
# custom-moderation-generaltopics-en

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("jaimevera1107/custom-moderation-generaltopics-en")

topic_model.get_topic_info()
```

## Topic overview

* Number of topics: 21
* Number of training documents: 1606

<details>
  <summary>Click here for an overview of all topics.</summary>
  
  | Topic ID | Topic Keywords | Topic Frequency | Label | 
|----------|----------------|-----------------|-------| 
| 0 | adult - adult explicit - explicit - sexual - adult sexual | 364 | 0_adult_adult explicit_explicit_sexual | 
| 1 | minor - minors - underage - child - suspected | 230 | 1_minor_minors_underage_child | 
| 2 | traffic - artificial traffic - artificial - low - party | 104 | 2_traffic_artificial traffic_artificial_low | 
| 3 | hate - hateful - bullying - slur - based | 89 | 3_hate_hateful_bullying_slur | 
| 4 | suicide - self harm - suicide self - self - harm | 85 | 4_suicide_self harm_suicide self_self | 
| 5 | property - destruction - trafficking - crime - hacking | 83 | 5_property_destruction_trafficking_crime | 
| 6 | drugs - drug - substances - controlled - controlled substances | 80 | 6_drugs_drug_substances_controlled | 
| 7 | violent - violence - fictional - fighting - violent fighting | 59 | 7_violent_violence_fictional_fighting | 
| 8 | animal - animals - animal mutilation - animals animal - slaughter | 58 | 8_animal_animals_animal mutilation_animals animal | 
| 9 | identifiable information - identifiable - personal - personally - personally identifiable | 56 | 9_identifiable information_identifiable_personal_personally | 
| 10 | human - gore - human body - shocking - mutilated human | 53 | 10_human_gore_human body_shocking | 
| 11 | dangerous - harm dangerous - promotion dangerous - challenges - stunts | 52 | 11_dangerous_harm dangerous_promotion dangerous_challenges | 
| 12 | terrorism - terrorist - nuclear - terrorist organizations - organizations | 52 | 12_terrorism_terrorist_nuclear_terrorist organizations | 
| 13 | false - conspiracy - misinformation - political - claims | 43 | 13_false_conspiracy_misinformation_political | 
| 14 | intellectual property - intellectual - copyrighted - copyright - stolen | 41 | 14_intellectual property_intellectual_copyrighted_copyright | 
| 15 | operations - uninformative content - brand - uninformative - enforcement | 33 | 15_operations_uninformative content_brand_uninformative | 
| 16 | hostility - degrading - degradation - defamation - target | 33 | 16_hostility_degrading_degradation_defamation | 
| 17 | culturally - cultural - appropriate - culturally appropriate - clothing | 30 | 17_culturally_cultural_appropriate_culturally appropriate | 
| 18 | controversial - social - incest - discussion - incest incestuous | 24 | 18_controversial_social_incest_discussion | 
| 19 | firearms - weapons - explosives - explosive - ammunition | 21 | 19_firearms_weapons_explosives_explosive | 
| 20 | scams - frauds scams - frauds - schemes - scams promotion | 16 | 20_scams_frauds scams_frauds_schemes |
  
</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: True

## 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: 4.24.0
* Numba: 0.58.1
* Plotly: 5.15.0
* Python: 3.10.12

{"library_name": "bertopic", "tags": ["bertopic"], "pipeline_tag": "text-classification"}
bertopic text-classification region:us