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

# MARTINI_enrich_BERTopic_UKcitizen2021

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("AIDA-UPM/MARTINI_enrich_BERTopic_UKcitizen2021")

topic_model.get_topic_info()
```

## Topic overview

* Number of topics: 11
* Number of training documents: 721

<details>
  <summary>Click here for an overview of all topics.</summary>
  
  | Topic ID | Topic Keywords | Topic Frequency | Label | 
|----------|----------------|-----------------|-------| 
| -1 | lockdown - wef - monkeypox - agenda - nuremberg | 22 | -1_lockdown_wef_monkeypox_agenda | 
| 0 | vaccinated - pfizer - vaxx - worldcouncilforhealth - injections | 313 | 0_vaccinated_pfizer_vaxx_worldcouncilforhealth | 
| 1 | ukcitizen2021 - amendments - supranational - pandemic - wgihr | 89 | 1_ukcitizen2021_amendments_supranational_pandemic | 
| 2 | constable - arrested - victims - allegations - thamesvalley | 73 | 2_constable_arrested_victims_allegations | 
| 3 | bbc - matt - tomorrow - southampton - everywhere | 51 | 3_bbc_matt_tomorrow_southampton | 
| 4 | vaccines - mhra - parliamentary - claims - chope | 42 | 4_vaccines_mhra_parliamentary_claims | 
| 5 | ukcitizen2021 - councils - mobilise - responses - nottinghamshire | 35 | 5_ukcitizen2021_councils_mobilise_responses | 
| 6 | ivermectin - hydroxychloroquine - quercetin - iodine - denied | 26 | 6_ivermectin_hydroxychloroquine_quercetin_iodine | 
| 7 | solicitors - mhra - regulatory - bayliss - allegations | 25 | 7_solicitors_mhra_regulatory_bayliss | 
| 8 | vaccination - nhs - consent - compulsory - jobsnotjabs | 23 | 8_vaccination_nhs_consent_compulsory | 
| 9 | digital - controligarchs - england - passport - currency | 22 | 9_digital_controligarchs_england_passport |
  
</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: False
* zeroshot_min_similarity: 0.7
* zeroshot_topic_list: None

## Framework versions

* Numpy: 1.26.4
* HDBSCAN: 0.8.40
* UMAP: 0.5.7
* Pandas: 2.2.3
* Scikit-Learn: 1.5.2
* Sentence-transformers: 3.3.1
* Transformers: 4.46.3
* Numba: 0.60.0
* Plotly: 5.24.1
* Python: 3.10.12