Add BERTopic model

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	---
	tags:
	- bertopic
	library_name: bertopic
	pipeline_tag: text-classification
	---

	# xsum_108_50000_25000_test

	This is a [BERTopic](https://github.com/MaartenGr/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

	<details>
	<summary>Click here for an overview of all topics.</summary>

	\| 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 \|

	</details>

	## 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


	---
	tags:
	- bertopic
	library_name: bertopic
	pipeline_tag: text-classification
	---

	# xsum_108_50000_25000_test

	This is a [BERTopic](https://github.com/MaartenGr/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

	<details>
	<summary>Click here for an overview of all topics.</summary>

	\| 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 \|

	</details>

	## 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