
# mods-general-topics-bertmodel

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/mods-general-topics-bertmodel")

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 - sexual - explicit - adult explicit - adult sexual | 364 | 0_adult_sexual_explicit_adult explicit | 
| 1 | minor - minors - underage - child - suspected | 230 | 1_minor_minors_underage_child | 
| 2 | traffic - artificial - artificial traffic - scary - low | 104 | 2_traffic_artificial_artificial traffic_scary | 
| 3 | hate - bullying - hateful - slur - based | 89 | 3_hate_bullying_hateful_slur | 
| 4 | suicide - self harm - suicide self harm - suicide self - self | 85 | 4_suicide_self harm_suicide self harm_suicide self | 
| 5 | property - destruction - trafficking - hacking - crime | 83 | 5_property_destruction_trafficking_hacking | 
| 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 - slaughter - animals animal | 58 | 8_animal_animals_animal mutilation_slaughter | 
| 9 | personal - information - identifiable - identifiable information - personally identifiable information | 56 | 9_personal_information_identifiable_identifiable information | 
| 10 | human - gore - human body - setting - shocking | 53 | 10_human_gore_human body_setting | 
| 11 | dangerous - harm dangerous - promotion dangerous - challenges - lead | 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 - intellectual property - stolen - copyright - copyrighted | 41 | 14_intellectual_intellectual property_stolen_copyright | 
| 15 | operations - uninformative content - uninformative - brand - enforcement | 33 | 15_operations_uninformative content_uninformative_brand | 
| 16 | hostility - degrading - defamation - degradation - target | 33 | 16_hostility_degrading_defamation_degradation | 
| 17 | culturally - cultural - culturally appropriate - appropriate - clothing | 30 | 17_culturally_cultural_culturally appropriate_appropriate | 
| 18 | controversial - social - incest - discussion - incest incestuous | 24 | 18_controversial_social_incest_discussion | 
| 19 | firearms - weapons - explosives - explosive - explosive weapons | 21 | 19_firearms_weapons_explosives_explosive | 
| 20 | scams - frauds - frauds scams - schemes - scamming | 16 | 20_scams_frauds_frauds scams_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