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

# parliament_topic_model

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("daniel-023/parliament_topic_model")

topic_model.get_topic_info()
```

## Topic overview

* Number of topics: 20
* Number of training documents: 2005

<details>
  <summary>Click here for an overview of all topics.</summary>
  
  | Topic ID | Topic Keywords | Topic Frequency | Label | 
|----------|----------------|-----------------|-------| 
| -1 | minister - singapore - member - time - government | 16 | -1_minister_singapore_member_time | 
| 0 | education - teachers - schools - school - minister | 541 | 0_education_teachers_schools_school | 
| 1 | water - reclamation - land - development - minister | 210 | 1_water_reclamation_land_development | 
| 2 | million - singapore - government - finance - year | 202 | 2_million_singapore_government_finance | 
| 3 | service - police - national - minister - officers | 187 | 3_service_police_national_minister | 
| 4 | law - council - house - members - committee | 140 | 4_law_council_house_members | 
| 5 | singapore - identity - citizenship - minister - cards | 112 | 5_singapore_identity_citizenship_minister | 
| 6 | bus - buses - taxis - transport - taxi | 88 | 6_bus_buses_taxis_transport | 
| 7 | property - land - tax - board - flats | 81 | 7_property_land_tax_board | 
| 8 | farmers - prices - minister - price - production | 79 | 8_farmers_prices_minister_price | 
| 9 | singapore - people - countries - government - foreign | 70 | 9_singapore_people_countries_government | 
| 10 | culture - cultural - programmes - films - people | 49 | 10_culture_cultural_programmes_films | 
| 11 | abortion - abortions - family - medical - women | 48 | 11_abortion_abortions_family_medical | 
| 12 | fund - pension - citizenship - age - years | 38 | 12_fund_pension_citizenship_age | 
| 13 | airport - telephone - passengers - singapore - terminal | 37 | 13_airport_telephone_passengers_singapore | 
| 14 | sports - games - national - singapore - national sports | 29 | 14_sports_games_national_singapore | 
| 15 | drug - drugs - medicines - advertisements - medical | 24 | 15_drug_drugs_medicines_advertisements | 
| 16 | health - mosquitoes - mosquito - hawkers - rubbish | 20 | 16_health_mosquitoes_mosquito_hawkers | 
| 17 | brigade - sports - minister - station - firefighting | 17 | 17_brigade_sports_minister_station | 
| 18 | hawkers - market - hawker - stalls - markets | 17 | 18_hawkers_market_hawker_stalls |
  
</details>

## Training hyperparameters

* calculate_probabilities: False
* language: english
* low_memory: False
* min_topic_size: 10
* n_gram_range: (1, 1)
* nr_topics: 20
* seed_topic_list: None
* top_n_words: 10
* verbose: False
* zeroshot_min_similarity: 0.7
* zeroshot_topic_list: None

## Framework versions

* Numpy: 1.26.4
* HDBSCAN: 0.8.37
* UMAP: 0.5.5
* Pandas: 2.2.0
* Scikit-Learn: 1.4.1.post1
* Sentence-transformers: 2.4.0
* Transformers: 4.43.3
* Numba: 0.60.0
* Plotly: 5.23.0
* Python: 3.12.1