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

# MARTINI_enrich_BERTopic_CI_Lib

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_CI_Lib")

topic_model.get_topic_info()
```

## Topic overview

* Number of topics: 7
* Number of training documents: 419

<details>
  <summary>Click here for an overview of all topics.</summary>
  
  | Topic ID | Topic Keywords | Topic Frequency | Label | 
|----------|----------------|-----------------|-------| 
| -1 | 1855 - 152 - 95 - lines - 139 | 22 | -1_1855_152_95_lines | 
| 0 | patrologiæ - volumes - preface - 161 - scanned | 71 | 0_patrologiæ_volumes_preface_161 | 
| 1 | 218 - 185 - 198 - 81 - 155 | 169 | 1_218_185_198_81 | 
| 2 | 1855 - 165 - 186 - 206 - 56 | 62 | 2_1855_165_186_206 | 
| 3 | 217 - 1855 - 157 - 197 - 57 | 37 | 3_217_1855_157_197 | 
| 4 | israelites - hamites - japhethites - miscegenation - simeon | 30 | 4_israelites_hamites_japhethites_miscegenation | 
| 5 | 215 - 205 - 210 - 204 - 25 | 28 | 5_215_205_210_204 |
  
</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