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---
tags:
- bertopic
library_name: bertopic
pipeline_tag: text-classification
---
# MARTINI_enrich_BERTopic_ConPy
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_ConPy")
topic_model.get_topic_info()
```
## Topic overview
* Number of topics: 9
* Number of training documents: 921
<details>
<summary>Click here for an overview of all topics.</summary>
| Topic ID | Topic Keywords | Topic Frequency | Label |
|----------|----------------|-----------------|-------|
| -1 | tucker - cuomo - supreme - immigration - subtitles | 38 | -1_tucker_cuomo_supreme_immigration |
| 0 | fauci - unvaxed - tyranny - jab - mandates | 374 | 0_fauci_unvaxed_tyranny_jab |
| 1 | tucker - fauci - tonight - taliban - democrats | 155 | 1_tucker_fauci_tonight_taliban |
| 2 | tucker - foxnews - tonight - presidential - 2020 | 86 | 2_tucker_foxnews_tonight_presidential |
| 3 | tucker - tonight - texas - migrants - governor | 71 | 3_tucker_tonight_texas_migrants |
| 4 | jfk - undercover - ufo - documentary - assassination | 62 | 4_jfk_undercover_ufo_documentary |
| 5 | tucker - carlson - zelensky - tonight - invading | 55 | 5_tucker_carlson_zelensky_tonight |
| 6 | fauci - unvaxed - david - podcast - tyranny | 41 | 6_fauci_unvaxed_david_podcast |
| 7 | crimea - sanctions - ussr - ukrainian - sovereignty | 39 | 7_crimea_sanctions_ussr_ukrainian |
</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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