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KingKazma/
cnn_dailymail_6789_3000_1500_test

Name: cnn_dailymail_6789_3000_1500_test
Availability: InStock

bertopic

text-classification

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cnn_dailymail_6789_3000_1500_test

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("KingKazma/cnn_dailymail_6789_3000_1500_test")

topic_model.get_topic_info()

Topic overview

Number of topics: 15
Number of training documents: 1500

Click here for an overview of all topics.

Topic ID	Topic Keywords	Topic Frequency	Label
-1	season - league - liverpool - player - club	12	-1_season_league_liverpool_player
0	said - one - police - year - people	151	0_said_one_police_year
1	madrid - league - champions - real - barcelona	1070	1_madrid_league_champions_real
2	chelsea - united - manchester - van - league	55	2_chelsea_united_manchester_van
3	fight - pacquiao - ticket - mayweather - boxing	43	3_fight_pacquiao_ticket_mayweather
4	race - hamilton - rosberg - marathon - vettel	28	4_race_hamilton_rosberg_marathon
5	england - cook - pietersen - cricket - test	25	5_england_cook_pietersen_cricket
6	villa - sherwood - benteke - aston - game	19	6_villa_sherwood_benteke_aston
7	try - minute - huddersfield - bristol - league	17	7_try_minute_huddersfield_bristol
8	celtic - scottish - rangers - game - inverness	15	8_celtic_scottish_rangers_game
9	mcilroy - masters - woods - augusta - golf	14	9_mcilroy_masters_woods_augusta
10	arsenal - wenger - arsenals - reading - coquelin	14	10_arsenal_wenger_arsenals_reading
11	newcastle - sunderland - advocaat - game - rangers	13	11_newcastle_sunderland_advocaat_game
12	cup - toulon - saracens - clermont - bath	12	12_cup_toulon_saracens_clermont
13	stadium - stand - fan - fa - final	12	13_stadium_stand_fan_fa

Training hyperparameters

calculate_probabilities: True
language: english
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

Framework versions

Numpy: 1.22.4
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: 5.13.1
Python: 3.10.6