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

# BERTopic_hurricane_tweet

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("cindyangelira/BERTopic_hurricane_tweet")

topic_model.get_topic_info()
```

## Topic overview

* Number of topics: 11
* Number of training documents: 811

<details>
  <summary>Click here for an overview of all topics.</summary>
  
  | Topic ID | Topic Keywords | Topic Frequency | Label | 
|----------|----------------|-----------------|-------| 
| -1 | medicare - theft - medical - harvey - identity | 1 | -1_medicare_theft_medical_harvey | 
| 0 | gofundme - donate - houstonstrong - texas - houston | 111 | Weather Updates | 
| 1 | plaza - txmedcenter - medical - instagram - here | 516 | Relief Efforts | 
| 2 | hurricaneharvey - houston - harvey - hurricane - houstonflood | 74 | Rescue Operations | 
| 3 | harvey - hurricane - flooded - houston - tx | 33 | Flooding Reports | 
| 4 | houston - astros - harvey - snow - houstonstrong | 28 | Storm Damage Reports | 
| 5 | harvey - reliefforharvey - hurricaneharvey - relief - houstonians | 23 | 5_harvey_reliefforharvey_hurricaneharvey_relief | 
| 6 | rescued - hurricaneharvey -  -  -  | 14 | 6_rescued_hurricaneharvey__ | 
| 7 | hurricaneharvey - hurricane -  -  -  | 4 | 7_hurricaneharvey_hurricane__ | 
| 8 | harvey - flooding - houston - floodwaters - flood | 4 | 8_harvey_flooding_houston_floodwaters | 
| 9 | houston - hurricaneseason - hurricaneharvey - harvey - weather | 3 | 9_houston_hurricaneseason_hurricaneharvey_harvey |
  
</details>

## Training hyperparameters

* calculate_probabilities: False
* language: None
* low_memory: False
* min_topic_size: 15
* n_gram_range: (1, 1)
* nr_topics: None
* seed_topic_list: None
* top_n_words: 10
* verbose: False
* zeroshot_min_similarity: 0.85
* zeroshot_topic_list: ['Weather Updates', 'Evacuation Information', 'Emergency Services', 'Relief Efforts', 'Rescue Operations', 'Flooding Reports', 'Traffic and Road Closures', 'Government and Local Authority Announcements', 'Personal Stories and Experiences', 'Storm Damage Reports', 'Others']

## Framework versions

* Numpy: 1.26.4
* HDBSCAN: 0.8.38.post1
* UMAP: 0.5.6
* Pandas: 2.2.2
* Scikit-Learn: 1.2.2
* Sentence-transformers: 3.1.0
* Transformers: 4.44.0
* Numba: 0.60.0
* Plotly: 5.22.0
* Python: 3.10.14