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KingKazma/
xsum_108_50000_25000_test

Name: xsum_108_50000_25000_test
Availability: InStock

bertopic

text-classification

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xsum_108_50000_25000_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/xsum_108_50000_25000_test")

topic_model.get_topic_info()

Topic overview

Number of topics: 84
Number of training documents: 11334

Click here for an overview of all topics.

Topic ID	Topic Keywords	Topic Frequency	Label
-1	said - mr - people - would - year	5	-1_said_mr_people_would
0	win - goal - game - league - foul	4854	0_win_goal_game_league
1	police - court - said - officer - mr	1637	1_police_court_said_officer
2	labour - party - eu - election - vote	872	2_labour_party_eu_election
3	health - care - nhs - patient - cancer	341	3_health_care_nhs_patient
4	olympic - sport - race - gold - medal	325	4_olympic_sport_race_gold
5	cricket - england - wicket - test - captain	278	5_cricket_england_wicket_test
6	animal - dog - bird - whale - specie	206	6_animal_dog_bird_whale
7	bridge - rail - council - said - transport	199	7_bridge_rail_council_said
8	school - education - student - teacher - university	193	8_school_education_student_teacher
9	bank - rate - growth - economy - market	181	9_bank_rate_growth_economy
10	syria - syrian - iraq - iran - force	145	10_syria_syrian_iraq_iran
11	energy - industry - wind - electricity - company	119	11_energy_industry_wind_electricity
12	film - actress - star - actor - character	80	12_film_actress_star_actor
13	president - boko - african - haram - mr	79	13_president_boko_african_haram
14	fire - blaze - service - smoke - said	79	14_fire_blaze_service_smoke
15	trump - mr - republican - trumps - president	75	15_trump_mr_republican_trumps
16	music - album - song - band - singer	70	16_music_album_song_band
17	race - hamilton - f1 - mercedes - lap	68	17_race_hamilton_f1_mercedes
18	space - earth - planet - solar - orbit	61	18_space_earth_planet_solar
19	lifeboat - rnli - beach - coastguard - rescue	55	19_lifeboat_rnli_beach_coastguard
20	flood - flooding - water - weather - rain	55	20_flood_flooding_water_weather
21	fight - boxing - champion - joshua - ali	54	21_fight_boxing_champion_joshua
22	plane - aircraft - flight - passenger - pilot	54	22_plane_aircraft_flight_passenger
23	earthquake - quake - flood - people - water	53	23_earthquake_quake_flood_people
24	russian - russia - ukraine - putin - ukrainian	49	24_russian_russia_ukraine_putin
25	murray - match - wimbledon - tennis - konta	47	25_murray_match_wimbledon_tennis
26	bitcoin - security - talktalk - data - tor	44	26_bitcoin_security_talktalk_data
27	round - birdie - bogey - par - shot	41	27_round_birdie_bogey_par
28	ireland - dup - sinn - northern - party	39	28_ireland_dup_sinn_northern
29	maduro - venezuela - president - venezuelan - opposition	36	29_maduro_venezuela_president_venezuelan
30	yn - ar - yr - ei - wedi	36	30_yn_ar_yr_ei
31	painting - art - gallery - portrait - museum	34	31_painting_art_gallery_portrait
32	unsupported - updated - bst - playback - media	33	32_unsupported_updated_bst_playback
33	migrant - eu - asylum - turkey - germany	31	33_migrant_eu_asylum_turkey
34	stone - cave - discovery - site - tree	30	34_stone_cave_discovery_site
35	parade - poppy - flag - jesus - statue	30	35_parade_poppy_flag_jesus
36	drug - cannabis - drugs - heroin - cocaine	27	36_drug_cannabis_drugs_heroin
37	church - pope - bishop - vatican - cardinal	27	37_church_pope_bishop_vatican
38	greek - greece - bailout - eurozone - bank	27	38_greek_greece_bailout_eurozone
