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

# MARTINI_enrich_BERTopic_gerasveikata

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_gerasveikata")

topic_model.get_topic_info()
```

## Topic overview

* Number of topics: 9
* Number of training documents: 1037

<details>
  <summary>Click here for an overview of all topics.</summary>
  
  | Topic ID | Topic Keywords | Topic Frequency | Label | 
|----------|----------------|-----------------|-------| 
| -1 | vakcinas - nustatyti - pfizer - 2021 - ukrainos | 20 | -1_vakcinas_nustatyti_pfizer_2021 | 
| 0 | konstitucijai - konstituciniu - nustatyti - respublikos - reikalavimas | 693 | 0_konstitucijai_konstituciniu_nustatyti_respublikos | 
| 1 | taisykliu - pasitikejimas - reikalauja - iliuzija - galimybiu | 63 | 1_taisykliu_pasitikejimas_reikalauja_iliuzija | 
| 2 | vakcinavimas - injekcija - susitvarkyti - sertifikatu - virusa | 60 | 2_vakcinavimas_injekcija_susitvarkyti_sertifikatu | 
| 3 | koronavirusu - virusologijos - laboratorijoje - institutas - ekspertai | 51 | 3_koronavirusu_virusologijos_laboratorijoje_institutas | 
| 4 | pfizer - vakcinas - fda - 2021 - dokumentu | 50 | 4_pfizer_vakcinas_fda_2021 | 
| 5 | vaers - omicron - 2021 - nepageidaujami - skaicius | 46 | 5_vaers_omicron_2021_nepageidaujami | 
| 6 | vakcinacija - израиля - izraeliui - kampanija - issamiai | 28 | 6_vakcinacija_израиля_izraeliui_kampanija | 
| 7 | virusus - bakterijas - patogenai - imunine - mikroskopa | 26 | 7_virusus_bakterijas_patogenai_imunine |
  
</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