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Rain4301/
beyondblue_BERTopic_MentalBERT

Name: beyondblue_BERTopic_MentalBERT
Availability: InStock

bertopic

text-classification

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beyondblue_BERTopic_MentalBERT

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("Rain4301/beyondblue_BERTopic_MentalBERT")

topic_model.get_topic_info()

Topic overview

Number of topics: 9
Number of training documents: 500

Click here for an overview of all topics.

Topic ID	Topic Keywords	Topic Frequency	Label
-1	im - like - feel - dont - know	41	-1_im_like_feel_dont
0	im - ive - anxiety - like - health	219	0_im_ive_anxiety_like
1	women - girl - ive - gay - im	97	1_women_girl_ive_gay
2	family - one - life - feel - years	12	2_family_one_life_feel
3	im - dont - really - know - pronouns	47	3_im_dont_really_know
4	im - things - would - dont - didnt	24	4_im_things_would_dont
5	mum - know - dont - didnt - feel	11	5_mum_know_dont_didnt
6	im - dont - feel - like - know	27	6_im_dont_feel_like
7	im - dont - like - feel - know	22	7_im_dont_like_feel

Training hyperparameters

calculate_probabilities: False
language: None
low_memory: False
min_topic_size: 10
n_gram_range: (1, 3)
nr_topics: 11
seed_topic_list: None
top_n_words: 15
verbose: False
zeroshot_min_similarity: 0.7
zeroshot_topic_list: None

Framework versions

Numpy: 1.26.4
HDBSCAN: 0.8.40
UMAP: 0.5.9.post2
Pandas: 2.3.0
Scikit-Learn: 1.7.0
Sentence-transformers: 5.0.0
Transformers: 4.52.4
Numba: 0.61.2
Plotly: 6.2.0
Python: 3.11.13