cnn_dailymail_55555_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_55555_3000_1500_train")

topic_model.get_topic_info()

Topic overview

• Number of topics: 61
• 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 - mr	10	-1_said_one_year_people
0	league - game - player - cup - goal	961	0_league_game_player_cup
1	police - death - said - murder - family	313	1_police_death_said_murder
2	obama - republican - senate - president - republicans	182	2_obama_republican_senate_president
3	fashion - hair - look - makeup - brand	91	3_fashion_hair_look_makeup
4	dog - animal - cat - bird - pet	69	4_dog_animal_cat_bird
5	syria - isis - syrian - iraq - fighter	54	5_syria_isis_syrian_iraq
6	mexico - said - cuba - president - cartel	53	6_mexico_said_cuba_president
7	police - court - cash - jailed - said	53	7_police_court_cash_jailed
8	space - nasa - mars - planet - earth	51	8_space_nasa_mars_planet
9	property - house - price - room - london	48	9_property_house_price_room
10	patient - hospital - nhs - doctor - cancer	48	10_patient_hospital_nhs_doctor
11	tax - bank - minister - mr - pay	46	11_tax_bank_minister_mr
12	car - fire - crash - bus - train	45	12_car_fire_crash_bus
13	milk - food - raw - restaurant - chocolate	44	13_milk_food_raw_restaurant
14	gold - olympic - horse - race - medal	36	14_gold_olympic_horse_race
15	album - song - joel - music - show	35	15_album_song_joel_music
16	show - film - movie - award - les	35	16_show_film_movie_award
17	baby - born - hospital - birth - pregnancy	34	17_baby_born_hospital_birth
18	prince - queen - royal - william - duchess	31	18_prince_queen_royal_william
19	chinese - china - bo - beijing - chen	30	19_chinese_china_bo_beijing
20	labour - mr - party - ukip - miliband	30	20_labour_mr_party_ukip
21	school - student - teacher - book - fraternity	29	21_school_student_teacher_book
22	somalia - dala - african - alshabaab - mali	28	22_somalia_dala_african_alshabaab
23	ukraine - russian - russia - putin - moscow	26	23_ukraine_russian_russia_putin
24	woods - golf - golfer - hole - round	26	24_woods_golf_golfer_hole
25	sterling - nba - clippers - donald - said	26	25_sterling_nba_clippers_donald
26	found - scientist - stonehenge - researcher - frog	26	26_found_scientist_stonehenge_researcher
27	apple - iphone - apples - phone - device	24	27_apple_iphone_apples_phone
28	formula - race - schumacher - prix - ecclestone	23	28_formula_race_schumacher_prix
29	ebola - virus - outbreak - health - vaccine	22	29_ebola_virus_outbreak_health
30	church - pope - priest - francis - vatican	21	30_church_pope_priest_francis
31	sharapova - open - wimbledon - tennis - slam	21	31_sharapova_open_wimbledon_tennis
32	pakistani - pakistan - taliban - musharraf - afghanistan	21	32_pakistani_pakistan_taliban_musharraf
33	storm - weather - tornado - water - rain	21	33_storm_weather_tornado_water
34	north - korea - korean - kim - south	21	34_north_korea_korean_kim
35	war - medal - soldier - army - afghanistan	21	35_war_medal_soldier_army
36	marijuana - cigarette - alcohol - drug - smoking	20	36_marijuana_cigarette_alcohol_drug
37	internet - google - user - facebook - online	19	37_internet_google_user_facebook
38	plane - flight - crash - passenger - airport	19	38_plane_flight_crash_passenger
39	weight - diet - fat - stone - food	18	39_weight_diet_fat_stone
40	israeli - israel - gaza - hamas - palestinian	17	40_israeli_israel_gaza_hamas
41	beach - art - resort - festival - painting	17	41_beach_art_resort_festival
42	petraeus - cia - broadwell - justice - fbi	17	42_petraeus_cia_broadwell_justice
43	garner - wilson - officer - police - black	16	43_garner_wilson_officer_police
44	ship - cruise - ships - crew - pirate	16	44_ship_cruise_ships_crew
45	nfl - patriots - rice - seahawks - chris	15	45_nfl_patriots_rice_seahawks
46	dolphin - sea - creature - cuttlefish - fisherman	14	46_dolphin_sea_creature_cuttlefish
47	weather - rain - winter - temperature - warm	14	47_weather_rain_winter_temperature
48	mandela - african - africa - south - mandelas	14	48_mandela_african_africa_south
49	disney - snow - million - wars - movie	14	49_disney_snow_million_wars
50	price - bag - plastic - cent - energy	13	50_price_bag_plastic_cent
51	spartan - cliff - parachute - matthew - obstacle	12	51_spartan_cliff_parachute_matthew
52	zoo - panda - cub - giraffe - park	12	52_zoo_panda_cub_giraffe
53	iran - iranian - irans - ahmadinejad - nuclear	12	53_iran_iranian_irans_ahmadinejad
54	bin - laden - us - qaeda - al	12	54_bin_laden_us_qaeda
55	crocodile - snake - python - bascoules - alligator	12	55_crocodile_snake_python_bascoules
56	woman - ivf - men - dna - fertility	11	56_woman_ivf_men_dna
57	driver - driving - police - meracle - text	11	57_driver_driving_police_meracle
58	mitchell - mr - evans - mp - gate	10	58_mitchell_mr_evans_mp
59	france - police - mosque - salah - donetsk	10	59_france_police_mosque_salah

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