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

# recipecomments-bertopic

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("daveripper0020/recipecomments-bertopic")

topic_model.get_topic_info()
```

## Topic overview

* Number of topics: 6
* Number of training documents: 386

<details>
  <summary>Click here for an overview of all topics.</summary>
  
  | Topic ID | Topic Keywords | Topic Frequency | Label | 
|----------|----------------|-----------------|-------| 
| -1 | 블로그 - 이형 - 많이 - 까지 - 항상 | 12 | -1_블로그_이형_많이_까지 | 
| 0 | 잼민 - 춘장 - 인가요 - 뚝딱 - 너무 | 54 | 0_잼민_춘장_인가요_뚝딱 | 
| 1 | 뚝딱 - 잼민 - 춘장 - 요리 - 수익 | 151 | 1_뚝딱_잼민_춘장_요리 | 
| 2 | 찜닭 - 마늘 - 레시피 - 넣고 - 간장 | 77 | 2_찜닭_마늘_레시피_넣고 | 
| 3 | 맛있어요 - 진짜 - 먹었는데 - 간단하고 - 너무 | 76 | 3_맛있어요_진짜_먹었는데_간단하고 | 
| 4 | 감사합니다 - 합니다 - 자는 - 저리 - 믿는 | 16 | 4_감사합니다_합니다_자는_저리 |
  
</details>

## Training hyperparameters

* calculate_probabilities: True
* 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: 1.23.5
* HDBSCAN: 0.8.33
* UMAP: 0.5.4
* Pandas: 1.5.3
* Scikit-Learn: 1.2.2
* Sentence-transformers: 2.2.2
* Transformers: 4.35.1
* Numba: 0.58.1
* Plotly: 5.15.0
* Python: 3.10.12