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

# MARTINI_enrich_BERTopic_healingivermectin

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_healingivermectin")

topic_model.get_topic_info()
```

## Topic overview

* Number of topics: 5
* Number of training documents: 260

<details>
  <summary>Click here for an overview of all topics.</summary>
  
  | Topic ID | Topic Keywords | Topic Frequency | Label | 
|----------|----------------|-----------------|-------| 
| -1 | ivermectin - fenbendazole - antitumor - cures - cyanide | 22 | -1_ivermectin_fenbendazole_antitumor_cures | 
| 0 | ivermectin - healing - bacterial - hcq - honey | 107 | 0_ivermectin_healing_bacterial_hcq | 
| 1 | parasitic - pinworms - cancers - schizophrenic - nematode | 54 | 1_parasitic_pinworms_cancers_schizophrenic | 
| 2 | vaccines - pfizer - conspiracy - deaths - mmr | 41 | 2_vaccines_pfizer_conspiracy_deaths | 
| 3 | download - recordings - telegram - 700mb - hello | 36 | 3_download_recordings_telegram_700mb |
  
</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