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

# string2-string

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("syntag/string2-string")

topic_model.get_topic_info()
```

## Topic overview

* Number of topics: 4
* Number of training documents: 20

<details>
  <summary>Click here for an overview of all topics.</summary>
  
  | Topic ID | Topic Keywords | Topic Frequency | Label | 
|----------|----------------|-----------------|-------| 
| 0 | life - make - adulting - worm - gives | 7 | 0_life_make_adulting_worm | 
| 1 | like - bar - walk - matter - coding | 7 | 1_like_bar_walk_matter | 
| 2 | break - version - vacation - told - succeed | 3 | 2_break_version_vacation_told | 
| 3 | don - skeletons - shame - scientists - parallel | 3 | 3_don_skeletons_shame_scientists |
  
</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

## Framework versions

* Numpy: 1.24.4
* HDBSCAN: 0.8.33
* UMAP: 0.5.4
* Pandas: 2.0.3
* Scikit-Learn: 1.3.1
* Sentence-transformers: 2.2.2
* Transformers: 4.34.1
* Numba: 0.58.1
* Plotly: 5.17.0
* Python: 3.10.12