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

# BERTopic-transcripts

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("nataliecastro/BERTopic-transcripts")

topic_model.get_topic_info()
```

## Topic overview

* Number of topics: 2
* Number of training documents: 4170

<details>
  <summary>Click here for an overview of all topics.</summary>
  
  | Topic ID | Topic Keywords | Topic Frequency | Label | 
|----------|----------------|-----------------|-------| 
| 0 | the - and - to - that - of | 4149 | 0_the_and_to_that | 
| 1 | music - and - to - the - of | 21 | 1_music_and_to_the |
  
</details>

## Training hyperparameters

* calculate_probabilities: False
* language: english
* low_memory: False
* min_topic_size: 10
* n_gram_range: (1, 1)
* nr_topics: None
* seed_topic_list: None
* top_n_words: 10
* verbose: True
* zeroshot_min_similarity: 0.7
* zeroshot_topic_list: None

## Framework versions

* Numpy: 1.24.3
* HDBSCAN: 0.8.29
* UMAP: 0.5.6
* Pandas: 1.5.3
* Scikit-Learn: 1.2.2
* Sentence-transformers: 3.1.0
* Transformers: 4.44.2
* Numba: 0.57.0
* Plotly: 5.9.0
* Python: 3.10.12