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

# saxa3-capstone

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.
This text-classification model was modeled from The Department of Veterans Affairs Advisory Committee on Women Veterans biennial reports, from
a period of 1996 - 2020. It was specifically generated from recommendations used within each of the reports. 

## 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("magica1/saxa3-capstone")

topic_model.get_topic_info()
```

## Topic overview

* Number of topics: 24
* Number of training documents: 1602



## Training hyperparameters

* calculate_probabilities: False
* 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

## Framework versions

* Numpy: 1.23.5
* HDBSCAN: 0.8.33
* UMAP: 0.5.4
* Pandas: 2.1.2
* Scikit-Learn: 1.2.2
* Sentence-transformers: 2.2.2
* Transformers: 4.35.0
* Numba: 0.56.4
* Plotly: 5.15.0
* Python: 3.10.12