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---
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
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