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Kamaljp/
topic_docs5000

Name: topic_docs5000
Rating: 2.5 (5 reviews)

bertopic

text-classification

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topic_docs5000

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("Kamaljp/topic_docs5000")

topic_model.get_topic_info()

Topic overview

Number of topics: 30
Number of training documents: 5000

Click here for an overview of all topics.

Topic ID	Topic Keywords	Topic Frequency	Label
-1	the - to - of - and - is	12	-1_the_to_of_and
0	the - in - to - he - game	1606	0_the_in_to_he
1	the - drive - to - with - for	450	1_the_drive_to_with
2	the - to - that - of - and	344	2_the_to_that_of
3	the - of - and - in - to	246	3_the_of_and_in
4	of - to - the - is - and	220	4_of_to_the_is
5	the - car - and - it - for	203	5_the_car_and_it
6	the - of - that - to - is	186	6_the_of_that_to
7	call - three - bittrolff - uhhhh - test	172	7_call_three_bittrolff_uhhhh
8	the - to - be - of - key	172	8_the_to_be_of
9	the - space - of - and - to	169	9_the_space_of_and
10	the - openwindows - to - window - and	169	10_the_openwindows_to_window
11	for - and - 100 - to - the	146	11_for_and_100_to
12	windows - dos - the - and - to	132	12_windows_dos_the_and
13	the - bike - to - my - was	105	13_the_bike_to_my
14	you - that - to - of - your	100	14_you_that_to_of
15	for - and - to - mail - send	100	15_for_and_to_mail
16	to - that - homosexual - of - is	94	16_to_that_homosexual_of
17	is - that - objective - of - science	66	17_is_that_objective_of
18	printer - fonts - deskjet - hp - the	56	18_printer_fonts_deskjet_hp
19	jpeg - image - gif - file - format	45	19_jpeg_image_gif_file
20	points - graeme - polygon - the - lines	44	20_points_graeme_polygon_the
21	radar - detector - detectors - is - the	28	21_radar_detector_detectors_is
22	hotel - dj - for - ticket - price	27	22_hotel_dj_for_ticket
23	insurance - health - private - the - and	26	23_insurance_health_private_the
24	water - battery - temperature - the - discharge	21	24_water_battery_temperature_the
25	oil - paint - it - wax - and	17	25_oil_paint_it_wax
26	drugs - cocaine - lsd - drug - license	16	26_drugs_cocaine_lsd_drug
27	motif - toolkit - cosecomplient - api - mean	15	27_motif_toolkit_cosecomplient_api
28	maxaxaxaxaxaxaxaxaxaxaxaxaxaxax - entry - entries - rules - we	13	28_maxaxaxaxaxaxaxaxaxaxaxaxaxaxax_entry_entries_rules

Training hyperparameters

calculate_probabilities: True
language: english
low_memory: False
min_topic_size: 10
n_gram_range: (1, 1)
nr_topics: 30
seed_topic_list: None
top_n_words: 10
verbose: True

Framework versions

Numpy: 1.22.4
HDBSCAN: 0.8.29
UMAP: 0.5.3
Pandas: 1.5.3
Scikit-Learn: 1.2.2
Sentence-transformers: 2.2.2
Transformers: 4.30.2
Numba: 0.56.4
Plotly: 5.13.1
Python: 3.10.12