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pszemraj/
BERTopic-booksum-ngram1-sentence-t5-xl-chapter

Name: BERTopic-booksum-ngram1-sentence-t5-xl-chapter
Availability: InStock

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BERTopic-booksum-ngram1-sentence-t5-xl-chapter

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 safetensors

You can use the model as follows:

python

from bertopic import BERTopic
topic_model = BERTopic.load("pszemraj/BERTopic-booksum-ngram1-sentence-t5-xl-chapter")

topic_model.get_topic_info()

Topic overview

Number of topics: 138
Number of training documents: 70840

Click here for an overview of all topics.

Topic ID	Topic Keywords	Topic Frequency	Label
-1	were - her - was - had - she	30	-1_were_her_was_had
0	were - had - was - could - miss	28715	0_were_had_was_could
1	artagnan - athos - musketeers - porthos - treville	16916	1_artagnan_athos_musketeers_porthos
2	rama - ravan - brahma - lakshman - raghu	4563	2_rama_ravan_brahma_lakshman
3	were - canoe - hist - huron - hutter	1268	3_were_canoe_hist_huron
4	slave - were - slavery - had - was	1011	4_slave_were_slavery_had
5	holmes - sherlock - watson - moor - baskerville	580	5_holmes_sherlock_watson_moor
6	prisoner - milady - felton - were - madame	549	6_prisoner_milady_felton_were
7	coriolanus - cassius - brutus - sicinius - titus	527	7_coriolanus_cassius_brutus_sicinius
8	confederation - constitution - federal - states - senate	511	8_confederation_constitution_federal_states
9	heathcliff - catherine - wuthering - cathy - hindley	498	9_heathcliff_catherine_wuthering_cathy
10	were - seemed - rima - was - had	492	10_were_seemed_rima_was
11	laws - lawes - law - civill - actions	452	11_laws_lawes_law_civill
12	fang - wolf - fangs - musher - growl	401	12_fang_wolf_fangs_musher
13	sigurd - thorgeir - thord - gunnar - skarphedinn	395	13_sigurd_thorgeir_thord_gunnar
14	achilles - troy - patroclus - aeneas - ulysses	385	14_achilles_troy_patroclus_aeneas
15	fogg - passengers - passed - phileas - travellers	376	15_fogg_passengers_passed_phileas
16	troy - trojans - aeneas - fates - trojan	370	16_troy_trojans_aeneas_fates
17	disciples - jesus - pharisees - temple - jerusalem	340	17_disciples_jesus_pharisees_temple
18	helsing - harker - diary - dr - he	324	18_helsing_harker_diary_dr
19	lama - who - no - kim - am	312	19_lama_who_no_kim
20	sara - princess - herself - she - minchin	301	20_sara_princess_herself_she
21	horses - horse - saddle - stable - were	293	21_horses_horse_saddle_stable
22	hester - pearl - scarlet - her - human	292	22_hester_pearl_scarlet_her
23	candide - inquisitor - friar - cunegonde - philosopher	286	23_candide_inquisitor_friar_cunegonde
24	dick - aunt - were - could - had	275	24_dick_aunt_were_could
25	wolves - wolf - cub - hunger - were	261	25_wolves_wolf_cub_hunger
26	god - gods - consequences - satan - som	241	26_god_gods_consequences_satan
27	modesty - women - behaviour - human - woman	240	27_modesty_women_behaviour_human
28	society - education - distribution - service - labour	240	28_society_education_distribution_service
29	siddhartha - buddha - gotama - kamaswami - om	237	29_siddhartha_buddha_gotama_kamaswami
30	ship - captain - aboard - squire - ll	229	30_ship_captain_aboard_squire
31	cyrano - roxane - montfleury - hark - love	227	31_cyrano_roxane_montfleury_hark
32	alice - were - rabbit - hare - hatter	225	32_alice_were_rabbit_hare
33	toto - kansas - dorothy - oz - scarecrow	211	33_toto_kansas_dorothy_oz
34	lancelot - camelot - merlin - guinevere - arthur	209	34_lancelot_camelot_merlin_guinevere
35	were - soldiers - seemed - soldier - th	201	35_were_soldiers_seemed_soldier
36	were - was - fields - seemed - hills	200	36_were_was_fields_seemed
37	reason - thyself - actions - thine - life	179	37_reason_thyself_actions_thine
38	hetty - her - she - judith - were	170	38_hetty_her_she_judith
39	othello - iago - desdemona - ll - roderigo	170	39_othello_iago_desdemona_ll
40	wildeve - yes - were - vye - was	165	40_wildeve_yes_were_vye
