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

# transformers_issues_topics

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("Maitorokku/transformers_issues_topics")

topic_model.get_topic_info()
```

## Topic overview

* Number of topics: 30
* Number of training documents: 9000

<details>
  <summary>Click here for an overview of all topics.</summary>
  
  | Topic ID | Topic Keywords | Topic Frequency | Label | 
|----------|----------------|-----------------|-------| 
| -1 | climate - environmental - research - analysis - study | 11 | -1_climate_environmental_research_analysis | 
| 0 | climate - climatic - ecological - environmental - precipitation | 2717 | 0_climate_climatic_ecological_environmental | 
| 1 | resilience - practices - innovation - preparedness - management | 2162 | 1_resilience_practices_innovation_preparedness | 
| 2 | ndvi - correlation - vegetation - pearson - formula | 642 | 2_ndvi_correlation_vegetation_pearson | 
| 3 | study - questionnaire - criteria - respondents - conducted | 427 | 3_study_questionnaire_criteria_respondents | 
| 4 | software - sustainable - research - researchers - development | 394 | 4_software_sustainable_research_researchers | 
| 5 | malaria - prevalence - population - mosquitoes - mosquito | 309 | 5_malaria_prevalence_population_mosquitoes | 
| 6 | pandemic - pandemics - covid19 - crisis - governments | 290 | 6_pandemic_pandemics_covid19_crisis | 
| 7 | journals - journal - article - articles - bibliometric | 252 | 7_journals_journal_article_articles | 
| 8 | propolis - antimicrobial - aloe - antibacterial - biofilm | 228 | 8_propolis_antimicrobial_aloe_antibacterial | 
| 9 | health - millennials - healthy - healthcare - changehealth | 216 | 9_health_millennials_healthy_healthcare | 
| 10 | deficiency - vitamin - diseases - supplementation - dietary | 184 | 10_deficiency_vitamin_diseases_supplementation | 
| 11 | quails - quail - insulation - temperatures - heatstress | 153 | 11_quails_quail_insulation_temperatures | 
| 12 | ethics - ethical - researchers - moral - research | 144 | 12_ethics_ethical_researchers_moral | 
| 13 | obesity - metabolic - diabetes - circadian - health | 140 | 13_obesity_metabolic_diabetes_circadian | 
| 14 | poultry - chickens - chicken - livestock - farms | 134 | 14_poultry_chickens_chicken_livestock | 
| 15 | lightning - incidence - fatalities - bangladesh - deaths | 80 | 15_lightning_incidence_fatalities_bangladesh | 
| 16 | harassment - gaming - behavior - games - narratives | 72 | 16_harassment_gaming_behavior_games | 
| 17 | indigenous - knowledge - concept - ik - principles | 68 | 17_indigenous_knowledge_concept_ik | 
| 18 | kinase - gene - signaling - phosphorylates - phosphorylation | 60 | 18_kinase_gene_signaling_phosphorylates | 
| 19 | camel - camels - meat - meats - beef | 44 | 19_camel_camels_meat_meats | 
| 20 | turkey - turkeyeu - turkeys - turkish - ankara | 43 | 20_turkey_turkeyeu_turkeys_turkish | 
| 21 | wasserwiedernutzung - treibhausgaseffekt - wiedernutzung - beschreibt - khleffekt | 41 | 21_wasserwiedernutzung_treibhausgaseffekt_wiedernutzung_beschreibt | 
| 22 | locusts - genome - locust - sequencing - genes | 41 | 22_locusts_genome_locust_sequencing | 
| 23 | chainsaw - noisy - noise - nonchainsaw - earmuffs | 37 | 23_chainsaw_noisy_noise_nonchainsaw | 
| 24 | evolution - evolved - brains - neuroplasticity - brain | 36 | 24_evolution_evolved_brains_neuroplasticity | 
| 25 | sinusitis - sinus - sinuses - aplasia - paranasal | 31 | 25_sinusitis_sinus_sinuses_aplasia | 
| 26 | mandarins - mandarin - citrus - fruits - fruit | 16 | 26_mandarins_mandarin_citrus_fruits | 
| 27 | hikikomori - otaku - japanese - anime - otakus | 16 | 27_hikikomori_otaku_japanese_anime | 
| 28 | travel - trips - businesses - business - corporate | 12 | 28_travel_trips_businesses_business |
  
</details>

## Training hyperparameters

* calculate_probabilities: False
* language: english
* low_memory: False
* min_topic_size: 10
* n_gram_range: (1, 1)
* nr_topics: 30
* seed_topic_list: None
* top_n_words: 10
* verbose: True

## Framework versions

* Numpy: 1.22.4
* HDBSCAN: 0.8.29
* UMAP: 0.5.3
* Pandas: 1.5.3
* Scikit-Learn: 1.2.2
* Sentence-transformers: 2.2.2
* Transformers: 4.30.2
* Numba: 0.56.4
* Plotly: 5.13.1
* Python: 3.10.12