You can deploy this model and start earning money today!

AIDA-UPM/
MARTINI_enrich_BERTopic_Planet_Earth

Name: MARTINI_enrich_BERTopic_Planet_Earth
Availability: InStock

bertopic

text-classification

Views

No views yet

MARTINI_enrich_BERTopic_Planet_Earth

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("AIDA-UPM/MARTINI_enrich_BERTopic_Planet_Earth")

topic_model.get_topic_info()

Topic overview

Number of topics: 5
Number of training documents: 202

Click here for an overview of all topics.

Topic ID	Topic Keywords	Topic Frequency	Label
-1	tower - tundra - lake - sicily - village	24	-1_tower_tundra_lake_sicily
0	dolphins - monkeys - moose - kangaroos - hear	46	0_dolphins_monkeys_moose_kangaroos
1	manali - pathankot - photos - varanasi - uttarakhand	63	1_manali_pathankot_photos_varanasi
2	natura - telegram - birthday - picture - 2015	35	2_natura_telegram_birthday_picture
3	wildlife - himalayas - giraffes - tanzania - endangered	34	3_wildlife_himalayas_giraffes_tanzania

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