41	utilitarian - morality - morals - virtue - moral	165	41_utilitarian_morality_morals_virtue
42	ransom - isaac - thine - thy - shekels	163	42_ransom_isaac_thine_thy
43	weasels - rat - ratty - toad - badger	157	43_weasels_rat_ratty_toad
44	philip - he - were - vicar - was	155	44_philip_he_were_vicar
45	macbeth - banquo - macduff - fleance - murderer	154	45_macbeth_banquo_macduff_fleance
46	lydgate - bulstrode - himself - he - had	145	46_lydgate_bulstrode_himself_he
47	capulet - romeo - juliet - verona - mercutio	142	47_capulet_romeo_juliet_verona
48	dying - her - were - helen - she	141	48_dying_her_were_helen
49	anne - avonlea - diana - her - marilla	141	49_anne_avonlea_diana_her
50	tartuffe - scene - dorine - pernelle - scoundrel	140	50_tartuffe_scene_dorine_pernelle
51	were - yes - had - was - no	139	51_were_yes_had_was
52	jekyll - hyde - were - myself - had	135	52_jekyll_hyde_were_myself
53	loved - were - philip - was - could	128	53_loved_were_philip_was
54	falstaff - mistress - ford - forsooth - windsor	127	54_falstaff_mistress_ford_forsooth
55	hurstwood - were - barn - had - was	127	55_hurstwood_were_barn_had
56	provost - capell - collier - conj - pope	126	56_provost_capell_collier_conj
57	gretchen - highness - chancellor - hildegarde - yes	125	57_gretchen_highness_chancellor_hildegarde
58	delamere - watson - dr - ll - no	124	58_delamere_watson_dr_ll
59	jem - her - were - felt - margaret	123	59_jem_her_were_felt
60	beowulf - grendel - hrothgar - wiglaf - hero	111	60_beowulf_grendel_hrothgar_wiglaf
61	verloc - seemed - was - were - had	102	61_verloc_seemed_was_were
62	hamlet - guildenstern - rosencrantz - fortinbras - polonius	102	62_hamlet_guildenstern_rosencrantz_fortinbras
63	corey - mrs - yes - business - lapham	101	63_corey_mrs_yes_business
64	projectiles - cannon - projectile - distance - satellite	99	64_projectiles_cannon_projectile_distance
65	piano - musical - music - played - beethoven	98	65_piano_musical_music_played
66	wedding - bridegroom - were - marriage - looked	93	66_wedding_bridegroom_were_marriage
67	juan - her - fame - some - had	92	67_juan_her_fame_some
68	were - looked - felt - her - had	91	68_were_looked_felt_her
69	staked - gambling - wildeve - stakes - dice	91	69_staked_gambling_wildeve_stakes
70	mistress - leonora - wanted - florence - was	89	70_mistress_leonora_wanted_florence
71	delano - ship - sailor - captain - benito	87	71_delano_ship_sailor_captain
72	yes - goring - no - robert - room	85	72_yes_goring_no_robert
73	stockmann - yes - horster - mayor - dr	81	73_stockmann_yes_horster_mayor
74	ll - were - looked - carl - was	80	74_ll_were_looked_carl
75	barber - philosophy - no - some - man	78	75_barber_philosophy_no_some
76	tom - maggie - came - had - tulliver	78	76_tom_maggie_came_had
77	middlemarch - hustings - candidate - brooke - may	75	77_middlemarch_hustings_candidate_brooke
78	inspector - verloc - yes - affair - police	75	78_inspector_verloc_yes_affair
79	scrooge - merry - no - christmas - man	73	79_scrooge_merry_no_christmas
80	coquenard - mutton - served - were - pudding	70	80_coquenard_mutton_served_were
81	yes - no - jack - ll - tell	69	81_yes_no_jack_ll
82	seth - lisbeth - th - ud - no	67	82_seth_lisbeth_th_ud
83	higgins - eliza - her - she - liza	66	83_higgins_eliza_her_she
84	yarmouth - were - went - had - was	65	84_yarmouth_were_went_had
85	servian - sergius - yes - catherine - no	64	85_servian_sergius_yes_catherine
86	service - army - salvation - institution - training	61	86_service_army_salvation_institution
87	condemn - ff - pray - mercy - conj	58	87_condemn_ff_pray_mercy
88	lucy - bartlett - were - could - she	57	88_lucy_bartlett_were_could
89	wills - seemed - bequest - were - testator	54	89_wills_seemed_bequest_were
90	scene - iii - malvolio - valentine - cesario	54	90_scene_iii_malvolio_valentine
91	fuss - think - ll - thinks - oh	53	91_fuss_think_ll_thinks
92	hermia - demetrius - helena - theseus - helen	50	92_hermia_demetrius_helena_theseus
