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

# MARTINI_enrich_BERTopic_returnofthesacred

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_returnofthesacred")

topic_model.get_topic_info()
```

## Topic overview

* Number of topics: 10
* Number of training documents: 1098

<details>
  <summary>Click here for an overview of all topics.</summary>
  
  | Topic ID | Topic Keywords | Topic Frequency | Label | 
|----------|----------------|-----------------|-------| 
| -1 | humanity - become - sun - years - mercola | 24 | -1_humanity_become_sun_years | 
| 0 | monday - meme - moments - loves - blessing | 666 | 0_monday_meme_moments_loves | 
| 1 | vaers - vaccinated - deaths - injections - 2021 | 71 | 1_vaers_vaccinated_deaths_injections | 
| 2 | fireweed - schisandra - herbal - mullein - tincture | 71 | 2_fireweed_schisandra_herbal_mullein | 
| 3 | spiritually - consciousness - negentropy - energeticsynthesis - become | 52 | 3_spiritually_consciousness_negentropy_energeticsynthesis | 
| 4 | dawn - darkness - becoming - whispers - bright | 50 | 4_dawn_darkness_becoming_whispers | 
| 5 | vaccinations - unvaccinated - exemptions - mandatory - employers | 49 | 5_vaccinations_unvaccinated_exemptions_mandatory | 
| 6 | solstice - capricorn - january - chiron - rebirth | 48 | 6_solstice_capricorn_january_chiron | 
| 7 | prayers - mercy - sanctity - responders - heroic | 40 | 7_prayers_mercy_sanctity_responders | 
| 8 | agrihood - sovereignty - empowering - gmo - mufi | 27 | 8_agrihood_sovereignty_empowering_gmo |
  
</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