xsum_108_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_108_3000_1500_train")

topic_model.get_topic_info()

Topic overview

• Number of topics: 32
• Number of training documents: 3000

Click here for an overview of all topics.

Topic ID	Topic Keywords	Topic Frequency	Label
-1	said - mr - people - would - also	7	-1_said_mr_people_would
0	win - game - right - goal - shot	841	0_win_game_right_goal
1	police - said - court - mr - told	815	1_police_said_court_mr
2	party - labour - mr - election - vote	438	2_party_labour_mr_election
3	care - nhs - patient - health - cancer	111	3_care_nhs_patient_health
4	rate - bank - growth - market - price	77	4_rate_bank_growth_market
5	film - song - show - story - one	76	5_film_song_show_story
6	school - education - student - teacher - child	71	6_school_education_student_teacher
7	syria - syrian - said - killed - force	46	7_syria_syrian_said_killed
8	trump - mr - clinton - russian - campaign	45	8_trump_mr_clinton_russian
9	rescue - helicopter - ship - search - crew	37	9_rescue_helicopter_ship_search
10	google - apple - mobile - said - company	37	10_google_apple_mobile_said
11	fire - torch - building - burner - blaze	35	11_fire_torch_building_burner
12	museum - coin - art - museums - work	32	12_museum_coin_art_museums
13	rail - train - network - service - passenger	32	13_rail_train_network_service
14	energy - gas - coal - fracking - industry	26	14_energy_gas_coal_fracking
15	wales - welsh - assembly - uk - government	25	15_wales_welsh_assembly_uk
16	facebook - company - social - said - site	24	16_facebook_company_social_said
17	president - maduro - mr - macri - venezuelan	23	17_president_maduro_mr_macri
18	president - mr - crocodile - boko - haram	22	18_president_mr_crocodile_boko
19	union - strike - rmt - staff - said	21	19_union_strike_rmt_staff
20	earthquake - quake - kathmandu - people - nepal	20	20_earthquake_quake_kathmandu_people
21	migrant - asylum - le - pen - hungary	18	21_migrant_asylum_le_pen
22	virus - disease - health - ebola - malaria	18	22_virus_disease_health_ebola
23	cat - animal - rspca - dog - said	17	23_cat_animal_rspca_dog
24	species - forest - frog - specie - tree	16	24_species_forest_frog_specie
25	space - earth - surface - mars - mission	15	25_space_earth_surface_mars
26	site - council - centre - pool - plan	14	26_site_council_centre_pool
27	mr - gandhi - minister - indias - state	13	27_mr_gandhi_minister_indias
28	plaque - memorial - died - war - akikusa	12	28_plaque_memorial_died_war
29	korea - north - missile - china - us	8	29_korea_north_missile_china
30	tax - rate - 50p - budget - chancellor	8	30_tax_rate_50p_budget

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