
# Trubnik1967

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("Trubnik1967/Trubnik1967")

topic_model.get_topic_info()
```

## Topic overview

* Number of topics: 18
* Number of training documents: 930

<details>
  <summary>Click here for an overview of all topics.</summary>
  
  | Topic ID | Topic Keywords | Topic Frequency | Label | 
|----------|----------------|-----------------|-------| 
| 0 | measurement - material - researcher - new - used | 396 | 0_measurement_material_researcher_new | 
| 1 | security - information - system - federal - control | 146 | 1_security_information_system_federal | 
| 2 | face - recognition - algorithm - study - data | 51 | 2_face_recognition_algorithm_study | 
| 3 | yous - standardization - international - program - trade | 32 | 3_yous_standardization_international_program | 
| 4 | framework - cybersecurity - critical - infrastructure - organization | 35 | 4_framework_cybersecurity_critical_infrastructure | 
| 5 | online - pilot - service - secure - solution | 25 | 5_online_pilot_service_secure | 
| 6 | data - environment - communication - model - measurement | 36 | 6_data_environment_communication_model | 
| 7 | cloud - computing - working - government - service | 21 | 7_cloud_computing_working_government | 
| 8 | cybersecurity - education - training - job - worker | 23 | 8_cybersecurity_education_training_job | 
| 9 | algorithm - key - public - comment - document | 19 | 9_algorithm_key_public_comment | 
| 10 | data - digital - function - system - algorithm | 28 | 10_data_digital_function_system | 
| 11 | building - code - report - recommendation - community | 32 | 11_building_code_report_recommendation | 
| 12 | mobile - security - device - organization - guide | 17 | 12_mobile_security_device_organization | 
| 13 | health - information - patient - security - electronic | 16 | 13_health_information_patient_security | 
| 14 | card - personal - federal - employee - publication | 14 | 14_card_personal_federal_employee | 
| 15 | smart - interoperability - framework - cyber - network | 13 | 15_smart_interoperability_framework_cyber | 
| 16 | safety - public - communication - network - emergency | 13 | 16_safety_public_communication_network | 
| 17 | comment - system - draft - guideline - yous | 13 | 17_comment_system_draft_guideline |
  
</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: True
* zeroshot_min_similarity: 0.7
* zeroshot_topic_list: None

## Framework versions

* Numpy: 1.23.5
* HDBSCAN: 0.8.33
* UMAP: 0.5.5
* Pandas: 1.5.3
* Scikit-Learn: 1.2.2
* Sentence-transformers: 2.3.1
* Transformers: 4.35.2
* 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