93	seemed - rochester - were - had - yes	50	93_seemed_rochester_were_had
94	sorrow - mourned - myself - had - was	48	94_sorrow_mourned_myself_had
95	gerty - sleepless - tea - weariness - tired	48	95_gerty_sleepless_tea_weariness
96	rushworth - crawford - were - sotherton - was	47	96_rushworth_crawford_were_sotherton
97	reasoning - syllogisme - names - signification - definitions	46	97_reasoning_syllogisme_names_signification
98	could - caleb - sure - work - no	46	98_could_caleb_sure_work
99	rose - tears - hope - tell - wish	46	99_rose_tears_hope_tell
100	peggotty - em - gummidge - he - ll	46	100_peggotty_em_gummidge_he
101	time - future - story - paradox - traveller	46	101_time_future_story_paradox
102	cleopatra - antony - caesar - loved - slave	45	102_cleopatra_antony_caesar_loved
103	appendicitis - doctors - doctor - dr - wanted	45	103_appendicitis_doctors_doctor_dr
104	slept - awoke - waking - sleep - seemed	44	104_slept_awoke_waking_sleep
105	parlour - room - seemed - sat - had	43	105_parlour_room_seemed_sat
106	prophets - scripture - prophet - moses - prophecy	43	106_prophets_scripture_prophet_moses
107	letter - honour - adieu - duval - evelina	43	107_letter_honour_adieu_duval
108	complications - cranky - had - tanis - was	43	108_complications_cranky_had_tanis
109	fled - armies - brussels - imperial - napoleon	42	109_fled_armies_brussels_imperial
110	philip - easel - greco - impressionists - manet	42	110_philip_easel_greco_impressionists
111	harlings - harling - frances - were - shimerdas	40	111_harlings_harling_frances_were
112	jane - mrs - janet - eyre - her	40	112_jane_mrs_janet_eyre
113	prisoner - confinement - prisoners - prison - gaoler	40	113_prisoner_confinement_prisoners_prison
114	hardcastle - marlow - impudence - constance - modesty	40	114_hardcastle_marlow_impudence_constance
115	horatio - murder - revenge - sorrow - hieronimo	40	115_horatio_murder_revenge_sorrow
116	traddles - had - married - room - horace	39	116_traddles_had_married_room
117	philip - tell - feelings - was - remember	38	117_philip_tell_feelings_was
118	nervous - countenance - seemed - he - huxtable	38	118_nervous_countenance_seemed_he
119	rogers - wanted - lapham - could - silas	38	119_rogers_wanted_lapham_could
120	titus - timon - varro - servilius - alcibiades	37	120_titus_timon_varro_servilius
121	morality - justice - moral - impartiality - unjust	37	121_morality_justice_moral_impartiality
122	willard - elmer - were - was - henderson	37	122_willard_elmer_were_was
123	had - was - could - circumstances - possession	37	123_had_was_could_circumstances
124	monkey - he - sahib - rat - sara	36	124_monkey_he_sahib_rat
125	mcmurdo - mcginty - cormac - police - scanlan	36	125_mcmurdo_mcginty_cormac_police
126	hetty - herself - she - her - had	36	126_hetty_herself_she_her
127	dimmesdale - reverend - chillingworth - clergyman - deacon	35	127_dimmesdale_reverend_chillingworth_clergyman
128	formerly - eliza - was - friend - friends	34	128_formerly_eliza_was_friend
129	were - seemed - had - was - felt	34	129_were_seemed_had_was
130	prisoner - jerry - lorry - tellson - court	33	130_prisoner_jerry_lorry_tellson
131	macmurdo - wenham - captain - steyne - crawley	33	131_macmurdo_wenham_captain_steyne
132	ducal - duchy - xv - fetes - theatre	32	132_ducal_duchy_xv_fetes
133	chapter - book - dows - unt - windowpane	32	133_chapter_book_dows_unt
134	money - riches - things - risk - thoughts	31	134_money_riches_things_risk
135	bethy - beth - seemed - sister - her	31	135_bethy_beth_seemed_sister
136	oliver - pickwick - were - was - inn	30	136_oliver_pickwick_were_was

Training hyperparameters

calculate_probabilities: True
language: None
low_memory: False
min_topic_size: 30
n_gram_range: (1, 1)
nr_topics: auto
seed_topic_list: None
top_n_words: 10
verbose: True

Framework versions

Numpy: 1.24.3
HDBSCAN: 0.8.29
UMAP: 0.5.3
Pandas: 2.0.2
Scikit-Learn: 1.2.2
Sentence-transformers: 2.2.2
Transformers: 4.30.2
Numba: 0.57.1
Plotly: 5.15.0
Python: 3.10.11