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

# BERTopic_teyakkuzhaber

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_teyakkuzhaber")

topic_model.get_topic_info()
```

## Topic overview

* Number of topics: 4
* Number of training documents: 2382

<details>
  <summary>Click here for an overview of all topics.</summary>
  
  | Topic ID | Topic Keywords | Topic Frequency | Label | 
|----------|----------------|-----------------|-------| 
| -1 | depremde - yu - vatandas - kaybedem - sondaki | 28 | -1_depremde_yu_vatandas_kaybedem | 
| 0 | alıs - kılıc - abd - yas - kars | 1 | 0_alıs_kılıc_abd_yas | 
| 1 | osmaniye - endonezya - konya - sondakika - adıyaman | 2323 | 1_osmaniye_endonezya_konya_sondakika | 
| 2 | 984tl - 971tl - 1093tl - kırılmaya - anlık | 30 | 2_984tl_971tl_1093tl_kırılmaya |
  
</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