BERTopic_andattakstruk_2

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("GiganticLemon/BERTopic_andattakstruk_2")

topic_model.get_topic_info()

Topic overview

• Number of topics: 62
• Number of training documents: 16559

Click here for an overview of all topics.

Topic ID	Topic Keywords	Topic Frequency	Label
-1	the - and - to - of - in	21	-1_the_and_to_of
0	the - to - of - and - is	8983	0_the_to_of_and
1	the - to - that - he - and	1232	1_the_to_that_he
2	her - she - to - and - is	605	2_her_she_to_and
3	and - the - of - to - in	506	3_and_the_of_to
4	the - of - earth - to - and	473	4_the_of_earth_to
5	the - and - to - he - his	459	5_the_and_to_he
6	the - and - to - of - ship	416	6_the_and_to_of
7	the - to - of - and - his	370	7_the_to_of_and
8	de - his - he - to - the	306	8_de_his_he_to
9	her - she - to - and - is	192	9_her_she_to_and
10	chinese - the - and - of - to	160	10_chinese_the_and_of
11	the - president - soviet - of - us	150	11_the_president_soviet_of
12	russian - the - his - to - of	145	12_russian_the_his_to
13	asterix - roman - obelix - the - rome	141	13_asterix_roman_obelix_the
14	doctor - tardis - the - ace - to	140	14_doctor_tardis_the_ace
15	of - that - the - in - or	138	15_of_that_the_in
16	socrates - theseus - the - of - and	130	16_socrates_theseus_the_of
17	vampire - vampires - darren - sookie - to	111	17_vampire_vampires_darren_sookie
18	kirk - enterprise - spock - federation - klingon	111	18_kirk_enterprise_spock_federation
19	reacher - hardy - frank - boys - hardys	101	19_reacher_hardy_frank_boys
20	cadfael - his - the - to - of	99	20_cadfael_his_the_to
21	jedi - vong - luke - leia - han	87	21_jedi_vong_luke_leia
22	german - szpilman - hitler - was - the	78	22_german_szpilman_hitler_was
23	jesus - judah - god - of - the	78	23_jesus_judah_god_of
24	animorphs - jake - visser - ax - cassie	67	24_animorphs_jake_visser_ax
25	spirou - fantasio - champignac - count - marsupilami	66	25_spirou_fantasio_champignac_count
26	henson - white - black - the - slaves	57	26_henson_white_black_the
27	novel - of - his - in - book	56	27_novel_of_his_in
28	dawkins - of - that - science - religion	55	28_dawkins_of_that_science
29	obiwan - jedi - quigon - kenobi - anakin	52	29_obiwan_jedi_quigon_kenobi
30	cats - clan - thunderclan - kits - firestar	48	30_cats_clan_thunderclan_kits
31	redwall - abbey - the - and - vermin	48	31_redwall_abbey_the_and
32	virus - the - to - is - of	47	32_virus_the_to_is
33	buffy - sunnydale - willow - slayer - giles	46	33_buffy_sunnydale_willow_slayer
34	time - machine - traveller - in - the	44	34_time_machine_traveller_in
35	confederate - lee - scarlett - rhett - the	38	35_confederate_lee_scarlett_rhett
36	bond - bonds - to - leiter - by	37	36_bond_bonds_to_leiter
37	baseball - hobbs - game - team - belichick	37	37_baseball_hobbs_game_team
38	sharpe - scene - french - sharpes - harper	36	38_sharpe_scene_french_sharpes
39	nancy - bess - nancys - george - mystery	33	39_nancy_bess_nancys_george
40	women - of - ellador - men - in	33	40_women_of_ellador_men
41	manticore - sten - haven - fleet - honor	32	41_manticore_sten_haven_fleet
42	billy - john - horse - ranch - harold	31	42_billy_john_horse_ranch
43	global - warming - climate - energy - carbon	30	43_global_warming_climate_energy
44	christmas - claus - santa - roger - mimi	30	44_christmas_claus_santa_roger
45	holmes - sherlock - watson - douglas - that	29	45_holmes_sherlock_watson_douglas
46	tarzan - ape - lion - tarzans - opar	28	46_tarzan_ape_lion_tarzans
47	conan - conans - dake - aquilonia - raseri	28	47_conan_conans_dake_aquilonia
48	angel - angels - quillon - archangel - alleluia	27	48_angel_angels_quillon_archangel
49	lone - wolf - kai - magnamund - darklords	27	49_lone_wolf_kai_magnamund
50	helm - matt - helms - mac - agency	27	50_helm_matt_helms_mac
51	dorothy - oz - elphaba - wizard - ozma	27	51_dorothy_oz_elphaba_wizard
52	max - fang - flock - roland - victor	26	52_max_fang_flock_roland
53	tom - swift - mr - airship - toms	25	53_tom_swift_mr_airship
54	tintin - haddock - calculus - snowy - the	25	54_tintin_haddock_calculus_snowy
55	robot - robots - derec - ariel - city	23	55_robot_robots_derec_ariel
56	bertie - jeeves - emsworth - gally - freddie	23	56_bertie_jeeves_emsworth_gally
57	alex - sarov - alexs - mi6 - to	23	57_alex_sarov_alexs_mi6
58	carson - rayford - tribulation - carpathia - buck	22	58_carson_rayford_tribulation_carpathia
59	dresden - harry - thomas - murphy - dresdens	22	59_dresden_harry_thomas_murphy
60	brigitta - major - life - novel - of	22	60_brigitta_major_life_novel

Training hyperparameters

• calculate_probabilities: False
• 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: True
• zeroshot_min_similarity: 0.7
• zeroshot_topic_list: None

Framework versions

• Numpy: 2.0.2
• HDBSCAN: 0.8.40
• UMAP: 0.5.7
• Pandas: 2.2.2
• Scikit-Learn: 1.6.1
• Sentence-transformers: 3.4.1
• Transformers: 4.51.3
• Numba: 0.60.0
• Plotly: 5.24.1
• Python: 3.11.12