Bertopic_Keybert_Champions

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:

from bertopic import BERTopic
topic_model = BERTopic.load("Noibu/Bertopic_Keybert_Champions")

topic_model.get_topic_info()

Topic overview

Number of topics: 10
Number of training documents: 11678

Click here for an overview of all topics.

Topic ID	Topic Keywords	Topic Frequency	Label
-1	short - powerblend - mesh short - shorts - big tall	78	-1_short_powerblend_mesh short_shorts
0	ny - york - new york - st - st apt	2099	0_ny_york_new york_st
1	available color - color - color black - grey - white	5852	1_available color_color_color black_grey
2	search result - search - short search - item search - pant search	2210	2_search result_search_short search_item search
3	address close - shipping address - address - shipping - michael	463	3_address close_shipping address_address_shipping
4	size xl - size guide - xl xl - xl available - xl	403	4_size xl_size guide_xl xl_xl available
5	code - code order - apply - new premium - premium	190	5_code_code order_apply_new premium
6	password - new password - login - account - enter	140	6_password_new password_login_account
7	shipping address - address address - address - address order - new address	131	7_shipping address_address address_address_address order
8	billing - credit card - card number - card - credit	112	8_billing_credit card_card number_card

Training hyperparameters

calculate_probabilities: True
language: None
low_memory: False
min_topic_size: 50
n_gram_range: (1, 2)
nr_topics: 10
seed_topic_list: [['ship', 'address', 'location', 'destination', 'post', 'deliver', 'florida', 'texas', 'united states', 'europe', 'asia'], ['password', 'account', 'login', 'sign in', 'email', 'id', 'authentication', 'username'], ['select', 'choose', 'sort', 'next', 'more', 'back', 'scroll', 'previous', 'search', 'results', 'catalog', 'find', 'lookup', 'query', 'browse', 'explore', 'filter'], ['first', 'last', 'name', 'username', 'middlename', 'surname', 'given name', 'alias'], ['cart', 'basket', 'bag', 'add', 'remove', 'edit', 'cancel', 'update', 'delete', 'modify', 'change'], ['checkout', 'payment', 'pay', 'order', 'purchase', 'billing', 'transaction'], ['small', 'medium', 'large', 'extra large', 's', 'm', 'l', 'xl', 'xxl', 'slim fit', 'size', 'fit', 'quantity'], ['promo', 'code', 'apply', 'welcome', 'offer']]
top_n_words: 10
verbose: False

Framework versions

Numpy: 1.23.5
HDBSCAN: 0.8.33
UMAP: 0.5.3
Pandas: 1.5.3
Scikit-Learn: 1.2.2
Sentence-transformers: 2.2.2
Transformers: 4.31.0
Numba: 0.56.4
Plotly: 5.15.0
Python: 3.10.12