topic_info()
```

## Topic overview

* Number of topics: 3
* Number of training documents: 10133

<details>
  <summary>Click here for an overview of all topics.</summary>
  
  | Topic ID | Topic Keywords | Topic Frequency | Label | 
|----------|----------------|-----------------|-------| 
| 0 | vyriausybe - paskelbe - pries - rusijos - ukrainos | 8779 | 0_vyriausybe_paskelbe_pries_rusijos | 
| 1 | vyriausybe - pries - visis - rusijos - ukrainos | 1336 | 1_vyriausybe_pries_visis_rusijos | 
| 2 | republics - pedophiles - awakenedspecies - booster - wins | 18 | 2_republics_pedophiles_awakenedspecies_booster |
  
</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: 1.23.5
* HDBSCAN: 0.8.38.post1
* UMAP: 0.5.6
* Pandas: 2.2.2
* Scikit-Learn: 1.5.1
* Sentence-transformers: 3.0.1
* Transformers: 4.44.2
* Numba: 0.60.0
* Plotly: 5.24.0
* Python: 3.10.12