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cindyangelira/
BERTopic_hurricane_tweet

Name: BERTopic_hurricane_tweet
Availability: InStock

bertopic

text-classification

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BERTopic_hurricane_tweet

This is a 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

Click here for an overview of all topics.

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

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