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

# MARTINI_enrich_BERTopic_grmedfa

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_grmedfa")

topic_model.get_topic_info()
```

## Topic overview

* Number of topics: 6
* Number of training documents: 582

<details>
  <summary>Click here for an overview of all topics.</summary>
  
  | Topic ID | Topic Keywords | Topic Frequency | Label | 
|----------|----------------|-----------------|-------| 
| -1 | everyone - vaccinated - ivermectin - 2022 - bbc | 26 | -1_everyone_vaccinated_ivermectin_2022 | 
| 0 | greekmedicalfreedomalliance - δηλωσε - ελλαδα - γιατι - ομαδα | 334 | 0_greekmedicalfreedomalliance_δηλωσε_ελλαδα_γιατι | 
| 1 | worldcouncilforhealth - betterwayconference - wai - parliament - virtual | 82 | 1_worldcouncilforhealth_betterwayconference_wai_parliament | 
| 2 | fauci - davos - physician - webinar - berberine | 60 | 2_fauci_davos_physician_webinar | 
| 3 | vaccinated - mhra - coronavirus - doses - january | 50 | 3_vaccinated_mhra_coronavirus_doses | 
| 4 | vaccins - fda - plasmids - cv19 - contaminated | 30 | 4_vaccins_fda_plasmids_cv19 |
  
</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