xsum_123_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/xsum_123_3000_1500_train")

topic_model.get_topic_info()

Topic overview

• Number of topics: 47
• Number of training documents: 3000

Click here for an overview of all topics.

Topic ID	Topic Keywords	Topic Frequency	Label
-1	said - mr - police - people - would	5	-1_said_mr_police_people
0	win - game - half - foul - league	1132	0_win_game_half_foul
1	eu - labour - party - would - uk	591	1_eu_labour_party_would
2	athlete - sport - gold - olympic - medal	149	2_athlete_sport_gold_olympic
3	nhs - health - care - patient - hospital	104	3_nhs_health_care_patient
4	growth - price - market - sale - economy	84	4_growth_price_market_sale
5	president - mr - government - maduro - rousseff	71	5_president_mr_government_maduro
6	crash - police - hospital - road - driver	58	6_crash_police_hospital_road
7	murray - match - set - tennis - seed	46	7_murray_match_set_tennis
8	syrian - us - syria - rebel - force	45	8_syrian_us_syria_rebel
9	school - education - pupil - schools - child	41	9_school_education_pupil_schools
10	animal - zoo - wildlife - bird - specie	40	10_animal_zoo_wildlife_bird
11	film - actor - star - series - drama	38	11_film_actor_star_series
12	abuse - court - sexual - police - victim	38	12_abuse_court_sexual_police
13	trump - mr - clinton - republican - president	31	13_trump_mr_clinton_republican
14	fire - blaze - building - service - firefighters	31	14_fire_blaze_building_service
15	suu - party - mr - government - election	29	15_suu_party_mr_government
16	china - korea - chinese - south - north	29	16_china_korea_chinese_south
17	album - band - song - music - best	25	17_album_band_song_music
18	ms - heard - court - death - said	24	18_ms_heard_court_death
19	wales - welsh - said - train - government	23	19_wales_welsh_said_train
20	road - police - death - seen - found	23	20_road_police_death_seen
21	passenger - crew - sea - boat - aircraft	23	21_passenger_crew_sea_boat
22	russian - ukraine - russia - mr - ukrainian	22	22_russian_ukraine_russia_mr
23	fight - joshua - title - khan - boxing	22	23_fight_joshua_title_khan
24	samsung - phone - app - android - user	20	24_samsung_phone_app_android
25	earthquake - particle - nepal - building - mars	19	25_earthquake_particle_nepal_building
26	highways - traffic - dartford - council - road	18	26_highways_traffic_dartford_council
27	vettel - hamilton - lap - race - alonso	18	27_vettel_hamilton_lap_race
28	park - building - visitor - festival - visitscotland	16	28_park_building_visitor_festival
29	site - council - street - project - plan	15	29_site_council_street_project
30	abdeslam - paris - attack - belgian - salah	15	30_abdeslam_paris_attack_belgian
31	virus - ebola - disease - hiv - sierra	14	31_virus_ebola_disease_hiv
32	security - data - attack - cyber - malware	14	32_security_data_attack_cyber
33	dog - dogs - stray - pet - owner	14	33_dog_dogs_stray_pet
34	birdie - pga - bogey - woods - open	13	34_birdie_pga_bogey_woods
35	man - police - wearing - incident - anyone	13	35_man_police_wearing_incident
36	energy - pipeline - waste - renewables - electricity	13	36_energy_pipeline_waste_renewables
37	silence - bishop - belfast - people - attended	11	37_silence_bishop_belfast_people
38	painting - art - work - artist - exhibition	11	38_painting_art_work_artist
39	eyre - gaunt - lyttle - peter - court	10	39_eyre_gaunt_lyttle_peter
40	crime - police - force - constable - chief	9	40_crime_police_force_constable
41	flood - river - rain - louisiana - flooded	9	41_flood_river_rain_louisiana
42	charity - abuse - yentob - porn - batmanghelidjh	7	42_charity_abuse_yentob_porn
43	india - nidar - gun - yrf - film	6	43_india_nidar_gun_yrf
44	driving - stirling - winn - fraser - road	6	44_driving_stirling_winn_fraser
45	boko - haram - shekau - militant - monguno	5	45_boko_haram_shekau_militant

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