39	nama - ireland - northern - cerberus - irish	26	39_nama_ireland_northern_cerberus
40	prison - prisoner - prisons - justice - turing	25	40_prison_prisoner_prisons_justice
41	radio - show - bbc - series - programme	24	41_radio_show_bbc_series
42	fifa - blatter - platini - fifas - football	23	42_fifa_blatter_platini_fifas
43	tesco - sale - store - supermarket - customer	23	43_tesco_sale_store_supermarket
44	china - taiwan - chinese - hong - taiwans	22	44_china_taiwan_chinese_hong
45	afghan - taliban - afghanistan - mansour - mullah	22	45_afghan_taliban_afghanistan_mansour
46	council - local - funding - government - authority	22	46_council_local_funding_government
47	nsa - encryption - cia - snowden - us	21	47_nsa_encryption_cia_snowden
48	ice - glacier - temperature - ocean - climate	21	48_ice_glacier_temperature_ocean
49	osullivan - world - snooker - beat - champion	21	49_osullivan_world_snooker_beat
50	book - prize - novel - author - award	20	50_book_prize_novel_author
51	auschwitz - jews - holocaust - camp - winton	20	51_auschwitz_jews_holocaust_camp
52	samsung - apple - phone - company - battery	19	52_samsung_apple_phone_company
53	picture - image - pictures - please - submit	19	53_picture_image_pictures_please
54	korea - north - korean - missile - koreas	19	54_korea_north_korean_missile
55	pension - worker - pay - work - hour	19	55_pension_worker_pay_work
56	pen - fillon - le - macron - mr	18	56_pen_fillon_le_macron
57	paris - eaw - french - attack - suspect	18	57_paris_eaw_french_attack
58	content - app - tv - digital - apple	18	58_content_app_tv_digital
59	israel - israeli - palestinians - palestinian - gaza	17	59_israel_israeli_palestinians_palestinian
60	housing - affordable - rent - homelessness - government	17	60_housing_affordable_rent_homelessness
61	prince - queen - birthday - duke - royal	17	61_prince_queen_birthday_duke
62	australia - australian - asylum - visa - abbott	15	62_australia_australian_asylum_visa
63	tax - spending - cut - osborne - fiscal	15	63_tax_spending_cut_osborne
64	updated - 2017 - bst - last - gmt	14	64_updated_2017_bst_last
65	refugee - uk - child - vulnerable - refugees	12	65_refugee_uk_child_vulnerable
66	ebola - sierra - leone - outbreak - liberia	12	66_ebola_sierra_leone_outbreak
67	shah - ahmed - mosque - muslims - prophet	11	67_shah_ahmed_mosque_muslims
68	broadband - 4g - ee - customer - internet	11	68_broadband_4g_ee_customer
69	pistorius - steenkamp - toilet - door - reeva	10	69_pistorius_steenkamp_toilet_door
70	eu - uk - population - migrant - trade	9	70_eu_uk_population_migrant
71	australia - marriage - turnbull - katter - samesex	9	71_australia_marriage_turnbull_katter
72	sugar - gin - sabmiller - inbev - ab	8	72_sugar_gin_sabmiller_inbev
73	suu - kyi - rohingya - rakhine - myanmar	8	73_suu_kyi_rohingya_rakhine
74	nadeau - field - aircraft - cordon - accidents	8	74_nadeau_field_aircraft_cordon
75	abortion - ireland - law - unborn - case	8	75_abortion_ireland_law_unborn
76	homosexuality - tor - homosexual - law - gay	7	76_homosexuality_tor_homosexual_law
77	castro - cuba - cuban - fidel - havana	7	77_castro_cuba_cuban_fidel
78	china - samsung - firm - business - cheil	7	78_china_samsung_firm_business
79	event - festival - technology - campsite - interactive	6	79_event_festival_technology_campsite
80	vw - volkswagen - production - emission - carmaker	6	80_vw_volkswagen_production_emission
81	mohammed - gjolla - sheriff - nca - terrorism	6	81_mohammed_gjolla_sheriff_nca
82	tb - tuberculosis - disease - badger - zoonotic	5	82_tb_tuberculosis_disease_badger

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: 0.57.1
Plotly: 5.13.1
Python: 3.10.12