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---
tags:
- bertopic
library_name: bertopic
pipeline_tag: text-classification
---
# MARTINI_enrich_BERTopic_TruthIsStillHateSpeech
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_TruthIsStillHateSpeech")
topic_model.get_topic_info()
```
## Topic overview
* Number of topics: 10
* Number of training documents: 820
<details>
<summary>Click here for an overview of all topics.</summary>
| Topic ID | Topic Keywords | Topic Frequency | Label |
|----------|----------------|-----------------|-------|
| -1 | jews - whites - racist - diversity - russia | 20 | -1_jews_whites_racist_diversity |
| 0 | antisemitism - jew - mossad - lbj - victims | 418 | 0_antisemitism_jew_mossad_lbj |
| 1 | racism - whites - supremacy - globohomo - republican | 87 | 1_racism_whites_supremacy_globohomo |
| 2 | lgbtq - pedophile - fundamentalism - california - satanic | 73 | 2_lgbtq_pedophile_fundamentalism_california |
| 3 | technocracy - vaxxed - plandemic - conspiracy - china | 57 | 3_technocracy_vaxxed_plandemic_conspiracy |
| 4 | britons - whites - colonisation - wales - populations | 45 | 4_britons_whites_colonisation_wales |
| 5 | rapist - muslims - riots - abused - leicester | 38 | 5_rapist_muslims_riots_abused |
| 6 | zelenskyy - ukrainians - crimea - volodymyr - holocaust | 34 | 6_zelenskyy_ukrainians_crimea_volodymyr |
| 7 | hitler - nationalsozialistiche - comrades - 1936 - chapter | 24 | 7_hitler_nationalsozialistiche_comrades_1936 |
| 8 | zionist - refugees - jewry - multiculturalism - hias | 24 | 8_zionist_refugees_jewry_multiculturalism |
</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
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