cnn_dailymail_22457_3000_1500_train

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("KingKazma/cnn_dailymail_22457_3000_1500_train")

topic_model.get_topic_info()

Topic overview

• Number of topics: 49
• Number of training documents: 3000

Click here for an overview of all topics.

Topic ID	Topic Keywords	Topic Frequency	Label
-1	said - one - year - people - police	10	-1_said_one_year_people
0	league - player - club - game - cup	1050	0_league_player_club_game
1	said - syria - government - iraq - islamic	317	1_said_syria_government_iraq
2	obama - president - house - state - republican	140	2_obama_president_house_state
3	cancer - hospital - baby - treatment - child	122	3_cancer_hospital_baby_treatment
4	google - apple - tablet - car - device	84	4_google_apple_tablet_car
5	fashion - dress - hair - look - woman	78	5_fashion_dress_hair_look
6	police - officer - shooting - said - shot	66	6_police_officer_shooting_said
7	film - movie - show - actor - comedy	65	7_film_movie_show_actor
8	murder - death - said - home - police	55	8_murder_death_said_home
9	mr - labour - minister - mp - blair	52	9_mr_labour_minister_mp
10	storm - water - weather - ice - rain	51	10_storm_water_weather_ice
11	shark - bear - turtle - crocodile - bird	50	11_shark_bear_turtle_crocodile
12	flight - plane - passenger - airport - pilot	49	12_flight_plane_passenger_airport
13	house - property - home - per - room	49	13_house_property_home_per
14	drug - police - court - stealing - robbery	40	14_drug_police_court_stealing
15	police - murder - mr - court - clavell	36	15_police_murder_mr_court
16	games - gold - olympic - race - sport	34	16_games_gold_olympic_race
17	student - school - teacher - said - cardosa	34	17_student_school_teacher_said
18	country - minister - energy - cent - greece	32	18_country_minister_energy_cent
19	golf - mcilroy - course - round - ryder	31	19_golf_mcilroy_course_round
20	police - harris - abuse - allegation - officer	30	20_police_harris_abuse_allegation
21	ebola - virus - africa - health - liberia	29	21_ebola_virus_africa_health
22	chinese - china - cable - bo - beijing	28	22_chinese_china_cable_bo
23	federer - tennis - murray - wimbledon - match	28	23_federer_tennis_murray_wimbledon
24	dog - animal - dogs - owner - simmons	26	24_dog_animal_dogs_owner
25	cent - per - woman - men - pickens	23	25_cent_per_woman_men
26	ship - boat - rescue - water - sea	23	26_ship_boat_rescue_water
27	hamilton - race - rosberg - mercedes - formula	22	27_hamilton_race_rosberg_mercedes
28	galaxy - planet - universe - earth - telescope	22	28_galaxy_planet_universe_earth
29	russian - russia - putin - ukraine - moscow	22	29_russian_russia_putin_ukraine
30	pakistan - pakistani - karachi - taliban - anwar	22	30_pakistan_pakistani_karachi_taliban
31	korea - north - korean - south - kim	21	31_korea_north_korean_south
32	car - driver - train - accident - cope	21	32_car_driver_train_accident
33	food - fruit - taste - cake - cream	20	33_food_fruit_taste_cake
34	painting - art - auction - artist - gallery	20	34_painting_art_auction_artist
35	base - drone - soldier - afghan - us	19	35_base_drone_soldier_afghan
36	weight - fat - eating - healthy - size	18	36_weight_fat_eating_healthy
37	mafia - wine - money - fraud - court	18	37_mafia_wine_money_fraud
38	aguilar - bravo - brewer - rambold - court	18	38_aguilar_bravo_brewer_rambold
39	missing - search - found - family - disappeared	17	39_missing_search_found_family
40	juarez - quezada - mexico - mexican - cartel	15	40_juarez_quezada_mexico_mexican
41	knicks - lin - chicago - blackhawks - game	15	41_knicks_lin_chicago_blackhawks
42	duchess - prince - kate - royal - william	15	42_duchess_prince_kate_royal
43	price - supermarket - asda - shop - food	14	43_price_supermarket_asda_shop
44	school - child - pupil - teacher - xxx	14	44_school_child_pupil_teacher
45	nhs - patient - ae - hospital - staff	13	45_nhs_patient_ae_hospital
46	zsa - francesca - rhodes - vongtau - gabor	12	46_zsa_francesca_rhodes_vongtau
47	medal - war - bomb - graf - vc	10	47_medal_war_bomb_graf

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